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Appendix: Indicator Measurement Definitions


Indicator Group: Alcohol

Indicator 1.1: Alcohol use among youths

Demographic group

Students in grades 9–12.

Numerator

Students in grades 9–12 who report consumption of at least one drink of alcohol in the past 30 days.

Denominator

Students in grades 9–12 who reported having a specific number of drinks of alcohol, including zero, in the past 30 days (excluding those who did not answer).

Measures of frequency

Biennial (odd years) prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

In 2011, 39% of high school students reported drinking alcohol on at least 1 day in the past 30 days (1). The prevalence of current drinking is similar for boys and girls but increases by grade. In 2011, among U.S. high school students, 80% had consumed alcohol by the 12th grade, even though the sale of alcohol to persons aged ≥21 years has been illegal in all states since 1988 (2). Current drinking by youths is correlated with current drinking by adults in states (3).

Significance

On average, alcohol is a factor in the deaths of approximately 4,300 youths in the United States per year, shortening their lives by an average of 60 years (4). Underage drinking cost the United States $24 billion in 2006 (5). Studies have determined that delaying the age when drinking is initiated until age 21 years or later substantially reduces the risk for experiencing alcohol-related problems (6). Underage drinking is also strongly associated with injuries, violence, fetal alcohol spectrum disorders, and risk for other acute and chronic health effects (7,8).

Limitations of indicator

The indicator does not convey the frequency of drinking or the specific amount of alcohol consumed. This indicator is available every other year.

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (1). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

Healthy People 2020 objective SA–13.1: Reduce the proportion of adolescents reporting use of alcohol or any illicit drugs in the past 30 days.

Related chronic disease indicator topics

School health.


References

  1. CDC. Youth risk behavior surveillance—United States, 2011. MMWR 2012;61(No. SS-4).
  2. O'Malley PM, Wagenaar AC. Effects of minimum drinking age laws on alcohol use, related behaviors, and traffic crash involvement among American youth: 1976–1987. J Stud Alcohol 1991;52:478–91.
  3. Nelson DE, Naimi TS, Brewer RD, Nelson HA. State alcohol-use estimates among youth and adults, 1993–2005. Am J Prev Med 2009;36:218–24.
  4. CDC. Alcohol-related disease impact (ARDI) application. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://apps.nccd.cdc.gov/DACH_ARDI/default/default.aspx.
  5. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  6. Hingson RW, Heeren T, Winter MR. Age at drinking onset and alcohol dependence: age at onset, duration, and severity. Pediatrics 2006;160:739–46.
  7. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 2011;34(1).
  8. Bonnie RJ, O'Connell ME, eds; National Research Council and Institute of Medicine Committee on Developing a Strategy to Reduce and Prevent Underage Drinking. Reducing underage drinking: a collective responsibility. Washington, DC: The National Academies Press; 2004.

Indicator Group: Alcohol

Indicator 1.2: Alcohol use before pregnancy

Demographic group

Women aged 18–44 years who have had a live birth.

Numerator

Respondents who reported that they drank any alcoholic beverages during the 3 months before becoming pregnant with their most recent live-born infant, including those having less than one drink in an average week.

Denominator

Respondents who reported the number of drinks they had in an average week, including none, during the 3 months before becoming pregnant with their most recent live-born infant, as well as those who reported that they did not have any alcoholic drinks in the past 2 years (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude prevalence with 95% confidence intervals weighted using the Pregnancy Risk Assessment Monitoring System method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage) and by demographic characteristics when feasible.

Time period of case definition

Three months before the pregnancy resulting in the most recent live birth.

Background

Preconception drinking patterns are highly predictive of alcohol use during pregnancy, which has been associated with adverse birth and infant outcomes, including fetal alcohol spectrum disorders (1,2). Therefore, medical guidelines advise against any alcohol use throughout pregnancy and around the time of conception because the effects of alcohol consumption on the fetus might occur before a woman is aware she is pregnant (3,4). According to 2004 PRAMS data collected from 26 reporting areas, the mean prevalence of alcohol use during the 3 months before the most recent pregnancy was 50.1% (5).

Significance

The U.S. Surgeon General has determined that no amount of alcohol consumption during pregnancy is known to be safe (3). The clinical workgroup of the Select Panel on Preconception Care recommends all women of childbearing aged be screened for alcohol use and provided with information regarding potential adverse health outcomes including the negative effects of alcohol consumption during pregnancy (6). In addition, women who exhibit signs of alcohol dependence or misuse should be directed to support programs that would assist them to achieve long-term cessation of alcohol use and be advised to delay any future pregnancies until they are able to abstain from alcohol use (6).

Limitations of indicator

The indicator does not convey the frequency of drinking or the number of drinks per day or per occasion. Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Pregnancy Risk Assessment Monitoring System (PRAMS).

Limitations of data resources

PRAMS data are collected only from women who delivered a live-born infant, not all women of reproductive age, and from 40 states and one city, not the entire United States. PRAMS data are self-reported and might be subject to recall bias and underreporting or overreporting of behaviors based on social desirability. Although most self-report surveys such as PRAMS might be subject to systematic error resulting from noncoverage (e.g., lower landline telephone coverage because of transition to cellular telephone–only households or undeliverable addresses), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement bias (e.g., recall bias), PRAMS attempts to contact potential respondents by mail and landline or cellular telephone to increase response rates. Women who experienced a fetal death or had an abortion are excluded. PRAMS estimates only cover the population of residents in each state who also deliver in that state; therefore, residents who delivered in a different state are not included in their resident state.

Related indicators or recommendations

Healthy People 2020 objective MICH-11: Increase abstinence from alcohol, cigarettes, and illicit drugs among pregnant women.

Related chronic disease indicator topics

Reproductive health.


References

  1. CDC. Alcohol use among women of childbearing age—United States 1991–1999. MMWR 2002;51:273–6.
  2. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 2011;34:4–14.
  3. US Department of Health and Human Services, Office of the Surgeon General. Surgeon General's advisory on alcohol use in pregnancy. Washington, DC: US Department of Health and Human Services, Office of the Surgeon General; 2005. Available at https://wayback.archive-it.org/3926/20140421162517/http://www.surgeongeneral.gov/news/2005/02/sg02222005.html.
  4. CDC. 2002 PRAMS surveillance report: multistate exhibits. Atlanta, GA: US Department of Health and Human Services, CDC; 2006. Available at http://www.surgeongeneral.gov/news/2005.
  5. D'Angelo D, Williams L, Morrow B, et al. Preconception and interconception health status of women who recently gave birth to a live-born infant—Pregnancy Risk Assessment Monitoring System (PRAMS), United States, 26 reporting areas, 2004. MMWR 2007;56(No. SS-10).
  6. Floyd RL, Jack BW, Cefalo R, et al. The clinical content of preconception care: alcohol, tobacco, and illicit drug exposures. Am J Obstet Gynecol 2008;199(Suppl B):S333–9.

Indicator Group: Alcohol

Indicator 2.1: Binge drinking prevalence among youths

Demographic group

Students in grades 9–12.

Numerator

Students in grades 9–12 who report having five or more drinks of alcohol within a couple of hours on ≥1 day in the past 30 days.

Denominator

Students in grades 9–12 who report having a specific number, including zero, of drinks of alcohol within a couple of hours on ≥1 day in the past 30 days (excluding those who did not answer).

Measures of frequency

Biennial (odd years) prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

In 2011, 22% of high school students in the United States reported binge drinking in the past 30 days (1). Binge drinking accounts for 90% of the alcohol consumed by youths (2); approximately two out of three high school students who report drinking also report binge drinking (3), usually on multiple occasions. In 2011, the prevalence of binge drinking among boys was 24% and 20% among girls (1). The prevalence of binge drinking was higher among white (24%) and Hispanic (24%) students than black students (12%), and prevalence increased with grade (1). Binge drinking by youths is correlated with binge drinking by adults in states (4).

Significance

Alcohol is a factor in the deaths of approximately 4,300 youths in the United States per year, shortening their lives by an average of 60 years (5). Underage drinking cost the United States $24 billion in 2006 (6). Binge drinking is a risk factor for many health and social problems, including motor-vehicle crashes, violence, suicide, hypertension, acute myocardial infarction, sexually transmitted diseases, unintended pregnancy, fetal alcohol spectrum disorders, and sudden infant death syndrome (7,8).

Limitations of indicator

The indicator does not convey the frequency of binge drinking or the specific amount of alcohol consumed. The definition of binge drinking used in the data source (Youth Risk Behavior Surveillance System) is not gender specific. This indicator is available every other year.

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (1). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

Healthy People 2020 objective SA–14.1: Reduce the proportion of students engaging in binge drinking during the past 2 weeks—high school seniors.

Healthy People 2020 objective SA–14.4: Reduce the proportion of persons engaging in binge drinking during the past month—persons aged 12–17 years.

• CDC Prevention Status Report: Excessive alcohol use (9).

Related chronic disease indicator topics

School health.


References

  1. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).
  2. Office of Juvenile Justice and Delinquency Prevention. Drinking in America: myths, realities, and prevention policy. Washington, DC: US Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention; 2005.
  3. CDC. Vital signs: binge drinking among high school students and adults—United States, 2009. MMWR 2010;59:1274–9.
  4. Nelson DE, Naimi TS, Brewer RD, Nelson HA. State alcohol-use estimates among youth and adults, 1993–2005. Am J Prev Med 2009;36:218–24.
  5. CDC. Alcohol-related disease impact (ARDI) application. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://apps.nccd.cdc.gov/DACH_ARDI/default/default.aspx.
  6. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  7. National Institute of Alcohol Abuse and Alcoholism. Tenth special report to the U.S. Congress on alcohol and health. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2000.
  8. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 2011;34:4–14.
  9. CDC. Prevention status reports, 2013: excessive alcohol use. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.cdc.gov/psr/alcohol/index.html.

Indicator Group: Alcohol

Indicator 2.2: Binge drinking prevalence among adults aged ≥18 years

Demographic group

Adults aged ≥18 years.

Numerator

Adults aged ≥18 years who report having five or more drinks (men) or four or more drinks (women) on an occasion in the past 30 days.

Denominator

Adults aged ≥18 years who report having a specific number, including zero, of drinks on an occasion in the past 30 days (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

In 2010, a total of 17.1% of adults reported binge drinking on an occasion in the past 30 days (2). Binge drinking prevalence is higher among men, persons aged 18–34 years, whites, and those with household incomes ≥$75,000 (2).

Significance

Excessive alcohol use accounted for an estimated average of 88,000 deaths and 2.5 million years of potential life lost (YPLL) in the United States each year during 2006–2010 (3), and an estimated $223.5 billion in economic costs in 2006 (4). Binge drinking accounted for more than half of those deaths, two thirds of the YPLL (5), and three fourths of the economic costs (4). Binge drinking also is a risk factor for many health and social problems, including motor-vehicle crashes, violence, suicide, hypertension, acute myocardial infarction, sexually transmitted diseases, unintended pregnancy, fetal alcohol spectrum disorders, and sudden infant death syndrome (6,7). In the United States, binge drinking accounts for more than half of the alcohol consumed by adults (8). However, most binge drinkers are not alcohol dependent (9,10).

Limitations of indicator

The indicator does not convey the frequency of binge drinking or the specific amount of alcohol consumed.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate. A recent study using BRFSS data found that self-reports identify only 22%–32% of presumed alcohol consumption in states, based on alcohol sales (11).

Related indicators or recommendations

Healthy People 2020 objective SA-14.3: Reduce the proportion of persons engaging in binge drinking in the past 30 days—adults aged ≥18 years.

• CDC Prevention Status Report: Excessive alcohol use (12).

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Vital signs: binge drinking prevalence, frequency, and intensity among adults—United States, 2010. MMWR 2012;61:14–9.
  3. CDC. Alcohol-related disease impact (ARDI) application. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://apps.nccd.cdc.gov/DACH_ARDI/default/default.aspx.
  4. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  5. Stahre M, Roeber J, Kanny D, Brewer RD, Zhang X. Contribution of excessive alcohol consumption to deaths and years of potential life lost in the United States. Prev Chronic Dis 2014;11:130293.
  6. National Institute of Alcohol Abuse and Alcoholism. Tenth special report to the U.S. Congress on alcohol and health. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2000.
  7. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 34;2011:4–14.
  8. Office of Juvenile Justice and Delinquency Prevention. Drinking in America: myths, realities, and prevention policy. Washington, DC: US Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention; 2005. Available at http://www.udetc.org/documents/Drinking_in_America.pdf.
  9. Dawson DA, Grant BF. LI T-K. Quantifying the risks associated with exceeding recommended drinking limits. Alcohol Clin Exp Res 2005;29:902–8.
  10. Woerle S, Roeber J, Landen MG. Prevalence of alcohol dependence among excessive drinkers in New Mexico. Alcohol Clin Exp Res 2007;31:293–8.
  11. Nelson DE, Naimi TS, Brewer RD, Roeber J. U.S. state alcohol sales compared to survey data, 1993–2006. Addiction 2010;105:1589–96.
  12. CDC. Prevention status reports, 2013: excessive alcohol use. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.cdc.gov/psr/alcohol/index.html.

Indicator Group: Alcohol

Indicator 2.3: Binge drinking prevalence among women aged 18–44 years

Demographic group

Women aged 18–44 years.

Numerator

Women aged 18–44 years who report having four or more drinks on an occasion in the past 30 days.

Denominator

Women aged 18–44 years who report a specific number, including zero, of drinks on an occasion in the past 30 days (excluding those who refused to answer, had a missing answer, or who answered "don't know/not sure").

Measures of frequency

Prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

In 2010, an estimated 14.0% of women aged 18–44 years reported binge drinking on an occasion in the past 30 days (1).

Significance

Approximately 23,000 deaths among females each year in the United States are attributed to excessive alcohol use (2). Excessive alcohol use, including binge drinking, is strongly associated with injuries, violence, chronic liver disease, and risk for other acute and chronic health effects (3). Binge drinking can lead to unintended pregnancies, and females who are not expecting to get pregnant might not find out that they are until later in their pregnancy (4). If women binge drink while pregnant, they risk exposing their developing fetus to high levels of alcohol, increasing the chances the fetus will be harmed by the mother's alcohol use (5). Alcohol use by pregnant women causes fetal alcohol spectrum disorders (FASDs) (6,7). FASDs, which are estimated to affect at least 1% of all births in the United States, result in physical and growth problems, neurodevelopmental deficits, and lifelong disability (8).

Limitations of indicator

The indicator does not convey the frequency of binge drinking or the specific amount of alcohol consumed. Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate. A recent study using BRFSS data found that self-reports identify only 22%–32% of presumed alcohol consumption in states, based on alcohol sales (9).

Related indicators or recommendations

Healthy People 2020 objective SA-14.3: Reduce the proportion of persons engaging in binge drinking in the past 30 days—adults aged ≥18 years.

Healthy People 2020 objective MICH-11: Increase abstinence from alcohol, cigarettes, and illicit drugs among pregnant women.

Related chronic disease indicator topics

Reproductive health.


References

  1. CDC. Alcohol use and binge drinking among women of childbearing age—United States, 2006–2010. MMWR 2012;61:534–8.
  2. CDC. Vital signs: binge drinking among women and high school girls—United States, 2011. MMWR 2013;62:9–13.
  3. National Institute of Alcohol Abuse and Alcoholism. Tenth special report to the U.S. Congress on alcohol and health. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2000.
  4. Naimi TS, Lipscomb L, Brewer B, Gilbert B. Binge drinking in the preconception period and the risk of unintended pregnancy: implications for women and their children. Pediatrics 2003;111:1136–41.
  5. Maier SE, West JR. Drinking patterns and alcohol-related birth defects. Alcohol Res Health 2001;25:168–74.
  6. Institute of Medicine. Fetal alcohol syndrome: diagnosis, epidemiology, prevention, and treatment. Washington, DC: National Academy Press; 1996.
  7. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 2011;34:4–14.
  8. May PA, Gossage JP. Estimating the prevalence of fetal alcohol syndrome: a summary. Alcohol Res Health 2001;25:159–67.
  9. Nelson DE, Naimi TS, Brewer RD, Roeber J. U.S. state alcohol sales compared to survey data, 1993–2006. Addiction 2010;105:1589–96.

Indicator Group: Alcohol

Indicator 3: Binge drinking frequency among adults aged ≥18 years who binge drink

Demographic group

Adults aged ≥18 years.

Numerator

Number of binge drinking episodes (five or more drinks for men or four or more drinks for women on an occasion) in the past 30 days.

Denominator

Adults aged ≥18 years who report having five or more drinks (men) or four or more drinks (women) on an occasion in the past 30 days.

Measures of frequency

Annual binge drinking frequency: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

Excessive alcohol use accounted for an estimated average of 88,000 deaths and 2.5 million years of potential life lost (YPLL) in the United States each year during 2006–2010 (2) and an estimated $223.5 billion in economic costs in 2006 (3). Binge drinking accounted for more than half of those deaths, two thirds of the YPLL (4), and three fourths of the economic costs (3). In 2010, 85% of all alcohol-impaired driving episodes were reported by persons who also reported binge drinking (5). Approximately 90% of excessive drinkers report binge drinking, and most persons who report binge drinking do so frequently (6). In 2010, among binge drinkers, the frequency of binge drinking was 4.4 episodes per month (7). Binge drinking frequency was highest among binge drinkers aged ≥65 years (5.5 episodes per month) and those with household incomes <$25,000 (5.0 episodes per month) (7).

Significance

Binge drinking also is a risk factor for many health and social problems, including motor-vehicle crashes, violence, suicide, hypertension, acute myocardial infarction, sexually transmitted diseases, unintended pregnancy, fetal alcohol spectrum disorders, and sudden infant death syndrome (8,9). In the United States, binge drinking accounts for more than half of the alcohol consumed by adults (10). However, most binge drinkers are not alcohol dependent (11,12). The risk for alcohol-attributable harms (e.g., injuries) increases with the number of binge drinking episodes (13). Assessing the frequency of binge drinking might be particularly useful for planning and evaluating strategies recommended by the Guide to Community Preventive Services for preventing excessive alcohol use (14).

Limitations of indicator

Unless age, sex, education, and race/ethnicity estimates are generated for this indicator, subpopulations at high risk for binge drinking are not identified.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate. A recent study using BRFSS data found that self-reports identify only 22%–32% of presumed alcohol consumption in states, based on alcohol sales (15).

Related indicators or recommendations

Healthy People 2020 objective SA-14.3: Reduce the proportion of persons engaging in binge drinking in the past 30 days — adults aged ≥18 years.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Alcohol-related disease impact (ARDI) application. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://apps.nccd.cdc.gov/DACH_ARDI/default/default.aspx.
  3. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  4. Stahre M, Roeber J, Kanny D, Brewer RD, Zhang X. Contribution of excessive alcohol consumption to deaths and years of potential life lost in the United States. Prev Chronic Dis 2014;11:130293.
  5. CDC. Vital signs: alcohol-impaired driving among adults—United States, 2010. MMWR 2011;60:1351–6.
  6. Town M, Naimi TS, Mokdad AH, Brewer RD. Health care access among U.S. adults who drink alcohol excessively: missed opportunities for prevention. Prev Chronic Dis 2006.
  7. CDC. Vital signs: binge drinking prevalence, frequency, and intensity among adults—United States, 2010. MMWR 2012;61:14–9.
  8. National Institute of Alcohol Abuse and Alcoholism. Tenth special report to the U.S. Congress on alcohol and health. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2000.
  9. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 2011;34:4–14.
  10. Office of Juvenile Justice and Delinquency Prevention. Drinking in America: myths, realities, and prevention policy. Washington, DC: US Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention; 2005.
  11. Dawson DA, Grant BF, LI T-K. Quantifying the risks associated with exceeding recommended drinking limits. Alcohol Clin Exp Res 2005;29:902–8.
  12. Woerle S, Roeber J, Landen MG. Prevalence of alcohol dependence among excessive drinkers in New Mexico. Alcohol Clin Exp Res 2007;31:293–8.
  13. Naimi TS, Brewer B, Mokdad AH, Serdula M, Denny C, Marks J. Binge drinking among U.S. adults. JAMA 2003;289:70–5.
  14. Guide to Community Preventive Services. Preventing excessive alcohol consumption. Atlanta, GA: US Department of Health and Human Services, CDC; 2011. Available at http://www.cdc.gov/psr/alcohol/index.html.
  15. Nelson DE, Naimi TS, Brewer RD, Roeber J. U.S. state alcohol sales compared to survey data, 1993–2006. Addiction 2010;105:1589–96.

Indicator Group: Alcohol

Indicator 4: Binge drinking intensity among adults aged ≥18 years who binge drink

Demographic group

Adults aged ≥18 years.

Numerator

Largest number of drinks consumed on an occasion in the past 30 days among adult binge drinkers aged ≥18 years.

Denominator

Adults aged ≥18 years who report having five or more drinks (men) or four or more drinks (women) on an occasion in the past 30 days.

Measures of frequency

Annual binge drinking intensity: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

Excessive alcohol use accounted for an estimated average of 88,000 deaths and 2.5 million years of potential life lost (YPLL) in the United States each year during 2006–2010 (2) and an estimated $223.5 billion in economic costs in 2006 (3). Binge drinking accounted for more than half of those deaths, two thirds of the YPLL (4), and three fourths of the economic costs (3). In 2010, 85% of all alcohol-impaired driving episodes were reported by persons who also reported binge drinking (5). In 2010, among binge drinkers, the binge drinking intensity was 7.9 drinks on occasion in the past 30 days (6). Binge drinking intensity was highest among persons aged 18–24 years (9.3 drinks on occasion) and those with household incomes <$25,000 (8.5 drinks on occasion) (6).

Significance

Binge drinking also is a risk factor for many health and social problems, including motor-vehicle crashes, violence, suicide, hypertension, acute myocardial infarction, sexually transmitted diseases, unintended pregnancy, fetal alcohol spectrum disorders, and sudden infant death syndrome (7,8). In the United States, binge drinking accounts for more than half of the alcohol consumed by adults. However, most binge drinkers are not alcohol dependent (9,10). The risk for alcohol-attributable harm (e.g., injuries) increases with the intensity of binge drinking (11). Assessing the intensity of binge drinking might be particularly useful for planning and evaluating strategies recommended by the Guide to Community Preventive Services for preventing excessive alcohol use (12).

Limitations of indicator

Unless age, sex, education, and race/ethnicity estimates are generated for this indicator, subpopulations at high risk are not identified.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate. A recent study using BRFSS data found that self-reports identify only 22%–32% of presumed alcohol consumption in states, based on alcohol sales (13).

Related indicators or recommendations

Healthy People 2020 objective SA-14.3: Reduce the proportion of persons engaging in binge drinking in the past 30 days—adults aged ≥18 years.

• CDC Prevention Status Report: Excessive alcohol use (14).

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Alcohol-related disease impact (ARDI) application. Atlanta, GA: US Department of Health and Human Services, CDC; 2013: http://apps.nccd.cdc.gov/DACH_ARDI/default/default.aspx.
  3. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  4. Stahre M, Roeber J, Kanny D, Brewer RD, Zhang X. Contribution of excessive alcohol consumption to deaths and years of potential life lost in the United States. Prev Chronic Dis 2014;11:130293.
  5. CDC. Vital signs: alcohol-impaired driving among adults—United States, 2010. MMWR 2011;60:1351–6.
  6. CDC. Vital signs: binge drinking prevalence, frequency, and intensity among adults—United States, 2010. MMWR 2012;61:14–9.
  7. National Institute of Alcohol Abuse and Alcoholism. Tenth special report to the U.S. Congress on alcohol and health. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2000.
  8. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 2011;34:4–14.
  9. Dawson DA, Grant BF. LI T-K. Quantifying the risks associated with exceeding recommended drinking limits. Alcohol Clin Exp Res 2005;29:902–8.
  10. Woerle S, Roeber J, Landen MG. Prevalence of alcohol dependence among excessive drinkers in New Mexico. Alcohol Clin Exp Res 2007;31:293–8.
  11. Naimi TS, Nelson DE, Brewer RD. The intensity of binge alcohol consumption among U.S. adults. Am J Prev Med 2010;38:201–7.
  12. Guide to Community Preventive Services. Preventing excessive alcohol consumption. Atlanta, GA: US Department of Health and Human Services, CDC; 2011. Available at http://www.thecommunityguide.org/alcohol/index.html.
  13. Nelson DE, Naimi TS, Brewer RD, Roeber J. US state alcohol sales compared to survey data, 1993–2006. Addiction 2010;105:1589–96.
  14. CDC. Prevention status reports, 2013: excessive alcohol use. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.cdc.gov/psr/alcohol/index.html.

Indicator Group: Alcohol

Indicator 5.1: Heavy drinking among adults aged ≥18 years

Demographic group

Adults aged ≥18 years.

Numerator

Adults aged ≥18 years who report weekly alcohol consumption of 15 or more drinks (men) or eight or more drinks (women).

Denominator

Adults aged ≥18 years who report a specific number, including zero, for the number of weekly drinks (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

In 2010, 5.4% of adult men reported heavy drinking and 4.5% of adult women reported heavy drinking (2). Heavy drinkers are more likely to binge drink than moderate drinkers (3).

Significance

Excessive alcohol use accounted for an estimated average of 88,000 deaths and 2.5 million years of potential life lost (YPLL) in the United States each year during 2006–2010 (4) and an estimated $223.5 billion in economic costs in 2006 (5). Excessive alcohol use, including heavy drinking, is strongly associated with injuries, violence, chronic liver disease, and risk for other acute and chronic health effects (6).

Limitations of indicator

The indicator does not convey the exact amount of alcohol consumed per day. Therefore, a weekly consumption of seven alcoholic drinks for a woman or 14 alcoholic drinks for a man could be consumed over 2-day weekend on one or two occasions rather than up to one drink for a woman and up to 2 drinks for a man each day.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate. A recent study using BRFSS data found that self-reports identify only 22%–32% of presumed alcohol consumption in states, based on alcohol sales (7).

Related indicators or recommendations

Healthy People 2020 objective SA–15: Reduce the proportion of adults who drank excessively in the previous 30 days.

Related chronic disease indicator topics

Oral health.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Behavioral Risk Factor Surveillance System survey data. Atlanta, GA: US Department of Health and Human Services, CDC; 2011. Available at http://apps.nccd.cdc.gov/brfss.
  3. Naimi TS, Brewer B, Mokdad AH, Serdula M, Denny C, Marks J. Binge drinking among U.S. adults. JAMA 2003;289:70–5.
  4. CDC. Alcohol-related disease impact (ARDI) application. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://apps.nccd.cdc.gov/DACH_ARDI/default/default.aspx.
  5. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  6. National Institute of Alcohol Abuse and Alcoholism. Tenth special report to the U.S. Congress on alcohol and health. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2000.
  7. Nelson DE, Naimi TS, Brewer RD, Roeber J. U.S. state alcohol sales compared to survey data, 1993–2006. Addiction 2010;105:1589–96.

Indicator Group: Alcohol

Indicator 5.2: Heavy drinking among women aged 18–44 years

Demographic group

Women aged 18–44 years.

Numerator

Women aged 18–44 years who reported having eight or more drinks per week on the days they drank alcohol in the past 30 days.

Denominator

Women aged 18–44 years who reported the number of drinks, including zero, on the days they drank alcohol as well as those who reported having had no drinks in the past 30 days (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude annual prevalence with 95% confidence intervals weighted using the Behavioral Risk Factor Surveillance System method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage) and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

Heavy alcohol use before pregnancy is predictive of continued use during pregnancy (1). CDC analysis of 2002 Behavioral Risk Factor Surveillance System data for women aged 18–44 years indicated that the prevalence of frequent drinking (seven or more drinks in a week or binge drinking) was 13.2% for all women of childbearing age overall (including pregnant women) and 13.1% for women who might become pregnant (2).

Significance

Alcohol consumption during pregnancy is associated with spontaneous abortions, birth defects, and developmental disorders, many of which occur early in gestation before the woman is aware that she is pregnant (2). Alcohol use during pregnancy is associated with fetal alcohol spectrum disorders, which might be characterized by specific physical features, impaired growth and abnormal development or functioning of the central nervous system (3). Even though a dose-response relationship has been observed between prenatal alcohol consumption and effects on the fetus, no amount of alcohol consumption during pregnancy is known to be safe (4). Therefore, medical guidelines, including the recommendations of the U.S. Surgeon General, the American Academy of Pediatrics, and the American College of Obstetricians and Gynecologists, advise against any alcohol use around the time of conception and throughout pregnancy. Furthermore, the clinical workgroup of the Select Panel on Preconception Care recommends that all women of childbearing age be screened for alcohol use and provided with information regarding potential adverse health outcomes including the negative effects of alcohol consumption during pregnancy. In addition, women who exhibit signs of alcohol dependence or misuse should be directed to support programs that will assist them to achieve long-term cessation of alcohol use and be advised to delay any future pregnancies until they are able to abstain from alcohol use.

Limitations of indicator

The indicator does not convey the exact amount of alcohol consumed per day. Therefore, a weekly consumption of seven alcoholic drinks for a woman can be consumed over 2-day weekend on one or two occasions rather than up to one drink for a woman each day. Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective MICH-11: Increase abstinence from alcohol, cigarettes, and illicit drugs among pregnant women.

Related chronic disease indicator topics

Reproductive health; oral health.


References

  1. CDC. Alcohol use among women of childbearing age—United States 1991–1999. MMWR 2002;51:273–6.
  2. Floyd RL, Decoufle P, Hungerford DW. Alcohol use prior to pregnancy recognition. Am J Prev Med 1999;17:101–7.
  3. Warren KR, Hewitt BG, Thomas JD. Fetal alcohol spectrum disorders. Alcohol Res Health 2011;34:4–14.
  4. Sokol RJ, Delaney-Black V, Nordstrom B. Fetal alcohol spectrum disorder. JAMA 2003;290:2996–9.
  5. Office of the Surgeon General. Surgeon General's advisory on alcohol use in pregnancy. Washington, DC: Office of the Surgeon General; 2005. Available at http://www.surgeongeneral.gov/news/2005.
  6. Floyd RL, Jack BW, Cefalo R, et al. The clinical content of preconception care: alcohol, tobacco, and illicit drug exposures. Am J Obstet Gynecol 2008;199(Suppl B):S333–9.
  7. Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soc Prev Med 2001;46(Suppl 1):S3–42.

Indicator Group: Alcohol

Indicator 6: Chronic liver disease mortality

Demographic group

Entire U.S. population.

Numerator

Deaths with International Classification of Diseases, 10th Revision codes K70 or K73–K74 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2010, a total of 31,903 persons died from chronic liver disease (2). The age-adjusted rate of death among males (12.7 per 100,000 population) was greater than the rate among females (6.1 per 100,000 population) (2).

Significance

Excessive alcohol use accounted for an estimated average of 88,000 deaths and 2.5 million years of potential life lost (YPLL) in the United States each year during 2006–2010 (3) and an estimated $223.5 billion in economic costs in 2006 (4). Sustained alcohol consumption is the leading cause of liver cirrhosis, the 12th leading cause of death (5). The risk for chronic liver disease and cirrhosis is directly to heavy and long-term consumption of alcohol (5).

Limitations of indicator

Because alcohol-related disease can have a long latency, changes in behavior or clinical practice affecting population mortality might not be apparent for years. Not all chronic liver disease deaths are alcohol-attributable. However, in 2009, almost 70% of cirrhosis deaths in the United States were alcohol-attributable (5), and the proportion of cirrhosis deaths coded as 100% alcohol-attributable has increased dramatically during 1970–2009 among United States adults aged 25–64 years (5).

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective SA–11: Reduce cirrhosis deaths.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC, National Center for Health Statistics. Health data interactive. Atlanta, GA: CDC. Available at http://www.cdc.gov/nchs/hdi.htm.
  3. CDC. Alcohol-related disease impact (ARDI) application. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://apps.nccd.cdc.gov/DACH_ARDI/default/default.aspx.
  4. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  5. National Institute on Alcohol Abuse and Alcoholism. Liver cirrhosis mortality in the United States, 1970–2009. Surveillance Report No. 93. Arlington, VA: National Institute on Alcohol Abuse and Alcoholism; 2012. Available at http://pubs.niaaa.nih.gov/publications/Surveillance93/Cirr09.htm.

Indicator Group: Alcohol

Indicator 7: Per capita alcohol consumption among persons aged ≥14 years

Demographic group

Resident persons aged ≥14 years.

Numerator

Gallons of pure alcohol consumed during a calendar year.

Denominator

Mid-year resident population aged ≥14 years for the same calendar year.

Measures of frequency

Annual alcohol consumption per capita.

Time period of case definition

Calendar year.

Background

The past 75 years of per capita consumption data show that alcohol consumption increased sharply at the end of Prohibition to a transient peak in 1945, followed by a decrease and plateau through the early 1960s (1). This was followed by a steady 20-year increase in per capita consumption that lasted through the early 1980s, after which consumption dropped again through 1995 (1). Since 1995, per capita consumption has once again been increasing, albeit more slowly than in the 1960s and 1970s (1). During 1995–2012, per capita alcohol consumption increased 8% (from to 2.15 to 2.33 gallons), driven by a 24% increase in per capita spirits consumption (from 0.63 to 0.78 gallons) and a 45% increase in wine consumption (from 0.29 to 0.42 gallons) (1). In contrast, beer consumption has been decreasing steadily since the early 1980s and decreased 8% (from 1.23 to 1.13 gallons) during 1995–2012 (1).

Significance

Strong scientific evidence supports the usefulness of per capita alcohol consumption as a proxy measure of excessive alcohol use (2). The independent, nonfederal Community Preventive Services Task Force reviewed this measure in 2009, subsequently endorsed this measure, and decided to use it as a recommendation outcome for subsequent reviews of alcohol control policies (3). This indicator provides a more complete accounting of alcohol consumption in states than self-reported consumption indicators (2). A recent study using Behavioral Risk Factor Surveillance System data found that self-reports identify only 22%–32% of the presumed alcohol consumption in states based on this indicator (4). This indicator also supports state-level surveillance of alcohol consumption by beverage type. This indicator could serve as a very broad environmental and system change indicator of changes in factors that influence excessive consumption, such as price, retail availability, and regulatory environment.

Limitations of indicator

This indicator does not support local analyses of alcohol consumption (e.g., by county or city) or the analysis of alcohol consumption among specific demographic groups (e.g., age, sex, race/ethnicity).

Data resources

Alcohol Epidemiologic Data System (AEDS).

Limitations of data resources

Many factors may result in inaccuracies in estimates of per capita alcohol consumption (1). These include the use of fixed ethanol conversion coefficients (ECC, the proportion of pure alcohol for each beverage type), despite evidence that ECCs may change over time by beverage type (1). The assumption is that changes in the average net ethanol content across all beverages have probably been minimal and not large enough to alter recent trends in overall per capita consumption. Other factors include the possibility that estimates in some states might be inflated by cross-border sales to buyers from neighboring states (e.g., in New Hampshire) or tourists' consumption of alcohol (e.g., in Washington DC) (1). Other factors include variations in state reporting practices for sales of alcoholic beverages, time delay between state taxation records and actual consumption, exclusion of alcohol contained in medications and foods, unrecorded legal home production, and illicit production, importation, and sales (1).

Related indicators or recommendations

Healthy People 2020 objective SA–16: Reduce average annual alcohol consumption.

CDC Prevention Status Report: excessive alcohol use (5).

Related chronic disease indicator topics

None.


References

  1. National Institute on Alcohol Abuse and Alcoholism. Apparent per capita alcohol consumption: national, state, and regional trends, 1977–2012. Surveillance Report No. 98. Arlington, VA: National Institute on Alcohol Abuse and Alcoholism; 2014. Available at http://pubs.niaaa.nih.gov/publications/surveillance98/CONS12.pdf.
  2. Elder RW, Lawrence B, Ferguson A, et al; Task Force on Community Preventive Services. The effectiveness of tax policy interventions for reducing excessive alcohol consumption and related harms. Am J Prev Med 2010;38:217–29.
  3. Task Force on Community Preventive Services. Increasing alcohol beverage taxes is recommended to reduce excessive alcohol consumption and related harms. Am J Prev Med 2010;38:230–2.
  4. Nelson DE, Naimi TS, Brewer RD, Roeber J. U.S. state alcohol sales compared to survey data, 1993–2006. Addiction 2010;105:1589–96.
  5. CDC. Prevention status reports, 2013: excessive alcohol use. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.cdc.gov/psr/alcohol/index.html.

Indicator Group: Alcohol

Indicator 8: Alcohol excise tax by beverage type

Demographic group

All resident persons.

Numerator

State taxes levied per gallon at the wholesale or retail level, by beverage type (reported separately for) 1) beer, 2) wine, or 3) distilled spirits.

Denominator

None.

Measures of frequency

Annual excise tax amount, by beverage type.

Time period of case definition

Annual as of January 1.

Background

The Community Preventive Services Task Force recommends increasing the unit price of alcohol by raising taxes based on strong evidence of effectiveness for reducing excessive alcohol consumption and related harms (1). Public health effects are expected to be proportional to the size of the tax increase (2). Alcohol consumption is particularly sensitive to the price of alcoholic beverages. (2) Across alcohol beverage types (i.e., beer, wine, and distilled spirits), the median price elasticity (a measure of the relationship between price and consumption) ranges from -0.50 for beer to -0.79 for spirits, and the overall price elasticity for ethanol is -0.77 (2). Thus, a 10% increase in the price of alcoholic beverages likely would reduce overall consumption by <7% (2). Recent analyses also note a substantial gap between the societal and governmental cost of excessive alcohol consumption (approximately $1.90 and $0.80 per drink, respectively) and the total federal and state taxes on alcoholic beverages (approximately $0.12 per drink) (3). Alcohol excise taxes are implemented at the state and federal level and are beverage specific (i.e., differ for beer, wine, and distilled spirits) (2). These taxes usually are based on the volume of alcohol sold and not on the sales price; therefore, their contribution to the total price of alcohol can erode over time because of inflation (2).

Significance

This indicator provides information about the level of state alcohol excise taxes. At the state and federal levels, inflation-adjusted alcohol taxes have decreased considerably since the 1950s (2). Concordant with this decrease in the real value of these taxes, the inflation-adjusted price of alcohol has decreased, reflecting the fact that changes in taxes are efficiently passed on through changes in prices (2). This indicator supports state-level surveillance of an important component of the price of alcohol (i.e., beverage-specific alcohol excise taxes), which has been strongly associated with changes in alcohol consumption (2).

Limitations of indicator

Additional taxes other than excise taxes that can affect the price of alcoholic beverages (e.g., sales taxes, which are levied as a percentage of the beverage's retail price) are not reported.

Data resources

Alcohol Policy Information System (APIS).

Limitations of data resources

Beverage-specific state tax levels are based on the taxes assessed on an index beverage within a particular beverage category (e.g., beer with 5% alcohol by volume) (4). APIS reports taxes for the most commonly sold container size and therefore does not include data on the taxes levied on alcoholic beverages sold in other container sizes. Tax amounts are not reported for states and beverage types for which the index beverage is available in state-run retail stores or through state-run wholesalers. In these cases, the state sets a price for each alcohol product that is some combination of cost, mark-up, and taxes, and determining the dollar value assigned to each of these components is not possible. Some states have separate tax rates for other types of alcoholic beverages (e.g., sparkling wine) that are not included in APIS. However, these beverages generally constitute a small segment of the alcohol retail market.

Related indicators or recommendations

CDC Prevention Status Report: Excessive alcohol use (5).

Related chronic disease indicator topics

None.


References

  1. Task Force on Community Preventive Services. Increasing alcohol beverage taxes is recommended to reduce excessive alcohol consumption and related harms. Am J Prev Med 2010;38:230–2.
  2. Elder RW, Lawrence B, Ferguson A, et al; Task Force on Community Preventive Services. The effectiveness of tax policy interventions for reducing excessive alcohol consumption and related harms. Am J Prev Med 2010;38:217–29.
  3. Bouchery EE, Harwood HJ, Sacks JJ, Simon CJ, Brewer RD. Economic costs of excessive alcohol consumption in the U.S., 2006. Am J Prev Med 2011;41:516–24.
  4. National Institute on Alcohol Abuse and Alcoholism. Alcohol policy information system (APIS). Rockville, MD: National Institute on Alcohol Abuse and Alcoholism. http://alcoholpolicy.niaaa.nih.gov.
  5. CDC. Prevention status reports, 2013: excessive alcohol use. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.cdc.gov/psr/alcohol/index.html.

Indicator Group: Alcohol

Indicator 9: Commercial host (dram shop) liability laws

Demographic group

All resident persons.

Numerator

State has at least one of the following: 1) commercial host liability with no major limitations; 2) commercial host liability with major limitations; or 3) no commercial host liability. (Major limitations include having commercial host liability for minors or intoxicated adults but not both, increased evidentiary requirements for finding liability, limitations on damage awards, or restrictions on who may be sued.)

Denominator

None.

Measures of frequency

State commercial host liability status.

Time period of case definition

Annual as of January 1.

Background

The Community Preventive Services Task Force has concluded on the basis of strong evidence that dram shop liability is effective in preventing and reducing alcohol-related harms (1). Dram shop liability (also known as commercial host liability) holds alcohol retailers liable for alcohol-attributable harms (e.g., injuries or deaths resulting from alcohol-related motor vehicle crashes) caused by a patron who was either intoxicated or aged <21 years (the minimum legal drinking age, a minor) at the time of service (2). This liability can be established in states either by case law or statute. Some states only have commercial host liability for service to minors (3). Some states also restrict commercial host liability by increasing evidence requirements, capping the amount of compensation allowed in suits, or restricting who may be sued. However, the existence of commercial host liability in a state is thought to improve compliance with laws prohibiting alcohol service to intoxicated patrons or minors (2).

Significance

This indicator provides information on the existence of commercial host liability and whether this liability has major restrictions in those states where it exists. In states with major restrictions on this liability, the impact of this intervention on excessive alcohol use and related harms is likely to be reduced (2,3).

Limitations of indicator

The legal research required to support this indicator is time-consuming and requires expertise in the area of alcohol control policies and legal analysis.

Data resources

Mosher, JF, Cohen, EN, Jernigan, DH. Commercial host (dram shop) liability: current status and trends. Am J Prev Med 2013;45:347–53.

Limitations of data resources

This indicator is updated annually only for dram shop liability for sales to underage youths; no time frame exists for updating dram shop liability status for sales to adults. Specialized legal consultation is required to interpret laws and regulations.

Related Indicators or Recommendations

CDC Prevention Status Report: Excessive alcohol use (4).

Related chronic disease indicator topics

None.


References

  1. Task Force on Community Preventive Services. Recommendations on dram shop liability and overservice law enforcement initiatives to prevent excessive alcohol consumption and related harms. Am J Prev Med 2011;41:344–6.
  2. Rammohan V, Hahn RA, Elder R, et al; Task Force on Community Preventive Services. Effects of dram shop liability and enhanced overservice law enforcement initiatives on excessive alcohol consumption and related harms: two Community Guide systematic reviews. Am J Prev Med 2011;41:334–43.
  3. Mosher JF, Cohen EN, Jernigan DH. Commercial host (dram shop) liability: current status and trends. Am J Prev Med 2013;45:347–53.
  4. CDC. Prevention status reports, 2013: excessive alcohol use. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.cdc.gov/psr/alcohol/index.html.

Indicator Group: Alcohol

Indicator 10: Local authority to regulate alcohol outlet density

Demographic group

All resident persons.

Numerator

State has at least one of the following: 1) exclusive local alcohol retail licensing, 2) joint local and state alcohol retail licensing, 3) exclusive state alcohol retail licensing but with local zoning authority, 4) mixed alcohol retail licensing policies, 5) nearly exclusive state alcohol retail licensing, or 6) exclusive state alcohol retail licensing.

Denominator

None.

Measures of frequency

Status of state's local authority to regulate alcohol outlet density.

Time period of case definition

Annual as of January 1.

Background

The Community Preventive Services Task Force has found sufficient evidence to recommend limiting alcohol outlet density through the use of regulatory authority (e.g., licensing and zoning) as a means of reducing or controlling excessive alcohol consumption and related harms (1). However, states vary in the extent to which they allow local governments to regulate the licensing of retail alcohol outlets and hence alcohol outlet density, ranging from the delegation of licensing authority to local governments to complete state control over alcohol licensing (2,3).

Significance

This indicator provides information on the degree of local control over the regulation of alcohol outlet density. In general, states that allow for greater local control over the regulation of alcohol outlet density (i.e., those that do not preempt local control over alcohol licensing) provide local governments with more opportunities to regulate alcohol outlet density and thereby reduce excessive alcohol consumption and related harms (2).

Limitations of indicator

The legal research required to support this indicator is time-consuming and requires expertise in the area of liquor control law.

Data resources

Mosher JF, Treffers R. State preemption, local control, and the regulation of alcohol retail outlet density. Am J Prev Med 2013; 44:399–405.

Limitations of data resources

No specified timeframe exists for updating this indicator. Specialized legal consultation is required to interpret laws and regulations.

Related indicators or recommendations

CDC Prevention Status Report: Excessive alcohol use (4).

Related chronic disease indicator topics

None.


References

  1. Task Force on Community Preventive Services. Recommendations for reducing excessive alcohol consumption and alcohol-related harms by limiting alcohol outlet density. Am J Prev Med 2009;37:570–1.
  2. Campbell CA, Hahn RA, Elder R, et al; Task Force on Community Preventive Services. The effectiveness of limiting alcohol outlet density as a means of reducing excessive alcohol consumption and alcohol-related harms. Am J Prev Med 2009;37:556–69.
  3. Mosher JF, Treffers RD. State preemption, local control, and the regulation of alcohol retail outlet density. Am J Prev Med 2013;44:399–405.
  4. CDC. Prevention status reports, 2013: excessive alcohol use. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. Available at http://www.cdc.gov/psr/alcohol/index.html.

Indicator Group: Arthritis

Indicator 1.1: Arthritis among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report having physician-diagnosed arthritis.

Denominator

Respondents aged ≥18 years who answered "yes" or "no" to the following question: "Have you ever been told by a doctor or other health professional that you have some form of arthritis, rheumatoid arthritis, gout, lupus, or fibromyalgia?" (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence (during 2011–2015) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Lifetime.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). As the population ages, arthritis is expected to affect an estimated 67 million adults in the United States by 2030 (2). In 2003, arthritis cost an estimated $128 billion in direct medical and indirect costs (3).

Significance

Monitoring the prevalence and public health effects of arthritis are important for estimating the state-specific need for interventions that reduce symptoms, improve physical function, and improve the quality of life for persons with arthritis. These interventions include self-management education programs that have been shown to reduce pain and improve psychological health and health behaviors and physical activity programs that have been shown to improve physical function, mental health, and quality of life.

Limitations of indicator

Physician-diagnosed arthritis is self-reported in the Behavioral Risk Factor Surveillance System and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be acceptable for surveillance purposes (4) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core).

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

In addition, CDC typically provides this estimate in standard arthritis BRFSS tables produced for each state for odd-numbered years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective AOCBC-1: Reduce the mean level of joint pain among adults with doctor-diagnosed arthritis.

Healthy People 2020 objective AOCBC-2: Reduce the proportion of adults with doctor-diagnosed arthritis who experience a limitation in activity because of arthritis or joint symptoms.

Healthy People 2020 objective AOCBC-4: Reduce the proportion of adults with doctor-diagnosed arthritis who have difficulty in performing two or more personal care activities, thereby preserving independence.

Healthy People 2020 objective AOCBC-5: Reduce the proportion of adults with doctor-diagnosed arthritis who report serious psychological distress.

Healthy People 2020 objective AOCBC-6: Reduce the impact of doctor-diagnosed arthritis on employment in the working-age population.

Healthy People 2020 objective AOCBC-7: Increase the proportion of adults with doctor-diagnosed arthritis who receive health-care provider counseling.

Healthy People 2020 objective AOCBC-8: Increase the proportion of adults with doctor-diagnosed arthritis who have had effective, evidence-based arthritis education as an integral part of the management of their condition.

Related chronic disease indicator topics

None.


References

  1. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR 2013;62:869–73.
  2. Hootman JM, Helmick CG. Projections of U.S. prevalence of arthritis and associated activity limitations. Arthritis Rheum 2006;54:226–9.
  3. CDC. National and state medical expenditures and lost earnings attributable to arthritis and other rheumatic conditions—United States, 2003. MMWR 2007;56:4–7.
  4. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Arthritis

Indicator 1.2: Arthritis among adults aged ≥18 years who are obese

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report physician-diagnosed arthritis and who are obese (body mass index ≥30.0 kg/m²), calculated from self-reported weight and height.

Denominator

Respondents aged ≥18 years who are obese (body mass index ≥30.0 kg/m² calculated from self-reported weight and height) (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). In 2003, arthritis resulted in an estimated $128 billion in direct medical and indirect costs (2). Obesity is common among persons with arthritis and is a modifiable risk factor associated with arthritis-related disease progression, activity limitation, disability, reduced quality-of-life, total joint replacement, and poor clinical outcomes after joint replacement (3). The prevalence of obesity among adults with arthritis is an average of 54% higher than among adults without arthritis (3).

Significance

Monitoring the prevalence of arthritis among adults who are obese is important because obesity can worsen arthritis-related joint pain. Reaching and maintaining a normal weight can lower the risk for developing osteoarthritis, the most common type of arthritis (representing approximately two thirds of arthritis cases) and can improve symptoms and function in persons who already have the condition (3).

Limitations of indicator

Physician-diagnosed arthritis is self-reported in the Behavioral Risk Factor Surveillance System and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be acceptable for surveillance purposes (4) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core). Height and weight are self-reported. Respondents tend to overestimate their height and underestimate their weight (3), likely leading to underestimation of body mass index and of the prevalence of obesity.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

In addition, CDC typically provides this estimate in standard arthritis BRFSS tables produced for each state for odd-numbered years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective AOCBC-7a: Increase the proportion of adults with physician-diagnosed arthritis who receive health-care provider counseling [...] for weight reduction among overweight and obese persons.

Related chronic disease indicator topics

Nutrition, physical activity, and weight status.


References

  1. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR 2013;62:869–73.
  2. CDC. National and state medical expenditures and lost earnings attributable to arthritis and other rheumatic conditions—United States, 2003. MMWR 2007;56:4–7.
  3. CDC. State-specific trends in obesity prevalence among adults with arthritis, Behavioral Risk Factor Surveillance System, 2003–2009. MMWR 2011;60:509–13.
  4. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Arthritis

Indicator 1.3: Arthritis among adults aged ≥18 years who have diabetes

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report ever having physician-diagnosed diabetes other than diabetes during pregnancy and who report physician-diagnosed arthritis.

Denominator

Respondents aged ≥18 years who report ever having physician-diagnosed diabetes other than diabetes during pregnancy (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). During 2005 and 2007, the prevalence of arthritis among adults aged ≥18 years with diabetes was 52%, compared with 27% for all adults aged ≥18 years (2). The prevalence of physical inactivity is higher among adults with both diabetes and arthritis than those with either condition alone; physical activity is a recommended self-management strategy for both diabetes and arthritis (2).

Significance

Monitoring the prevalence of arthritis among adults with diabetes is important because more than half of the adults with diabetes also have arthritis (2). Diabetes and arthritis occur more frequently in older adults, women, and those who are obese (2). Arthritis might be an unaddressed barrier for adults with diabetes seeking to manage their condition through physical activity. Persons with arthritis report that increased joint pain is the number one barrier to participating in physical activities (3). Physical activity helps control blood glucose for persons with diabetes and can reduce pain, improve function, and delay disability among adults with arthritis (2). This indicator can be used to estimate the number of persons with diabetes who might need special interventions to help them become more physically active and manage their disease (e.g., through the Chronic Disease Self-Management Program or EnhanceFitness).

Limitations of indicator

Physician-diagnosed arthritis is self-reported in the Behavioral Risk Factor Surveillance System and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be acceptable for surveillance purposes (4) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core). Diabetes also is self-reported.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

In addition, CDC typically provides this estimate in standard arthritis BRFSS tables produced for each state for odd-numbered years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Diabetes.


References

  1. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR 2013;62:869–73.
  2. CDC. Arthritis as a potential barrier to physical activity among adults with diabetes—United States, 2005 and 2007. MMWR 2008;57:486–9.
  3. Brittain DR, Gyurcsik NC, McElroy M, Hillard SA. General and arthritis-specific barriers to moderate physical activity in women with arthritis. Womens Health Issues 2011;21:57–63.
  4. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Arthritis

Indicator 1.4: Arthritis among adults aged ≥18 years who have heart disease

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report ever being told by a doctor, nurse, or other health professional that they had heart disease (myocardial infarction or coronary heart disease) and who report having physician-diagnosed arthritis.

Denominator

Respondents aged ≥18 years who report ever being told by a doctor, nurse, or other health professional that they had heart disease (myocardial infarction or coronary heart disease) (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). During 2005 and 2007, the prevalence of arthritis among adults aged ≥18 years with heart disease was 57%, compared with 27% for all adults aged ≥18 years (2). The prevalence of physical inactivity is higher among adults with both heart disease and arthritis than those with either condition alone (2); physical activity is a recommended self-management strategy for both heart disease and arthritis (2).

Significance

Monitoring the prevalence of arthritis among adults with heart disease is important because approximately half of the adults with heart disease also have arthritis. Heart disease and arthritis occur more frequently in older adults and those who are obese (2). Arthritis might be an unaddressed barrier for adults with heart disease seeking to manage their condition through physical activity. Persons with arthritis report that increased joint pain is the most common barrier to participating in physical activities (3). Physical activity helps control blood pressure and helps persons reach and maintain a healthy weight for persons with heart disease and can reduce pain, improve function, and delay disability among adults with arthritis (4). This indicator can be used to estimate the number of persons with heart disease who might need special interventions to help them become more physically active and manage their disease (e.g., through the Chronic Disease Self-Management Program or EnhanceFitness).

Limitations of indicator

Physician-diagnosed arthritis is self-reported in the Behavioral Risk Factor Surveillance System and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be valid for surveillance purposes (5) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core). Physician-diagnosed heart disease also was self-reported.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).
In addition, CDC typically provides this estimate in standard arthritis BRFSS tables produced for each state for odd-numbered years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Cardiovascular disease.


References

  1. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR 2013;62:869–73.
  2. CDC. Arthritis as a potential barrier to physical activity among adults with heart disease—United States, 2005 and 2007. MMWR 2009;58:165–9.
  3. Brittain DR, Gyurcsik NC, McElroy M, Hillard SA. General and arthritis-specific barriers to moderate physical activity in women with arthritis. Womens Health Issues 2011;21:57–63.
  4. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.
  5. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Arthritis

Indicator 2: Activity limitation due to arthritis among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report having physician-diagnosed arthritis and an activity limitation because of arthritis or joint symptoms.

Denominator

Respondents aged ≥18 years (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biannual prevalence with 95% confidence intervals (odd-numbered years) and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). By 2030, 25 million (9.3% of the adult population) are projected to report arthritis-attributable activity limitations (2). In 2003, arthritis cost an estimated $128 billion in direct medical and indirect costs (3).

Significance

Monitoring the prevalence of arthritis-attributable activity limitation among the general population of adults is important for estimating the state-specific impact of arthritis, the need for interventions to reduce the disabling effects of arthritis, and how well existing interventions are working. These interventions include self-management education programs that have been shown to reduce pain and improve psychological health and health behaviors, and physical activity programs that have been shown to improve physical function, mental health, and quality of life (4).

Limitations of indicator

Physician-diagnosed arthritis is self-reported in the Behavioral Risk Factor Surveillance System and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be acceptable for surveillance purposes (5) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core). Activity limitation also is self-reported.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS), Arthritis Burden Module (odd-numbered years only).

In addition, CDC typically provides this estimate in standard arthritis years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective AOCBC-2: Reduce the proportion of adults with doctor-diagnosed arthritis who experience a limitation in activity because of arthritis or joint symptoms.

Related chronic disease indicator topics

Disability.


References

  1. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR 2013;62:869–73.
  2. Hootman JM, Helmick CG. Projections of U.S. prevalence of arthritis and associated activity limitations. Arthritis Rheum 2006;54:226–9.
  3. CDC. National and state medical expenditures and lost earnings attributable to arthritis and other rheumatic conditions—United States, 2003. MMWR 2007;56:4–7.
  4. Hootman JM, Helmick CG, Brady TJ. A public health approach to addressing arthritis in older adults: the most common cause of disability. Am J Public Health 2012;102:426–33.
  5. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Arthritis

Indicator 3: Physical inactivity among adults aged ≥18 years with arthritis

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report physician-diagnosed arthritis and no leisure-time physical activity. Includes respondents reporting no activity when asked six questions about frequency and duration of participation in nonoccupational activities of moderate and vigorous intensity (i.e., lifestyle activities). All other respondents were classified as active.

Denominator

Respondents aged ≥18 years who report physician-diagnosed arthritis (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). By 2030, 25 million (9.3% of the adult population) are projected to report arthritis-attributable activity limitations (2). Staying physically active and maintaining a healthy weight through diet and exercise are recommended for persons with arthritis to reduce and delay disability (3). In 2003, arthritis cost an estimated $128 billion in direct medical and indirect costs (4).

Significance

Monitoring the prevalence of inactivity among persons with arthritis is important because increasing physical activity has significant benefits for persons with arthritis, including reductions in pain and improvements in physical function, mental health, and quality of life (5).

Limitations of indicator

Physician-diagnosed arthritis is self-reported in the Behavioral Risk Factor Surveillance System and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be acceptable for surveillance purposes (6) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core). Physical activity also is self-reported. Unadjusted data are presented in this report to provide actual estimates to help in state-level program planning.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).
In addition, CDC typically provides this estimate in standard arthritis BRFSS tables produced for each state for odd-numbered years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective AOCBC-7.2: Increase the proportion of adults with doctor-diagnosed arthritis who receive health-care provider counseling[...]for physical activity or exercise.

Related chronic disease indicator topics

Nutrition, physical activity, and weight status.


References

  1. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR 2013;62:869–73.
  2. Hootman JM, Helmick CG. Projections of U.S. prevalence of arthritis and associated activity limitations. Arthritis Rheum 2006;54:226–9.
  3. Hochberg MC, Altman RD, April KT, et al; American College of Rheumatology. American College of Rheumatology 2012 recommendations for the use of nonpharmacologic and pharmacologic therapies in osteoarthritis of the hand, hip and knee. Arthritis Care Res 2012;64:465–74.
  4. CDC. National and state medical expenditures and lost earnings attributable to arthritis and other rheumatic conditions—United States, 2003. MMWR 2007;56:4–7.
  5. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.
  6. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Arthritis

Indicator 4: Fair or poor health among adults aged ≥18 years with arthritis

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report physician-diagnosed arthritis and who report that their health is fair or poor.

Denominator

Respondents aged ≥18 years who report physician-diagnosed arthritis (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). Based on combined 2003, 2005, and 2007 BRFSS data, of persons aged ≥18 years with arthritis, 27% reported fair/poor health, compared with 12% without arthritis (2).

Significance

Monitoring health-related quality of life among adults with arthritis is important because persons with arthritis report worse health related quality of life than adults without arthritis (2). Self-management education can help improve physical function and quality of life among adults with arthritis (3) As self-management education becomes more widespread in states, this measure can help track improvements in quality of life of persons with arthritis.

Limitations of indicator

Physician-diagnosed arthritis is self-reported in BRFSS and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be acceptable for surveillance purposes (4) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core). General health status also is self-reported.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

In addition, CDC typically provides this estimate in standard arthritis BRFSS tables produced for each state for odd-numbered years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR 2013;62:869–73.
  2. Furner SE, Hootman JM, Helmick CG, Bolen J, Zack MM. Health-related quality of life of U.S. adults with arthritis: analysis of data from the Behavioral Risk Factor Surveillance System, 2003, 2005, and 2007. Arthritis Care Res 2011;63:788–99.
  3. Hootman JM, Helmick CG, Brady TJ. A public health approach to addressing arthritis in older adults: the most common cause of disability. Am J Public Health 2012;102:426–33.
  4. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Arthritis

Indicator 5: Adults aged ≥18 years with arthritis who have taken a class to learn how to manage arthritis symptoms

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report physician-diagnosed arthritis and who report ever taking a course or class on managing their arthritis or joint symptoms.

Denominator

Respondents aged ≥18 years who report physician-diagnosed arthritis (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biannual prevalence with 95% confidence intervals (for odd-numbered years) and by demographic characteristics when feasible.

Time period of case definition

Lifetime.

Background

An estimated 52.5 million adults have physician-diagnosed arthritis, and 22.7 million report arthritis-attributable activity limitations (1). CDC recommends evidence-based programs that are proven to improve the quality of life of persons with arthritis, including self-management education classes or courses. Based on Healthy People 2010 data, only 11% of adults with arthritis have taken such recommended classes (2).

Significance

Self-management education programs can reduce pain and health-care costs and are an important arthritis intervention (3). The Arthritis Foundation's Self-Help Program teaches persons how to manage arthritis and decrease its effects. This 6-week course reduces arthritis pain by 20% (3). More widespread use of this course and similar programs (e.g., the Chronic Disease Self-Management Program, which addresses arthritis along with other chronic diseases) could improve function and quality of life for persons with arthritis. This measure will indicate the proportion of adults with arthritis who have ever taken a course or class to manage their symptoms.

Limitations of indicator

Physician-diagnosed arthritis is self-reported in the Behavioral Risk Factor Surveillance System and was not confirmed by a health-care provider or objective monitoring; however, such self-reports have been shown to be valid for surveillance purposes (4) (despite minor changes made in 2011 to the case-finding question to include arthritis on the chronic conditions core). Participation in self-management programs is also self-reported. Unadjusted data are presented in this report to provide actual estimates for state-level program planning. Because this question comes from a Behavioral Risk Factor Surveillance System optional module, data are missing for some states.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS), Arthritis Management optional module (odd-numbered years only).

In addition, CDC typically provides this estimate in standard arthritis BRFSS tables produced for each state for odd-numbered years. Unadjusted data are usually presented in these tables to provide actual estimates to help in state-level program planning.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective AOCBC-8: Increase the proportion of adults with physician-diagnosed arthritis who have had effective, evidence-based arthritis education as an integral part of the management of their condition.

Related chronic disease indicator topics

None.


References

  1. Hootman JM, Helmick CG. Projections of US prevalence of arthritis and associated activity limitations. Arthritis Rheum 2006;54:226–9.
  2. US Department of Health and Human Services. Healthy people 2020. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.healthypeople.gov/2020/topicsobjectives2020/default.
  3. Stanford School of Medicine. Arthritis self-management program. Stanford, CA: Stanford School of Medicine. Available at http://patienteducation.stanford.edu/programs/asmp.html.
  4. Sacks JJ, Harrold LR, Helmick CG, Gurwitz JH, Emani S, Yood RA. Validation of a surveillance case definition for arthritis. J Rheumatol 2005;32:340–7.

Indicator Group: Asthma

Indicator 1.1: Current asthma prevalence

Demographic group

Civilian noninstitutionalized population.

Numerator

Weighted number of respondents who answer "yes" both to both of the following questions: "Have you ever been told by a doctor, nurse, or other health professional that you have asthma?" and "Do you still have asthma?"

Denominator

Weighted number of respondents to Behavioral Risk Factor Surveillance System (BRFSS) or National Survey of Children's Health (NSCH), excluding "don't know" and refused responses to the question "Do you still have asthma?".

Measures of frequency

Annual number of state residents with current asthma and annual current asthma prevalence percentage with 95% confidence intervals and by demographic characteristics when feasible.

Annual number of adults (aged ≥18 years) with current asthma and percentage of adults with current asthma with 95% confidence intervals and by demographic characteristics when feasible.

Annual number of children (aged 0–17 years) with current asthma and percentage of children with current asthma with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year of survey.

Background

Estimates of asthma prevalence indicate the number and percentage of the population with asthma at a given point in time. National estimates indicate that both adult and child current asthma prevalence estimates have been increasing from 20.3 million persons in 2001 to 25.7 million persons in 2010, of which 7.0 million were children (1). Adult current asthma prevalence, available for states from BRFSS since 2001, varies by state and region as well as by many demographic characteristics (2). Current asthma prevalence among children is available for a subset of states from BRFSS annually since 2005 (2). Current asthma prevalence among children is available for all states from the National Survey of Children's Health for 2003, 2007, and 2011 (3).

Significance

Asthma prevalence describes the size of a state's population with asthma as well as the overall asthma prevalence relative to other chronic conditions. The greater the prevalence of asthma, the greater the likelihood of adverse outcomes from asthma including emergency department visits, hospitalizations, and death (1). Compared with persons without asthma, persons with asthma have more days of activity limitation and missed school and missed work and are more likely to report comorbid depression (48).

Limitations of indicator

Although all states have collected adult BRFSS data annually since 2001, not all states collect child data using the child asthma module of the BRFSS. States that do not collect child asthma data from BRFSS cannot produce the total indicator for all years; they can only produce the adult indicator. However, child asthma prevalence data for all states is available every 4 years using the National Survey of Children's Health (2003, 2007, and 2011). (Because NSCH is being redesigned, its mode and future periodicity is unknown at this time.) For these years a total indicator can be produced for all states by combining the adult prevalence from BRFSS with the child prevalence from NSCH. This survey-based indicator requires a doctor diagnosis of asthma, which may not include all persons with asthma. The child information is provided by an adult proxy respondent.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS) for adults (all states) and for children (some states).

National Survey of Children's Health for those states not collecting child data with BRFSS.

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  2. CDC. Behavioral Risk Factor Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.cdc.gov/brfss/annual_data/annual_data.htm.
  3. CDC. National Survey of Children's Health. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.cdc.gov/nchs/slaits/nsch.htm.
  4. Moonie S, Sterling D, Figgs L, Castro M. Asthma status and severity affects missed school days. J School Health 2006;76:18–24.
  5. King ME. Serious psychological distress and asthma [Chapter 5]. In: Preedy VR, ed. Scientific basis of healthcare. London, England: Science Publishers; 2012:86–107. Available at http://www.crcnetbase.com/doi/abs/10.1201/b11607-6?cookieSet=.
  6. Strine TW, Mokdad AH, Balluz LS, et al. Depression and anxiety in the United States: findings from the 2006 Behavioral Risk Factor Surveillance System. Psychiatr Serv 2008;59:1383–90.
  7. Chapman DP, Perry GS, Strine TW. The vital link between chronic disease and depressive disorders. Prev Chronic Dis 2005;2:A14.
  8. Scott KM, Von Korff M, Ormel J, et al. Mental disorders among adults with asthma: results from the World Mental Health Survey. Gen Hosp Psychiatry 2007;29:123–33.

Indicator Group: Asthma

Indicator 1.2: Asthma prevalence among women aged 18–44 years

Demographic group

Women aged 18–44 years.

Numerator

Female respondents aged 18–44 years who reported that they had ever been told by a doctor, nurse, or other health professional that they had asthma and reported that they still have asthma.

Denominator

Female respondents aged 18–44 years who reported that they had or had not ever been told by a doctor, nurse, or other health professional that they had asthma (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual number of women (aged 18–44 years) with current asthma with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year of survey.

Background

Asthma affects at least 8.2% of pregnant women and 9.4% of women of reproductive age in the United States (1). For approximately 30% of women with asthma, the severity of the disease worsens during pregnancy (2).

Significance

Although outcomes of pregnancy in which the woman's asthma is mild or well-controlled are usually good, severe and poorly controlled asthma during pregnancy might be associated with an increased likelihood of premature delivery, cesarean delivery, preeclampsia, growth restriction, other perinatal complications, and maternal morbidity and mortality (3). Furthermore, subsequent pregnancies tend to follow a course similar as the first pregnancy with respect to status of asthma severity (4). The clinical workgroup of the Select Panel on Preconception Care recommends that women of reproductive age with asthma be counseled about the importance of achieving asthma control before pregnancy and the potential for their asthma control to decline during pregnancy (2). The panel also recommends that those women with poor control of their asthma should be encouraged to use effective birth control until they achieve symptom relief (2). Finally, preventive therapy with inhaled corticosteroids is highly recommended for women with chronic asthma who are planning to become pregnant or who could become pregnant as use of these medications before pregnancy has been shown to reduce the rate of asthma-related health-care use during pregnancy (4).

Limitations of indicator

This survey-based indicator requires a doctor diagnosis of asthma, which might not include all persons with asthma.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Reproductive health.


References

  1. Kwon HL, Belanger K, Bracken MB. Asthma prevalence among pregnant and childbearing-aged women in the United States: estimates from national health surveys. Ann Epidemiol 2003;13:317–24.
  2. Dunlop AL, Jack BW, Bottalico JN, et al. The clinical content of preconception care: women with chronic medical conditions. Am J Obstet Gynecol 2008;199(Suppl B):S310–27.
  3. American College of Obstetricians and Gynecologists. Asthma in pregnancy. ACOG Practice Bulletin No. 90. Obstet Gynecol 2008;111:457–64.
  4. Schatz M, Dombrowski MP, Wise R, et al. Asthma morbidity during pregnancy can be predicted by severity classification. J Allergy Clin Immunol 2003;112:283–8.

Indicator Group: Asthma

Indicator 2.1: Emergency department visit rate for asthma

Demographic group

All resident persons.

Numerator

1) Emergency department (ED) visits with a principal diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 493, 2) (if not already included) hospitalizations with the ED as the source of admission and an admission diagnosis of ICD-9-CM code 493, and 3) (if not already included) 24-hour observation beds where the source of the admission was the ED with a principal admission diagnosis of ICD-9-CM code 493 among residents during a calendar year. When possible, include ED visits, 24-hour observations, and hospitalizations for residents who have an ED visit, 24-hour observation, or hospitalization in another state.

Denominator

Midyear resident population for the same calendar year, obtained from the U.S. Census Bureau.

Measures of frequency

Annual number of ED visits. Annual ED visit rate per 10,000: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) and by demographic characteristics when feasible.

Time period of case definition

Use the ED discharge date used if available for the calendar year. For ED cases found in the hospitalization data that did not have an ED discharge date, use the hospital admission date as the ED discharge date.

Background

Each year, approximately 3.2 million ED visits related to asthma occur in the United States (2). As of 2010, an estimated 25.7 million U.S. residents had asthma, which is a 27% increase over 10 years (3). The cost of ED care is substantially higher than the cost of outpatient and pharmaceutical services (4).

Significance

Scientific and clinical consensus is that the majority of acute asthma events, particularly ED visits, can be prevented if asthma is properly managed according to established medical guidelines (5). Effective management includes control of exposure to factors that trigger exacerbations, adequate pharmacological management, continual monitoring of the disease, and patient education in asthma care (5).

Limitations of indicator

This indicator might overestimate the impact of asthma exacerbations because persons sometime use the ED inappropriately (i.e., for primary care). This indicator is calculated using the entire resident population, which includes persons who do not have asthma. A more appropriate measure is a rate that is calculated using the population that has asthma (i.e., based on those at risk). (See also the following indicator: At-Risk Emergency Department Visit Rate for Asthma.) A subset of patients who appear in the numerator of ED indicators (those who were hospitalized with the ED as source of admission) also appear in the numerator for both hospitalization indicators. Because resources were used by the patients at each site (ED and inpatient), counting the events in both indicators is important as an indicator of asthma hospital visit rates.

Data resources

State ED visit, observation unit, and hospitalization discharge data (numerator) from Agency for Healthcare Research and Quality and population estimates from the U.S. Census Bureau (denominator).

Limitations of data resources

Not all states have access to administrative billing ED data. The diagnosis information contained in this data source might not match perfectly with information on the medical records, which is considered the gold standard. State ED data sets might not include all facilities or populations. They might exclude Veterans Administration hospitals, Indian Health Service facilities, or institutionalized (prison) populations. For most states, this measure only includes state residents who visited the ED in their own state.

Related indicators or recommendations

Healthy People 2020 objective RD-3: Reduce hospital emergency department visits for asthma. (RD-3.1 is specific for children aged <5 years; RD-3.2 is specific for children and adults aged 5–64 years; RD-3.3 is specific for adults aged ≥65 years.)

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Vital signs. Asthma prevalence, disease characteristics, and self-management education—United States, 2001–2009. MMWR 2011;60:547–52.
  3. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  4. Barnett SBL, Nurmagambetov TA. Costs of asthma in the United States: 2002–2007. J Allergy Clin Immunol 2011; 127:145–152.
  5. National Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda MD: National Institutes of Health; 2007.

Indicator Group: Asthma

Indicator 2.2: Risk-based emergency department visit rate for asthma (emergency department visits for asthma per persons with asthma)

Demographic group

Residents with asthma.

Numerator

1) Emergency department (ED) visits with a principal diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 493, 2) (if not already included) hospitalizations with the ED as the source of admission and an admission diagnosis of ICD-9-CM code 493, and 3) (if not already included) 24-hour observation beds where the source of the admission was the ED with a principal admission diagnosis of ICD-9-CM code 493 among residents during a calendar year. When possible, include ED visits, 24-hour observations, and hospitalizations for residents who have an ED visit, 24-hour observation, or hospitalization in another state.

Denominator

Estimate of the number of state residents with current asthma for the same calendar year. For adults the estimate is obtained from the Behavioral Risk Factor Surveillance System (BRFSS) and, for children, from the BRFSS child asthma module, if implemented. If not implemented, the child estimate can be obtained from the National Survey of Children's Health (NSCH).

Measures of frequency

Annual number of state residents with current asthma, annual number of asthma ED visits, and annual at-risk asthma ED visit rate by demographic characteristics when feasible.

• Children (aged 0–17 years) if BRFSS data are available.

  • Annual number of ED visits for children aged 0–17 years.
  • Annual number of state residents aged 0–17 years with current asthma.
  • Annual risk-based child asthma ED rate per 10,000 population, crude and age-adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 19 [1]).

• Children (aged 0–17 years) if BRFSS data are not available.

  • Number of ED visits for children aged 0–17 years for 2003, 2007, and 2011 (and every 4 years thereafter).
  • Number of state residents aged 0–17 years with current asthma for 2003, 2007, and 2011 (and every 4 years thereafter).
  • Risk-based child asthma ED visit rate per 10,000, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 19 [1]) for 2003, 2007, and 2011 (and every 4 years thereafter).

• Adults (aged ≥18 years).

  • Annual number of ED visits for adults aged ≥18 years.
  • Annual number of state residents aged ≥18 years with current asthma: Annual risk-based adult asthma ED visit rate per 10,000, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]).

• Total (all ages).

  • Annual number of ED visits for states and years with child prevalence and adult prevalence.
  • Annual number of state residents with current asthma for states and years with child prevalence and adult prevalence.
  • Annual risk-based asthma ED visit rate per 10,000, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 3 [1]) for states and years with child prevalence and adult prevalence).

Time period of case definition

Use the ED discharge date if available for the calendar year. For ED cases found in the hospitalization data that did not have an ED discharge date, use the hospital admission date as the ED discharge date.

Background

In 2009, 2.1 million ED visits were related to asthma in the United States (2). As of 2010, an estimated 25.7 million U.S. residents had asthma (2), which is a 27% increase over 10 years. The at-risk–based asthma ED rate controls for the increase in the number of state residents with asthma that has occurred over time or differences in the underlying asthma prevalence across states. The population-based rates can increase simply because more persons have asthma each year or are in specific geographic areas. The at-risk–based rate reflects the use of EDs for asthma care per person with asthma and is therefore independent of the number of persons with asthma.

The cost of ED care is substantially higher than the cost of outpatient and pharmaceutical services. An at-risk–based asthma ED rate better reflects the prevalence and asthma independent of the increase or differences in the number of persons with asthma. As more persons with asthma achieve higher levels of asthma control, the at-risk rate should decrease while the population-based rate might increase because more persons have asthma.

Significance

Scientific and clinical consensus is that the majority of acute asthma events, particularly ED visits, can be prevented if asthma is properly managed according to established medical guidelines. Effective management includes control of exposure to factors that trigger exacerbations, adequate pharmacological management, continual monitoring of the disease, and patient education in asthma care (3).

Limitations of indicator

As with the population-based rate, recognizing that myriad environmental factors affect asthma control is crucial, including the use of the ED (as opposed to physicians' offices) as the location to medically manage asthma. Diagnoses listed in ED data might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to see patients in the ED.

Because universal state ED data are not available, aggregations of state data to produce nationwide estimates are incomplete. Because repeat ED visits by the same person in a single calendar year are included in the numerator, the ED rate is a visit-level rate, not a person-level rate. A limited number of states have personal identifiers in the ED data and are able to deduplicate ED visit data. (As used here, the term deduplicated means that persons with multiple admissions during the calendar year are counted only once. However, deduplication of multiple billing records for the same ED visit is assumed.) However, assessing the full impact on the medical care system is important. Accordingly, the ED rate includes multiple visits by the same person and the rate calculated is a visit-level rate, not a person-level rate. However, multiple billing records for the same ED visit should only be counted as a single event. A subset of patients who appear in the numerator of ED indicators (those who were hospitalized with the ED as source of admission) also are included in the numerator for both hospitalization indicators. Because resources were used by the patients at each site (ED and inpatient), counting the events in both indicators as an indicator of asthma impact on the hospital system is important.

Data resources

State ED visit, observation unit, and state hospital discharge data (numerator) from the Agency for Healthcare Research and Quality and estimates of the number of state residents with current asthma from the Behavioral Risk Factor Surveillance System (BRFSS, adults and children) or the National Survey of Children's Health (NSCH) (children) if the Behavior Risk Factor Surveillance System child asthma module was not conducted (denominator).

Limitations of data resources

If the BRFSS child module was not implemented, annual child prevalence estimates are only available for 2003, 2007, and 2011 from NSCH, which is conducted every 4 years. (Because NSCH is being redesigned, its mode and future periodicity is unknown at this time.) For ED cases found in the hospitalization data, cases with an ED discharge date or a hospital admit date in the calendar year of interest might require use of the hospital discharge data file from the next calendar year. Some admissions at the end of the calendar year are discharged the following calendar year and are found in the hospital discharge file for the next year.

ED data are not available for all states. State ED data sets may not include all facilities or populations. They might exclude Veterans Administration hospitals, Indian Health Service facilities, or institutionalized (prison) populations. For most states, this measure only includes state residents who visited the ED in their own state.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001.
  2. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  3. National Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program; 2007.

Indicator Group: Asthma

Indicator 3.1: Hospitalizations for asthma

Demographic group

All resident persons.

Numerator

Inpatient hospitalizations with a principal discharge diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification code 493 among residents during a calendar year. When possible, include hospitalizations for residents who are hospitalized in another state.

Denominator

Midyear resident population for the same calendar year, obtained from the US Census Bureau.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rate per 10,000: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) and by demographic characteristics when feasible.

Time period of case definition

Calendar year based on the hospital discharge date.

Background

Each year, approximately 480,000 hospitalizations related to asthma occur in the United States (2). As of 2010, an estimated 25.7 million U.S. residents have asthma, which is a 27% increase over 10 years (3). Although inpatient hospitalization for asthma is less frequently used than outpatient and pharmaceutical services, its cost is substantially higher.

Significance

Hospitalizations for asthma could be reduced if asthma were managed according to established guidelines. Effective management includes control of exposure to factors that trigger exacerbations, adequate pharmacological management, continual monitoring of the disease, and patient education in asthma care (4).

Limitations of indicator

Although reducing hospitalizations for asthma is theoretically a function of better care and self-management knowledge, the economy and the health-care system also greatly affect this measure. Practice patterns and payment mechanisms can affect decisions by health-care providers to hospitalize patients. The use of a population-based measure can be misleading because the measure is affected by changes in prevalence over space or time. Because one person can have multiple hospitalizations for asthma in a single calendar year, this indicator describes rate of events, not rate of persons hospitalized.

Data resources

State hospital discharge data (numerator) from the Agency for Healthcare Research and Quality and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

The diagnosis information contained in this data source might not match perfectly with information on the medical records, which is considered the gold standard. Hospital discharge data might not be available for all states. State hospitalization data sets might not include all facilities or populations. They might exclude Veterans Administration hospitals, Indian Health Service facilities, or institutionalized (prison) populations. For most states, this measure only includes state residents who were hospitalized in their own state. Hospital inpatient data files are usually organized by discharge date. Some inpatient stays may have been initiated in the previous calendar year since some admissions at the end of one calendar year are discharged the following calendar year. Consequently, rates based on discharge date may differ from other indicators based on admission date.

Related indicators or recommendations

Healthy People 2020 objective RD-2: Reduce hospitalizations for asthma. (RD-2.1 is specific for children aged <5 years; RD-2.2 is specific for children and adults aged 5–64 years; RD-2.3 is specific for adults aged ≥65 years.)

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Number and rate of discharges from short-stay hospitals and of days of care, with average length of stay, and standard error, by selected first-listed diagnostic categories: United States, 2009. National Hospital Discharge Survey. Atlanta, GA: US Department of Health and Human Services, CDC; 2009. Available at http://www.cdc.gov/nchs/data/nhds/2average/2009ave2_firstlist.pdf.
  3. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001. National Center for Health Statistics. Vital Health Stat 2012;3(35).
  4. National Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program; 2007.

Indicator Group: Asthma

Indicator 3.2: Risk-based hospital discharge rate for asthma (hospitalizations for asthma per persons with asthma)

Demographic group

Residents with asthma.

Numerator

In-patient hospitalizations with a principal discharge diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification code 493 among state residents during a calendar year. When possible, include hospitalizations for state residents who have been hospitalized in another state.

Denominator

Estimate of the number of state residents with current asthma for the same calendar year. For adults the estimate is obtained from the Behavioral Risk Factor Surveillance System (BRFSS) and, for children, from the BRFSS child asthma module, if implemented. If not implemented, the child estimate can be obtained from the National Survey of Children's Health (NSCH).

Measures of frequency

Annual number of state residents with current asthma, annual number of asthma inpatient hospitalizations, and annual at-risk asthma hospital discharge rate by demographic characteristics when feasible.

  • Children (aged 0–17 years) if BRFSS data are available.
    • Annual number of hospital discharges for children aged 0–17 years.
    • Annual number of state residents aged 0–17 years with current asthma.
    • Annual risk-based child asthma hospital discharge rate per 10,000 population, crude and age-adjusted (standardized by the direct method to the year 2000 standard U.S. population distribution [1]).
  • Children (aged 0–17 years) if BRFSS data are not available.
    • Number of hospital discharges for children aged 0–17 years for 2003, 2007, and 2011 (and every 4 years thereafter).
    • Number of state residents aged 0–17 years with current asthma for 2003, 2007, and 2011 (and every 4 years thereafter).
    • Risk-based child asthma hospital discharge rate per 10,000 population, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population distribution [1]) for 2003, 2007, and 2011 (and every 4 years thereafter).
  • Adults (aged ≥18 years)
    • Annual number of hospital discharges for adults aged ≥18 years.
    • Annual number of state residents aged ≥18 years with current asthma.
    • Annual risk-based adult asthma hospital discharge rate per 10,000 population, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population distribution [1]).
  • Total (all ages)
    • Annual number of hospital discharges for states and years with child prevalence and adult prevalence.
    • Annual number of state residents with current asthma for states and years with child prevalence and adult prevalence.
    • Annual risk-based asthma hospital discharge rate per 10,000 population, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population distribution [1]) for states and years with child prevalence and adult prevalence.

Time period of case definition

Calendar year based on the hospital discharge date.

Background

Each year, approximately 500,000 inpatient hospital stays for asthma occur in the United States (2). As of 2010, an estimated 25.7 million U.S. residents had asthma (2), which is a 27% increase over 10 years. The at-risk–based asthma hospital discharge rate controls for the increase in the number of state residents with asthma that has occurred over time or differences in the underlying asthma prevalence across states. The population-based rates can increase simply because more persons have asthma each year or are in specific geographic areas. The at-risk–based rate reflects hospitalizations for asthma per person with asthma and is therefore independent of the number of persons with asthma. The cost of inpatient stays is substantially higher than the cost of outpatient and pharmaceutical services (3). An at-risk–based asthma hospital discharge rate better reflects the prevalence and asthma independent of the increase or differences in the number of persons with asthma. As more persons with asthma achieve higher levels of asthma control, the at-risk rate should decrease while the population-based rate might increase because more persons have asthma.

Significance

Scientific and clinical consensus is that the majority of acute asthma events, including inpatient stays, can be prevented if asthma is properly managed according to established medical guidelines. Effective management includes control of exposure to factors that trigger exacerbations, adequate pharmacological management, continual monitoring of the disease, and patient education in asthma care (4).

Limitations of indicator

As with the population based rate, recognizing that myriad environmental factors affect asthma control is crucial, including hospitalizations (as opposed to physicians' offices) as the location to medically manage asthma. Diagnoses listed in hospital data might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to admit patients to the hospital. Because universal state hospital discharge data are not available, aggregations of state data to produce nationwide estimates are incomplete or differ from those resulting from the national surveys. Because repeat stays by the same person in a single calendar year are included in the numerator, the at-risk–based hospital discharge rate is an in-patient stay-level rate, not a person-level rate. A limited number of states have personal identifiers in the hospital discharge data and are able to deduplicate inpatient data. (The term deduplicated means that persons with multiple admissions during the calendar year are counted only once. However, deduplication of multiple billing records for the same hospital admission is assumed.) However, assessing the full impact on the medical care system is important. Accordingly, the hospital discharge rate includes multiple stays by the same person and the rate calculated is a stay-level rate, not a person-level rate. However, multiple billing records for the same hospital admission should only be counted as a single admission.

Data resources

State hospital discharge data (numerator) and estimates of the number of state residents with current asthma from the Behavioral Risk Factor Surveillance System (BRFSS) (adults and children) or the National Survey of Children's Health (NSCH) (children) if the BRFSS child asthma module was not conducted (denominator).

Limitations of data resources

If the BRFSS child module was not implemented, annual child prevalence estimates are only available for 2003, 2007, and 2011 from NSCH. (Because NSCH is being redesigned, its mode and future periodicity is unknown at this time.) Hospital inpatient data files are usually organized by discharge date. Some inpatient stays might have been initiated in the previous calendar year because some admissions at the end of one calendar year are discharged the following calendar year. Consequently, rates based on discharge date might differ from other indicators based on admission date. Hospital discharge data might not be available for all states. State hospital discharge data sets might not include all facilities or populations. They might exclude Veterans Administration hospitals, Indian Health Service facilities, or institutionalized (prison) populations. For most states, this measure only includes state residents who were hospitalized in their own state.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001.
  2. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  3. Barnett SBL, Nurmagambetov TA. Costs of asthma in the United States: 2002–2007. J Allergy Clin Immunol 2011;127:145–52.
  4. National Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute; 2007.

Indicator Group: Asthma

Indicator 4.1: Asthma mortality rate

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision code J45–J46 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year obtained from the U.S. Census Bureau.

Measures of frequency

Annual number of deaths. Annual mortality rate per million: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) and by demographic characteristics when feasible.

Time period of case definition

Calendar year. States with <20 deaths in a calendar year should calculate 3- to 5-year moving averages to increase reliability.

Background

The number of deaths with asthma as the underlying cause decreased from 4,483 in 2000 (2) to 3,816 in 2004 (2) and then to 3,388 in 2009 (3). A very slight increase to 3,404 occurred in 2010 (4). The population-based asthma mortality rate decreased from 16.1 in per million population in 2000 (2) to 10.6 in 2010, whereas the risk-based asthma mortality rate decreased from 2.1 per 10,000 persons with asthma in 2001 to 1.3 in 2010 (4).

Significance

The majority of the problems associated with asthma are preventable if asthma is managed according to established guidelines. Effective management includes control of exposure to factors that trigger exacerbations, adequate pharmacological management, continual monitoring of the disease, and patient education in asthma care. With proper management, deaths from asthma are theoretically preventable (5).

Limitations of indicator

The reliability of death certificate data for asthma has been questioned, particularly for older age groups. Asthma might be overreported or underreported for adults because of misreporting the cause of death, particularly in persons with confounding medical conditions. In one study, inconsistencies in death certificate completion resulted in asthma automatically overriding the underlying cause chosen, leading to an overestimation of asthma deaths among persons aged ≥55 years (6,7). In contrast, a larger and well-designed study concluded that 1) asthma death coding had very high specificity (99%) and low sensitivity (42%), 2) asthma as a cause of death was underreported rather than overreported in preference to chronic obstructive pulmonary disease (58% false negative, 1% false positive), 3) and age had no effect (8). This study casts some doubt on the assumption that coding of asthma deaths in older persons is unreliable in the United States. However, no studies representative of the entire U.S. vital statistics system have been published.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective RD-1: Reduce asthma deaths. (RD-1.1 is specific for children and adults aged <35 years; RD-1.2 is specific for adults aged 35–64 years; RD-1.3 is specific for adults aged ≥65 years.)

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National surveillance for asthma—United States, 1980–2004. MMWR 2007;56(No. SS-8).
  3. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  4. CDC. Wonder online databases. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://wonder.cdc.gov.
  5. National Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute; 2007.
  6. Brunner WM, Ross SK, Johnson JE. Review of the asthma mortality rate for Minnesota residents aged 55 years or older, 2004–2005: when death certificates deserve a second look. Prev Chronic Dis 2009;6:A92.
  7. Rosenman KD, Hanna E, Wasilevich EA, Lyon-Callo SK. 2007 annual report on asthma deaths among individuals aged 2–34 and 45–54 years in Michigan. East Lansing, MI: Michigan State University Department of Medicine; 2010. Available at http://getasthmahelp.org.
  8. Hunt LW, Silverstein MD, Reed CE, O'Connell EJ, O'Fallon WM, Yunginger JW. Accuracy of the death certificate in a population-based study of asthmatic patients. JAMA 1993;269:1947–52.

Indicator Group: Asthma

Indicator 4.2: Risk-based asthma mortality rate (mortality from asthma among persons with asthma)

Demographic group

Residents with asthma.

Numerator

Deaths with International Classification of Diseases, 10th Revision code J45-J46 as the underlying cause of death among residents during a calendar year.

Denominator

Estimate of the number of state residents with current asthma for the same calendar year, obtained from the Behavioral Risk Factor Surveillance System (for adults), and for children from the BRFSS child module, if implemented, or from the National Survey of Children's Health (NSCH) if the BRFSS child module was not implemented.

Measures of frequency

Annual number of residents with current asthma, annual number of asthma deaths, and annual at-risk asthma death rate by demographic characteristics when feasible. States with < 20 deaths in a calendar year should calculate 3- to 5-year moving averages to increase reliability.

  • Children (aged 0–17 years) from BRFSS if available.
    • Annual number of deaths for children aged 0–17 years.
    • Annual number of state residents aged 0–17 years with current asthma.
    • Annual risk-based child asthma mortality rate per million, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 19 [1]).
  • Children (aged 0–17 years) from NSCH if BRFSS data are not available.
    • Number of deaths for children aged 0–17 years for 2003, 2007, and 2011 (and every four years thereafter).
    • Number of state residents aged 0–17 years with current asthma for 2003, 2007, and 2011 (and every four years thereafter).
    • Risk-based child asthma mortality rate per million, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 19 [1]) for 2003, 2007, and 2011 (and every four years thereafter).
  • Adults (≥18 years).
    • Annual number of deaths for adults aged ≥18 years.
    • Annual number of state residents aged ≥18 years with current asthma.
    • Annual risk-based adult asthma mortality rate per million, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9[1]).
  • Total (all ages).
    • Annual number of deaths.
    • Annual number of state residents with current asthma (for states and years with child prevalence and adult prevalence).
    • Annual risk-based asthma mortality rate per million, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 3 [1]).

Time period of case definition

Calendar year. States with <20 deaths in a calendar year should calculate 3- to 5-year moving averages to increase reliability.

Background

The number of deaths with asthma as the underlying cause decreased from 4,483 in 2000 (2) to 3,816 in 2004 (2) and then to 3,388 in 2009 (3). A very slight increase to 3,404 occurred in 2010 (4). The population-based asthma mortality rate decreased from 16.1 in 2000 (2) to 10.6 per million population in 2010 (4), while the risk-based asthma mortality rate decreased from 2.1 in 2001 (2) to 1.3 per 100,000 persons with asthma in 2010 (Carol Johnson, CDC, National Asthma Control Program, personal communication, August 28, 2014). As of 2010, an estimated 25.7 million U.S. residents had asthma (3), which is a 27% increase over 10 years. The at-risk–based asthma mortality rate controls for the increase in the number of state residents with asthma that has occurred over time and differences in the underlying asthma prevalence across states. Population-based asthma mortality rates could increase simply because more persons have asthma each year or are in specific geographic areas who are at risk for death from asthma. The at-risk–based rate reflects deaths among persons with asthma and is therefore independent of the number of persons with asthma.

Significance

The majority of the problems associated with asthma are preventable if asthma is managed according to established guidelines. Effective management includes control of exposure to factors that trigger exacerbations, adequate pharmacological management, continual monitoring of the disease, and patient education in asthma care (5). With proper management, deaths from asthma are theoretically preventable.

Limitations of indicator

The reliability of death certificate data for asthma has been questioned, particularly for older age groups. The cause of death in persons with confounding medical conditions might be misreported (68). Some studies have reported inconsistencies in death certificate completion that resulted in asthma automatically overriding the underlying cause chosen, leading to an overestimate of asthma deaths. In contrast, a larger and well-designed study concluded that asthma death coding had very high specificity (99%) and low sensitivity (42%), that asthma as a cause of death was underreported rather than overreported in preference to chronic obstructive pulmonary disease (58% false negative, 1% false positive), and that age had no effect. This study casts some doubt on the assumption that coding of asthma deaths in older persons is unreliable in the United States. However, no studies representative of the entire U.S. vital statistics system have been published.

Data resources

Death certificate data from vital statistics agencies (numerator) and estimates of the state population with current asthma from BRFSS and NSCH (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

If the BRFSS child module was not implemented, annual child prevalence estimates are only be available for 2003, 2007, and 2011 from NSCH. Because NSCH is being redesigned, its mode and future periodicity is unknown.

Related indicators or recommendations

Healthy People 2020 objective RD-1: Reduce asthma deaths. (RD-1.1 is specific for children and adults aged <35 years; RD-1.2 is specific for adults aged 35–64 years; RD-1.3 is specific for adults aged ≥65 years.)

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National surveillance for asthma—United States, 1980–2004. MMWR 2007;56(No. SS-8).
  3. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  4. CDC. Wonder online databases. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://wonder.cdc.gov.
  5. National Asthma Education and Prevention Program. Expert panel report 3: guidelines for the diagnosis and management of asthma. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute, 2007.
  6. Brunner WM, Ross SK, Johnson JE. Review of the asthma mortality rate for Minnesota residents aged 55 years or older, 2004–2005: when death certificates deserve a second look. Prev Chronic Dis. 2009;6(3):A92.
  7. Rosenman KD, Hanna E, Wasilevich EA, Lyon-Callo SK. 2007 annual report on asthma deaths among individuals aged 2–34 and 45–54 years in Michigan. Michigan State University Department of Medicine. September 2010.
  8. Hunt LW, Silverstein MD, Reed CE, O'Connell EJ, O'Fallon WM, Yunginger JW. Accuracy of the death certificate in a population-based study of asthmatic patients. JAMA 1993;269:1947–52.

Indicator Group: Asthma

Indicator 5.1: Influenza vaccination among noninstitutionalized adults aged 18–64 years with asthma

Demographic group

Noninstitutionalized resident adults aged 18–64 years.

Numerator

Respondents aged 18–64 years who report having ever been told that they have asthma and who still have asthma and who report having received an influenza vaccination in the past 12 months.

Denominator

Respondents aged 18–64 years who report having ever been told that they have asthma and who still have asthma, and who report having received influenza vaccination in the past 12 months or not having received influenza vaccination in the past 12 months (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of Behavioral Risk Factor Surveillance System respondents at the state-level who have a history of asthma, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Current (still has asthma).

Past 12 months (vaccinated).

Background

Asthma has a significant public health impact. In the United States, approximately 18.5 million adults have asthma (2). During the 2010–2011 influenza season, 48.4% of adults aged 18–64 years at high risk for influenza received influenza vaccine (3).

Significance

Asthma appears to be related to influenza infection. Children and adults with asthma are at higher risk for influenza-related adverse health outcomes, including pneumonia, hospitalization for acute respiratory disease, and death. Because 5%–10% of the U.S. population has asthma, the potential public health impact of influenza infection on this vulnerable subgroup is enormous (4).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage surveillance is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Related chronic disease indicator topics

Immunization.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  3. CDC. Interim results: state-specific seasonal influenza vaccination coverage—United States, August 2010–February 2011. MMWR 2011;60:737–43 .
  4. Eisner MD. Asthma and influenza vaccination. Chest 2003;124:775–7.

Indicator Group: Asthma

Indicator 5.2: Influenza vaccination among noninstitutionalized adults aged ≥65 years with asthma

Demographic group

Noninstitutionalized resident adults aged ≥65 years.

Numerator

Respondents aged ≥65 years who report having ever been told that they have asthma and who still have asthma and who report having received influenza vaccination in the past 12 months.

Denominator

Respondents aged ≥65 years who report having ever been told that they have asthma and who still have asthma and who report having received influenza vaccination in the past 12 months or not having received influenza vaccination in the past 12 months (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of asthma, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Current (asthma).

Past 12 months (vaccinated).

Background

Asthma has a significant public health impact. In the United States, approximately 18.5 million adults have asthma (2). During the 2010–2011 influenza season, 68.6% of adults aged >65 years received influenza vaccine (3).

Significance

Asthma appears to be related to influenza infection. Children and adults with asthma are at higher risk for influenza-related adverse health outcomes, including pneumonia, hospitalization for acute respiratory disease, and death. Because 5%–10% of the U.S. population has asthma, the potential public health impact of influenza infection on this vulnerable subgroup is enormous (4).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage surveillance is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  3. CDC. Interim results: state-specific seasonal influenza vaccination coverage—United States, August 2010–February 2011. MMWR 2011;60:737–43.
  4. Eisner MD. Asthma and influenza vaccination. Chest 2003;124:775–7.

Indicator Group: Asthma

Indicator 6.1: Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years with asthma

Demographic group

Noninstitutionalized resident adults aged 18–64 years.

Numerator

Respondents aged 18–64 years who report having ever been told that they have asthma and who still have asthma and who report ever having received a pneumococcal vaccination.

Denominator

Respondents aged 18–64 years who report having ever been told that they have asthma and who still have asthma and who report ever having or not ever having a pneumococcal vaccination (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age-stratified and age-adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of asthma, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Current (still has asthma).

Lifetime (ever vaccinated).

Background

Asthma has a significant public health impact. In the United States, approximately 18.5 million adults have asthma (2). In 2012, only 20.0% of high-risk persons aged 19–64 years reported ever receiving a pneumococcal vaccination (3).

Significance

In 2011, an estimated 8.2% of adults aged ≥18 years reported current asthma (4) . A case-control study conducted in Tennessee, which identified cases through active, population-based, and laboratory-based surveillance and verified history of asthma from the Tennessee Medicaid database, showed that among adults aged 18–49 years, invasive pneumococcal disease (IPD) was more common among persons with asthma than persons without asthma (adjusted odds ratio = 2.4; 95% confidence interval = 1.8–3.3) (5). Among persons with high-risk asthma, the risk for IPD was nearly twice that for persons with low-risk asthma (5). In contrast, in a study conducted among a cohort of older veterans (average age: 53 years), persons with asthma did not have higher rates of hospitalization for pneumococcal pneumonia compared with persons in a group without asthma or chronic obstructive pulmonary disease (COPD) who were matched to the asthma patients by age, sex, and region (6). However, in the same study, hospitalization rates for pneumococcal pneumonia among persons with COPD were higher than among persons in the control group (6). Because distinguishing between COPD and asthma becomes more difficult with advancing age, misclassification of persons in this study is a possibility (7). The Advisory Committee on Immunization Practices recommends that persons aged 19–64 years who have asthma should receive a single dose of pneumococcal vaccine (7).

Limitations of indicator

Although self-reported pneumococcal vaccination has been validated (8), the reliability and validity of this measure is unknown.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-13: Increase the percentage of adults who are vaccinated against pneumococcal disease. IID-13.2 is specific to noninstitutionalized adults aged 18–64 years at high risk for influenza complications.
  • Promoting Preventive Services for Adults Aged 50–64 Years, Community and Clinical Partnerships.
  • Percentage of adults who reported current smoking, diabetes, asthma, or cardiovascular disease who have ever had a pneumococcal vaccination.

Related chronic disease indicator topics

Immunization.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 2001–2010. Vital Health Stat 2012;3(35).
  3. CDC. Noninfluenza vaccination coverage among adults—United States, 2012. MMWR 2014;63:95–102.
  4. CDC. National Health Interview Survey. Atlanta, GA: CDC; 2012. Available at http://www.cdc.gov/nchs/data/series/sr_10/sr10_255.pdf.
  5. Talbot TR, Hartert TV, Mitchel E, et al. Asthma as a risk factor for invasive pneumococcal disease. N Engl J Med 2005;352:2082–90.
  6. Lee TA, Weaver FM, Weiss KB. Impact of pneumococcal vaccination on pneumonia rates in patients with COPD and asthma. J Gen Intern Med 2007;22:62–7.
  7. CDC. Updated recommendations for prevention of invasive pneumococcal disease among adults using the 23-valent pneumococcal polysaccharide vaccine (PPSV23). MMWR 2010;59:1102–6.
  8. Shenson D, DiMartino D, Bolen J, Campbell M, Lu PJ, Singleton JA. Validation of self-reported pneumococcal vaccination in behavioral risk factor surveillance surveys: experience from the sickness prevention achieved through regional collaboration (SPARC) program. Vaccine 2005;23:1015–20.

Indicator Group: Asthma

Indicator 6.2: Pneumococcal vaccination among noninstitutionalized adults aged ≥65 years with asthma

Demographic group

Noninstitutionalized resident adults aged ≥65 years.

Numerator

Respondents aged ≥65 years who report having ever been told that they have asthma and who still have asthma and who report ever having received a pneumococcal vaccination.

Denominator

Respondents aged ≥65 years who report having ever been told that they have asthma and who still have asthma and who report ever having or not ever having a pneumococcal vaccination (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of asthma, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Current (still has asthma).

Lifetime (ever vaccinated).

Background

Asthma has a significant public health impact. In the United States, approximately 18.5 million adults have asthma (2). In 2012 in the United States, pneumococcal vaccination coverage among adults aged ≥65 years was 59.9% overall (3).

Significance

In 2011, an estimated 8.2% of adults aged ≥18 years reported current asthma (4). A case-control study conducted in Tennessee, which identified cases through active, population-based and laboratory-based surveillance and verified history of asthma from the Tennessee Medicaid database, showed that among adults aged 18–49 years, invasive pneumococcal disease (IPD) was more common among persons with asthma than persons without asthma (adjusted odds ratio = 2.4; 95% confidence interval = 1.8–3.3). Among persons with high-risk asthma, the risk for IPD was nearly twice that for persons with low-risk asthma (5). In contrast, in a study conducted among a cohort of older veterans (average age: 53 years), persons with asthma did not have higher rates of hospitalization for pneumococcal pneumonia compared with persons in a group without asthma or chronic obstructive pulmonary disease (COPD) who were matched to the asthma patients by age, sex, and region (6). However, in the same study, hospitalization rates for pneumococcal pneumonia among persons with COPD were higher compared with persons in the control group (6). Because distinguishing between COPD and asthma becomes more difficult with advancing age, misclassification of persons in this study is a possibility (7).

Limitations of indicator

Although self-reported pneumococcal vaccination has been validated (8), the reliability and validity of this measure is unknown.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-13: Increase the percentage of adults who are vaccinated against pneumococcal disease. IID-13.1 is specific to noninstitutionalized adults aged ≥65 years.
  • Healthy People 2020 objective OA-2: Increase the proportion of older adults who are up to date on a core set of clinical preventive services.

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Moorman JE, Akinbami LJ, Bailey CM, et al. National surveillance of asthma: United States, 1980–2004. MMWR. 2007;56(No. SS-8).
  3. CDC. Noninfluenza vaccination coverage among adults—United States, 2012. MMWR 2014;63:95–102.
  4. CDC. National Health Interview Survey. Atlanta, GA: CDC; 2012. Available at http://www.cdc.gov/nchs/data/series/sr_10/sr10_255.pdf.
  5. Talbot TR, Hartert TV, Mitchel E, et al. Asthma as a risk factor for invasive pneumococcal disease. N Engl J Med 2005;352:2082–90.
  6. Lee TA, Weaver FM, Weiss KB. Impact of pneumococcal vaccination on pneumonia rates in patients with COPD and asthma. J Gen Intern Med 2007;22:62–7.
  7. CDC. Updated recommendations for prevention of invasive pneumococcal disease among adults using the 23-valent pneumococcal polysaccharide vaccine (PPSV23). MMWR 2010;59:1102–6.
  8. Shenson D, DiMartino D, Bolen J, Campbell M, Lu PJ, Singleton JA. Validation of self-reported pneumococcal vaccination in behavioral risk factor surveillance surveys: experience from the sickness prevention achieved through regional collaboration (SPARC) program. Vaccine 2005;23:1015–20.

Indicator Group: Cancer

Indicator 1: Mammography use among women aged 50–74 years

Demographic group

Resident women aged 5074 years.

Numerator

Female respondents aged 5074 years who report having had a mammogram within the previous 2 years.

Denominator

Female respondents aged 5074 years who report ever having or never having had a mammogram (excluding unknowns and refusals).

Measures of frequency

Prevalence, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 2 years.

Background

In 2010, 20% of women aged 50–74 years had not had a mammogram with the past 2 years (2). Breast cancer is the most common cancer among women. In 2010, female breast cancer caused approximately 41,000 deaths (3). Approximately 207,000 new cases of invasive female breast cancer are diagnosed annually (3).

Significance

Strong evidence shows that mammography screening can reduce breast cancer deaths by 17% among women aged 50–69 years (4). The USPSTF recommends biennial screening for women aged 50–74 years (5). Evidence supporting mammography among women aged 40–49 years is lower but with higher false positives that result in less net benefit (5).

Limitations of indicator

Recommendations for mammography screening are not always consistent among national groups.

Data resources

Behavioral Risk Factor Surveillance Survey (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective C-17: Increase the proportion of women who receive breast cancer screening based on the most recent guidelines.

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Breast cancer screening among adult women—Behavioral Risk Factor Surveillance System, United States, 2010. MMWR 2012;61(Suppl; June 15, 2012):45–50.
  3. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  4. Mandelblatt JS, Cronin KA, Bailey S, et al. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms Ann Intern Med 2009;151:738–47.
  5. US Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2009;151:716–26.

Indicator Group: Cancer

Indicator 2.1: Papanicolaou testing among women aged 21–65 years

Demographic group

Resident women aged 21–65 years who have not had a hysterectomy.

Numerator

Female respondents aged 21–65 years who do not report having had a hysterectomy and who report having had a Papanicolaou (Pap) test within the past 3 years.

Denominator

Female respondents aged 21–65 years who do not report having had a hysterectomy and who report ever having or never having had a Pap test (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Prevalence, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 3 years.

Background

In 2010, 87% of women aged 21–65 years had a Pap test within the past 3 years (2). In 2010, cancer of the cervix caused approximately 4,000 deaths, and approximately 11,800 new cases are diagnosed annually (3). Black women have a higher incidence of and mortality from cervical cancer than do white women (3).

Significance

Approximately 40%–60% of cervical cancer deaths can be prevented by increased use of the Pap test (especially among women never screened) and effective, timely treatment (4). The dramatic decrease in cervical cancer incidence and mortality during the past 50 years is mainly the result of the widespread use of the Pap test (2).

Limitations of indicator

Recommendations for screening frequency vary by risk factor, and a 3-year interval might not be appropriate for some women.

Data resources

Behavioral Risk Factor Surveillance Survey (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

National recommendations also include use of human papillomavirus (HPV) testing along with Pap testing for cervical cancer screening. BRFSS does not contain questions about HPV status or testing.

Related indicators or recommendations

Healthy People 2020 objective C-15: Increase the proportion of women who receive a cervical cancer screening based on the most recent guidelines.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Jemal A, Simard EP, Dorell C, et al. annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.
  3. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  4. American College of Obstetricians and Gynecologists. Screening for cervical cancer. Practice Bulletin No. 131. Obstet Gynecol 2012;120:1222–38.

Indicator Group: Cancer

Indicator 2.2: Recent Papanicolaou testing among women aged 21–44 years

Demographic group

Women aged 21–44 years who have not had a hysterectomy.

Numerator

Female respondents aged 21–44 years who do not report having had a hysterectomy and who reported that they had a Papanicolaou (Pap) test within the previous 3 years.

Denominator

Female respondents aged 21–44 years who do not report having had a hysterectomy and who reported ever having or never having had a Pap test (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude prevalence with 95% confidence intervals weighted using the Behavioral Risk Factor Surveillance System method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage) and by demographic characteristics when feasible.

Time period of case definition

Past 3 years.

Background

In 2010, 87% of women aged 21–65 years had a Pap test within the previous 3 years (1). In 2010, cancer of the cervix caused approximately 4,000 deaths, and approximately 11,800 new cases are diagnosed annually (2).

Significance

Approximately 40%–60% of cervical cancer deaths can be prevented by increased use of the Pap test and effective, timely treatment (3). The dramatic decrease in cervical cancer incidence and deaths during the past 50 years is primarily the result of the widespread use of the Pap test (1).

The CDC's Select Panel on Preconception Care recommends that clinicians screen for preconception risk factors and provide treatment or other interventions as necessary. The office visit during which a Pap test is most often performed, sometimes referred to as the annual examination, is a prime opportunity for clinicians to conduct this screening. In addition, women could be screened routinely for abnormalities of the cervix associated with human papillomavirus (HPV) and that recommended subgroups receive the HPV vaccine. Use of the vaccine, in conjunction with regular pap screening to detect HPV abnormalities early on, can reduce or eliminate the need for procedures that could decrease cervical competency during pregnancy (4).

Limitations of indicator

Pap tests are recommended beginning at age 21 years (5). The prevalence of Pap testing might be limited by any changes in age distribution over time, because younger women have had less opportunity to be in the age group recommended for the test. The reliability of the BRFSS item assessing having ever had a Pap test is high. Regarding the validity of recall periods for this item, sensitivity is high but specificity is low to moderate, with recollection being better with shorter periods of time (6).

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

National recommendations also include use of HPV testing along with Pap testing for cervical cancer screening. BRFSS does not currently contain questions about HPV status or testing.

Related indicators or recommendations

Healthy People 2020 objective C-15: Increase the proportion of women who receive a cervical cancer screening based on the most recent guidelines.

Related chronic disease indicator topics

Reproductive health.


References

  1. Jemal A, Simard EP, Dorell C, et al. Annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. American College of Obstetricians and Gynecologists. Screening for cervical cancer. Practice Bulletin No. 131. Obstet Gynecol 2012;120:1222–38.
  4. Jack B, Atrash H, Coonrod D, Moos M-K, O'Donnell J, Johnson K. The clinical content of preconception care: an overview and preparation of this supplement. Am J Obstet Gynecol 2008;199(Suppl B):S266–79.
  5. Whitlock EP, Vesco KK, Eder M, et al. Liquid based cytology and human papillomavirus testing to screen for cervical cancer: a systematic review for the U.S. Preventive Services Task Force. Ann Intern Med 2011;155:687–97.
  6. Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soc Prev Med 2001;46(Suppl 1):S3–42.

Indicator Group: Cancer

Indicator 3: Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50–75 years

Demographic group

Resident adults aged 50–75 years.

Numerator

Respondents aged 50–75 years who report having had 1) a fecal occult blood test (FOBT) within the past year, 2) a sigmoidoscopy within the past 5 years and an FOBT within the past 3 years, or 3) a colonoscopy within the past 10 years.

Denominator

Respondents aged 50–75 years who report ever having or never having an FOBT, sigmoidoscopy, or colonoscopy (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Prevalence, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past year for FOBT alone, both past 5 years for sigmoidoscopy and past 3 years for FOBT, past 10 years for colonoscopy.

Background

In 2010, 35% of adults aged 50–75 years had not received a recommended colorectal cancer screening test within the appropriate time interval (2). Among adults aged 50–75 years, 60% reported having had colonoscopy within 10 years as their most recent colorectal cancer screening test (2). In 2010, colorectal cancer caused approximately 52,000 deaths (3). Approximately 131,600 cases are diagnosed annually (3).

Significance

Colorectal cancer screening can both prevent the occurrence of cancer by detecting and removing precancerous lesions, and detect colorectal cancer early when treatment is more effective (4). Colorectal cancer screening has been shown to significantly reduce deaths from the disease (4).

Limitations of indicator

National colorectal cancer screening guidelines vary regarding the choice of screening test, the appropriate screening interval, and the age at which screening should occur.

Data resources

Behavioral Risk Factor Surveillance Survey (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective C-16: Increase the proportion of adults who receive a colorectal cancer screening based on the most recent guidelines.

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Prevalence of colorectal cancer screening among adults—Behavioral Risk Factor Surveillance System, United States, 2010. MMWR 2012;61(Suppl; June 15, 2012):51–56.
  3. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available http://apps.nccd.cdc.gov/uscs.
  4. Whitlock EP, Lin JS, Liles E, et al. Screening for colorectal cancer: a targeted systematic review for the U.S. Preventive Services Task Force. Ann Intern Med 2008;149:638–58.

Indicator Group: Cancer

Indicator 4.1: Invasive cancer (all sites combined), incidence

Demographic group

All resident persons.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C00–C80 and a behavior code of 3 (malignant, primary site), C67.0–C67.9 (bladder cancer) and a behavior code of 2 or 3 (in situ or malignant, primary site) among residents during a calendar year. (Certain histologic types are excluded.)

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

Approximately 1.5 million new cases of cancer are diagnosed annually (2). Cancer is the second leading cause of death in the United States (3). Approximately one in two males and one in three females will receive a diagnosis of cancer over their lifetime (3).

Significance

Information on cancer at all sites combined provides a measure of, and means of tracking, the substantial impact of cancer. Morbidity and mortality from cancers of the lung, colon, female breast, cervix, oral cavity and pharynx, and multiple other cancers can be reduced through known interventions.

Limitations of indicator

Cancer is not a single disease but rather numerous diseases with different causes, risks, and potential interventions. Interpretation of trends or patterns in cancer incidence can be made only by examination of specific types of cancers. Because certain cancers have a long latency period, years might pass before changes in behavior or clinical practice patterns affect the incidence of new cancer cases. In addition, certain cancers are not amenable to primary prevention or screening.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Data from certain statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Howlader N, Noone AM, Krapcho M, et al, eds. SEER cancer statistics review, 1975–2010. Bethesda, MD: National Cancer Institute; 2013. Available at http://seer.cancer.gov/archive/csr/1975_2010.

Indicator Group: Cancer

Indicator 4.2: Invasive cancer (all sites combined), mortality

Demographic group

All resident persons.

Numerator

Incident cases of cancer with an International Classification of Diseases, 10th Revision codes C00-C97 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

Approximately 1.5 million new cases of cancer are diagnosed annually (2). Cancer is the second leading cause of death in the United States (3). Approximately one in two males and one in three females will receive a diagnosis of cancer over their lifetime (3).

Significance

Information on cancer at all sites combined provides a measure of, and means of tracking, the substantial impact of cancer. Morbidity and mortality from cancers of the lung, colon, female breast, cervix, oral cavity and pharynx, and multiple other cancers can be reduced through known interventions.

Limitations of indicator

Cancer is not a single disease but rather numerous diseases with different causes, risks, and potential interventions. Interpretation of trends or patterns in cancer mortality can be made only by examination of specific types of cancers. Because certain cancers have a long latency period, years might pass before changes in behavior or clinical practice patterns affect cancer mortality. In addition, certain cancers are not amenable to primary prevention or screening.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-1: Reduce the overall cancer death rate.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Howlader N, Noone AM, Krapcho M, et al, eds. SEER cancer statistics review, 1975–2010. Bethesda, MD: National Cancer Institute; 2013. Available at http://seer.cancer.gov/archive/csr/1975_2010.

Indicator Group: Cancer

Indicator 5.1: Invasive cancer of the female breast, incidence

Demographic group

All female residents.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C50 and a behavior code of 3 (malignant, primary site, excluding histologic types M9590–M9989) among female residents during a calendar year.

Denominator

Midyear female resident population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 207,000 women received a breast cancer diagnosis, and approximately 41,000 died from the disease (2). Except for nonmelanoma skin cancer, breast cancer is the most common cancer among women (2).

Significance

Screening for breast cancer with mammography can reduce deaths from breast cancer (3). Although scientific controversy remains regarding the benefits versus risks of screening, particularly among women aged 40–49 years, mammography is recommended for women aged 50–74 years (4).

Limitations of indicator

Breast cancer has a long latency period, and years might pass before changes in behavior or clinical practice patterns affect the incidence of breast cancer.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Data from some statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

Healthy People 2020 objective C-11: Reduce late-stage breast cancer.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Mandelblatt JS, Cronin KA, Bailey S, et al. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Ann Intern Med 2009;151:738–47.
  4. US Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2009;151:716–26.

Indicator Group: Cancer

Indicator 5.2: Cancer of the female breast, mortality

Demographic group

All female residents.

Numerator

Deaths with International Classification of Diseases, 10th Revision code C50 as the underlying cause of death among female residents during a calendar year.

Denominator

Midyear resident female population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age-adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 207,000 women received a diagnosis of breast cancer and approximately 41,000 died from the disease (2). Except for nonmelanoma skin cancer, breast cancer is the most common cancer among women (2).

Significance

Screening for breast cancer with mammography can reduce deaths from breast cancer (3).Although scientific controversy remains regarding the benefits versus risks of screening, particularly among women aged 40–49 years, mammography is recommended for women aged 50–74 years (4).

Limitations of indicator

Because breast cancer can have a long latency period, years might pass before changes in behavior or clinical practice patterns affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-3: Reduce the female breast cancer death rate.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Mandelblatt JS, Cronin KA, Bailey S, et al. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Ann Intern Med 2009;151:738–47.
  4. US Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2009;151:716–26.

Indicator Group: Cancer

Indicator 6.1: Invasive cancer of the cervix, incidence

Demographic group

All female residents.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C53 and a behavior code of 3 (malignant, primary site, excluding histologic types M9590–M9989) among female residents during a calendar year.

Denominator

Midyear female resident population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 11,800 women received a diagnosis of cervical cancer, and 4,000 died from the disease (2). Black and Hispanic women have higher incidence rates of cervical cancer compared to white women (2).

Significance

The dramatic decrease in cervical cancer incidence and mortality during the past 45 years is mainly the result of the widespread use of the Papanicolaou test (3). Cervical cancer rates were markedly higher among most women living in lower socioeconomic status areas than among those in higher socioeconomic status areas (3).

Limitations of indicator

Cervical cancer has a long latency period, and years might pass before changes in behavior or clinical practice patterns affect the incidence of cervical cancer.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Data from some statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

Healthy People 2020 objective C-10: Reduce invasive uterine cervical cancer.

Related chronic disease indicator topics

Reproductive health; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Jemal A, Simard EP, Dorell C, et al. Annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.

Indicator Group: Cancer

Indicator 6.2: Cancer of the female cervix, mortality

Demographic group

All female residents.

Numerator

Deaths with International Classification of Diseases, 10th Revision code C53 as the underlying cause of death among female residents during a calendar year.

Denominator

Midyear resident female population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 11,800 women received a diagnosis of cervical cancer, and 4,000 died from the disease (2). Black and Hispanic women have higher incidence rates of cervical cancer than white women (2).

Significance

The dramatic decrease in cervical cancer incidence and mortality during the past 45 years is mainly the result of the widespread use of the Papanicolaou test (3). Cervical cancer rates were markedly higher among most women living in low socioeconomic status areas than among those in high socioeconomic status areas (3).

Limitations of indicator

Because cervical cancer can have a long latency period, years might pass before changes in behavior or clinical practice patterns affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-4: Reduce the death rate from cancer of the uterine cervix.

Related chronic disease indicator topics

Reproductive health; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Jemal A, Simard EP, Dorell C, et al. Annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.

Indicator Group: Cancer

Indicator 7.1: Cancer of the colon and rectum (colorectal), incidence

Demographic group

All resident persons.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C18–C20, C26.0 and a behavior code of 3 (malignant, primary site, excluding histologic types M9590–M9989) among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

Colorectal cancer is the second leading cause of cancer death among cancers that affect both men and women (2). In 2010, approximately 131,600 persons received a diagnosis of colorectal cancer, and 52,000 persons died from the disease (2). The incidence of colorectal cancer rises sharply after age 50 years (2).

Significance

Screening for colorectal cancer with a fecal occult blood test, flexible sigmoidoscopy, or colonoscopy can prevent colorectal cancer by detecting and removing precancerous polyps and can detect cancer early when treatment is more likely to be effective (3).

Limitations of indicator

Colorectal cancer has a long latency period, and years might pass before changes in behavior or clinical practice patterns affect the incidence of colorectal cancer.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Data from some statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

Healthy People 2020 objective C-9: Reduce invasive colorectal cancer.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Whitlock EP, Lin JS, Liles E, et al. Screening for colorectal cancer: a targeted systematic review for the U.S. Preventive Services Task force. Ann Intern Med 2008;149:638–58.

Indicator Group: Cancer

Indicator 7.2: Cancer of the colon and rectum (colorectal), mortality

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision codes C18–C20 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

Colorectal cancer is the second leading cause of cancer death among cancers that affect both men and women (2). In 2010, approximately 131,600 persons received a diagnosis of colorectal cancer, and 52,000 persons died from the disease (2). The incidence of colorectal cancer rises sharply after age 50 years (2).

Significance

Screening for colorectal cancer with a fecal occult blood test, flexible sigmoidoscopy, or colonoscopy can prevent colorectal cancer by detecting and removing precancerous polyps and can detect cancer early when treatment is more likely to be effective (3).

Limitations of indicator

Because colorectal cancer can have a long latency period, years might pass before changes in behavior or clinical practice patterns affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-5: Reduce the colorectal cancer death rate.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Whitlock EP, Lin JS, Liles E, et al. Screening for colorectal cancer: a targeted systematic review for the U.S. Preventive Services Task Force. Ann Intern Med 2008;149:638–58.

Indicator Group: Cancer

Indicator 8.1: Cancer of the lung and bronchus, incidence

Demographic group

All resident persons.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C34 and a behavior code of 3 (malignant, primary site, excluding histologic types M9590–M9989) among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

More persons die from lung cancer than from any other cancer (2). In 2008, approximately 201,100 persons received a diagnosis of lung cancer, and 158,200 died from the disease (2). During the past 10 years, lung cancer incidence and mortality have decreased among men and women but at a faster rate among men (3).

Significance

Cigarette smoking accounts for 80%–90% of lung cancer cases (4). Lung cancer is also associated with secondhand tobacco smoke and certain environmental exposures, such as radon (4).

Limitations of indicator

Lung cancer has a long latency period, and years might pass before changes in behavior or clinical practice patterns affect the incidence of lung cancer.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Data from some statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Jemal A, Simard EP, Dorell C, et al. Annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.
  4. Humphrey LL, Deffebach M, Pappas M, et al. Screening for lung cancer with low-dose computed tomography: a systematic review to update the U.S. Preventive Services Task Force recommendation. Ann Intern Med 2004;140:740–53.

Indicator Group: Cancer

Indicator 8.2: Cancer of the lung and bronchus, mortality

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision code C34 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

More persons die from lung cancer than from any other cancer (2). In 2010, approximately 201,100 persons received a diagnosis of lung cancer, and 158,200 died from the disease (2). During the past 10 years, lung cancer incidence and mortality have continued to decrease among men and women but at a faster rate among men (3).

Significance

Cigarette smoking accounts for 80%–90% of lung cancer cases (4). Lung cancer also is associated with secondhand tobacco smoke and certain environmental exposures, such as radon (4).

Limitations of indicator

Because lung cancer can have a long latency period, years might pass before changes in behavior or clinical practice patterns affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-2: Reduce the lung cancer death rate.

Related chronic disease indicator topics

Tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Jemal A, Simard EP, Dorell C, et al. Annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.
  4. Humphrey LL, Deffebach M, Pappas M, et al. Screening for lung cancer with low-dose computed tomography: a systematic review to update the U.S. Preventive Services Task Force recommendation. Ann Intern Med 2004;140:740–53.

Indicator Group: Cancer

Indicator 9.1: Invasive melanoma, incidence

Demographic group

All resident persons.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C44 and a behavior code of 3 (malignant, primary site) and histologic types 8720–8790 among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 61,100 persons received a diagnosis of melanoma, and approximately 9,200 died from the disease (2). The incidence of melanoma has continued to increase among both men and women over the past decade (3).

Significance

Exposure to ultraviolet light causes 65%–90% of melanomas (4). Risk factors for melanoma include a lighter natural skin color, a history of sunburns early in life, and a history of indoor tanning device use (5,6). In 2010, using sunscreen (37%) and staying in the shade (35%) were the most common protective behaviors reported among women aged 18–29 years, compared with wearing long clothing to the ankles (33%), staying in the shade (26%), and using sunscreen (16%) among men of the same age (7). According to the 2010 National Health Interview Survey, 19% of women aged 18–29 years reported using an indoor tanning device at least once during the past 12 months (8).

Limitations of indicator

Melanoma has a long latency period, and years might pass before changes in behavior or clinical practice patterns affect the incidence of melanoma.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Melanoma is frequently diagnosed outside of the hospital and therefore might be underreported by a central cancer registry. Data from some statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Jemal A, Simard EP, Dorell C, et al. Annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.
  4. Armstrong BK, Kricker A. How much melanoma is caused by sun exposure? Melanoma Res 1993;3:395–401.
  5. Gandini S, Sera F, Cattaruzza MS, et al. Meta-analysis of risk factors for cutaneous melanoma: II. Sun exposure. Eur J Cancer 2005;41:45–60.
  6. Boniol M, Autier P, Boyle P, Gandini S. Cutaneous melanoma attributable to sunbed use: systematic review and meta-analysis. BMJ 2012;345:e4757.
  7. CDC. Sunburn and sun protective behaviors among adults aged 18–29 years—United States, 2000–2010. MMWR 2012;61:317–22.
  8. CDC. Use of indoor tanning devices by adults—United States, 2010. MMWR 2012;61:323–6.

Indicator Group: Cancer

Indicator 9.2: Melanoma, mortality

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision code C43 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, 61,100 persons received a diagnosis of melanoma, and approximately 9,200 died from the disease (2). Approximately 76% of all skin cancer–associated deaths are caused by melanoma (2). The incidence of melanoma has continued to increase among both men and women over the past decade (3).

Significance

Exposure to ultraviolet light causes about 65%–90% of melanomas (4). Risk factors for melanoma include a lighter natural skin color, a history of sunburns early in life, and a history of indoor tanning use (5,6). In 2010, using sunscreen (37 %) and staying in the shade (35%) were the most common protective behaviors reported among women aged 18–29 years, compared with wearing long clothing to the ankles (33%), staying in the shade (26%) and using sunscreen (16%) among men of the same age (7). According to the 2010 National Health Interview Survey, 19% of women aged 18–29 years reported using an indoor tanning device at least once during the past 12 months (8).

Limitations of indicator

Because melanoma can have a long latency period, years might pass before changes in behavior or clinical practice patterns affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-8: Reduce the melanoma cancer death rate.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Jemal A, Simard EP, Dorell C, et al. Annual report to the nation on the status of cancer, 1975–2009, featuring the burden and trends in human papillomavirus (HPV)–associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst 2013;105:175–201.
  4. Armstrong BK, Kricker A. How much melanoma is caused by sun exposure? Melanoma Res 1993;3:395–401.
  5. Gandini S, Sera F, Cattaruzza MS, et al. Meta-analysis of risk factors for cutaneous melanoma: II. Sun exposure. Eur J Cancer 2005;41:45–60.
  6. Boniol M, Autier P, Boyle P, Gandini S. Cutaneous melanoma attributable to sunbed use: systematic review and meta-analysis. BMJ 2012;345:e4757.
  7. CDC. Sunburn and sun protective behaviors among adults aged 18–29 years—United States, 2000–2010. MMWR 2012;61:317–22.
  8. CDC. Use of indoor tanning devices by adults—United States, 2010. MMWR 2012;61:323–6.

Indicator Group: Cancer

Indicator 10.1: Invasive cancer of the oral cavity or pharynx, incidence

Demographic group

All resident persons.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C00.0–C14.8 and a behavior code of 3 (malignant, primary site, excluding histologic types M9590–M9989) among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 36,300 persons received a diagnosis of cancer of the oral cavity or pharynx, and approximately 8,500 persons died from the disease (2). The incidence of and mortality from cancer of the oral cavity and pharynx is more than twice as high among man as among women (2).

Significance

Cancer of the oral cavity and pharynx is associated with use of tobacco products, excessive alcohol use, and human papillomavirus (HPV) infection (3,4). Together, alcohol and tobacco use account for approximately 75% of oral and pharyngeal cancers in the United States (3,4). A significant percentage (63%) of cancers primarily involving the base of the tongue and tonsils (i.e., oropharynx) have been linked to HPV infection (5).

Limitations of indicator

Cancer of the oral cavity and pharynx has a long latency period, and years might pass before changes in behavior or clinical practice patterns affect incidence.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Data from some statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Oral health; tobacco; alcohol.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Sturgis EM, Cinciripini PM. Trends in head and neck cancer incidence in relation to smoking prevalence: an emerging epidemic of human papillomavirus-associated cancers? Cancer 2007;110:1429–35.
  4. Ragin CC, Modugno F, Gollin SM. The epidemiology and risk factors of head and neck cancer: a focus on human papillomavirus. J Dent Res 2007;86:104–14.
  5. CDC. Human papillomavirus–associated cancers—United States, 2004–2008. MMWR 2012;61:258–61.

Indicator Group: Cancer

Indicator 10.2: Cancer of the oral cavity and pharynx, mortality

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision codes C00–C14 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 36,300 persons received a diagnosis of cancer of the oral cavity or pharynx, and approximately 8,500 persons died from the disease (2). The incidence of and mortality from cancer of the oral cavity and pharynx is more than twice as high among men as among women (2).

Significance

Cancer of the oral cavity and pharynx is associated with use of tobacco products, excessive alcohol use, and human papillomavirus (HPV) infection (3,4). Together, alcohol and tobacco use account for approximately 75% of oral and pharyngeal cancers in the United States (3,4). A significant percentage (63%) of cancers primarily involving the base of the tongue and tonsils (i.e., oropharynx) have been linked to HPV infection (5).

Limitations of indicator

Because cancer of the oral cavity and pharynx can have a long latency period, years might pass before changes in behavior or clinical practice patterns affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-6: Reduce oropharyngeal cancer death rate.

Related chronic disease indicator topics

Oral health; tobacco; alcohol.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Sturgis EM, Cinciripini PM. Trends in head and neck cancer incidence in relation to smoking prevalence: an emerging epidemic of human papillomavirus-associated cancers? Cancer 2007;110:1429–35.
  4. Ragin CC, Modugno F, Gollin SM. The epidemiology and risk factors of head and neck cancer: a focus on human papillomavirus. J Dent Res 2007;86:104–14.
  5. CDC. Human papillomavirus–associated cancers—United States, 2004–2008. MMWR 2012;61:258–61.

Indicator Group: Cancer

Indicator 11.1: Invasive cancer of the prostate, incidence

Demographic group

All male residents.

Numerator

Incident cases of cancer with an International Classification of Diseases for Oncology, Second Edition, or International Classification of Diseases for Oncology, Third Edition (for cases diagnosed after January 1, 2001) code C61.9 and a behavior code of 3 (malignant, primary site, excluding histologic types M9590–M9989) among male residents during a calendar year.

Denominator

Midyear resident male population for the same calendar year.

Measures of frequency

Average annual number of incident cases. Average annual incidence rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 196,000 men received a diagnosis of prostate cancer, and approximately 28,600 men died from the disease (2). Prostate cancer is the most common cancer among men (2). Black males have higher rates of prostate cancer incidence and mortality than white males (2).

Significance

Although screening for prostate cancer is not recommended by the United States Preventive Services Task Force, screening for prostate cancer has increased the number of new cases and the percentage of new cases diagnosed at an early stage (3).

Limitations of indicator

The impact of screening for prostate cancer on prostate cancer mortality is unknown. Current methods do not allow for differentiation between cases of prostate cancer that might result in death from indolent cases that are unlikely to result in death.

Data resources

Cancer incidence data from statewide central cancer registries (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Data from some statewide central cancer registries might not meet standards for data completeness and quality. Therefore, nationwide estimates calculated from aggregated state data might not include data from each state. However, state registry data should accurately represent state cancer incidence in the majority of states, particularly where completeness and quality of registry data are high.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Chou R, Croswell JM, Dana T, et al. Screening for prostate cancer: a review of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 2011;155:762–71.

Indicator Group: Cancer

Indicator 11.2: Cancer of the prostate, mortality

Demographic group

All male residents.

Numerator

Deaths with International Classification of Diseases, 10th Revision code C61 as the underlying cause of death among male residents during a calendar year.

Denominator

Midyear resident male population for the same calendar year.

Measures of frequency

Average annual number of deaths. Average annual death rate, crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1] based on single years of age from the U.S. census P25–1130 series estimates, which are combined to form 5-year age groups) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Five years.

Background

In 2010, approximately 196,000 men received a diagnosis of prostate cancer, and approximately 28,600 men died from the disease (2). Prostate cancer is the most common cancer among men (2). Black males have higher rates of prostate cancer incidence and mortality than white males (2).

Significance

Although substantial evidence exists that prostate-specific antigen (PSA) can detect early-stage prostate cancer (3), evidence is inconclusive regarding the ability of early detection to improve health outcomes, including mortality.

Limitations of indicator

Because prostate cancer can have a long latency period, years might pass before changes in behavior or clinical practice patterns affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Bureau of the Census or suitable alternative (denominator).

Limitations of data resources

Causes of death or other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective C-7: Reduce the prostate cancer death rate.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: US Department of Health and Human Services, CDC; National Cancer Institute; 2013. Available at http://apps.nccd.cdc.gov/uscs.
  3. Chou R, Croswell JM, Dana T, et al. Screening for prostate cancer: a review of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 2011;155:762–71.

Indicator Group: Cardiovascular Disease

Indicator 1.1: Mortality from total cardiovascular diseases

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision codes I00–I99 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

Cardiovascular disease (CVD) is the leading cause of death in the United States, with nearly 800,000 persons dying in the United States each year from CVDs, or one out of every three deaths (2). About 150,000 persons in the United States who died from CVD in 2009 were aged <65 years (2).

Significance

Modifiable risk factors for CVD include behaviors (e.g., tobacco use, physical inactivity, and improper nutrition), health status (e.g., hypertension, hyperlipidemia, overweight, or diabetes), and policies (e.g., smoking policies in restaurants and work sites) (3). Substantial differences in CVD death rates exist by race, age, sex, place of residence, and other demographic factors (4).

Limitations of indicator

CVD is not a single disease but rather is multiple diseases with different causes, risks, and potential interventions. Interpretation of trends or patterns in deaths from CVD can be made only by examination of specific types of CVD. Because certain types of cardiovascular disease have a long latency period, years might pass before changes in behavior or clinical practice affect CVD mortality. Certain types of CVD (e.g., valvular and congenital heart disease) are not amenable to primary prevention or screening.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

  • Healthy People 2020 objective HDS-2: Reduce coronary heart disease deaths.
  • Healthy People 2020 objective HDS-3: Reduce stroke deaths.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Kochanek KD, Xu JQ, Murphy SL, Miniño AM, Kung HC. Deaths: final data for 2009. Natl Vital Stat Rep 2011;60.
  3. Fryar CD, Chen T, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999–2010. NCHS Data Brief, No. 103. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2012.
  4. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2012; e2–241.

Indicator Group: Cardiovascular Disease

Indicator 1.2: Mortality from diseases of the heart

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision codes I00–I09, I11, I13, I20–I51 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

Nearly 600,000 persons in the United States died from heart disease (one out of every four deaths) in 2009 (2). In 2009, the age-adjusted rate among males (202.9 per 100,000 population) is greater than the age-adjusted rate among females (187.89 per 100,000 population) (2).

Significance

Modifiable risk factors for heart disease include behaviors (e.g., tobacco use, physical inactivity, and improper nutrition), health status (e.g., hypertension, hyperlipidemia, overweight, or diabetes), and policies (e.g., smoking policies in restaurants and work sites) (3). Substantial differences in heart disease death rates and preventive measures exist by race, age, sex, place of residence, and other demographic factors (4).

Limitations of indicator

Heart disease is not a single disease but rather is multiple diseases with different causes, risks, and potential interventions. Interpretation of trends or patterns in deaths from heart disease can be made only by examination of specific types of heart disease. Because certain types of heart disease have a long latency period, years might pass before changes in behavior or clinical practice affect heart disease mortality. Certain types of heart disease (e.g., valvular and congenital heart disease) are not amenable to primary prevention or screening.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective HDS-2: Reduce coronary heart disease deaths.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Kochanek KD, Xu JQ, Murphy SL, Miniño AM, Kung HC. Deaths: final data for 2009. Natl Vital Stat Rep 2011;60.
  3. Fryar CD, Chen T, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999–2010. NCHS Data Brief, No. 103. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics, US Department of Health and Human Services; 2012
  4. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Cardiovascular Disease

Indicator 1.3: Mortality from coronary heart disease

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision (ICD-10) codes I20–I25 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2009, coronary heart disease (CHD) is the largest category of heart disease, resulting in approximately 385,000 deaths annually (2). In 2009, age-adjusted rate among males (138.7 per 100,000 population) is greater than the age-adjusted rate among females (113.3 per 100,000 population) (2).

Significance

Modifiable risk factors for CHD include behaviors (e.g., tobacco use, physical inactivity, and improper nutrition), health status (e.g., hypertension, hyperlipidemia, overweight, or diabetes), and policies (e.g., smoking policies in restaurants and work sites) (3). Substantial differences in CHD death rates and preventive measures exist by race, age, sex, place of residence, and other demographic factors (4).

Limitations of indicator

Historically, epidemiologists have used different groups of ICD-10 rubrics to monitor CHD mortality. This has created differences in published mortality measures. Because CHD might have a long preclinical phase, years might pass before changes in behavior or clinical practice affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective HDS-2: Reduce CHD deaths.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Kochanek KD, Xu JQ, Murphy SL, Miniño AM, Kung HC. Deaths: final data for 2009. Natl Vital Stat Rep 2011;60.
  3. Fryar CD, Chen T, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999–2010. NCHS Data Brief, No. 103. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics, US Department of Health and Human Services; 2012.
  4. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Cardiovascular Disease

Indicator 1.4: Mortality from heart failure

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision code I50 as the underlying or contributing (any mentioned) cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence interval; and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

Heart failure is the primary cause of approximately 55,000 of the 600,000 heart disease deaths in 2009 and heart failure was mentioned as a contributing cause in approximately 270,000 deaths (1 in 9) in 2009 (2). Congestive heart failure (CHF) is the leading principal diagnosis for Medicare hospital claims (3).

Significance

Approximately 75% of persons with CHF have antecedent hypertension (4). During 1979–1996, hospitalization for CHF increased by 130% (5). Substantial differences in CHF death rates and preventive measures exist by race, age, sex, place of residence, and other demographic factors.

Limitations of indicator

Because congestive heart failure is a chronic disease and can have a long preclinical phase, years might pass before changes in behavior or clinical practice affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Kochanek KD, Xu JQ, Murphy SL, Miniño AM, Kung HC. Deaths: final data for 2009. Natl Vital Stat Rep 2011;60.
  3. Hall MJ, Levant S, DeFrances CJ. Hospitalization for congestive heart failure: United States, 2000–2010. NCHS data brief, no 108. Hyattsville, MD: National Center for Health Statistics. 2012.
  4. Institute of Medicine. Cardiovascular disease [Chapter 2]. In: Institute of Medicine, Committee on a National Surveillance System for Cardiovascular and Select Chronic Diseases. A nationwide framework for surveillance of cardiovascular and chronic lung diseases. Washington, DC: National Academies Press; 2011:19–32. Available at http://www.ncbi.nlm.nih.gov/books/NBK83160.
  5. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Cardiovascular Disease

Indicator 1.5: Mortality from cerebrovascular disease (stroke)

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision codes I60–I69 as the underlying cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2009, stroke accounted almost 130,000 of the 800,000 deaths among persons in the United States who died of cerebrovascular disease, or one out of every 19 deaths from all causes (2). Historically, the southeastern United States has experienced high stroke death rates (2).

Significance

Modifiable risk factors for stroke include behaviors (e.g., tobacco use, physical inactivity, and improper nutrition) and health status (e.g., untreated hypertension, hyperlipidemia, overweight, or diabetes) (3). Approximately 26% of stroke deaths in the United States are attributable to high blood pressure and 12% to smoking (3). Substantial differences in risk and preventive factors exist by race, age, sex, place of residence, and other demographic factors.

Limitations of indicator

Although the two major types of stroke (hemorrhagic, which accounts for approximately 10% of strokes [4] and ischemic, which accounts for approximately 65% of strokes [4]) share certain risk factors, their treatment varies. Consequently, accurate interpretation of trends or patterns in total mortality from cerebrovascular disease is difficult. Because cerebrovascular disease has a long latency period, years might pass before changes in behavior or clinical practice patterns affect cerebrovascular disease mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective HDS-3: Reduce stroke deaths.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Kochanek KD, Xu JQ, Murphy SL, Miniño AM, Kung HC. Deaths: final data for 2009. Natl Vital Stat Rep 2011;60.
  3. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.
  4. Furie KL, Kasner SE, Adams RJ, et al; American Heart Association Stroke Council, Council on Cardiovascular Nursing, Council on Clinical Cardiology, and Interdisciplinary Council on Quality of Care and Outcomes Research. Guidelines for the prevention of stroke in patients with stroke or transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2011;42:227–76.

Indicator Group: Cardiovascular Disease

Indicator 2: Hospitalization for heart failure among Medicare-eligible adults aged ≥65 years

Demographic group

Medicare-eligible resident adults aged ≥65 years.

Numerator

Hospitalizations with principal diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification code 428 among Medicare-eligible resident adults aged ≥65 years.

Denominator

Residents aged ≥65 years who were eligible for Medicare Part A benefits on July 1 of the calendar year, excluding members of health maintenance organizations.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rates: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 18 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

Heart failure has become a major public health concern in the United States, especially among older adults. Data from the National Institutes of Health Heart, Lung, and Blood Institute Framingham Heart Study indicate that heart failure incidence is about 10 per 1,000 population at age >65 years (2). Heart failure is the leading principal diagnosis for Medicare hospital claims (3). In 2007, the rate was 188.3 per 10,000 population (3).

Significance

Approximately 75% of persons with heart failure have antecedent hypertension (4). During 1979–1996, hospitalizations for heart failure increased by 130% (2).

Limitations of indicator

Because heart failure is a chronic disease that can have a long preclinical phase, years might pass before changes in behavior or clinical practice affect population morbidity and mortality.

Data resources

Centers for Medicare and Medicaid Services (CMS) Part A claims data (numerator) and CMS estimates of the population of persons eligible for Medicare (denominator).

Limitations of data resources

Diagnoses listed on Medicare claims data might be inaccurate. Practice patterns and payment mechanisms could affect decisions by health-care providers to hospitalize patients. Indicator is limited to Medicare-eligible population. Multiple admissions for an individual patient can inaccurately increase the number of persons with heart failure. The Medicare claims dataset cannot provide incident (new) hospitalizations for heart failure.

Related indicators or recommendations

Healthy People 2020 objective HDS-24: Reduce hospitalizations of older adults with heart failure as the principal diagnosis. (24.1 is specific for adults aged 65–74 years; 24.2 is specific for adults aged 75–84 years; 24.3 is specific for adults aged ≥85 years.)

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; older adults; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.
  3. Hall MJ, DeFrances CJ, Williams SN, Golosinskiy A, Schwartzman A. National hospital discharge survey: 2007 summary. National Health Statistics Report; no 29. Hyattsville MD: National Center for Health Statistics; 2010.
  4. Institute of Medicine. Cardiovascular disease [Chapter 2]. In: Institute of Medicine, Committee on a National Surveillance System for Cardiovascular and Select Chronic Diseases. A nationwide framework for surveillance of cardiovascular and chronic lung diseases. Washington, DC: National Academies Press; 2011:19–32. Available at http://www.ncbi.nlm.nih.gov/books/NBK83160.

Indicator Group: Cardiovascular Disease

Indicator 3.1: Hospitalization for stroke

Demographic group

All resident persons.

Numerator

Hospitalizations with principal diagnosis International Classification of Diseases, Ninth Revision, Clinical Modification codes 430–434 and 436–438 among residents during a calendar year. When possible, include discharges for residents who are hospitalized in another state.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rates: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2009, stroke accounted almost 130,000 of the 800,000 deaths among persons in the United States who died of cerebrovascular disease, or one out of every 19 deaths from all causes (2). Approximately 800,000 persons have a stroke each year in the United States. A total of 610,000 of these strokes are first, or new, strokes, and 185,000, or nearly one out of four, are recurrent strokes (3).

Significance

Modifiable risk factors for stroke include behaviors (e.g., tobacco use, physical inactivity, and improper nutrition) and health status (e.g., untreated hypertension, hyperlipidemia, overweight, or diabetes) (3). Approximately 26% of stroke deaths in the United States are attributable to high blood pressure, and 12% are attributable to smoking (3). Substantial differences in stroke death rates and preventive measures exist by race, age, sex, place of residence, and other demographic factors. Historically, the southeastern United States has had high stroke death rates.

Limitations of indicator

Although the two major types of stroke (hemorrhagic, which accounts for approximately 10% of strokes [4] and ischemic, which accounts for approximately 65% of strokes [4]) share certain risk factors, their treatment varies. Because cerebrovascular disease has a long latency period, years might pass before changes in behavior or clinical practice patterns affect cerebrovascular disease morbidity and mortality.

Data resources

State hospital discharge data (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Diagnoses listed on hospital discharge data might be inaccurate. Practice patterns and payment mechanisms could affect decisions by health-care providers to hospitalize patients. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Multiple admissions for an individual patient can inaccurately increase the number of persons with stroke. Because state hospital discharge data are not universally available, aggregation of state data to produce nationwide estimates are incomplete. State discharge records cannot identify incident (new) hospitalizations for stroke.

Related indicators or recommendations

  • Healthy People 2020 objective HDS-3: Reduce stroke deaths.
  • Healthy People 2020 objective HDS-17 (Developmental): Increase the proportion of adults aged 20 years and older who are aware of the symptoms of and how to respond to a stroke.
  • Healthy People 2020 objective HDS-23 (Developmental): Increase the proportion of adult stroke survivors who are referred to a stroke rehabilitation program at discharge.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Kochanek KD, Xu JQ, Murphy SL, Miniño AM, Kung HC. Deaths: final data for 2009. Natl Vital Stat Rep 2011;60.
  3. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.
  4. Furie KL, Kasner SE, Adams RJ, et al; American Heart Association Stroke Council, Council on Cardiovascular Nursing, Council on Clinical Cardiology, and Interdisciplinary Council on Quality of Care and Outcomes Research. Guidelines for the prevention of stroke in patients with stroke or transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2011;42:227–76.

Indicator Group: Cardiovascular Disease

Indicator 3.2: Hospitalization for acute myocardial infarction

Demographic group

All resident persons.

Numerator

Hospitalizations with principal diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification code 410 among residents during a calendar year. When possible, include hospitalizations for residents who are hospitalized in another state.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rates: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

National Hospital Discharge survey showed that age-adjusted hospitalization rate for myocardial infarction increased during 1979–1987, stabilized during 1987–1996, and then decreased during 1996–2005 (2). Although the trends were similar between men and women, men had the hospitalization rate almost twice as those of women (2). Myocardial infarction hospitalization rates increased with age (2).

Significance

Modifiable risk factors for coronary heart disease include behaviors (e.g., tobacco use, physical inactivity, and improper nutrition), health status (e.g., hypertension, hyperlipidemia, overweight, or diabetes), and policies (e.g., smoking policies in restaurants and work sites) (3). Rapid identification and treatment of heart attack reduces heart muscle damage, improves heart muscle function, and lowers the heart attack death rate (4). Substantial differences in coronary heart disease death rates and preventive measures exist by race, age, sex, place of residence, and other demographic factors.

Limitations of indicator

Substantial numbers of persons with acute myocardial infarction die before reaching a hospital (5). Because heart disease is a chronic disease that can have a long preclinical phase, years might pass before changes in behavior or clinical practice affect population morbidity and mortality. A substantial number of misdiagnoses, particularly among women, have been reported (5).

Data resources

State hospital discharge data (numerator) and population estimates from the U.S. Bureau of the Census or suitable alternative (denominator).

Limitations of data resources

Diagnoses listed on hospital discharge data might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to hospitalize patients. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Multiple admissions for an individual patient can inaccurately increase the number of persons with acute myocardial infarctions. Because state hospital discharge data are not universally available, aggregation of state data to produce nationwide estimates are incomplete. State hospital discharge data does not allow identification of incident (new) hospitalizations for acute myocardial infarction.

Related indicators or recommendations

  • Healthy People 2020 objective HDS-16: Increase the proportion of adults aged 20 years and older who are aware of the symptoms of and how to respond to a heart attack.
  • Healthy People 2020 objective HDS-18: (Developmental) Increase the proportion of out-of-hospital cardiac arrests in which appropriate bystander and emergency medical services are administered.
  • Healthy People 2020 objective HDS-19.1: Increase the proportion of eligible patients with heart attacks who receive fibrinolytic therapy within 30 minutes of hospital arrival.
  • Healthy People 2020 objective HDS-19.2: Increase the proportion of eligible patients with heart attacks who receive percutaneous intervention within 90 minutes of hospital arrival.
  • Healthy People 2020 objective HDS-22: (Developmental) Increase the proportion of adult heart attack survivors who are referred to a cardiac rehabilitation program at discharge.
  • Million Hearts: brings together communities, health systems, nonprofit organizations, federal agencies, and private-sector partners from across the country to fight heart disease and stroke (available at http://millionhearts.hhs.gov/index.html).

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Fang J, Alderman MH, Keenan NL, Ayala C. Acute myocardial infarction hospitalization in the United States, 1979 to 2005. Am J Med 2010;123:259–66.
  3. Fryar CD, Chen T, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999–2010. NCHS Data Brief, No. 103. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics, US Department of Health and Human Services; 2012.
  4. American College of Emergency Physicians; Society for Cardiovascular angiography and Interventions. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: executive summary. A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 2013;127:529–55.
  5. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Cardiovascular Disease

Indicator 4: Cholesterol screening among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report having their cholesterol checked within the previous 5 years.

Denominator

Respondents aged ≥18 years who report having their cholesterol checked within the past 5 years (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biennial (odd years) prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Previous 5 years.

Background

25% of adults aged ≥18 years still have not had their cholesterol checked within the past 5 years (2). Among those who had ever been screened for high blood cholesterol, the percentage who reported ever being told by a health-care provider their blood cholesterol was high was 35.0% in 2009 (2).

Significance

Elevated levels of serum cholesterol can lead to development of atherosclerosis (3). Approximately 30%–40% of coronary heart disease and 10%–20% of strokes in the United States are attributable to elevated serum cholesterol (3). Elevated cholesterol has been associated with physical inactivity, high fat intake, smoking cigarettes, diabetes, and obesity (3). Lifestyle changes and medications can reduce cholesterol and prevent heart disease among persons with elevated serum cholesterol (3).

Limitations of indicator

Validity and reliability of this indicator can be low because patients might not be aware of the specific tests conducted on their blood samples collected in clinical settings.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., lower telephone coverage among populations of low socioeconomic status), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective HDS-6: Increase the proportion of adults who have had their blood cholesterol checked within the preceding 5 years.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Prevalence of cholesterol screening and high blood cholesterol among adults—United States, 2005, 2007, and 2009 MMWR 2012;61;697–702.
  3. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–421.

Indicator Group: Cardiovascular Disease

Indicator 5: High cholesterol prevalence among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report having been told by a doctor, nurse, or other health professional that they had high cholesterol.

Denominator

Respondents aged ≥18 years who report having their cholesterol checked within the past 5 years (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biennial (odd years) prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 5 years.

Background

Among those who had ever been screened for high blood cholesterol, the percentage who reported ever being told by a health-care provider their blood cholesterol was high was 35.0% in 2009 (2). Only 33.5% (one out of three) adults with high cholesterol have the condition under control, and approximately half of adults with high cholesterol get treatment (3).

Significance

Elevated levels of serum cholesterol can lead to development of atherosclerosis (4). Approximately 30%–40% of coronary heart disease and 10%–20% of strokes in the United States are attributable to elevated serum cholesterol (4). Elevated cholesterol has been associated with physical inactivity, high fat intake, smoking cigarettes, diabetes, and obesity (4). Lifestyle changes and medications can reduce cholesterol and prevent heart disease among persons with elevated serum cholesterol (4).

Limitations of indicator

Validity and reliability of this indicator can be low because patients might not be aware of the specific tests conducted on their blood samples collected in clinical settings, or the patients cannot afford to visit a physician for a cholesterol check.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective HDS-6: Increase the proportion of adults who have had their blood cholesterol checked within the preceding 5 years.
  • Healthy People 2020 objective HDS-7: Reduce the proportion of adults with high total blood cholesterol levels.

Related chronic disease indicator topics

Diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Prevalence of cholesterol screening and high blood cholesterol among adults—United States, 2005, 2007, and 2009 MMWR 2012;61;697–702
  3. CDC. Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol: United States, 1999–2002 and 2005–2008. MMWR 2011;60:109–14.
  4. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–421.

Indicator Group: Cardiovascular Disease

Indicator 6.1: Awareness of high blood pressure among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report ever having been told by a doctor, nurse, or other health professional that they have high blood pressure. Women who were told high blood pressure only during pregnancy and those who were told they had borderline hypertension were not included.

Denominator

Respondents aged ≥18 years (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biennial (odd years) prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past year.

Background

In the United States, one out of three adults had hypertension (2003–2010 National Health and Nutrition Examination Survey) (2). Behavioral Risk Factor Surveillance System 2009 data showed that the overall age-adjusted prevalence of self-reported high blood pressure in the United States was 28.3% (3).

Significance

Approximately 348,000 American deaths in 2009 included high blood pressure as a primary or contributing cause (4). Approximately 20%–30% of coronary heart disease and 20%–50% of strokes in the United States are attributable to uncontrolled hypertension (4). Blood pressure–related cardiovascular complications can occur before the onset of established hypertension (4). Lifestyle risk factors for hypertension include high sodium intake, excessive caloric intake, physical inactivity, excessive alcohol consumption, and deficient potassium intake. Lifestyle changes and medications can be used to reduce blood pressure (4).

Limitations of indicator

Indicator does not measure the proportion of adults who currently have diagnosed high blood pressure and might result in an underestimate of the prevalence of high blood pressure. Indicator is based on having been told that one has high blood pressure and is subject to recall and actually having been told. In addition, reports are not validated against actual blood pressure measurements or medical records.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., lower telephone coverage among populations of low socioeconomic status), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective HDS-4: Increase the proportion of adults who have had their blood pressure measured within the preceding 2 years and can state whether their blood pressure was normal or high.
  • Healthy People 2020 objective HDS-5.1: Reduce the proportion of adults with hypertension.

Related chronic disease indicator topics

Alcohol; diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Vital signs: awareness and treatment of uncontrolled hypertension among adults—United States, 2003–2010. MMWR 2012;61:703–9.
  3. CDC. Self-reported hypertension and use of antihypertensive medication among adults—United States, 2005–2009. MMWR 2013;62:237–44.
  4. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Cardiovascular Disease

Indicator 6.2: Awareness of high blood pressure among women aged 18–44 years

Demographic group

Women aged 18–44 years.

Numerator

Female respondents aged 18–44 years who reported ever being told by a doctor, nurse, or other health professional that they have high blood pressure. Women with high blood pressure during pregnancy are included in the numerator, whereas women with borderline high blood pressure or prehypertension are not.

Denominator

Female respondents aged 18–44 years who reported that they had or had never been told by a doctor, nurse, or other health professional that they have high blood pressure (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biennial (odd years) crude annual prevalence with 95% confidence intervals weighted using the Behavioral Risk Factor Surveillance System Method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage), and by demographic characteristics when feasible.

Time period of case definition

Lifetime.

Background

In 2002, national data estimate that 3% of women of reproductive age had hypertension (1). As the number of pregnancies among women aged ≥35 years increases, this proportion is likely to grow.

Significance

Pregnancies among women with chronic hypertension can lead to preeclampsia or eclampsia, damage to the central nervous system, and kidney damage (2,3). Potential life-threatening conditions related to chronic hypertension during pregnancy include preterm delivery, intrauterine growth retardation, placental abruption, and fetal demise (4). The clinical workgroup of the Select Panel on Preconception Care recommends that all women of reproductive age with chronic hypertension be counseled before pregnancy about medication management and about the maternal and infant risks associated with hypertension during pregnancy (5).

Limitations of indicator

Estimates are based on self-reported high blood pressure, which has not been confirmed by a physician. Studies have reported high reliability for this BRFSS item (6). However, based on studies comparing self-reports with clinical data, validity is deemed to be moderate as self-reported high blood pressure status may result in an underestimate of true hypertension prevalence (7). However, this underestimation is consistent with other research (6). Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective HDS-5.1: Reduce the proportion of adults with hypertension.

Related chronic disease indicator topics

Reproductive health.


References

  1. U.S. Department of Health and Human Services. Health Resources and Services Administration, Maternal and Child Health Bureau, Women's Health USA 2002. Rockville, MD: U.S. Department of Health and Human Services; 2002.
  2. Report of the National High Blood Pressure Education Program Working Group on High Blood Pressure in Pregnancy. Am J Obstet Gynecol 2000;183:S1–22.
  3. Agency for Healthcare Research and Quality. Management of chronic hypertension during pregnancy. Evidence Report/Technology Assessment no. 14. AHRQ publication no. 00E011. Rockville, MD: Agency for Healthcare Research and Quality; 2000.
  4. Ferrer RL, Sibai BM, Morrow CD, Chiquette E, Stevens KR, Cornell J. Management of mild chronic hypertension during pregnancy: a review. Obstet Gynecol 2000;96:849–60.
  5. Dunlop AL, Jack BW, Bottalico JN, et al. The clinical content of preconception care: women with chronic medical conditions. Am J Obstet Gynecol 2008;199(Suppl B):S310–27.
  6. Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soc Prev Med 2001;46(Suppl 1):S3–42.
  7. Joint National Committee. Hypertension prevalence and the status of awareness, treatment, and control in the United States: final report. Hypertension 1985;7:456–68.

Indicator Group: Cardiovascular Disease

Indicator 7: Taking medicine for high blood pressure control among adults aged ≥18 years with high blood pressure

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report taking medicine for high blood pressure.

Denominator

Respondents aged ≥18 years who report having been told by a doctor, nurse, or other health professional of having high blood pressure other than during pregnancy (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biennial (odd years) prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Previous year.

Background

CDC showed that approximately half (47%) of persons with high blood pressure have their condition under control (2). Improving the control rate to reduce the risk for heart attack and stroke is very important (3). Studies showed that 46,000 deaths might be averted each year if 70% of patients with high blood pressure were treated according to goals established in current clinical guidelines (3). Reducing average population systolic blood pressure by only 12–13 mmHg could reduce stroke by 37%, coronary heart disease by 21%, cardiovascular disease deaths by 25%, and deaths from all causes by 13% (3).

Significance

Approximately 20%–30% of coronary heart disease and 20%–50% of strokes in the United States are attributable to uncontrolled hypertension (3). Blood pressure–related cardiovascular complications can occur before the onset of established hypertension. Lifestyle risk factors for hypertension include high sodium intake, excessive caloric intake, physical inactivity, excessive alcohol consumption, and deficient potassium intake (3). Lifestyle changes and medications can be used to reduce blood pressure (3).

Limitations of indicator

Indicator does not measure the proportion of adults with diagnosed hypertension who have their blood pressure successfully controlled. In addition, the indicator does not include persons with hypertension who have their blood pressure successfully controlled through lifestyle changes and without medication. Indicator only measures those aware of being told they have high blood pressure and not those who have been told they have hypertension.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., lower telephone coverage among populations of low socioeconomic status), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective HDS-11: Increase the proportion of adults with hypertension who are taking prescribed medications to lower their blood pressure.

Related chronic disease indicator topics

Alcohol; diabetes; nutrition, physical activity, and weight status; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Vital signs: awareness and treatment of uncontrolled hypertension among adults—United States, 2003–2010. MMWR 2012;61:703–9.
  3. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Cardiovascular Disease

Indicator 8: Prepregnancy hypertension

Demographic group

Women aged 18–44 years who have had a live birth.

Numerator

Respondents who reported having high blood pressure or hypertension before they became pregnant with their most recent live-born infant.

Denominator

Women who did or did not report having high blood pressure before they became pregnant with their most recent live-born infant (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude prevalence with 95% confidence intervals weighted using the Pregnancy Risk Assessment Monitoring System method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage); and by demographic characteristics when feasible.

Time period of case definition

Before the pregnancy resulting in the most recent live birth.

Background

In 2002, national data estimate that 3% of women of reproductive age had hypertension (1). As the number of pregnancies among women aged 35 years and older increases, this proportion is likely to grow.

Significance

Pregnancies among women with chronic hypertension can lead to preeclampsia or eclampsia, damage to the central nervous system, and kidney damage (2,3). Potential life threatening conditions related to chronic hypertension during pregnancy include preterm delivery, intrauterine growth retardation, placental abruption, and fetal demise (4). The clinical workgroup of the Select Panel on Preconception Care recommends that all women of reproductive age with chronic hypertension be counseled before pregnancy about medication management and about the maternal and infant risks associated with hypertension during pregnancy (5). Based on studies making comparisons with clinical data, self- reports of hypertension status may underestimate hypertension prevalence (6).

Limitations of indicator

Estimates are based on self-reported high blood pressure, which has not been confirmed by a physician. Based on studies comparing self-reports with clinical data, validity is deemed to be moderate as self-reported high blood pressure status may result in an underestimate of true hypertension prevalence (6). However, this underestimation is consistent with other research (6). Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Pregnancy Risk Assessment Monitoring System (PRAMS).

Limitations of data resources

PRAMS data are only collected from women who delivered a live-born infant, not all women of reproductive age, and from 40 states and one city, not the entire United States. PRAMS data are self-reported and might be subject to recall bias and underreporting or overreporting of behaviors based on social desirability. Although most self-report surveys such as PRAMS might be subject to systematic error resulting from noncoverage (e.g., lower landline telephone coverage because of transition to cellular telephone–only households or undeliverable addresses), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement bias (e.g., recall bias), PRAMS attempts to contact potential respondents by mail and landline or cellular telephone to increase response rates. Women who experienced a fetal death or had an abortion are excluded. PRAMS estimates only cover the population of residents in each state who also deliver in that state; therefore, residents who delivered in a different state are not included in their resident state.

Related indicators or recommendations

Healthy People 2020 objective HDS-5.1: Reduce the proportion of adults with hypertension.

Related chronic disease indicator topics

Reproductive health.


References

  1. D'Angelo D, Williams L, Morrow B, et al. Preconception and interconception health status of women who recently gave birth to a live-born infant, Pregnancy Risk Assessment Monitoring System (PRAMS), United States, 26 reporting area, 2004. MMWR 2007;56:1–35.
  2. Jain L. The effect of pregnancy-induced and chronic hypertension on pregnancy outcome. J Perinatol 1997;17:425–7.
  3. Thorngren-Jereck K, Herbst A. Perinatal factors associated with cerebral palsy in children born in Sweden. Obstet Gynecol 2006;108:1499–505.
  4. Barton J, Sibai B. Prediction and prevention of recurrent preeclampsia. Obstet Gynecol 2008;112:359–72.
  5. Dunlop AL, Jack BW, Bottalico JN, et al. The clinical content of preconception care: women with chronic medical conditions. Am J Obstet Gynecol 2008;199(Suppl 2):S310–27.
  6. Joint National Committee. Hypertension prevalence and the status of awareness, treatment, and control in the United States: final report. Hypertension 1985;7:456–68.

Indicator Group: Cardiovascular Disease

Indicator 9.1: Influenza vaccination among noninstitutionalized adults aged 18–64 years with a history of coronary heart disease or stroke

Demographic group

Resident adults aged 18–64 years.

Numerator

Respondents aged 18–64 years ever told by a doctor or health professional that they have had a heart attack or stroke or have angina or other coronary heart disease who report having received an influenza vaccination in the past 12 months.

Denominator

Respondents age 18–64 years ever told by a doctor or health professional that they have had a heart attack or stroke or have angina or other coronary heart disease who report having or not having an influenza vaccination in the past 12 months (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of coronary heart disease or stroke, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Past 12 months (influenza vaccination).

Lifetime (history of heart attack, stroke, angina, or other coronary heart disease).

Background

In 2005, only 34% of adults with coronary heart disease reported receiving an influenza vaccination in the past 12 months (2).

Significance

Annual vaccination against seasonal influenza prevents cardiovascular morbidity and all-cause mortality in persons with cardiovascular conditions (3). The American Heart Association and American College of Cardiology recommend influenza vaccination with inactivated vaccine as part of comprehensive secondary prevention in persons with coronary and other atherosclerotic vascular disease (2). The American Heart Association estimates that 16.3 million persons in the U.S. have a history of coronary heart disease and 7.0 million have a history of stroke (4). Influenza vaccination coverage levels in this population are well below national goals (2). Persons with cardiovascular disease (excluding isolated hypertension) are considered by the Advisory Committee on Immunization Practices to be at high risk for severe complications from influenza (5,6).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-12.6: Increase the percentage of noninstitutionalized high-risk adults aged 18–64 years who are vaccinated annually against seasonal influenza. The Healthy People 2020 influenza vaccination objectives have been consolidated since the original publication of Healthy People 2020 but will continue to be monitored as part of Healthy People 2020 data reporting. (Additional information is available at http://www.cdc.gov/vaccines/acip/meetings/downloads/slides-oct-2013/03-Influenza-Singleton.pdf and from the Healthy People 2020 website at http://www.healthypeople.gov/2020/topics-objectives/topic/immunization-and-infectious-diseases/objectives.)
  • Healthy People 2020 objective HDS-2: Reduce coronary heart disease deaths.
  • Healthy People 2020 objective HDS-3: Reduce stroke deaths.
  • Promoting Preventive Services for Adults Aged 50–64 Years, Community and Clinical Partnerships: Percentage of adults who reported influenza vaccination within the past year.
  • Percentage of adults who reported current smoking, diabetes, asthma or cardiovascular disease who have ever had a pneumococcal vaccination.

Related chronic disease indicator topics

Immunization.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Davis MM, Taubert K, Benin AL, et al. Influenza vaccination as secondary prevention for cardiovascular disease: a Science Advisory from the American Heart Association/American College of Cardiology. Circulation 2006;114:1549–53.
  3. Gurfinkel EP, Leon de la Fuente R, Mendiz O, et al. Flu vaccination in acute coronary syndromes and planned percutaneous coronary interventions (FLUVACS) Study. Eur Heart J 2004;25:25–31.
  4. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.
  5. CDC. Prevention and control of seasonal influenza with vaccines. Recommendations of the Advisory Committee on Immunization Practices—United States, 2013-2014. MMWR 2013;62(No. RR-7):1–43 .
  6. CDC. Errata: Vol. 62, No. RR-7. MMWR 2013;62:906 .

Indicator Group: Cardiovascular Disease

Indicator 9.2: Influenza vaccination among noninstitutionalized adults aged ≥65 years with a history of coronary heart disease or stroke

Demographic group

Resident adults aged ≥65 years.

Numerator

Respondents aged ≥65 years ever told by a doctor or health professional that they have had a heart attack or stroke or have angina or other coronary heart disease who report having received an influenza vaccination in the past 12 months.

Denominator

Respondents aged ≥65 years ever told by a doctor or health professional that they have had a heart attack or stroke or have angina or other coronary heart disease who report having or not having an influenza vaccination in the past 12 months (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method) (1), with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of coronary heart disease or stroke, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Past 12 months (influenza vaccination).

Lifetime (history of heart attack, stroke, angina or other coronary heart disease).

Background

In 2005, only 34% of adults with coronary heart disease reported receiving an influenza vaccination in the past 12 months (2).

Significance

Annual vaccination against seasonal influenza prevents cardiovascular morbidity and all-cause mortality in persons with cardiovascular conditions (3). The American Heart Association and American College of Cardiology recommend influenza vaccination with inactivated vaccine as part of comprehensive secondary prevention in persons with coronary and other atherosclerotic vascular disease (2). The American Heart Association estimates that 16.3 million persons in the United States have a history of coronary heart disease, and 7.0 million have a history of stroke (4). Influenza vaccination coverage levels in this population are well below national goals (2). Persons with cardiovascular disease (excluding isolated hypertension) are considered by the Advisory Committee on Immunization Practices to be at high risk for severe complications from influenza (5,6).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage surveillance is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Davis MM, Taubert K, Benin AL, et al. Influenza vaccination as secondary prevention for cardiovascular disease: a science advisory from the American Heart Association/American College of Cardiology. Circulation 2006;114:1549–53.
  3. Gurfinkel EP, Leon de la Fuente R, Mendiz O, et al. Flu vaccination in acute coronary syndromes and planned percutaneous coronary interventions (FLUVACS) Study. Eur Heart J 2004;25:25–31.
  4. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.
  5. CDC. Prevention and control of seasonal influenza with vaccines. Recommendations of the Advisory Committee on Immunization Practices—United States, 2013–2014. MMWR 2013;62(No. RR-7).
  6. CDC. Errata: vol. 62, no. RR-7. MMWR 2013;62:906.

Indicator Group: Cardiovascular Disease

Indicator 10.1: Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years with a history of coronary heart disease

Demographic group

Resident adults aged 18–64 years.

Numerator

Respondents aged 18–64 years ever told by a doctor or health professional that they have had a heart attack or have angina or other coronary heart disease who report ever having received a pneumococcal vaccination.

Denominator

Respondents aged 18–64 years ever told by a doctor or health professional that they have had a heart attack or have angina or other coronary heart disease who report ever having or not ever having a pneumococcal vaccination (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of coronary heart disease, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Lifetime (ever vaccinated).

Lifetime (history of heart attack, angina or other coronary heart disease).

Background

In 2012, only 20.0% of adults aged 19–64 years at high risk for pneumococcal disease and its complications reported ever receiving a pneumococcal vaccination (2).

Significance

Invasive pneumococcal infection is a major cause of illness and death in the United States, with an estimated 43,500 cases and 5,000 deaths among persons of all ages in 2009 (3). Persons with chronic heart disease (excluding hypertension) are considered by the Advisory Committee on Immunization Practices to be a group at high risk for pneumococcal disease and its complications who should receive pneumococcal vaccination (4). The American Heart Association estimates that 16.3 million persons in the U.S. have a history of coronary heart disease (5). Pneumococcal vaccination rates among adults at high risk for pneumococcal disease and its complications are well below national goals (2).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-13.2: Increase the percentage of noninstitutionalized high-risk adults aged 18–64 years who are vaccinated against pneumococcal disease.
  • Healthy People 2020 objective HDS-2: Reduce coronary heart disease deaths.
  • Healthy People 2020 objective HDS-3: Reduce stroke deaths.
  • Promoting Preventive Services for Adults Aged 50–64 Years, Community and Clinical Partnerships: Percentage of adults who reported influenza vaccination within the past year.
  • Percentage of adults who reported current smoking, diabetes, asthma or cardiovascular disease who have ever had a pneumococcal vaccination.

Related chronic disease indicator topics

Immunization.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Noninfluenza vaccination coverage among adults—United States, 2012. MMWR 2014;63:95–102.
  3. CDC. Active Bacterial Core Surveillance (ABCs) Report: Emerging Infections Program Network. Streptococcus pneumonia, provisional-2009. Atlanta, GA: US Department of Health and Human Services, CDC:2010. Available at http://www.cdc.gov/abcs/reports-findings/survreports/spneu09.pdf.
  4. CDC. Prevention of pneumococcal disease: recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR 1997;46(No. RR-8).
  5. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Cardiovascular Disease

Indicator 10.2: Pneumococcal vaccination among noninstitutionalized adults aged ≥65 years with a history of coronary heart disease

Demographic group

Resident adults aged ≥65 years.

Numerator

Respondents aged ≥65 years ever told by a doctor or health professional that they have had a heart attack or have angina or other coronary heart disease who report ever having received a pneumococcal vaccination.

Denominator

Respondents aged ≥65 years ever told by a doctor or health professional that they have had a heart attack or have angina or other coronary heart disease who report ever having or not ever having a pneumococcal vaccination (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of coronary heart disease, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Lifetime (ever vaccinated).

Lifetime (history of heart attack, angina or other coronary heart disease).

Background

In 2012, only 59.9% of persons aged >65 years reported ever receiving a pneumococcal vaccination (2).

Significance

Invasive pneumococcal infection is a major cause of illness and death in the United States, with an estimated 43,500 cases and 5,000 deaths among persons of all ages in 2009 (3). Persons with chronic heart disease (excluding hypertension) are considered by the Advisory Committee on Immunization Practices to be a group at high risk for pneumococcal disease and its complications who should receive pneumococcal vaccination (4). The American Heart Association estimates that 16.3 million persons in the U.S. have a history of coronary heart disease (5). Pneumococcal vaccination rates among adults at high risk for pneumococcal disease and its complications are well below national goals (2).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-13.1: Increase the percentage of noninstitutionalized adults aged ≥65 years who are vaccinated against pneumococcal disease.
  • Healthy People 2020 objective HDS-2: Reduce coronary heart disease deaths.
  • Healthy People 2020 objective HDS-3: Reduce stroke deaths.
  • Healthy People 2020 objective OA-2: Increase the proportion of older adults who are up to date on a core set of clinical preventive services.

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Noninfluenza vaccination coverage among adults—United States, 2012. MMWR 2014;63:95–102.
  3. CDC. Active Bacterial Core Surveillance (ABCs) Report: Emerging Infections Program Network. Streptococcus pneumonia, provisional-2009. Atlanta, GA: US Department of Health and Human Services, CDC:2010. Available at http://www.cdc.gov/abcs/reports-findings/survreports/spneu09.pdf.
  4. CDC. Prevention of pneumococcal disease: recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR 1997;46(No. RR-8).
  5. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245.

Indicator Group: Chronic Kidney Disease

Indicator 1: Mortality with end-stage renal disease

Demographic group

All resident persons.

Numerator

Deaths with International Classifications of Diseases, Tenth Revision code N03-N05, N13.0-N13.3, N17-N19, N25-N26, N28.0, and N28.8 as the underlying or contributing cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 1 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2011, approximately 113,000 new cases of end-stage renal disease (ESRD), or kidney failure, were reported in the United States (2). Diabetes and hypertension are the leading causes of ESRD in the United States, accounting for 44% and 28%, respectively, of the new cases of ESRD in 2011 (2).

Significance

The complications of diabetes and hypertension, including ESRD, can be prevented through improved patient education and self-management and provision of adequate and timely medical care, including blood glucose and blood pressure control (3).

Limitations of indicator

The denominator is the general population and not specific to the population at risk for developing ESRD.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate. The number of contributing causes of death listed on the death certificate might vary by person completing the death certificate and geographic region.

Related indicators or recommendations

Healthy People 2020 objective CKD-14: Reduce deaths in persons with ESRD.

Related chronic disease indicator topics

Diabetes; cardiovascular disease.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. US Renal Data System. Incidence, prevalence, patient characteristics, and treatment modalities [Chapter 2]. In: US Renal Data System. 2013 annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2013. Available at http://www.usrds.org/2013/pdf/v2_ch1_13.pdf.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.

Indicator Group: Chronic Kidney Disease

Indicator 2.1: Incidence of treated end-stage renal disease

Demographic group

All resident persons.

Numerator

Initial claims for either renal dialysis or renal transplant among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of incident cases. Annual incidence: crude and adjusted (standardized by the method used by the US Renal Data System) [1]) and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2011, approximately 113,000 new cases of end-stage renal disease (ESRD), or kidney failure, were reported in the United States (2). Diabetes and hypertension are the leading causes of ESRD in the United States, accounting for 44% and 28%, respectively, of the new cases of ESRD in 2011 (2).

Significance

The complications of diabetes and hypertension, including ESRD, can be prevented through improved patient education and self-management and provision of adequate and timely medical care, including blood glucose and blood pressure control (3).

Limitations of indicator

The denominator is the general population and not specific to the population at risk for developing ESRD.

Data resources

ESRD incidence data (numerator) from the U.S. Renal Data System and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Because data are available only for patients whose ESRD therapy is reported to the Centers for Medicare and Medicaid Services (CMS), patients who die of ESRD before receiving treatment or whose therapy is not reported to CMS are not included in the database (1).

Related indicators or recommendations

Healthy People 2020 objective CKD-8: Reduce the rate of new cases of ESRD.

Related chronic disease indicator topics

Diabetes; cardiovascular disease.


References

  1. US Renal Data System. Analytical methods: ESRD [Appendix]. In: US Renal Data System. USRDS 2013 annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2013. Available at http://www.usrds.org/2013/pdf/v2_z_appendix__13.pdf.
  2. US Renal Data System. Incidence, prevalence, patient characteristics, and treatment modalities. In: USRDS 2013 Annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2013. Available at http://www.usrds.org/2013/pdf/v2_ch1_13.pdf.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.

Indicator Group: Chronic Kidney Disease

Indicator 2.2: Incidence of treated end-stage renal disease attributed to diabetes

Demographic group

All resident persons.

Numerator

Initial claims for either renal dialysis or renal transplant with diabetes listed as the primary cause of disease among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of incident cases. Annual incidence: crude and adjusted (standardized by the method used by the U.S. Renal Data System [1]) and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2011, approximately 113,000 new cases of end-stage renal disease (ESRD), or kidney failure, were reported in the United States (2). Diabetes and hypertension are the leading causes of ESRD in the United States, accounting for 44% and 28%, respectively, of the new cases of ESRD in 2011 (2).

Significance

The complications of diabetes and hypertension, including ESRD, can be prevented through improved patient education and self-management, and provision of adequate and timely medical care, including blood glucose and blood pressure control (3).

Limitations of indicator

The ESRD primary diagnosis, taken from the Medical Evidence Report provided by the ESRD networks, is based on the physician's assessment of the patient and might be affected if the patient has multiple comorbidities. In 2005–2008, three out of four persons with diabetes also had hypertension (3).

Data resources

ESRD incidence data (numerator) from the U.S. Renal Data System and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Because data are available only for patients whose ESRD therapy is reported to the Centers for Medicare and Medicaid Services (CMS), patients who die of ESRD before receiving treatment or whose therapy is not reported to CMS are not included in the database (1).

Related indicators or recommendations

Healthy People 2020 objective CKD-9: Reduce kidney failure due to diabetes.

Related chronic disease indicator topics

Diabetes; cardiovascular disease.


References

  1. US Renal Data System. Analytical methods: ESRD [Appendix]. In: US Renal Data System. USRDS 2013 annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2013. Available at http://www.usrds.org/2013/pdf/v2_ch1_13.pdf.
  2. US Renal Data System. Incidence, prevalence, patient characteristics, and treatment modalities [Chapter 2]. In: USRDS 2013 annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2013. Available at http://www.usrds.org/2013/pdf/v2_ch1_13.pdf.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.

Indicator Group: Chronic Kidney Disease

Indicator 3: Prevalence of chronic kidney disease among adults aged ≥18 years

Demographic group

All adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report ever having been told by a doctor or other health professional that they have kidney disease.

Denominator

Respondents aged ≥18 years who report or do not report ever having been told by a doctor or other health professional that they have kidney disease (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9) (1) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2011, kidney disease was the ninth leading cause of death in the United States (2). Although approximately 600,000 persons have end-stage renal disease (3), approximately 20 million U.S. adults aged ≥20 years are estimated to have chronic kidney disease (CKD) (4), and most of them are unaware of their condition (5).

Significance

If left untreated, CKD can lead to kidney failure, requiring dialysis or transplantation for survival (4) However, persons with CKD are more likely to die from cardiovascular disease than develop end-stage renal disease (4). Controlling blood glucose, blood pressure, and cholesterol can prevent or delay these conditions and improve health outcomes (4). For the first time, Healthy People 2020 included objectives addressing the earlier stages of kidney disease (6).

Limitations of indicator

Reducing the proportion of the U.S. population with CKD is likely to be difficult because of the growing prevalence of major risk factors such as diabetes, hypertension, and aging of the population (7). Many years may pass before declining trends are observed.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

The estimated prevalence of CKD in the U.S. population is likely to be an underestimate because BRFSS is a telephone survey that excludes the institutionalized population, in whom the prevalence is likely to be higher (8), and because prevalence is based on self-report. Most persons with CKD are unaware of their condition (5). In addition, one data point is available. The following question was asked for the first time in the 2011 BRFSS core questionnaire: "Has a doctor, nurse, or other health professional EVER told you have kidney disease? Do NOT include kidney stones, bladder infection or incontinence." (9).

Related indicators or recommendations

Healthy People 2020 objective CKD-1: Reduce the proportion of the U.S. population with chronic kidney disease.

Related chronic disease indicator topics

Diabetes; cardiovascular disease.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Hoyert DL, Xu JQ. Deaths: Preliminary data for 2011. Natl Vital Stat Rep 2012;61(6).
  3. US Renal Data System. Incidence, prevalence, patient characteristics, and treatment modalities [Chapter 2]. In: USRDS 2013 annual data report: atlas of chronic kidney disease and end-stage renal disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2013. Available at http://www.usrds.org/2013/pdf/v2_ch1_13.pdf.
  4. CDC. National chronic kidney disease fact sheet: general information and national estimates on chronic kidney disease in the United States, 2010. Atlanta, GA: US Department of Health and Human Services, CDC; 2010.
  5. Plantinga LC, Boulware LE, Coresh J, et al. Patient awareness of chronic kidney disease: trends and predictors. Arch Intern Med 2008;168:2268–75.
  6. US Department of Health and Human Services. Healthy people 2020. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.healthypeople.gov.
  7. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the U.S. adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr 2010;8:29.
  8. McClellan WM, Resnick B, Lei L, et al. Prevalence and severity of chronic kidney disease and anemia in the nursing home population. J Am Med Dir Assoc 2010;11:33–41.
  9. CDC. Behavioral Risk Factor Surveillance System: questionnaires. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.cdc.gov/brfss/questionnaires.htm.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 1.1: Mortality with chronic obstructive pulmonary disease as underlying cause among adults aged ≥45 years

Demographic group

Resident adults aged ≥45 years.

Numerator

Deaths with International Classification of Diseases, 10th Revision code J40–J44 as underlying cause death among residents aged ≥45 years.

Denominator

Midyear resident population aged ≥45 years.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted per 100,000 population (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible (age groups: 45–54, 55–64, 65–74, 75–84, and ≥85 years).

Time period of case definition

Calendar year.

Background

In 2010, a total of 133,575 deaths (63.1 per 100,000 population) occurred with chronic obstructive pulmonary disease (COPD) as the underlying cause of death for U.S. adults aged >25 years (2). During 1999–2010, COPD deaths decreased for U.S. men (p = 0.001) but not for women (p = 0.127) (2). More than 99% of deaths from COPD occur among adults aged ≥45 years (2).

Significance

Elimination of tobacco use or exposure might be the most effective way to reduce COPD because approximately 80% of COPD deaths are attributable to smoking (3). Other risk factors for COPD include occupational exposure, ambient air pollution, and long-term severe asthma (4). Public education and awareness of COPD symptoms and earlier diagnosis and treatment might slow additional lung damage, improve COPD symptoms, and reduce COPD-related disability and deaths (5).

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect population mortality. Other comorbid conditions such as cardiovascular disease may displace COPD as the underlying cause of death that is reported on the death certificate (6,7).

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate.

Related indicators or recommendations

Healthy People 2020 objective RD-10: Reduce deaths from COPD among adults.

Related chronic disease indicator topics

Asthma; tobacco; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.
  3. CDC. Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000–2004. MMWR 2008;57:1226–8.
  4. Mannino DM. Epidemiology and global impact of chronic obstructive pulmonary disease. Semin Respir Crit Care Med 2005;26:204–10.
  5. Qaseem A, Wilt TJ, Weinberger SE, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med 2011;155:179–91.
  6. Hansell AL, Walk JA, Soriano JB. What do chronic obstructive pulmonary disease patients die from? A multiple cause coding analysis. Eur Respir J 2003;22:809–14.
  7. Jensen HH, Godtfredsen NS, Lange P, Vestbo J. Potential misclassification of death from COPD. Eur Respir J 2006;28:781–5.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 1.2: Mortality with chronic obstructive pulmonary disease as underlying or contributing cause among adults aged ≥45 years

Demographic group

Resident adults aged ≥45 years

Numerator

Deaths with International Classification of Diseases, 10th Revision Code J40–J44 as either underlying or contributing cause of death among residents aged ≥45 years

Denominator

Midyear resident population aged ≥45 years

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted per 100,000 population (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible (age groups: 45–54, 55–64, 65–74, 75–84, and ≥85 years).

Time period of case definition

Calendar year.

Background

More than 99% of deaths from chronic obstructive pulmonary disease (COPD) occur among adults aged ≥45 years (2). COPD became the third leading cause of death in 2008 (3). However, other comorbid conditions such as cardiovascular disease might displace COPD as the underlying cause of death that is reported on the death certificate (4,5).

Significance

Deaths from COPD might be underestimated; therefore, a much more serious public health impact might be masked. Public education and awareness of COPD symptoms and earlier diagnosis and treatment can slow further lung damage, improve COPD symptoms, and reduce COPD-related disability and deaths (6).

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect population mortality.

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate. The number of contributing causes of death listed on the death certificate might vary by person completing the death certificate and geographic region.

Related indicators or recommendations

Healthy People 2020 objective RD-10: Reduce deaths from COPD among adults.

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD Surveillance—United States, 1999–2011. Chest 2013;144:284–305.
  3. Minino AM, Xu J, Kochanek KD. Deaths: preliminary data for 2008. Natl Vital Stat Rep 2010;59:1–52.
  4. Hansell AL, Walk JA, Soriano JB. What do chronic obstructive pulmonary disease patients die from? A multiple cause coding analysis. Eur Respir J 2003;22:809–14.
  5. Jensen HH, Godtfredsen NS, Lange P, Vestbo J. Potential misclassification of death from COPD. Eur Respir J 2006;28:781–5.
  6. Qaseem A, Wilt TJ, Weinberger SE, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med 2011;155:179–91.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 2: Prevalence of chronic obstructive pulmonary disease

Demographic group

1. Resident adults aged ≥18 years.

2. Resident adults aged ≥45 years.

Numerator

Respondents aged ≥18 years (or ≥45 years) who report ever having physician-diagnosed chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis.

Denominator

Respondents aged ≥18 years (or ≥45 years) who report or do not report ever having physician-diagnosed COPD, emphysema, or chronic bronchitis (excluding refusals).

Measures of frequency

Annual prevalence (percentage), crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible (age groups: 1] 18–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥85 years and 2] 45–54, 55–64, 65–74, 75–84,and ≥85 years).

Time period of case definition

Lifetime (ever diagnosed) with COPD, which includes emphysema and chronic bronchitis.

Background

In 2011, 6.3% (15 million) of adults aged ≥18 years reported that they had COPD (2). Another estimated 15 million adults have impaired pulmonary function and COPD symptoms but are unaware of having COPD because the disease has not been diagnosed by their physician with the use of spirometry (3). Approximately 80%–90% of identified COPD cases occur among persons aged ≥45 years (2,4).

Significance

Elimination of tobacco use or exposure might be the most effective way to reduce COPD because almost 80% of COPD deaths are attributable to smoking (5). Other risk factors for COPD include occupational exposure, ambient air pollution, and long-term severe asthma (6). Public education and awareness of COPD symptoms and earlier diagnosis with spirometry and treatment might slow additional lung damage, improve COPD symptoms, and reduce COPD-related disability and deaths (7).

Limitations of indicator

The indicator is based on being diagnosed by a physician and respondent recall of the diagnosis and might underestimate the true prevalence.

Data resources

Prevalence data from Behavioral Risk Factor Surveillance System (BRFSS) (numerator) and population estimates from the U.S. Census Bureau (denominator).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective RD-13: (Developmental) Increase the proportion of adults with abnormal lung function whose underlying obstructive disease has been diagnosed.

Related chronic disease indicator topics

Asthma; tobacco; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Chronic obstructive pulmonary disease among adults—United States, 2011. MMWR 2012;61:938–43.
  3. Mannino DM, Gagnon RC, Petty TL, Lydick E. Obstructive lung disease and low lung function in adults in the United States: data from the national health and nutrition examination survey, 1988–1994. Arch Intern Med 2000;160:1683–9.
  4. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.
  5. CDC. Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000–2004. MMWR 2008;57:1226–8.
  6. Mannino DM. Epidemiology and global impact of chronic obstructive pulmonary disease. Semin Respir Crit Care Med 2005;26:204–10.
  7. Qaseem A, Wilt TJ, Weinberger SE, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med 2011;155:179–91.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 3: Prevalence of current smoking among adults with diagnosed chronic obstructive pulmonary disease

Demographic group

1. Resident adults aged ≥18 years.

2. Resident adults aged ≥45 years.

Numerator

Respondents aged ≥18 years (or ≥45 years) who report being current smokers and ever having physician-diagnosed chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis.

Denominator

Respondents aged ≥18 years (or ≥45 years) who report ever having physician-diagnosed COPD, emphysema, or chronic bronchitis.

Measures of frequency

Annual prevalence (percentage): crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible. (age groups: 1] 18–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥85 years and 2] 45–54, 55–64, 65–74, 75–84, and ≥85 years).

Time period of case definition

Lifetime (ever diagnosed) with COPD, which includes emphysema and chronic bronchitis.

Background

In 2011, 6.3% (15 million) of adults aged ≥18 years reported that they had COPD; 75% of these adults had a history of smoking and 39% continued to smoke despite awareness of having COPD (2). Approximately 80%–90% of identified COPD cases occur among persons aged ≥45 years (2,3). Approximately 80% of COPD deaths are attributable to tobacco use (4).

Significance

Elimination of tobacco use or exposure might be the most effective way to improve COPD symptoms among persons with COPD and the most effective way to prevent most COPD cases (5,6).

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect population prevalence.

Data resources

Prevalence data from Behavioral Risk Factor Surveillance System (BRFSS) and population estimates from the U.S. Census Bureau (denominator).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Older adults; tobacco.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Chronic obstructive pulmonary disease among adults—United States, 2011. MMWR 2012;61:938–43.
  3. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.
  4. CDC. Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000–2004. MMWR 2008;57:1226–8.
  5. Lee PN, Fry JS. Systematic review of the evidence relating FEV1 decline to giving up smoking. BMC Med 2010;8:84.
  6. Eisner MD, Balmes J, Yelin EH, et al. Directly measured secondhand smoke exposure and COPD health outcomes. BMC Pulm Med 2006;6:12.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 4: Prevalence of activity limitation among adults with diagnosed chronic obstructive pulmonary disease

Demographic group

1. Resident adults aged ≥18 years.

2. Resident adults aged ≥45 years.

Numerator

Respondents aged ≥18 years (or ≥45 years) who report any health-related activity limitation (≥1 day in past 30 days) and ever having physician-diagnosed chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis.

Denominator

Respondents aged ≥18 years (or ≥45 years) who report ever having physician-diagnosed COPD, emphysema, or chronic bronchitis.

Measures of frequency

Annual prevalence (percentage): crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1]) with 95% confidence intervals and by demographic characteristics when feasible (age groups: 1] 18–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥85 years and 2] 45–54, 55–64, 65–74, 75–84, and ≥85 years).

Time period of case definition

Lifetime (ever diagnosed) with COPD, which includes emphysema and chronic bronchitis.

Background

In 2011, 6.3% (15 million) of adults aged ≥18 years reported that they had COPD; 80% were aged ≥45 years (2). COPD is an important cause of activity limitation and disability. In the 2007–2009 North Carolina BRFSS, 5.7% of adults aged ≥18 years reported COPD; adults with COPD were more likely to report moderate to severe disability (37.0% versus 9.1%) than adults without COPD (3).

Significance

Public education and awareness of COPD symptoms and earlier diagnosis with spirometry and treatment might slow additional lung damage, improve COPD symptoms, and reduce COPD-related disability and deaths (4).

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect population prevalence.

Data resources

Prevalence data from Behavioral Risk Factor Surveillance System (BRFSS) (numerator) and population estimates from the U.S. Census Bureau (denominator).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective RD-9: Reduce activity limitations among adults with chronic obstructive pulmonary disease (COPD).

Related chronic disease indicator topics

Disability; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Chronic obstructive pulmonary disease among adults—United States, 2011. MMWR 2012;61:938–43.
  3. CDC. Chronic obstructive pulmonary disease and associated health-care resource use—North Carolina, 2007 and 2009. MMWR 2012;61:143–6.
  4. Qaseem A, Wilt TJ, Weinberger SE, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med 2011;155:179–91.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 5.1: Hospitalization for chronic obstructive pulmonary disease as first-listed diagnosis

Demographic group

Resident adults aged ≥45 years.

Numerator

Hospitalizations with first-listed diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification codes 490, 491, 492, 466, and 496 or International Classification of Diseases, Tenth Revision, Clinical Modification codes J40–44 among residents aged ≥45 years. When possible, include hospitalizations for residents who are hospitalized in another state.

Denominator

Midyear resident population aged ≥45 years.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1], age categories: 45–54, 55–64, 65–74, 75–84, and ≥85 years) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2010, a total of 699,000 (34.4 per 10,000 U.S. civilian population) hospitalizations with a first-listed diagnosis of chronic obstructive pulmonary disease (COPD) occurred among adults aged ≥25 years; 97.5% occurred among those aged ≥45 years (2). Hospitalizations for a first-listed diagnosis of COPD decreased during 1999–2010 (p = 0.018) (2).

Significance

Decreasing the frequency and severity of acute exacerbations of COPD might affect the rate of hospital and emergency visits.

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect populations who had COPD with serious complications. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. State discharge records cannot identify incident (new) hospitalizations for COPD. Patients with COPD are often hospitalized with pneumonia, cardiovascular diseases, or both, which might be the first-listed diagnosis.

Data resources

State hospital discharge data (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Diagnoses listed on hospital discharge data might be inaccurate. Practice patterns and payment mechanisms could affect decisions by health-care providers to hospitalize patients. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. Because state hospital discharge data are not universally available, aggregation of state data to produce nationwide estimates are incomplete. State discharge records cannot identify incident (new) hospitalizations.

Related indicators or recommendations

Healthy People 2020 objective RD-11: Reduce hospitalizations for COPD.

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 5.2: Hospitalization for chronic obstructive pulmonary disease as any diagnosis

Demographic group

Resident adults aged ≥45 years.

Numerator

Hospitalizations with any diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification codes 490, 491, 492, 466, and 496 or International Classification of Diseases, Tenth Revision, Clinical Modification codes J40–44 among residents aged ≥45 years. When possible, include hospitalizations for residents who are hospitalized in another state.

Denominator

Midyear resident population aged ≥45 years.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1], age categories: 45–54, 55–64, 65–74, 75–84, and ≥85 years) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2010, a total of 699,000 (34.4 per 10,000 U.S. civilian population) hospitalizations occurred with a first-listed diagnosis of chronic obstructive pulmonary disease (COPD) among adults aged >25 years; 97.5% occurred among adults aged ≥45 years (2). Hospitalizations for a first-listed diagnosis of COPD declined during 1999–2010 (p = 0.018) (2). Patients with COPD might be often hospitalized with pneumonia, cardiovascular diseases, or both, which might be the first-listed diagnosis.

Significance

Decreasing the frequency and severity of acute exacerbations of COPD might affect the rate of hospital and emergency visits.

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect populations who had COPD with serious complications. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. State discharge records cannot identify incident (new) hospitalizations for COPD.

Data resources

State hospital discharge data (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Diagnoses listed on hospital discharge data might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to hospitalize patients. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. Because state hospital discharge data are not universally available, aggregation of state data to produce nationwide estimates will be incomplete.

Related indicators or recommendations

Healthy People 2020 objective RD-11: Reduce hospitalizations for COPD.

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 5.3: Hospitalization for chronic obstructive pulmonary disease as first-listed diagnosis among Medicare-eligible adults aged ≥65 years

Demographic group

Medicare-eligible resident adults aged ≥65 years.

Numerator

Hospitalizations with first-listed diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification codes 490, 491, 492, 466, and 496 or International Classification of Diseases, Tenth Revision, Clinical Modification codes J40–44 among Medicare-eligible resident adults aged ≥65 years.

Denominator

Residents aged ≥65 years who were eligible for Medicare Part A benefits on July 1 of the calendar year.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population (1), age categories: 65–74, 75–84, and ≥85 years) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2010, a total of 312,654 (11.1 per 1000 Medicare enrollees) hospitalizations occurred for a first-listed diagnosis of chronic obstructive pulmonary disease (COPD) among Medicare enrollees aged ≥65 years (2). Hospitalizations decreased during 1999–2010 for men (p = 0.022) but not for women (p = 0.138) (2).

Significance

Decreasing the frequency and severity of acute exacerbations of COPD might affect the rate of hospital and emergency visits.

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect population prevalence. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. Medicare claims records cannot identify incident (new) hospitalizations for COPD. Patients with COPD are often hospitalized with pneumonia, cardiovascular diseases, or both, which might be the first-listed diagnosis.

Data resources

Centers for Medicare and Medicaid Services (CMS) Part A claims data (numerator) and CMS estimates of the population of persons eligible for Medicare (denominator).

Limitations of data resources

Diagnoses listed on Medicare hospital claims might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to hospitalize patients and to report COPD as the first-listed diagnosis. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD.

Related indicators or recommendations

Healthy People 2020 objective RD-11: Reduce hospitalizations for COPD.

. indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 5.4: Hospitalization for chronic obstructive pulmonary disease as any diagnosis among Medicare-eligible adults aged ≥ 65 years

Demographic group

Medicare-eligible resident adults aged ≥65 years.

Numerator

Hospitalizations with any diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification codes 490, 491, 492, 466, and 496 or International Classification of Diseases, Tenth Revision, Clinical Modification codes J40–44 among Medicare-eligible resident adults aged ≥65 years.

Denominator

Residents aged ≥65 years who were eligible for Medicare Part A benefits on July 1 of the calendar year.

Measures of frequency

Annual number of hospitalizations. Annual hospitalization rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1], age categories: 65–74, 75–84, and ≥85 years) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2010, 312,654 (11.1 per 1000 Medicare enrollees) hospitalizations occurred for a first-listed diagnosis of chronic obstructive pulmonary disease (COPD) among Medicare enrollees aged ≥65 years (2). Hospitalizations decreased during 1999–2010 for men (p = 0.022) but not for women (p = 0.138) (2). Patients with COPD are often hospitalized with pneumonia, cardiovascular diseases, or both, which might be the first-listed diagnosis.

Significance

Decreasing the frequency and severity of acute exacerbations of COPD might affect the rate of hospital and emergency visits.

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect populations who had COPD with serious complications. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. Medicare claims records cannot identify incident (new) hospitalizations for COPD.

Data resources

Centers for Medicare and Medicaid Services (CMS) Part A claims data (numerator) and CMS estimates of the population of persons eligible for Medicare (denominator).

Limitations of data resources

Diagnoses listed on Medicare hospital claims might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to hospitalize patients and to identify COPD as the first-listed diagnosis. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD.

Related indicators or recommendations

Healthy People 2020 objective RD-11: Reduce hospitalizations for chronic obstructive pulmonary disease (COPD).

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 6.1: Emergency department visit rate for chronic obstructive pulmonary disease as first-listed diagnosis

Demographic group

Resident adults aged ≥45 years.

Numerator

Number of emergency department (ED) visits with a first-listed diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification codes 490, 491, 492, 466, and 496 or International Classification of Diseases, Tenth Revision, Clinical Modification codes J40–44 among residents aged ≥45 years.

Denominator

Midyear resident population aged ≥45 years.

Measures of frequency

Annual number of ED visits. Annual ED visit rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population [1], age categories: 45–54, 55–64, 65–74, 75–84, and ≥85 years) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2010, a total of 1,468,000 (72.0 per 10,000 U.S. civilian population) ED visits occurred with a first-listed diagnosis of chronic obstructive pulmonary disease (COPD) among adults aged ≥25 years; 73.6% of these visits occurred among persons aged ≥45 years (2). ED visits have not changed during 1999–2010 (2).

Significance

Public education and awareness of COPD symptoms and earlier diagnosis and treatment can slow additional lung damage, improve COPD symptoms, and reduce COPD-related disability and deaths (3). Decreasing the frequency and severity of acute exacerbations of COPD will affect the rate of hospitalizations and ED visits and possibly number of deaths.

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect populations who had COPD with serious complications.

Data resources

State ED databases (visits that do not result in an admission) from the Healthcare Cost and Utilization Project (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Diagnoses listed on ED visit data might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to identify COPD as the first-listed diagnosis. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. Data might not be available for all states.

Related indicators or recommendations

Healthy People 2020 objective RD-12: Reduce hospital emergency department visits for COPD.

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.
  3. Qaseem A, Wilt TJ, Weinberger SE, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med 2011;155:179–91.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 6.2: Emergency department visits rate for chronic obstructive pulmonary disease as any diagnosis

Demographic group

Resident adults aged ≥45 years.

Numerator

Number of emergency department (ED) visits with any diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification codes 490, 491, 492, 466, and 496 or International Classification of Diseases, Tenth Revision, Clinical Modification codes J40–44 among residents aged ≥45 years.

Denominator

Midyear resident population aged ≥45 years.

Measures of frequency

Annual number of ED visits. Annual ED visit rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population (1), age categories: 45–54, 55–64, 65–74, 75–84, ≥85) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2010, a total of 1,468,000 (72.0 per 10,000 U.S. civilian population) ED visits occurred with a first-listed diagnosis of chronic obstructive pulmonary disease (COPD) among adults aged ≥25 years; 73.6% of these visits occurred among persons aged ≥45 years (2). ED visits have not changed during 1999–2010 (2).

Significance

Public education and awareness of COPD symptoms and earlier diagnosis and treatment can slow additional lung damage, improve COPD symptoms, and reduce COPD-related disability and deaths (3). Decreasing the frequency and severity of acute exacerbations of COPD will affect the rate of hospitalizations and ED visits and possibly number of deaths.

Limitations of indicator

Because COPD is a chronic disease, years might pass before changes in behavior or clinical practice affect populations who had COPD with serious complications.

Data resources

State ED databases (visits that do not result in an admission) from the Healthcare Cost and Utilization Project (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Diagnoses listed on ED visit data might be inaccurate. Practice patterns and payment mechanisms can affect decisions by health-care providers to identify COPD as the first-listed diagnosis. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Multiple admissions for an individual patient can inaccurately increase the number of persons with COPD. Data might not be available for all states.

Related indicators or recommendations

Healthy People 2020 objective RD-12: Reduce hospital emergency department visits for COPD.

Related chronic disease indicator topics

Older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Ford ES, Croft JB, Mannino DM, Wheaton AG, Zhang X, Giles WH. COPD surveillance—United States, 1999–2011. Chest 2013;144:284–305.
  3. Qaseem A, Wilt TJ, Weinberger SE, et al. Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline update from the American College of Physicians, American College of Chest Physicians, American Thoracic Society, and European Respiratory Society. Ann Intern Med 2011;155:179–91.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 7: Influenza vaccination among noninstitutionalized adults aged ≥45 years with chronic obstructive pulmonary disease

Demographic group

Noninstitutionalized resident adults aged ≥45 years.

Numerator

Respondents aged ≥45 years who report having ever been told that they have chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis and who report having received influenza vaccination in the past 12 months.

Denominator

Respondents aged ≥45 years having ever been told that they have COPD, emphysema, or chronic bronchitis and who report having received influenza vaccination in the past 12 months or not having received influenza vaccination in the past 12 months (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of COPD, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Lifetime COPD, which includes emphysema and chronic bronchitis.

Past 12 months (vaccinated).

Background

In 2011, 6.3% (15 million) of adults aged ≥18 years reported that they had COPD; 80% were aged ≥45 years (2). During the 2010–2011 influenza season, 48.4% of adults aged 18–64 years at high risk for complications from influenza and 68.6% of adults aged ≥65 years received influenza vaccine (3).

Significance

In patients with underlying lung diseases such as COPD, influenza viruses cause respiratory tract infections that are associated with exacerbations and excess morbidity and mortality (4).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage surveillance is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-12: Increase the percentage of children and adults who are vaccinated annually against seasonal influenza. (IID-12.6 is specific for noninstitutionalized high-risk adults aged 18–64 years, and IID-12.7 is specific to noninstitutionalized adults aged ≥65 years). The Healthy People 2020 influenza vaccination objectives have been consolidated since the original publication of Healthy People 2020 but will continue to be monitored as part of Healthy People 2020 data reporting. (Additional information is available at http://www.cdc.gov/vaccines/acip/meetings/downloads/slides-oct-2013/03-Influenza-Singleton.pdf and from the Healthy People 2020 website at http://www.healthypeople.gov/2020/topics-objectives/topic/immunization-and-infectious-diseases/objectives.)
  • Healthy People 2020 objective OA-2: Increase the proportion of older adults who are up to date on a core set of clinical preventive services.
  • Promoting Preventive Services for Adults Aged 50–64 Years, Community and Clinical Partnerships: Percentage of adults who reported influenza vaccination within the past year.

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Chronic obstructive pulmonary disease among adults—United States, 2011. MMWR 2012;61:938–43.
  3. CDC. Interim results: state-specific seasonal influenza vaccination coverage—United States, August 2010—February 2011. MMWR 2011;60:737–43.
  4. Wesseling G. Occasional review: Influenza in COPD: pathogenesis, prevention, and treatment. Int J Chron Obstruct Pulmon Dis 2007;2:5–10.

Indicator Group: Chronic Obstructive Pulmonary Disease

Indicator 8: Pneumococcal vaccination among adults aged ≥45 years with chronic obstructive pulmonary disease

Demographic group

Noninstitutionalized resident adults aged ≥45 years.

Numerator

Respondents aged ≥45 years who reported having ever been told that they chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis and who reported ever having received pneumococcal vaccine.

Denominator

Respondents aged ≥45 years having ever been told that they have COPD, emphysema, or chronic bronchitis and who report ever having or not ever having a pneumococcal vaccination (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of COPD, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Lifetime COPD, which includes emphysema and chronic bronchitis.

Lifetime (ever been vaccinated).

Background

In 2011, 6.3% (15 million) of adults aged ≥18 years reported that they had COPD; 80% were aged ≥45 years (2). In 2012 in the United States, pneumococcal vaccination coverage among adults aged 19–64 years at high risk for pneumococcal disease and its complications was 20.0% overall; among adults aged ≥65 years, coverage was 59.9% (3).

Significance

In a study conducted among a cohort of older veterans (average age: 53 years), hospitalization rates for pneumococcal pneumonia among persons with COPD were higher than among persons in the control group (4).

Limitations of indicator

Although self-reported pneumococcal vaccination has been validated (5), the reliability and validity of this measure is unknown.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-13: Increase the percentage of adults who are vaccinated against pneumococcal disease. (IID-13.1 is specific to noninstitutionalized adults aged ≥65 years, and IID-13.2 is specific to noninstitutionalized high-risk adults aged 18–64 years.)
  • Healthy People 2020 objective OA-2: Increase the proportion of older adults who are up to date on a core set of clinical preventive services.
  • Promoting Preventive Services for Adults Aged 50–64 Years, Community and Clinical Partnerships: Percentage of adults who reported current smoking, diabetes, asthma or cardiovascular disease who have ever had a pneumococcal vaccination.

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Chronic obstructive pulmonary disease among adults—United States, 2011. MMWR 2012;61:938–43.
  3. CDC. Noninfluenza vaccination coverage among adults—United States, 2012. MMWR 2014;63:95–102.
  4. CDC. Updated recommendations for prevention of invasive pneumococcal disease among adults using the 23-valent pneumococcal polysaccharide vaccine (PPSV23). MMWR 2010;59:1102–6.
  5. Shenson D, DiMartino D, Bolen J, Campbell M, Lu PJ, Singleton JA. Validation of self-reported pneumococcal vaccination in behavioral risk factor surveillance surveys: experience from the sickness prevention achieved through regional collaboration (SPARC) program. Vaccine 2005;23:1015–20.

Indicator Group: Diabetes

Indicator 1.1: Mortality with diabetes reported as any listed cause of death

Demographic group

All resident persons.

Numerator

Deaths with International Classification of Diseases, 10th Revision codes E10–E14 as an underlying or a contributing cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual death rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 4 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

During 2011, diabetes was the seventh leading cause of death in the United States, resulting in approximately 73,000 deaths (2). Diabetes is two times as likely to be listed as a contributing cause of death than as the underlying cause of death (3). In 2004, heart disease was noted on 68% of diabetes-related death certificates among adults aged ≥65 years (4).

Significance

Multiple long-term complications of diabetes can be prevented through regular, optimal blood glucose, blood lipid, and blood pressure monitoring and through screening and treatment for eye, foot, and kidney abnormalities (3). Methods to prevent complications include improved patient education and self-management and provision of adequate and timely screening services and medical care (3).

Limitations of indicator

Approximately one fourth of cases of diabetes are undiagnosed (3). Diabetes is likely to be underreported as a cause of death, listed on the death certificates of only approximately 40% of decedents who actually had diabetes (5).

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate. The number of contributing causes of death listed on the death certificate can vary by person completing the death certificate and geographic region. If this estimate is calculated within the diabetes population, restrict the denominator to only persons with diabetes.

Related indicators or recommendations

Healthy People 2020 objective D-3: Reduce the diabetes death rate from 73.1 per 100,000 population in 2007 to 65.8 per 100,000 population (age adjusted to the year 2000 population).

Related chronic disease indicator topics

Cardiovascular disease; nutrition, physical activity, and weight status.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. Hoyert DL, Xu JQ. Deaths: Preliminary data for 2011. Natl Vital Stat Rep 61(6); 2012.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  4. Gorina Y, Lentzer H. Multiple causes of death in old age. Aging Trends, no. 9. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2008. Available at http://www.cdc.gov/nchs/data/ahcd/agingtrends/09causes.pdf.
  5. McEwen LN, Kim C, Haan M, et al; TRIAD Study Group. Diabetes reporting as a cause of death: results from the Translating Research Into Action for Diabetes (TRIAD) study. Diabetes Care 2006;29:247–53.

Indicator Group: Diabetes

Indicator 1.2: Mortality with diabetic ketoacidosis reported as any listed cause of death

Demographic group

All resident persons.

Numerator

Deaths from International Classification of Diseases, 10th Revision codes E10.1, E11.1, E12.1, E13.1, and E14.1 as an underlying or contributing cause of death among residents during a calendar year.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of deaths. Annual mortality rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 4 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

During 2009, hyperglycemic crisis, which includes diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state, was listed as the underlying cause of death for approximately 2,400 persons (2). DKA is more frequent among persons with type 1 diabetes than among persons with type 2 (3). Diabetes is two times as likely to be listed as a contributing cause of death than as the underlying cause of death (4). In 2004, heart disease was noted on 68% of diabetes-related death certificates among adults aged ≥65 years (5).

Significance

DKA is a life-threatening condition. Among persons with diagnosed diabetes, DKA is substantially preventable through improved patient education and self-management and provision of adequate and timely medical care (3).

Limitations of indicator

Although the percentage of persons who are aware of having the disease (diabetes) is higher among persons with type 1 diabetes than among those with type 2 diabetes, approximately one fourth of all cases of diabetes are undiagnosed (4). In addition, although DKA is an acute event and would be expected to be listed more frequently than diabetes as the underlying cause of death, diabetes is listed on the death certificates of only approximately 40% of decedents who actually had diabetes (6).

Data resources

Death certificate data from vital statistics agencies (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Causes of death and other variables listed on the death certificate might be inaccurate. The number of contributing causes of death listed on the death certificate might vary by the person completing the death certificate and geographic region. If this estimate is calculated within the diabetes population, restrict the denominator to only persons with diabetes.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. Nyenwe EA, Kitabchi AE. Evidence-based management of hyperglycemic emergencies in diabetes mellitus. Diabetes Res Clin Pract 2011;94:340–51.
  4. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  5. Gorina Y, Lentzer H. Multiple causes of death in old age. Aging Trends, No. 9. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2008. Available at http://www.cdc.gov/nchs/data/ahcd/agingtrends/09causes.pdf.
  6. McEwen LN, Kim C, Haan M, et al; TRIAD Study Group. Diabetes reporting as a cause of death: results from the Translating Research Into Action for Diabetes (TRIAD) study. Diabetes Care 2006;29:247–53.

Indicator Group: Diabetes

Indicator 2.1: Prevalence of diagnosed diabetes among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report ever been told by a doctor or other health professional that they have diabetes other than diabetes during pregnancy.

Denominator

Respondents aged ≥18 years who report or do not report ever been told by a doctor or other health professional that they have diabetes (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Lifetime (ever diagnosed).

Background

In 2011, 9% of the U.S. adult population aged ≥18 years had diagnosed diabetes (2). Substantial differences in diabetes prevalence exist by age, race, and ethnicity (3).

Significance

The impact of diabetes in the United States has increased with the increasing prevalence of obesity (3). Multiple long-term complications of diabetes can be prevented through improved patient education and self-management and provision of adequate and timely screening services and medical care (4).

Limitations of indicator

Approximately one fourth of cases of diabetes are undiagnosed (4).

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (5).

Related indicators or recommendations

None.

Related chronic disease indicator topics

Nutrition, physical activity, and weight status.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. Geiss LS, Cowie C. Type 2 diabetes and persons at high risk of diabetes. In: Venkat Narayan KM, Williams D, Gregg EW, Cowie C, eds. Diabetes public health: from data to policy. New York, NY: Oxford University Press; 2011:15–32.
  4. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  5. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 2.2: Diabetes prevalence among women aged 18–44 years

Demographic group

Women aged 18–44 years.

Numerator

Female respondents aged 18–44 years who reported ever being told by a doctor that they have diabetes. Women with gestational diabetes are included in the numerator, whereas women with prediabetes or borderline diabetes are not.

Denominator

Female respondents aged 18–44 years who did or did not report ever being told by a doctor that they have diabetes (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude annual prevalence with 95% confidence intervals weighted using the Behavioral Risk Factor Surveillance System method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage) and by demographic characteristics when feasible.

Time period of case definition

Lifetime.

Background

During 2005–2006, approximately 8% of women aged ≥20 years had diagnosed diabetes (age adjusted to the year 2000 population) (1). In 2003–2004, 4.2% of pregnant women aged 15–44 years had gestational diabetes (2).

Significance

Diabetes is the seventh leading cause of death in the United States and is associated with serious health complications including heart disease, blindness, kidney failure, and lower extremity amputations (3). In addition, gestational diabetes can cause serious problems for both mothers and infants (4). Because preconceptional and prenatal control of diabetes reduces the risk for congenital malformations, pregnancy loss, and perinatal mortality, the clinical workgroup of the Select Panel on Preconception Care recommends that all diabetic women of reproductive age be counseled before pregnancy about the importance of diabetes control (4,5).

Limitations of indicator

The indicator is based on self-reported data that were not confirmed by a physician. However, self-reported diabetes data from BRFSS has consistently yielded high reliability and moderate validity, which is also consistent with other research demonstrating underreporting of diabetes (6). Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (7).

Related indicators or recommendations

None.

Related chronic disease indicator topics

Reproductive health.


References

  1. Cowie CC, Rust KF, Ford ES, et al. Full accounting of diabetes and pre-diabetes in the U.S. population in 1988–1994 and 2005–2006. Diabetes Care 2009;32(2):287–94.
  2. Getahun D, Nath C, Ananth CV, Chavez MR, Smulian JC. Gestational diabetes in the United States: temporal trends, 1989 through 2004. Am J Obstet Gynecol 2008;198:525.e1–5.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  4. Dunlop AL, Jack BW, Bottalico JN, et al. The clinical content of preconception care: women with chronic medical conditions. Am J Obstet Gynecol 2008;199(Suppl 2):S310–27.
  5. CDC. Recommendations to improve preconception health and health care—United States: a report of the CDC/ATSDR Preconception Care Work Group and the Select Panel on Preconception Care. MMWR 2006;55(No. RR-6).
  6. Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soc Prev Med 2001;46(Suppl 1):S3–42.
  7. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 3.1: Prevalence of prepregnancy diabetes

Demographic group

Women aged 18–44 years who have had a live birth.

Numerator

Respondents aged 18–44 years who reported ever being told by a doctor, nurse, or health-care worker that they had type 1 or type 2 diabetes before the pregnancy that resulted in their most recent live birth.

Denominator

Respondents aged 18–44 years who reported that they had or had not ever been told by a doctor, nurse, or health-care worker that they had type 1 or type 2 diabetes before the pregnancy that resulted in their most recent live birth (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude prevalence with 95% confidence intervals weighted using the Pregnancy Risk Assessment Monitoring System method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage) and by demographic characteristics when feasible.

Time period of case definition

Lifetime.

Background

Based on 2005 clinical data, among women aged 13–58 years in southern California with singleton deliveries of ≥20 weeks' gestation, preexisting diabetes was identified in 1.8 per 100 pregnancies, and gestational diabetes was diagnosed in 7.4 per 100 pregnancies (1). In 2005, in this study of racially diverse pregnant women with diabetes, 21% had preexisting diabetes and 79% had gestational diabetes (1).

Significance

Women with diabetes are at an increased risk for complications during pregnancy and are more likely than nondiabetic women to experience adverse infant health outcomes such birth weight that is large for gestational age and birth defects (2,3). Macrosomia (i.e., large for gestational age) increases the risk for labor complications, cesarean delivery, intracranial hemorrhage, shoulder dystocia, and respiratory distress (2,3). Because preconceptional and prenatal control of diabetes reduces the risk for congenital malformations, pregnancy loss, and perinatal mortality, the clinical workgroup of the Select Panel on Preconception Care recommends that all diabetic women of reproductive age be counseled about the importance of diabetes control before pregnancy and appropriately treated to achieve diabetes control (4,5).

Limitations of indicator

Women who experienced a fetal death or had an abortion are excluded. These data are self-reported, were not confirmed by a physician, and might be subject to misclassification bias. In addition, no method exists to differentiate between type 1 and type 2 diabetes. Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Pregnancy Risk Assessment Monitoring System (PRAMS).

Limitations of data resources

PRAMS data are only collected from women who delivered a live-born infant, not all women of reproductive age, and from 40 states and one city, not the entire United States. PRAMS data are self-reported and might be subject to recall bias and underreporting or overreporting of behaviors based on social desirability. Although most self-report surveys such as PRAMS might be subject to systematic error resulting from noncoverage (e.g., lower landline telephone coverage because of transition to cell phone–only households or undeliverable addresses), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement bias (e.g., recall bias), PRAMS attempts to contact potential respondents by mail and landline or cellular telephone to increase response rates. Women who experienced a fetal death or had an abortion are excluded. PRAMS estimates only cover the population of residents in each state who also deliver in that state; therefore, residents who delivered in a different state are not included in their resident state.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Reproductive health.


References

  1. Lawrence JM, Contreras R, Chen W, Sacks DA. Trends in the prevalence of preexisting diabetes and gestational diabetes mellitus among a racially/ethnically diverse population of pregnant women, 1999–2005. Diabetes Care 2008;31:899–904.
  2. Temple RC, Aldridge VJ, Murphy HR. Prepregnancy care and pregnancy outcomes in women with type 1 diabetes. Diabetes Care 2006;29:1744–9.
  3. Clausen TD, Mathiesen E, Ekbom P, et al. Poor pregnancy outcome in women with type 2 diabetes. Diabetes Care 2005;28:323–8.
  4. Dunlop AL, Jack BW, Bottalico JN, et al. The clinical content of preconception care: women with chronic medical conditions. Am J Obstet Gynecol 2008;199(Suppl B):S310–27.
  5. CDC. Recommendations to improve preconception health and health care—United States: a report of the CDC/ATSDR Preconception Care Work Group and the Select Panel on Preconception Care. MMWR 2006;55(No. RR-6).

Indicator Group: Diabetes

Indicator 3.2: Prevalence of gestational diabetes

Demographic group

Women aged 18–44 years who have had a live birth.

Numerator

Number of women who have had a live birth with gestational diabetes listed on the birth certificate.

Denominator

Number of women who have had a live birth.

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

The prevalence of gestational diabetes in the United States is increasing, and during 2003–2004, 4.2% of pregnant women aged 15–44 years had gestational diabetes (1). In 2011, rates of gestational diabetes in some populations ranged from 2% to 10% (2), which means that approximately 200,000 U.S. women were at risk for subsequently developing type 2 diabetes.

Significance

Gestational diabetes is defined as having abnormally high blood glucose levels first detected in pregnancy (2). This abnormality usually disappears after pregnancy; however, women who have had gestational diabetes have a 35%–60% chance of developing diabetes in the next 10–20 years (2). Infants born to women with gestational diabetes also have a higher risk for developing type 2 diabetes (3). Among women at risk, maintaining a healthy weight and increasing physical activity can reduce the risk for type 2 diabetes by approximately 50% (4).

Limitations of indicator

Difficulties in documenting and reaching consensus on the prevalence of gestational diabetes exist for a number of reasons including the use of various diagnostic criteria, past confusion about the specific criteria used to diagnose gestational diabetes, and the lack of a universal recommendation for screening and diagnosis. Decreasing the proportion of pregnant women with gestational diabetes was a developmental Healthy People 2010 objective but was discontinued midterm for lack of data. Based on a Gestational Diabetes Consensus Panel convened on March 2013, the National Institutes of Health released a draft consensus statement on screening and diagnosis of gestational diabetes (5). Electronic medical records are expected to enhance accuracy of hospital records data. Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Birth certificate, National Vital Statistics System.

Limitations of data resources

Not all states use the 2003 version of birth certificates and might not identify gestational diabetes separately from diabetes. Birth certificates might not accurately document maternal health status.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Reproductive health.


References

  1. Getahun D, Nath C, Ananth CV, Chavez MR, Smulian JC. Gestational diabetes in the United States: temporal trends 1989 through 2004. Am J Obstet Gynecol 2008;198:525.e1–5.
  2. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  3. Correa-Villaseñor A. Marcinkevage JA, Cowie C. Diabetes in pregnancy. In: Venkat Narayan KM, Williams D, Gregg EW, Cowie C, eds. Diabetes public health: from data to policy. New York, NY: Oxford University Press; 2011:195–223.
  4. Knowler WC, Barrett-Connor E, Fowler SE, et al; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346:393–403.
  5. Vandorsten JP, Dodson WC, Espeland MA, et al. NIH consensus development conference: diagnosing gestational diabetes mellitus. NIH Consens State Sci Statements. 2013;29:1–31.

Indicator Group: Diabetes

Indicator 4: Amputation of a lower extremity attributable to diabetes

Demographic group

All resident persons.

Numerator

Hospitalizations with a first-listed or contributing diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 250 and a procedure of ICD-9-CM code 84.1 and not having ICD-9-CM codes 895–897 (traumatic amputation) among residents during a calendar year. Search all diagnostic fields. When possible, include hospitalizations for residents who are hospitalized in another state.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of persons hospitalized. Annual hospitalization rates: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 4 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

Diabetes is the leading cause of nontraumatic amputation in the United States, listed as a diagnosis in approximately 68,000 hospital discharges for nontraumatic amputations in 2009 (2).

Significance

Multiple long-term complications of diabetes, including amputation, can be prevented through glucose, lipid, and blood pressure regulation and through screening and treatment for foot abnormalities (3). Methods to prevent amputation include improved patient education and self-management (3).

Limitations of indicator

Approximately one fourth of diabetes cases are undiagnosed (3).

Data resources

State hospital discharge data (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Diagnoses and procedures listed on hospital discharge data might be inaccurate. Practice patterns and payment mechanisms might affect decisions by health-care providers to hospitalize patients. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Hospital discharge data does not allow identification of amputations that are new (incident case) versus a second amputation for an individual. Multiple but unrecognized admissions for one person can inaccurately increase the number of persons hospitalized for lower extremity amputations. Because state hospital discharge data are not universally available, aggregation of state data to produce nationwide estimates is incomplete.

Related indicators or recommendations

Healthy People 2020 objective D-4: Reduce the rate of lower extremity amputations in persons with diagnosed diabetes to less than 3.5 lower extremity amputations per 1,000 persons with diagnosed diabetes (age adjusted to the year 2000 population).

Related chronic disease indicator topics

Disability.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.

Indicator Group: Diabetes

Indicator 5: Foot examination among adults aged ≥18 years with diagnosed diabetes

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told having diabetes only when pregnant) who report having received at least one clinical foot examination within the previous year.

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told having diabetes only when pregnant, refusals, and unknowns).

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past year.

Background

In 2010, approximately 68% of adults with diagnosed diabetes reported having received at least one foot examination in the past 12 months (age adjusted to the year 2000 population) (2).

Significance

Persons with diabetes are at increased risk for pathologic changes of their lower extremities that, when combined with minor trauma and infection, can lead to serious foot problems, including amputation (3). Diabetes is the leading cause of nontraumatic amputation in the United States, listed as a diagnosis in approximately 68,000 hospital discharges for nontraumatic amputations in 2009 (2). Routine and periodic foot examination can greatly reduce rates of lower-extremity amputation (4).

Limitations of indicator

The reliability and validity of this indicator are unknown.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (5).

Related indicators or recommendations

Healthy People 2020 objective D-9: Increase the proportion of adults with diabetes who have at least an annual foot examination from 68.0% in 2008 to 74.8% (age adjusted to the year 2000 population).

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. Boulton AJM, Bowling FL. Diabetes and lower extremity diseases. In: Venkat Narayan KM, Williams D, Gregg EW, Cowie C, eds. Diabetes public health: from data to policy. New York, NY: Oxford University Press; 2011:161–171.
  4. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  5. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 6: Glycosylated hemoglobin measurement among adults aged ≥18 years with diagnosed diabetes

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who have ever told by a doctor or other health professional that they have diabetes (excluding women who were told having diabetes only when pregnant, refusals and unknowns) who report having their glycosylated hemoglobin checked at least twice in past 12 months by a doctor, nurse or other health professional.

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told having diabetes only when pregnant, refusals, and unknowns).

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Previous year.

Background

In 2010, approximately 69% of adults ≥18 years with diagnosed diabetes reported having a glycosylated hemoglobin measurement at least twice in the past 12 months (age adjusted to the year 2000 population) (2).

Significance

Testing for glycosylated hemoglobin at least twice per year is recommended for persons with diabetes (3). The number of glycosylated hemoglobin tests is an indicator of diabetes care and provider effort in monitoring the glycemic control of patients.

Limitations of indicator

Although testing for glycosylated hemoglobin is recommended for persons with diabetes (3), the number of tests received is not an indicator of their current glycemic control, or lack thereof. Persons who reported they have never heard of an A1c test are not counted as not having the test. Some of the respondents might have had the test but reported "don't know/not sure" because of unawareness, or communication or cognitive difficulties.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (4).

Related indicators or recommendations

Healthy People 2020 objective D-11: Increase the proportion of adults with diabetes who have a glycosylated hemoglobin measurement at least twice a year from 64.6% in 2008 to 71.1% (age adjusted to the year 2000 population).

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013;36(Suppl. 1):S11–66. Available at http://care.diabetesjournals.org/content/36/Supplement_1/S11.full.pdf+html.
  4. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 7: Dilated eye examination among adults aged ≥18 years with diagnosed diabetes

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant) who report having received a dilated eye examination within the previous year.

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns).

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past year.

Background

In 2010, approximately 63% of adults with diabetes reported having received a dilated eye examination in the past year (age adjusted to the year 2000 population) (2).

Significance

Routine dilated eye examinations can lead to early detection and effective treatment of complications (3). Persons with diabetes are at increased risk for blindness as a result of retinopathy (3). Diabetes is the leading cause of new cases of blindness among adults aged 20–74 years (3).

Limitations of indicator

Respondents might not distinguish between dilated and nondilated eye examinations.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (4).

Related indicators or recommendations

Healthy People 2020 objective D-10: Increase the proportion of adults with diabetes who have an annual dilated eye examination from 53.4% in 2008 to 58.7% (age adjusted to the year 2000 population).

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  4. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 8: Visits to dentist or dental clinic among adults aged ≥18 years with diagnosed diabetes

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns) who report having visited a dentist or dental clinic within the previous year.

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns).

Measures of frequency

Prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, using age groups 18–54 years, 55–64 years, and ≥65 years [1]), with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past year.

Background

In 2004, among the 50 states, the District of Columbia, and the U.S. territories, the median estimated age-adjusted percentage of dentate adults with diabetes who had a dental visit during the preceding 12 months was 67.3% (range: 49.1%–83.3%) (2).

Significance

Periodontal disease is a major complication of diabetes (3). The American Diabetes Association recommends a dentist referral for a comprehensive periodontal examination for persons with diabetes (4).

Limitations of indicator

Approximately one fourth of cases of diabetes are undiagnosed (3). The dental visit indicator does not convey the reasons for the visit or whether dental care was actually received.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (5).

Related indicators or recommendations

Healthy People 2020 objective D-8: Increase the proportion of persons with diagnosed diabetes who have at least an annual dental examination from 55.6% in 2008 to 61.2% (age adjusted to the year 2000 population).

Related chronic disease indicator topics

Oral health.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Dental visits among dentate adults with diabetes—United States, 1999 and 2004. MMWR 2005;54:1181–3.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  4. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013;36(Suppl. 1):S11–66.
  5. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 9: Hospitalization with diabetes as a listed diagnosis

Demographic group

All resident persons.

Numerator

Hospitalizations with a first-listed or contributing diagnosis of International Classification of Diseases, Ninth Revision, Clinical Modification code 250. Search all diagnostic fields among residents during a calendar year. When possible, include hospitalizations for residents who are hospitalized in another state.

Denominator

Midyear resident population for the same calendar year.

Measures of frequency

Annual number of hospitalizations. Annual hospital rate: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 4 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

In 2009, approximately 5.5 million hospitalizations had diabetes listed as a diagnosis, including 688,000 (12.5%) hospitalizations with diabetes listed as the principal diagnosis (2). In 2010, among hospital discharges with diabetes as any-listed diagnosis in adults aged ≥18 years, circulatory diseases were the most frequent first-listed diagnosis, accounting for approximately one fourth of all discharges (2).

Significance

Long-term complications of diabetes requiring hospitalization can be prevented through glucose, lipid, and blood pressure regulation, as well as screening and treatment for eye, foot, and kidney abnormalities (3). Patient education, self-management, and medical care can prevent complications.

Limitations of indicator

Because diabetes is a chronic disease, and approximately one fourth of cases are undiagnosed (3). The number of diagnoses listed on discharge abstracts might vary by person completing the abstract and geographic region of the United States. Hospital discharge records cannot identify incident (new) hospitalizations for diabetes.

Data resources

State hospital discharge data (numerator) and population estimates from the U.S. Census Bureau or suitable alternative (denominator).

Limitations of data resources

Diagnoses listed on hospital discharge data might be inaccurate. Practice patterns and payment mechanisms might affect decisions by health-care providers to hospitalize patients. Residents of one state might be hospitalized in another state and not be reflected in the first state's hospital data set. Multiple admissions for one person might inaccurately increase the number of persons with diabetes. Because no universal availability of state hospital discharge data exists, aggregation of state data to produce nationwide estimates will be incomplete.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.

Indicator Group: Diabetes

Indicator 10: Adults aged ≥18 years with diagnosed diabetes who have taken a diabetes self-management course

Demographic group

All resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns) who report ever taking a course or class in how to self-manage diabetes.

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns).

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past year.

Background

In 2010, approximately 57% of adults aged ≥18 years with diagnosed diabetes reported ever receiving diabetes self-management education (age adjusted to the year 2000 population) (2).

Significance

The American Diabetes Association recommends that persons with diabetes receive diabetes self-management education (DSME) as outlined in the national standards for DSME at the time of their diagnosis and as needed thereafter (3). DSME is an essential component of diabetes care, and the national standards are based on evidence of its benefits. DSME assists persons with diabetes in effectively managing their disease when they receive the initial diagnosis and helps them continue a high-quality level of self-care that is essential for optimizing metabolic control, managing complications, and having an acceptably high quality of life.

Limitations of indicator

Data are limited to those states that ask the optional BRFSS diabetes module.

Definition of "course or class" is not well defined.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (4).

Related indicators or recommendations

Healthy People 2020 objective D-14: Increase the proportion of persons with diagnosed diabetes who receive formal diabetes education from 56.8% in 2008 to 62.5% (age adjusted to the year 2000 population).

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013;36(Suppl. 1):S11–66.
  4. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 11.1: Prevalence of self-reported high cholesterol among adults aged ≥18 years with diagnosed diabetes

Demographic group

All resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns) who report having ever been told by a doctor, nurse, or other health professional that they had high cholesterol.

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns).

Measures of frequency

Biannual (odd years) prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard US population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Diabetes: lifetime (ever diagnosed).

High cholesterol: lifetime (ever diagnosed).

Background

In 2009, approximately 58% of U.S. adults aged ≥18 years with diagnosed diabetes reported having high blood cholesterol (age adjusted to the year 2000 population) (2).

Significance

Diabetes is a major risk factor for coronary heart disease and other forms of cardiovascular disease (3). Reducing cholesterol levels in persons with diabetes reduces the risk for cardiovascular complications (3).

Limitations of indicator

BRFSS measures whether respondents were ever told they had high cholesterol, which might result in an overestimate of the diagnosis of high cholesterol. In addition, the parameters for high cholesterol are not defined.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (4).

Related indicators or recommendations

Healthy People 2020 objective D-6: Improve lipid control among persons with diagnosed diabetes from 53.1% of adults aged ≥18 years with low-density lipoprotein cholesterol <100 mg/dL in 2005–2008 to 58.4%.

Related chronic disease indicator topics

Cardiovascular disease.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  4. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 11.2: Prevalence of self-reported high blood pressure among adults aged ≥18 years with diagnosed diabetes

Demographic group

All resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals and unknowns) who report having ever been told by a doctor, nurse, or other health professional that they had high blood pressure (excluding during pregnancy).

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals and unknowns).

Measures of frequency

Biannual (odd years) prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard US population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Diabetes: lifetime (ever diagnosed).

High blood pressure: lifetime (ever diagnosed).

Background

In 2009, approximately 57% of adults aged ≥18 years with diagnosed diabetes reported having high blood pressure (age adjusted to the year 2000 population) (2).

Significance

Hypertension is an extremely common comorbidity in patients with diabetes (3). In 2005–2008, approximately two thirds of adults aged ≥20 years with self-reported diabetes had blood pressure ≥140/90 mmHg or used prescription medications for high blood pressure (3). The development of hypertension in patients with diabetes is particularly harmful because the incidence of cardiovascular complications, including heart attack, stroke, and peripheral vascular disease, is strongly associated with increasing systolic blood pressure (4). Hypertension also is thought to play a major role in the development of retinopathy, nephropathy, and possibly neuropathy (3,4). Early detection and treatment is essential to prevent these complications.

Limitations of indicator

BRFSS measures whether respondents were ever told they had high cholesterol, which might result in an overestimate of the diagnosis of high cholesterol. In addition, the parameters for high cholesterol are not defined.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (5).

Related indicators or recommendations

Healthy People 2020 objective D-7: Increase the proportion of the population with diagnosed diabetes whose blood pressure is under control from 51.8% of adults aged ≥18 years with blood pressure under control in 2005–2008 to 57.0% (age adjusted to the year 2000 population).

Related chronic disease indicator topics

Cardiovascular disease.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  4. Adler AI, Stratton IM, Neil HA, et al. Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (UKPDS 36): prospective observational study. BMJ 2000;321:412–9.
  5. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 11.3: Prevalence of depressive disorders among adults aged ≥18 years with diagnosed diabetes

Demographic group

All resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals and unknowns) who report having ever been told that they had a depressive disorder (including depression, major depression, dysthymia or minor depression).

Denominator

Respondents aged ≥18 years ever told by a doctor or other health professional that they have diabetes (excluding women who were told only when pregnant, refusals and unknowns).

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 8 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Diabetes: lifetime (ever diagnosed).

Depressive disorders: lifetime (ever diagnosed).

Background

Persons with diabetes are twice as likely to have depression, which can complicate diabetes management, than persons without diabetes (2). In addition, depression is associated with a 60% increased risk for developing type 2 diabetes (2).

Significance

Poor glucose control is associated with depression, a factor to be considered when developing diabetes treatment programs (3). Screening for and treatment of depression is appropriate and might improve glycemic control (3).

Limitations of indicator

BRFSS measures whether respondents were ever told they had a depressive disorder, which might result in an overestimate of the diagnosis of depressive disorders. Some respondents might not understand the meaning of the terms in the question because descriptions of disorders are not precise, or they might experience a stigma associated with depression and alter their responses accordingly.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (4).

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  3. American Diabetes Association. Standards of medical care in diabetes—2013. Diabetes Care 2013;36(Suppl. 1):S11–66.
  4. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 12.1: Influenza vaccination among noninstitutionalized adults aged 18–64 years with diagnosed diabetes

Demographic group

Noninstitutionalized persons aged 18–64 years.

Numerator

Respondents aged 18–64 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant) who report having received an influenza vaccination in the past 12 months.

Denominator

Respondents aged 18–64 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns) who report having received or not having received an influenza vaccination in the past 12 months (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of diabetes, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Diabetes: Lifetime (ever diagnosed).

Vaccination: Past 12 months.

Background

In 2010, 50% of adults aged ≥18 years with diagnosed diabetes reported having received influenza vaccination in the last year (age adjusted to the year 2000 population) (2).

Significance

Influenza and pneumonia are associated with high morbidity and mortality in persons with diabetes (3). However, among patients with diabetes , influenza vaccination was associated with a 56% reduction in any complication, a 54% reduction in hospitalizations, and a 58% reduction in deaths (4). Because an annual influenza vaccination might prevent or attenuate the clinical course of respiratory illness attributable to influenza, the CDC Advisory Committee on Immunization Practices recommends yearly influenza vaccination of persons with diabetes (5).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage surveillance is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (6).

Related indicators or recommendations

Related chronic disease indicator topics

Immunization.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. Egede LE, Soule JB. Diabetes and acute metabolic complications, infections, and inflammation. In: Venkat Narayan KM, Williams D, Gregg EW, Cowie C, eds. Diabetes public health: from data to policy. New York, NY: Oxford University Press; 2011:95–110.
  4. Looijmans-Van den Akker I, Verheij TJ, Buskens E, et al. Clinical effectiveness of first and repeat influenza vaccination in adult and elderly diabetic patients. Diabetes Care 2006;29:1771–6.
  5. Smith SA, Poland GA. Immunization and the prevention of influenza and pneumococcal disease in people with diabetes. Diabetes Care 2003;26(Suppl. 1):S126–8.
  6. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 12.2: Influenza vaccination among noninstitutionalized adults aged ≥65 years with diagnosed diabetes

Demographic group

Noninstitutionalized adults aged ≥65 years.

Numerator

Respondents aged ≥65 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant) who report having received an influenza vaccination in the past 12 months.

Denominator

Respondents aged ≥65 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns) who report having received or not having received an influenza vaccination in the past 12 months (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of diabetes, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Diabetes: Lifetime (ever diagnosed).

Vaccination: Past 12 months.

Background

In 2011, approximately 20% of adults aged ≥65 years reported they had ever been told they had diabetes (2). Among adults aged ≥65 years with diagnosed diabetes, approximately 70% reported in 2010 having received influenza vaccination in the past 12 months (2).

Significance

Influenza and pneumonia are associated with high morbidity and mortality in persons with diabetes (3). However, among patients with diabetes, influenza vaccination was associated with a 56% reduction in any complication, a 54% reduction in hospitalizations, and a 58% reduction in deaths (4). Because an annual influenza vaccination might prevent or attenuate the clinical course of respiratory illness attributable to influenza, the CDC Advisory Committee on Immunization Practices recommends yearly influenza vaccination of persons with diabetes (5).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage surveillance is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (7). Self-report of influenza vaccination among adults aged ≥66 years compared with determining vaccination status from the medical record is a sensitive source of information (6).

Related indicators or recommendations

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. Egede LE, Soule JB. Diabetes and acute metabolic complications, infections, and inflammation. In: Venkat Narayan KM, Williams D, Gregg EW, Cowie C, eds. Diabetes public health: from data to policy. New York, NY: Oxford University Press; 2011:95–110.
  4. Looijmans-Van den Akker I, Verheij TJ, Buskens E, et al. Clinical effectiveness of first and repeat influenza vaccination in adult and elderly diabetic patients. Diabetes Care 2006;29:1771–6.
  5. Smith SA, Poland GA. Immunization and the prevention of influenza and pneumococcal disease in people with diabetes. Diabetes Care 2003;26(Suppl. 1):S126–8.
  6. Zimmerman RK, Raymund M, Janosky JE, et al. Sensitivity and specificity of patient self-report of influenza and pneumococcal poly-saccharide vaccinations among elderly outpatients in diverse patient care strata. Vaccine 2003;21:1486–91.
  7. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 13.1: Pneumococcal vaccination among noninstitutionalized adults aged 18–64 years with diagnosed diabetes

Demographic group

Noninstitutionalized adults aged 18–64 years.

Numerator

Respondents aged 18–64 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant) and who report having ever received a pneumococcal vaccination.

Denominator

Respondents aged 18–64 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns) and who report ever having received or not ever having received a pneumococcal vaccination (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of diabetes, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Diabetes: Lifetime (ever diagnosed).

Vaccination: Lifetime (ever been vaccinated).

Background

In 2011, 9% of adults aged ≥18 years reported they had ever been told they had diabetes, and among those who reported having diabetes, 43% reported having ever received pneumococcal vaccination (2).

Significance

Persons with diabetes are more susceptible to pneumonia and more likely to die of pneumococcal infections than those without diabetes (3). Because a pneumococcal vaccination might prevent or attenuate the clinical course of respiratory illness attributable to Streptococcus pneumonia, the CDC Advisory Committee on Immunization Practices recommends pneumococcal vaccination of persons with diabetes (4).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. The reliability and validity of this measure is unknown.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (5). The National Health Interview Survey (NHIS) can be used as an alternative data source; however, the size of the sample from NHIS might not be adequate for calculating stable, state-specific estimates.

Related indicators or recommendations

Healthy People 2020 objective IID-13: Increase the percentage of adults who are vaccinated against pneumococcal disease. (IID-13.2 is specific for noninstitutionalized high-risk adults aged 18–64 years.)

Related chronic disease indicator topics

Immunization.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. Egede LE, Soule JB. Diabetes and acute metabolic complications, infections, and inflammation. In: Venkat Narayan KM, Williams D, Gregg EW, Cowie C, eds. Diabetes public health: from data to policy. New York, NY: Oxford University Press; 2011:95–110.
  4. Smith SA, Poland GA. Immunization and the prevention of influenza and pneumococcal disease in people with diabetes. Diabetes Care 2003;26(Suppl. 1):S126–8.
  5. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.

Indicator Group: Diabetes

Indicator 13.2: Pneumococcal vaccination among noninstitutionalized adults aged ≥65 years with diagnosed diabetes

Demographic group

Noninstitutionalized adults aged ≥65 years.

Numerator

Respondents aged ≥65 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant) and who report having ever received a pneumococcal vaccination.

Denominator

Respondents aged ≥65 years ever told by a doctor or health professional that they have diabetes (excluding women who were told only when pregnant, refusals, and unknowns) and who report ever having received or not ever having received a pneumococcal vaccination (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude, age stratified, and age adjusted (to the 2000 U.S. standard population using the direct method [1]) with 95% confidence intervals and by demographic characteristics when feasible. Because of the relatively small numbers of BRFSS respondents at the state level who have a history of diabetes, 2- or 3-year averages might be needed to provide stable state-level estimates. U.S. estimates might be based on single years of data.

Time period of case definition

Diabetes: Lifetime (ever diagnosed).

Vaccination: Lifetime (ever been vaccinated).

Background

In 2011, approximately 20% of adults aged ≥65 years reported they had ever been told they had diabetes (2). Among adults with diagnosed diabetes aged ≥65 years, 70% of those aged 65–74 years and 80% of those aged ≥75 years reported in 2010 ever having received pneumococcal vaccination (2).

Significance

Persons with diabetes are more susceptible to pneumonia and more likely to die of pneumococcal infections than those without diabetes (3). Because a pneumococcal vaccination might prevent or attenuate the clinical course of respiratory illness attributable to Streptococcus pneumoniae, the CDC Advisory Committee on Immunization Practices recommends pneumococcal vaccination of persons with diabetes (4).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate (5). The National Health Interview Survey (NHIS) can be used as an alternative data source; however, the size of the sample from NHIS might not be adequate for calculating stable, state-specific estimates. Self-report of pneumococcal vaccination among adults aged ≥65 years compared with determining vaccination status from the medical record or from Medicare claim is a sensitive source of information (6,7).

Related indicators or recommendations

  • Healthy People 2020 objective IID-13: Increase the percentage of adults who are vaccinated against pneumococcal disease. (IID-13.1 is specific for noninstitutionalized high-risk adults aged ≥65 years.)
  • Healthy People OA-2: Increase the proportion of older adults who are up to date on a core set of clinical preventive services.

Related chronic disease indicator topics

Immunization; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. National Diabetes Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/DDTSTRS/default.aspx.
  3. Egede LE, Soule JB. Diabetes and acute metabolic complications, infections, and inflammation. In: Venkat Narayan KM, Williams D, Gregg EW, Cowie C, eds. Diabetes public health: from data to policy. New York, NY: Oxford University Press; 2011:95–110.
  4. Smith SA, Poland GA. Immunization and the prevention of influenza and pneumococcal disease in people with diabetes. Diabetes Care 2003;26(Suppl. 1):S126–8.
  5. CDC. Behavioral Risk Factor Surveillance System: methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/surveillancepractice/reports/brfss/brfss.html.
  6. Shenson D, DiMartino D, Bolen J, Campbell M, Lu PJ, Singleton JA. Validation of self-reported pneumococcal vaccination in behavioral risk factor surveillance surveys: experience from the sickness prevention achieved through regional collaboration (SPARC) program. Vaccine 2005;23:1015–20.
  7. Zimmerman RK, Raymund M, Janosky JE, et al. Sensitivity and specificity of patient self-report of influenza and pneumococcal poly-saccharide vaccinations among elderly outpatients in diverse patient care strata. Vaccine 2003;21:1486–91.

Indicator Group: Disability

Indicator 1: Disability among adults aged ≥65 years

Demographic group

All resident adults aged ≥65 years.

Numerator

Number of adults aged ≥65 years who responded "yes" to at least one of the following questions:

"Are you deaf or do you have serious difficulty hearing?"

"Are you blind or do you have serious difficulty seeing, even when wearing glasses?"

"Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?"

"Do you have serious difficulty walking or climbing stairs?"

"Do you have difficulty dressing or bathing?"

"Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone such as visiting a doctor's office or shopping?"

Denominator

Number of adults aged ≥65 years.

Measures of frequency

Annual prevalence and by demographic characteristics when feasible.

Time period of case definition

Calendar year.

Background

The six-item set of questions used on the American Community Survey and other major surveys to measure disability was developed by a federal interagency committee and reflects the change in how disability is conceptualized consistent with the International Classification of Functioning, Disability, and Health (1). The question set defines disability from a functional perspective and was developed so that disparities between populations with and without disabilities can be monitored. The question set went through several rounds of cognitive and field testing and has been adopted in many federal data collection systems. The Office of Management and Budget has encouraged the use of this question set by other federal agencies conducting similar population studies because of the extensive testing used in the development of these measures, including the findings that alternative measures did not test as well. Cognitive testing of these questions revealed that the six questions must be used as a set to assure a meaningful measure of disability (1). In 2005, 47.4 million U.S. adults reported a disability, of whom 18.1 million were aged ≥65 years (2). Disability prevalence increases with age, doubling with successive age groups (18–44 years, 11.0%; 45–64 years, 23.9%; and ≥65 years, 51.8%) (3).

Significance

Assessment of disability allows for a description of the older adult population from a functional perspective. Disability measures highlight opportunities and areas for improvement for persons with disabilities, including opportunities to fully participate in and benefit from public health activities, receive well-timed interventions and services, interact with their environment without barriers, and participate in everyday life activities (Additional information is available at http://www.healthypeople.gov/2020/topics-objectives/topic/disability-and-health/objectives.)

Limitations of indicator

The six-item disability standard represents a minimum standard, and the questions and answer categories cannot be changed. Therefore, a combined measure must be used. Additional questions on disability might be added to any survey as long as the minimum standard is included. If the American Community Survey changes the disability questions in the future, the U.S. Department of Health and Human Services will revisit the standard and modify as necessary. The six questions provide a conservative prevalence estimate in that they emphasize "serious" difficulty with various activities, potentially excluding persons with less severe impairments.

Data resources

American Community Survey, 1-year estimates, U.S. Census Bureau.

Limitations of data resources

The U.S. Census Bureau introduced a new set of disability questions in the 2008 American Community Survey questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from previous years are not recommended.

Related indicators or recommendations

  • Healthy People 2020 objective OA-5: Reduce the proportion of older adults who have moderate to severe functional limitations.
  • Healthy People 2020 objective OA-6: Increase the proportion of older adults with reduced physical or cognitive function who engage in light, moderate, or vigorous leisure-time physical activities.
  • Healthy People 2020 objective DH-9 (Developmental): Reduce the proportion of persons with disabilities who encounter barriers to participating in home, school, work, or community activities.

Related chronic disease indicator topics

Older adults.


References

  1. Brault M, Stern S, Raglin D. Evaluation report covering disability. American Community Survey Content Test Report P. 4. Washington, DC: US Census Bureau; 2007.
  2. Brault M. Americans with disabilities: 2005. Current Population Reports, P70–117. Washington, DC: US Census Bureau; 2008.
  3. CDC. Prevalence and most common causes of disability among adults—United States, 2005. MMWR 2009;58:421–6.

Indicator Group: Immunization

Indicator 1: Influenza vaccination among noninstitutionalized adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who report having received an influenza vaccination within the previous year.

Denominator

Respondents aged ≥18 years who report having or not having an influenza vaccination within the previous year (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, master list [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past year.

Background

Although vaccination rates have increased, during the 2012–13 influenza season, only approximately 41.5% of adults aged ≥18 years were vaccinated against influenza (2).

Significance

In the United States during 1976–2007, estimates of annual influenza deaths ranged from 3,349 (during 1986–87) to 48,614 (during 2003–04) (3). During these annual epidemics, rates of serious illness and death are highest among adults aged ≥65 years, children aged <2 years, and persons of any age who have medical conditions that place them at increased risk for complications from influenza (4,5). During seasonal influenza epidemics from 1979–80 to 2000–01, the estimated annual overall mean number of influenza-associated hospitalizations in the United States was 226,000 (6). An annual influenza vaccination might prevent or attenuate the clinical course of respiratory illness attributable to influenza. The Advisory Committee on Immunization Practices has recommended annual influenza vaccination for all persons aged ≥6 months in the United States (4,5).

Limitations of indicator

Respondents might not distinguish between influenza and pneumococcal (Streptococcus pneumoniae) vaccinations. Indicator does not measure vaccination rates among persons at high risk (e.g., persons with chronic illness) who should also be vaccinated. Estimates are not specific to one influenza season; influenza vaccinations reported in the past 12 months could have been received for one or more of up to three previous influenza seasons. (Additional information on influenza vaccination coverage surveillance is available at http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6204a1.htm?s_cid=ss6204a1_w. Additional information on influenza vaccination coverage estimates by season is available at http://www.cdc.gov/flu/fluvaxview.)

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective IID-12: Increase the percentage of children and adults who are vaccinated annually against seasonal influenza. (IID-12.6 is specific for noninstitutionalized high-risk adults aged 18–64 years, and IID-12.7 is specific to noninstitutionalized adults aged ≥65 years). The Healthy People 2020 influenza vaccination objectives have been consolidated since the original publication of Healthy People 2020 but will continue to be monitored as part of Healthy People 2020 data reporting. (Additional information is available at http://www.cdc.gov/vaccines/acip/meetings/downloads/slides-oct-2013/03-Influenza-Singleton.pdf and from the Healthy People 2020 website at http://www.healthypeople.gov/2020/topics-objectives/topic/immunization-and-infectious-diseases/objectives.)
  • Healthy People 2020 objective OA-2: Increase the proportion of older adults who are up to date on a core set of clinical preventive services.
  • Promoting Preventive Services for Adults Aged 50–64 Years, Community and Clinical Partnerships: Percentage of adults who reported influenza vaccination within the past year.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Flu vaccination coverage—United States, 2012–13 influenza season. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.cdc.gov/flu/fluvaxview/coverage-1213estimates.htm.
  3. CDC. Estimates of deaths associated with seasonal influenza—United States, 1976–2007. MMWR 2010;59:1057.
  4. CDC. Prevention and control of seasonal influenza with vaccines. Recommendations of the Advisory Committee on Immunization Practices—United States, 2013–2014. MMWR 2013;62(No. RR-7).
  5. CDC. Errata: vol. 62, no. RR-7. MMWR 2013;62:906.
  6. Thompson WW, Shay DK, Weintraub E, et al. Influenza-associated hospitalizations in the United States. JAMA 2004;292:1333–40.

Indicator Group: Mental Health

Indicator 1: Recent mentally unhealthy days among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Sum of the number of days in the past 30 days for which respondents aged ≥18 years report that their mental health (including stress, depression, and problems with emotions) was not good.

Denominator

Total number of respondents aged ≥18 years who report 0 days in the past 30 days for which their mental health was not good (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Mean number of mentally unhealthy days in the past 30 days: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

In 2009, the mean number of mentally unhealthy days (days when mental health was not good) in the past 30 days was 3.5 (2). This is the best available measure of population mental health.

Significance

Poor mental health interferes with social functioning, is associated with health behavior, and should be monitored as an overall indicator of chronic disease impact (3). Recent mentally unhealthy days is used with recent physically unhealthy days to estimate the mean number of unhealthy days (days with impaired physical or mental health) in the past 30 days, a summary measure of population health (3).

Limitations of indicator

Although this indicator is based on a self-assessment, it has good reliability, validity, and responsiveness.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Health-related quality of life, surveillance and data. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. Available at http://www.cdc.gov/hrqol/data/tables/table3a.htm.
  3. CDC. Measuring healthy days. Atlanta, GA: US Department of Health and Human Services, CDC; 2000.

Indicator Group: Mental Health

Indicator 2: At least 14 recent mentally unhealthy days among women aged 18–44 years

Demographic group

Women aged 18–44 years.

Numerator

Female respondents aged 18–44 years who reported that their mental health was not good for ≥14 days in the past month.

Denominator

Female respondents aged 18–44 years who reported the number of days in the past month when their mental health was not good, including none (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude annual prevalence with 95% confidence intervals weight using the Behavioral Risk Factor Surveillance System method (to compensate for unequal probabilities of selection and adjust for nonresponse and telephone noncoverage) and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

Mental health conditions, including depression and anxiety, are common among pregnant, postpartum, and nonpregnant women of reproductive age (1–3). Poor mental health might adversely affect women's family relationships, social life, and their ability to function at school or work (4). Poor mental health is associated with substance use and might put women at risk for future chronic disease, such as diabetes and heart disease (5,6). Poor mental health might adversely affect pregnancy, mother-infant bonding, maternal functioning, and infant and child health and development (1,7).

Significance

Research has shown that poor mental health is a major source of distress and disability and has a substantial social impact (8). Furthermore, poor mental health can interfere with social functioning and negatively affect physical well-being as well as the practice of health-promoting behaviors (9).

Limitations of indicator

Reliability of data on the number of poor mental health days is unknown. However, the measure has been shown to be moderately valid (10). Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Behavioral Risk Factors Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Reproductive health.


References

  1. O'Hara MW. Postpartum depression: what we know. J Clin Psychol 2009;65:1258–69.
  2. Ko JY, Farr SL, Dietz PM, Robbins CL. Depression and treatment among U.S. pregnant and nonpregnant women of reproductive age, 2005–2009. J Womens Health (Larchmt) 2012;21:830–6.
  3. Farr SL, Bitsko RH, Hayes DK, Dietz PM. Mental health and access to services among U.S. women of reproductive age. Am J Obstet Gynecol 2010;203:541–49.
  4. Substance Abuse and Mental Health Services Administration. Results from the 2011 National Survey on Drug Use and Health: mental health findings. NSDUH Series H-45, HHS Publication No. (SMA) 12-4725. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2012.
  5. Le Strat Y, Dubertret C, Le Foll B. Prevalence and correlates of major depressive episode in pregnant and postpartum women in the United States. J Affect Disord 2011;135:128–38.
  6. Farr SL, Hayes DK, Bitsko RH, Bansil P, Dietz PM. Depression, diabetes, and chronic disease risk factors among U.S. women of reproductive age. Prev Chronic Dis 2011;8:A119.
  7. Yonkers KA, Wisner KL, Stewart DE, et al. The management of depression during pregnancy: a report from the American Psychiatric Association and the American College of Obstetricians and Gynecologists. Gen Hosp Psychiatry 2009;31:403–13.
  8. Murray CJL, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Boston, MA: Harvard University Press; 1996.
  9. US Department of Health and Human Services. Mental health: a report of the Surgeon General. Rockville, MD: US Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services, National Institutes of Health, National Institute of Mental Health; 1999. Available at http://profiles.nlm.nih.gov/ps/retrieve/ResourceMetadata/NNBBHS.
  10. Nelson DE, Holtzman D, Bolen J, et al. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soc Prev Med 2001;46(Suppl 1):S3–42.

Indicator Group: Mental Health

Indicator 3: Postpartum depressive symptoms

Demographic group

Women aged 18–44 years who have had a live birth.

Numerator

Respondents who reported that they felt down, depressed, or hopeless, often or always after their most recent live birth.

Denominator

Respondents who reported that they felt down, depressed, or hopeless never, rarely, sometimes, often, or always after delivery of their most recent live birth (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Crude prevalence and 95% confidence intervals weighted using the Pregnancy Risk Assessment Monitoring System method (to compensate for unequal probabilities of selection and adjust for nonresponse and for mail and telephone noncoverage) and by demographic characteristics when feasible.

Time period of case definition

Since the most recent live birth.

Background

Depressive disorders after delivery range from the "baby blues," which occur within the first several weeks after delivery, to depression of postpartum onset (postpartum depression), which is more severe, requires treatment, and can manifest up to 1 year after delivery (1). Postpartum depression is estimated to affect 14%–15% of mothers, has an adverse effect on marital relationships and mother-infant bonding, and can contribute to unfavorable parenting and infant health practices (28).

Significance

Depressive disorders generally have high recurrence rates, and previous depression or postpartum depression is predictive of depression during and after subsequent pregnancies (9). Screening for depression has been shown to be simple and safe, and various treatments are available (10). Identifying in the preconception period women who are at high risk for depression might prevent the emergence of depressive disorders during pregnancy and postpartum. Recommended screening for depression during well-baby visits during the postpartum period is also being considered by the American Academy of Pediatrics (11).

Limitations of indicator

Distinguishing preexisting depressive symptoms from those that manifested after delivery is impossible. This indicator represents self-reported depressive symptoms only and cannot be used to determine actual depression status. Various similar tools assessing self-reported depressive symptoms, including feelings of being down, depressed, sad, or hopeless, have been recommended for depression case finding (9). Sensitivity measures for these tools is generally high, with moderate to high specificity measures (1214). The response option "slowed down" was excluded from the case definition because this experience might be common among new mothers as a result of lack of appropriate rest. The measure for this indicator is a new item on the Pregnancy Risk Assessment Monitoring System Phase 6 questionnaire, which was implemented in 2009. Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Pregnancy Risk Assessment Monitoring System (PRAMS).

Limitations of data resources

PRAMS data are only collected from women who delivered a live-born infant, not all women of reproductive age, and from 40 states and one city, not the entire United States. PRAMS data are self-reported and might be subject to recall bias and underreporting or overreporting of behaviors based on social desirability. Although most self-report surveys such as PRAMS might be subject to systematic error resulting from noncoverage (e.g., lower landline telephone coverage because of transition to cellular telephone–only households or undeliverable addresses), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement bias (e.g., recall bias), PRAMS attempts to contact potential respondents by mail and landline or cellular telephone to increase response rates. Women who experienced a fetal death or had an abortion are excluded. PRAMS estimates only cover the population of residents in each state who also deliver in that state; therefore, residents who delivered in a different state are not included in their resident state.

Related indicators or recommendations

None.

Related chronic disease indicator topics

None.


References

  1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 4th ed, text revision. Washington, DC: American Psychiatric Publishing, Inc; 2000.
  2. Dietz PM, Williams SB, Callaghan WM, et al. Clinically identified maternal depression before, during, and after pregnancies ending in live births. Am J Psychiatry 2007;164:1515–20.
  3. Gaynes BN, Gavin N, Meltzer-Brody S, et al. Perinatal depression: prevalence, screening accuracy, and screening outcomes. Evidence Report/Technology Assessment 119; AHRQ Publication No. 05-E006-2. Rockville, MD: Agency for Healthcare Research and Quality; 2005.
  4. Chung EK, McCollum KF, Elo IT, et al. Maternal depressive symptoms and infant health practices among low-income women. Pediatrics 2004;113:e523–9.
  5. Galler JR, Harrison RH, Ramsey F. Bed-sharing, breastfeeding and maternal moods in Barbados. Infant Behav Dev 2006;29:526–34.
  6. Leiferman J. The effect of maternal depressive symptomatology on maternal behaviors associated with child health. Health Educ Behav 2002;29:596–607.
  7. McLearn KT, Minkovitz CS, Strobino DM, et al. Maternal depressive symptoms at 2 to 4 months post partum and early parenting practices. Arch Pediatr Adolesc Med 2006;160:279–84.
  8. McLennan JD, Kotelchuck M. Parental prevention practices for young children in the context of maternal depression. Pediatrics 2000;105:1090–5.
  9. Frieder A, Dunlop AL, Culpepper L, et al. The clinical content of preconception care: women with psychiatric conditions. Am J Obstet Gynecol 2008;199(Suppl B):S328–32.
  10. US Preventive Services Task Force. Screening for depression: recommendations and rationale. Ann Intern Med. 2002;136:760–4.
  11. Chaudron LH, Szilagyi PG, Campbell AT, et al. Legal and ethical considerations: risks and benefits of postpartum depression screening at well-child visits. Pediatrics 2007;119:123–8.
  12. Whooley MA, Avins AL, Miranda J, et al. Case-finding instruments for depression. Two questions are as good as many. J Gen Intern Med 1997;12:439–45.
  13. Beck CT, Gable RK. Comparative analysis of the performance of the Postpartum Depression Screening Scale with two other depression instruments. Nurs Res 2001;50:242–50.
  14. Kroenke K, Spitzer RL, Williams JBW. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care 2003;41:1284–92.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 1.1: Obesity among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who have a body mass index (BMI) ≥30.0 kg/m² calculated from self-reported weight and height. Exclude the following:

  • Height: data from respondents measuring <3 ft or ≥8 ft
  • Weight: data from respondents weighing <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2
  • Pregnant women

Denominator

Respondents aged ≥18 years for whom BMI can be calculated from their self-reported weight and height (excluding unknowns, refusals to provide weight or height and exclusions listed below):

  • Height: data from respondents measuring <3 ft or ≥8 ft
  • Weight: data from respondents weighing <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2
  • Pregnant women

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

In 2012, state prevalences for obesity ranged from 20.5% to 34.7% based on self-reported BRFSS data (2).

Significance

Being overweight or obese increases the risk for multiple chronic diseases, including heart disease, stroke, hypertension, type 2 diabetes, osteoarthritis, and certain cancers (3).

Limitations of indicator

Self-reports of height and weight lead to lower BMI estimates compared with estimates obtained when height and weight are measured (4,5).

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS now includes (started in 2011) cellular telephone–only users and a new data weighting method in the data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective NWS-9: Reduce the proportion of adults who are obese.

Related chronic disease indicator topics

Arthritis; cancer; cardiovascular disease; diabetes; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Behavioral Risk Factor Surveillance System: prevalence and trends data: overweight and obesity (BMI)—2012. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/brfss/list.asp?cat=OB&yr=2012&qkey=8261&state=All.
  3. CDC. Overweight and obesity: causes and consequences. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://wwwdev.cdc.gov/obesity/adult/causes/index.html.
  4. Kuczmarski MF, Kuczmarski RJ. Matthew Najjar. Effects of age on validity of self-reported height, weight, and body mass index: findings from the third National Health and Nutrition Examination Survey, 1988–1994. J Am Diet Assoc 2001;101:28–34.
  5. Merrill RM, Richardson JS. Validity of self-reported height, weight, and body mass index: findings from the National Health and Nutrition Examination Survey, 2001–2006. Prev Chronic Dis 2009;6:A121.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 1.2: Obesity among high school students

Demographic group

Students in grades 9–12.

Numerator

Students in grades 9–12 with a body mass index (BMI) at or above the sex- and age-specific 95th percentile from the CDC U.S. growth charts (1).

Denominator

Students in grades 9–12 who answer height, weight, sex and age questions. Youth Risk Behavior Surveillance System self-reported height and weight are edited for plausibility. Age- and sex-specific weight, height, and BMI cutpoints are used to exclude implausible values. (Additional information is available at ftp://ftp.cdc.gov/pub/data/YRBS/2011/YRBS_2011_National_User_Guide.pdf .)

Measures of frequency

Biennial (odd years) prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

In 2011, 13.0% of students in grades 9–12 were obese (2). The prevalence of obesity among high school students has not changed significantly since 2003 based on self-reported data (2). In 2011, the state-specific prevalence of obesity ranged from 7.3% to 17.0% based on self-reported Youth Risk Behavior Survey data (2).

Significance

Obese children are more likely to have high blood pressure, high cholesterol, impaired glucose tolerance, asthma, joint problems, and other physical, social, and psychological problems (3). Obese children are more likely to become obese adults, which increases the risk for multiple chronic diseases in adulthood, including heart disease, stroke, hypertension, type 2 diabetes, osteoarthritis, and certain cancers (3).

Limitations of indicator

Self-reported data underestimate obesity prevalence among adolescents (4).

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (5). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

Healthy People 2020 objective NWS-10: Reduce the proportion of children and adolescents who are considered obese. (NWS-10.4 is specific for adolescents aged 12–19 years.)

Related chronic disease indicator topics

Asthma; arthritis; cancer; cardiovascular disease; diabetes; school health.


References

  1. Kuczmarksi RJ, Ogden CL, Grummer-Strawn LM, et al. CDC growth charts: United States. Advance data from vital and health statistics, no. 314. Washington, DC: US Department of Health and Human Services, CDC, National Center for Health Statistics. Available at http://www.cdc.gov/nchs/data/ad/ad314.pdf.
  2. CDC. Youth online: high school YRBS. Atlanta, GA: US Department of Health and Human Services. Available at http://nccd.cdc.gov/YouthOnline/App/Default.aspx.
  3. CDC. Basics about childhood obesity. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://wwwdev.cdc.gov/obesity/childhood/basics.html.
  4. Sherry B, Jefferds ME, Grummer-Strawn LM. Accuracy of adolescent self-report of height and weight in assessing overweight status: a literature review. Arch Pediatr Adolesc Med 2007;161:1154–61.
  5. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 2.1: Overweight or obesity among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who have a body mass index (BMI) 25.0 kg/m² calculated from self-reported weight and height. Exclude the following:

  • Height: data from respondents measuring <3 ft or ≥8 ft
  • Weight: data from respondents weighing <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2
  • Pregnant women

Denominator

Respondents aged 18 years for whom BMI can be calculated from their self-reported weight and height (excluding unknowns, refusals to provide weight or height and exclusions listed below):

  • Height: data from respondents measuring <3 ft or ≥8 ft
  • Weight: data from respondents weighing <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2
  • Pregnant women

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

In 2012, state prevalences (including the District of Columbia) for overweight adults ranged from 30.0% to 39.1% and those for obesity ranged from 20.5% to 34.7% based on self-reported BRFSS data (2).

Significance

Being overweight or obese increases the risk for multiple chronic diseases, including heart disease, stroke, hypertension, type 2 diabetes, osteoarthritis, and certain cancers (3).

Limitations of indicator

Self-reports of height and weight lead to lower BMI estimates compared to estimates obtained when height and weight are measured (4,5).

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective NWS-8: Increase the proportion of adults who are at a healthy weight.
  • Healthy People 2020 objective NWS-9: Reduce the proportion of adults who are obese.

Related chronic disease indicator topics

Arthritis; cancer; cardiovascular disease; diabetes; older adults.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Behavioral Risk Factor Surveillance System: prevalence and trends data: overweight and obesity (BMI)—2012. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/brfss/list.asp?cat=OB&yr=2012&qkey=8261&state=All.
  3. CDC. Overweight and obesity: causes and consequences. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://wwwdev.cdc.gov/obesity/adult/causes/index.html.
  4. Kuczmarski MF, Kuczmarski RJ. Matthew Najjar. Effects of age on validity of self-reported height, weight, and body mass index: findings from the third National Health and Nutrition Examination Survey, 1988–1994. J Am Diet Assoc 2001;101:28–34.
  5. Merrill RM, Richardson JS. Validity of self-reported height, weight, and body mass index: Findings from the National Health and Nutrition Examination Survey, 2001–2006. Prev Chronic Dis 2009;6:A121.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 2.2: Overweight or obesity among high school students

Demographic group

Students in grades 9–12.

Numerator

Students in grades 9–12 with a body mass index (BMI) at or above the sex- and age-specific 85th percentile from CDC U.S. growth charts (1).

Denominator

Students in grades 9–12 who answer height, weight, sex and age questions. YRBSS self-reported height and weight are edited for plausibility. Age- and sex-specific weight, height, and BMI cutpoints are used to exclude implausible values. Details can be found at ftp://ftp.cdc.gov/pub/data/YRBS/2011/YRBS_2011_National_User_Guide.pdf starting on page 3. Details on editing for plausibility start on page 5.

Measures of frequency

Biennial (odd years) prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

In 2011, 15.2% of students in grades 9–12 were overweight and 13.0% were obese based on self-reported data (2). The prevalence of overweight and obesity among high school students has not changed significantly since 2003 (2). The state-specific prevalence of overweight ranged from 10.7% to 19.5%. The state-specific prevalence of obesity ranged from 7.3% to 17.0% (2).

Significance

Obese children are more likely to have high blood pressure, high cholesterol, impaired glucose tolerance, asthma, joint problems, and other physical, social, and psychological problems (3). Obese children are more likely to become obese adults, which increases the risk for multiple chronic diseases in adulthood, including heart disease, stroke, hypertension, type 2 diabetes, osteoarthritis, and certain cancers (3).

Limitations of indicator

Self-reported data underestimate obesity prevalence among adolescents (4).

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (5). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

Healthy People 2020 objective NWS-10: Reduce the proportion of children and adolescents who are considered obese. (NWS-10.4 is specific for adolescents aged 12–19 years.)

Related chronic disease indicator topics

Asthma; arthritis; cancer; cardiovascular disease; diabetes; school health.


References

  1. Kuczmarksi RJ, Ogden CL, Grummer-Strawn LM, et al. CDC growth charts: United States. Advance data, no. 314. Washington, DC: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2000. Available at http://www.cdc.gov/nchs/data/ad/ad314.pdf.
  2. CDC. Youth on line: high school YRBS. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://nccd.cdc.gov/YouthOnline/App/Default.aspx.
  3. CDC. Basics about childhood obesity. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://wwwdev.cdc.gov/obesity/childhood/basics.html.
  4. Sherry B, Jefferds ME, Grummer-Strawn LM. Accuracy of adolescent self-report of height and weight in assessing overweight status: a literature review. Arch Pediatr Adolesc Med 2007;161:1154–61.
  5. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 2.3: Overweight or obesity among women aged 18–44 years

Demographic group

Women aged 18–44 years.

Numerator

Overweight: Women aged 18–44 years who have a body mass index (BMI) of ≥25 kg/m2 but <30 kg/m2.

Obesity: Women aged 18–44 years who have a BMI >30 kg/m2.

Exclude the following:

  • Height: data from respondents <3 ft or ≥8 ft
  • Weight: data from respondents <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2

Denominator

Women aged 18–44 years for whom BMI can be calculated using their self-reported weight and height (excluding unknowns, refusals to provide weight or height and exclusions listed below):

  • Height: data from respondents <3 ft or ≥8 ft
  • Weight: data from respondents <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2

Measures of frequency

Crude annual prevalence and 95% confidence intervals and by demographic characteristics when feasible, weighted using the Behavioral Risk Factor Surveillance System method (to compensate for unequal probabilities of selection. and adjust for non-response and telephone noncoverage).

Time period of case definition

Current.

Background

Nearly two thirds of reproductive-age women in the United States are overweight or obese, placing them at increased risk for adverse health outcomes (1,2). The prevalence of severe obesity, once a relatively rare condition, has increased dramatically among women of childbearing age (3).

Significance

In the nonpregnant state, obesity contributes to numerous adverse health conditions, including type 2 diabetes, hypertension, heart disease, various cancers, and infertility (47). Obesity is also associated with numerous unfavorable perinatal health outcomes including neural tube defects, labor and delivery complications, fetal and neonatal death, and maternal complications such as gestational diabetes and preeclampsia (812). Although health risks are better established for obese persons, overweight is a predictor of subsequent obesity (6). Therefore, several professional health organizations and councils, in addition to the clinical workgroup of the Select Panel on Preconception Care recommends that all women have their BMI calculated at least annually (13). Overweight and obese women should be offered healthy strategies to achieve a healthier body weight, especially before any future pregnancies.

Limitations of indicator

Height and weight are self-reported by the participant but are not verified using medical records data. However, women have been shown to underreport weight, which might lead to an underestimation of BMI (14). Analysis for this indicator requires use of a calculated variable named _BMI4CAT. (Additional information available at http://ftp.cdc.gov/pub/data/brfss/calcvar_07.rtf.) Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective NWS-8: Increase the proportion of adults who are at a healthy weight.
  • Healthy People 2020 objective NWS-9: Reduce the proportion of adults who are obese.

Related chronic disease indicator topics

Reproductive health.


References

  1. Hillemeier MM, Weisman CS, Chuang C, Downs DS, McCall-Hosenfeld J, Camacho F. Transition to overweight or obesity among women of reproductive age. J Womens Health (Larchmt). 2011; 20:703–10.
  2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among U.S. adults, 1999–2008. JAMA 2010;303:235–41.
  3. Institute of Medicine and National Research Council Committee to Reexamine IOM Pregnancy Weight Guidelines. Descriptive epidemiology and trends [Chapter 2]. In: Rasmussen KM, Yaktine AL, eds. Institute of Medicine and National Research Council Committee to Reexamine IOM Pregnancy Weight Guidelines. Weight gain during pregnancy: reexamining the guidelines. Washington, DC: National Academies Press; 2009. Available at http://www.ncbi.nlm.nih.gov/books/NBK32810.
  4. Institute of Medicine. Influence of pregnancy weight on maternal child health: a workshop report. Washington, DC: National Academy Press; 2007.
  5. Sarwer DB, Allison KC, Gibbons LM, Markowitz JT, Nelson DB. Pregnancy and obesity: a review and agenda for future research. J Womens Health (Larchmt) 2006;15:720–33.
  6. McTigue KM, Harris R, Hemphill B, et al. Screening and interventions for obesity in adults: summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 2003;139:933–49.
  7. Dixit A, Girling JC. Obesity and pregnancy. J Obstet Gynaecol 2008;28:14–23.
  8. Rich-Edwards JW, Goldman MB, Willett WC, et al. Adolescent body mass index and infertility caused by ovulatory disorder. Am J Obstet Gynecol 1994;171:171–7.
  9. Watkins ML, Rasmussen SA, Honein MA, Botto LD, Moore CA. Maternal obesity and risk for birth defects. Pediatrics 2003;111:1152–8.
  10. Cedergren MI. Maternal morbid obesity and the risk of adverse pregnancy outcome. Obstet Gynecol 2004;103:219–24.
  11. Cnattingius S, Bergstrom R, Lipworth L, Kramer MS. Prepregnancy weight and the risk of adverse pregnancy outcomes. N Engl J Med 1998;338:147–52.
  12. Baeten JM, Bukusi EA, Lambe M. Pregnancy complications and outcomes among overweight and obese nulliparous women. Am J Public Health 2001;91:436–40.
  13. Gardiner PM, Nelson L, Shellhaas CS, et al. The clinical content of preconception care: nutrition and dietary supplements. Am J Obstet Gynecol 2008;199(Suppl B):S345–56.
  14. Gillum RF, Sempos CT. Ethnic variation in validation of classification of overweight and obesity using self-reported weight and height in American women and men: the Third National Health and Nutrition Examination Survey. Nutr J 2005;4:27.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 2.4: Prepregnancy overweight or obesity

Demographic group

Women aged 18–44 years who have had a live birth.

Numerator

Overweight: Women aged 18–44 years whose prepregnancy body mass index (BMI) was ≥25 kg/m2 but <30 kg/m2. BMI is calculated from self-reported weight and height.

Obesity: Women aged 18–44 years whose prepregnancy BMI was >30 kg/m2. BMI is calculated from self-reported weight and height.

Denominator

Women aged 18–44 years for whom BMI can be calculated using their self-reported weight and height.

Measures of frequency

Crude annual prevalence and 95% confidence intervals.

Time period of case definition

Before the pregnancy resulting in the most recent live birth.

Background

Prepregnancy obesity poses risks to both pregnant women and their infants. Obese women are at increased risk for infertility, pregnancy loss, complications during pregnancy such as hypertensive disorders and gestational diabetes, and cesarean delivery. Fetal risks include prematurity, stillbirth, neural tube defects, and an increased risk for the child becoming obese or developing heart disease in the future (14). In addition, obese mothers are less likely than those of normal weight to begin breastfeeding their new infant or to continue breastfeeding after initiation (5).

Based on data from the Pregnancy Risk Assessment Monitoring System, one fifth of American women are obese (BMI >29 kg/m2) at the start of pregnancy, a proportion that has increased 70% in the past decade (6,7).

Significance

In the nonpregnant state, obesity contributes to numerous adverse health conditions including type 2 diabetes, hypertension, heart disease, various cancers, and infertility (811). Obesity is also associated with numerous unfavorable perinatal health outcomes including neural tube defects, labor and delivery complications, fetal and neonatal death, and maternal complications such as gestational diabetes and preeclampsia (1216). Although health risks are better established for obese persons, overweight is a predictor of subsequent obesity (12). In addition to steadily increasing obesity rates in the general U.S. population, a notable increase toward higher prepregnancy BMI in the United States has been demonstrated (17). Therefore, several professional health organizations and councils, in addition to the clinical workgroup of the Select Panel on Preconception Care recommends that all women have their BMI calculated at least annually (18). Overweight and obese women should be offered healthy strategies to achieve a healthier body weight, especially before any future pregnancies.

Limitations of indicator

Maternal weight and height as recorded on the birth certificate are based on either maternal recall or prenatal records (19). Unpublished data demonstrates that birth certificate data underestimate the prevalence of obesity, although the data have a satisfactory reliability and validity for surveillance and research purposes (unpublished data, Florida birth certificates, 2005). Other age group definitions are recognized for reproductive age; however, these measurements will consistently use the age range of 18–44 years.

Data resources

Birth certificates, National Vital Statistics System.

Limitations of data resources

Missing values: definitions of exclusion criteria are not provided, nor is a method to address biologically implausible values. Not all states use the 2003 version of birth certificates, which include prepregnancy height and weight. Birth certificates might not accurately document maternal health status.

Related indicators or recommendations

  • Healthy People 2020 objective NWS-8: Increase the proportion of adults who are at a healthy weight.
  • Healthy People 2020 objective NWS-9: Reduce the proportion of adults who are obese.

Related chronic disease indicator topics

Reproductive health.


References

  1. Cnattingius S, Bergstrom R, Lipworth L, Kramer MS. Prepregnancy weight and the risk of adverse pregnancy outcomes. N Engl J Med 1998;338:147–52.
  2. Catalano PM, Ehrenberg HM. The short-and long-term implications of maternal obesity on the mother and her offspring. BJOG 2006;113:1126–33.
  3. Begum KS, Sachchithanantham K, De Somsubhra S. Maternal obesity and pregnancy outcome. Clin Exp Obstet Gynecol 2011;38:14–20.
  4. Weiss JL, Malone FD, Emig D, et al; FASTER Research Consortium. Obesity, obstetric complications and cesarean delivery rate—a population-based screening study. Am J Obstet Gynecol 2004;190:1091–7.
  5. Guelinckx I, Devlieger R, Bogaerts A, Pauwels S, Vansant G. The effect of pre-pregnancy BMI on intention, initiation and duration of breast-feeding. Public Health Nutr 2012;15:840–8.
  6. Kim SY, Dietz PM, England L, Morrow B, Callaghan WM. Trends in prepregnancy obesity in nine states, 1993–2003. Obesity (Silver Spring) 2007;15:986–93.
  7. Institute of Medicine and National Research Council Committee to Reexamine IOM Pregnancy Weight Guidelines. Descriptive epidemiology and trends [Chapter 2]. In: Rasmussen KM, Yaktine AL, eds. Institute of Medicine and National Research Council Committee to Reexamine IOM Pregnancy Weight Guidelines. Weight gain during pregnancy: reexamining the guidelines. Washington, DC: National Academies Press; 2009. Available at http://www.ncbi.nlm.nih.gov/books/NBK32810.
  8. Van Lieshout RJ, Taylor VH, Boyle MH. Pre-pregnancy and pregnancy obesity and neurodevelopmental outcomes in offspring: a systematic review. Obes Rev 2011;12:e548–59.
  9. Institute of Medicine. Influence of pregnancy weight on maternal child health: a workshop report. Washington, DC: National Academies Press; 2007.
  10. Sarwer DB, Allison KC, Gibbons LM, Markowitz JT, Nelson DB. Pregnancy and obesity: a review and agenda for future research. J Womens Health (Larchmt) 2006;15:720–33.
  11. McTigue KM, Harris R, Hemphill B, et al. Screening and interventions for obesity in adults: summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 2003;139:933–49.
  12. Dixit A, Girling JC. Obesity and pregnancy. J Obstet Gynaecol 2008;28:14–23.
  13. Rich-Edwards JW, Goldman MB, Willett WC, et al. Adolescent body mass index and infertility caused by ovulatory disorder. Am J Obstet Gynecol 1994;171:171–7.
  14. Watkins ML, Rasmussen SA, Honein MA, Botto LD, Moore CA. Maternal obesity and risk for birth defects. Pediatrics 2003;111:1152–8.
  15. Cedergren MI. Maternal morbid obesity and the risk of adverse pregnancy outcome. Obstet Gynecol 2004;103:219–24.
  16. Baeten JM, Bukusi EA, Lambe M. Pregnancy complications and outcomes among overweight and obese nulliparous women. Am J Public Health 2001;91:436–40.
  17. Yeh J, Shelton JA. Increasing prepregnancy body mass index: analysis of trends and contributing variables. Am J Obstet Gynecol 2005;193:1994–8.
  18. Gardiner PM, Nelson L, Shellhaas CS, et al. The clinical content of preconception care: nutrition and dietary supplements. Am J Obstet Gynecol 2008;199(Suppl B):S345–56.
  19. CDC, National Center for Health Statistics. 2003 revisions of the U.S. standard certificates of live birth and death and the fetal death report. Rockville, MD: CDC, National Center for Health Statistics; 2009. Available at http://www.cdc.gov/nchs/vital_certs_rev.htm.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 3.1: Healthy weight among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents aged ≥18 years who have a body mass index (BMI) of 18.5–24.9 kg/m² calculated from self-reported weight and height. Exclude the following:

  • Height: data from respondents <3 ft or ≥8 ft
  • Weight: data from respondents <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2
  • Pregnant women

Denominator

Respondents aged ≥ 18 years for whom BMI can be calculated from their self-reported weight and height (excluding unknowns or refusals to provide weight or height and exclusions listed below):

  • Height: data from respondents <3 ft or ≥8 ft
  • Weight: data from respondents <50 lbs or ≥650 lbs
  • BMI: data from respondents with BMI <12 kg/m2 or ≥100 kg/m2
  • Pregnant women

Measures of frequency

Annual prevalence: crude and age adjusted (standardized by the direct method to the year 2000 standard U.S. population, distribution 9 [1]) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

In 2012, state prevalences (including the District of Columbia) for healthy weight ranged from 28.5% to 46.0% based on self-reported BRFSS data (2).

Significance

Being at a healthy weight is associated with more favorable health outcomes (lowered risk for development of hypertension, dyslipidemia, diabetes, heart disease, and certain cancers) (3), greater longevity (4), and lowered health-care costs (5).

Limitations of indicator

Self-reports of height and weight lead to lower BMI estimates compared to estimates obtained when height and weight are measured (6,7).

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective NWS-8: Increase the proportion of adults who are at a healthy weight.

Related chronic disease indicator topics

None.


References

  1. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no. 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001. Available at http://www.cdc.gov/nchs/data/statnt/statnt20.pdf.
  2. CDC. Behavioral Risk Factor Surveillance System: prevalence and trends data: overweight and obesity (BMI)—2012. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://apps.nccd.cdc.gov/brfss/list.asp?cat=OB&yr=2012&qkey=8261&state=All.
  3. National Heart, Lung, and Blood Institute. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. Obes Res 1998;6(Suppl 2):51S–209S.
  4. Adams KF, Schatzkin A, Harris TB, et al. Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. N Engl J Med 2006;355:763–78.
  5. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer- and service-specific estimates. Health Aff 2009;28:w822–31.
  6. Kuczmarski MF, Kuczmarski RJ. Matthew Najjar. Effects of age on validity of self-reported height, weight, and body mass index: findings from the third National Health and Nutrition Examination Survey, 1988–1994. J Am Diet Assoc 2001;101:28–34.
  7. Merrill RM, Richardson JS. Validity of self-reported height, weight, and body mass index: findings from the National Health and Nutrition Examination Survey, 2001–2006. Prev Chronic Dis 2009;6:A121.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 3.2: Healthy weight among high school students

Demographic group

Students in grades 9–12.

Numerator

Students in grades 9–12 with a body mass index (BMI) at the sex- and age-specific 5th percentile to <85th percentile from CDC U.S. growth charts (1).

Denominator

Students in grades 9–12 who answer height, weight, sex and age questions. Youth Risk Behavior Surveillance System self-reported height and weight are edited for plausibility. Age- and sex-specific weight, height, and BMI cutpoints are used to exclude implausible values. (Additional information is available at ftp://ftp.cdc.gov/pub/data/YRBS/2011/YRBS_2011_National_User_Guide.pdf.)

Measures of frequency

Biennial (odd years) prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Current.

Background

In 2011, 69% of high school students in the United States were a healthy weight (2).

Significance

Being at a healthy weight in adolescence is associated with lower risk for obesity during adulthood (3).

Limitations of indicator

Self-reported data are associated with biased prevalence estimates for weight status (4).

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (5). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

Healthy People 2020 objective NWS-10: Reduce the proportion of children and adolescents who are considered obese (NWS-10.4 is specific for adolescents aged 12–19 years.)

Related chronic disease indicator topics

School health.


References

  1. Kuczmarksi RJ, Ogden CL, Grummer-Strawn LM, et al. CDC growth charts: United States. Washington, DC: US Department of Health and Human Services, CDC, National Center for Health Statistics. Advance data; December 4, 2000 (revised). Publication no. 314. Available at http://www.cdc.gov/nchs/data/ad/ad314.pdf.
  2. Analysis of data from: CDC. Youth Risk Behavior Survey Data. Atlanta, GA: US Department of Health and Human Services, CDC; 2011.
  3. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med 1997;337:869–73.
  4. Sherry B, Jefferds ME, Grummer-Strawn LM. Accuracy of adolescent self-report of height and weight in assessing overweight status: a literature review. Arch Pediatr Adolesc Med 2007;161:1154–61.
  5. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 4.1: Median daily frequency of fruit consumption among high school students

Demographic group

Students in grades 9–12.

Numerator

Number of total daily intake of fruit consumption (100% fruit juice and fruit).

Denominator

All respondents for whom these data are available (excluding unknowns or refusals to provide responses to fruit consumption question).

Measures of frequency

Biennial median frequency with interquartile range and by demographic characteristics when feasible.

Time period of case definition

Past 7 days.

Background

The Dietary Guidelines for Americans, 2010 and Healthy People 2020 objectives call for persons in the United States to increase their intake of fruits and vegetables (1,2). Dietary intake recommendations for fruit intake are about 11/2 - to 2-cup equivalents for girls aged 14–18 years and 2- to 21/2-cup equivalents for boys aged 14–18 years, depending on age and physical activity level (1). In 2011, median daily intake of fruit among high school students was 1 time daily (3).

Significance

The Dietary Guidelines for Americans, 2010 recommends that persons in the United States eat more fruits and vegetables as part of a healthy diet because they contribute important nutrients, can reduce the risk for many chronic diseases, and can help with weight management (1).

Limitations of indicator

The indicator is based on the median frequency of fruit consumption among high school students surveyed. However, because the indicator does not include the cup equivalents of fruits consumed, the objective cannot be compared with or used to assess progress toward Healthy People 2020 objectives, which measure progress based on cup equivalents per 1,000 kilocalories of intake. YRBSS data are used to track increased frequency of fruit consumption, a key recommendation of the Dietary Guidelines for Americans.

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (4). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

Healthy People 2020 objective NWS-14: Increase the contribution of fruits to the diets of the population aged ≥2 years.

Related chronic disease indicator topics

School health.


References

  1. US Departments of Agriculture, US Department of Health and Human Services. Dietary guidelines for Americans, 2010. 7th ed. Washington, DC: US Government Printing Office; 2010.
  2. US Department of Health and Human Services. Healthy people 2020. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.healthypeople.gov.
  3. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; 2013.
  4. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 4.2: Median daily frequency of fruit consumption among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Number of total daily intake of fruit consumption (100% fruit juice and fresh, frozen, or canned fruit).

Denominator

All respondents for whom these data are available (excluding unknowns or refusals to provide responses to fruit consumption question or those with a total intake >16).

Measures of frequency

Biennial median frequency with interquartile range; and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

The Dietary Guidelines for Americans, 2010 and Healthy People 2020 objectives call for persons in the United States to increase their intake of fruits and vegetables (1,2). Dietary intake recommendations for fruit intake are about 11/2 - to 2-cup equivalents for girls aged 14–18 years and 2- to 21/2-cup equivalents for boys aged 14–18 years, depending on age and physical activity level (1). In 2011, median daily intake of fruit among high school students was 1 time daily (3).

Significance

The Dietary Guidelines for Americans, 2010 recommends Americans eat more fruits and vegetables as part of a healthy diet because they contribute important nutrients, can reduce the risk for many chronic diseases, and can help with weight management (1).

Limitations of indicator

The indicator is based on the median frequency of fruit consumption among high school students surveyed. However, because the indicator does not include the cup equivalents of fruits consumed, the objective cannot be compared with or used to assess progress toward Healthy People 2020 objectives, which measure progress based on cup equivalents per 1,000 kilocalories of intake. YRBSS data are used to track increased frequency of fruit consumption, a key recommendation of the Dietary Guidelines for Americans.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective NWS-14: Increase the contribution of fruits to the diets of the population aged ≥2 years.

Related chronic disease indicator topics

None.


References

  1. US Departments of Agriculture and Health and Human Services. Dietary Guidelines for Americans, 2010. 7th ed. Washington, DC: US Government Printing Office; 2010.
  2. US Department of Health and Human Services. Healthy people 2020. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.healthypeople.gov.
  3. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services, 2013.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 5.1: Median daily frequency of vegetable consumption among high school students

Demographic group

Students in grades 9–12.

Numerator

Number of total daily intake of vegetable consumption (green salad, potatoes, carrots, and other vegetables).

Denominator

All respondents for whom these data are available (excluding unknowns or refusals to provide responses to any of the vegetable consumption questions).

Measures of frequency

Biennial median frequency with interquartile range and by demographic characteristics when feasible.

Time period of case definition

Past 7 days.

Background

The Dietary Guidelines for Americans, 2010 and Healthy People 2020 objectives call for persons in the United States to increase their intake of fruits and vegetables (1,2). Dietary intake recommendations for fruit intake are about 11/2- to 2-cup equivalents for girls aged 14–18 years and 2- to 21/2-cup equivalents for boys aged 14–18 years, depending on age and physical activity level (1). In 2011, median daily intake of fruit among high school students was 1 time daily (3).

Significance

The Dietary Guidelines for Americans, 2010 recommends that persons in the United States eat more fruits and vegetables as part of a healthy diet because they contribute important nutrients, can reduce the risk for many chronic diseases, and can help with weight management (1).

Limitations of indicator

The indicator is based on the median frequency of fruit consumption among high school students surveyed. However, because the indicator does not include the cup equivalents of fruits consumed, the objective cannot be compared with or used to assess progress toward Healthy People 2020 objectives, which measure progress based on cup equivalents per 1,000 kilocalories of intake. YRBSS data are used to track increased frequency of fruit consumption, a key recommendation of the Dietary Guidelines for Americans.

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (4). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

Healthy People 2020 objective NWS-15.1: Increase the contribution of total vegetables to the diets of the population aged ≥2 years.

Related chronic disease indicator topics

School health.


References

  1. US Department of Agriculture, US Department of Health and Human Services. Dietary guidelines for Americans, 2010. 7th ed. Washington, DC: US Government Printing Office; 2010.
  2. US Department of Health and Human Services. Healthy people 2020. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.healthypeople.gov.
  3. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.
  4. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 5.2: Median daily frequency of vegetable consumption among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Number of total daily intake of vegetable consumption (beans (legumes), dark green vegetables, orange vegetables, and other vegetables).

Denominator

All respondents for whom these data are available (excluding unknowns or refusals to provide responses to any of the vegetable consumption questions, or those with a total intake >23).

Measures of frequency

Biennial median frequency with interquartile range; and by demographic characteristics when feasible.

Time period of case definition

Past 30 days.

Background

The Dietary Guidelines for Americans, 2010 and Healthy People 2020 objectives call for persons in the United States to increase their intake of fruits and vegetables (1,2). Dietary intake recommendations for fruit intake are about 11/2- to 2-cup equivalents for girls aged 14–18 years and 2- to 21/2-cup equivalents for boys aged 14–18 years, depending on age and physical activity level (1). In 2011, median daily intake of fruit among high school students was 1 time daily (3).

Significance

The Dietary Guidelines for Americans, 2010 recommends that persons in the United States eat more fruits and vegetables as part of a healthy diet because they contribute important nutrients, can reduce the risk for many chronic diseases, and can help with weight management (1).

Limitations of indicator

The indicator is based on the median frequency of fruit consumption among high school students surveyed. However, because the indicator does not include the cup equivalents of fruits consumed, the objective cannot be compared with or used to assess progress toward Healthy People 2020 objectives, which measure progress based on cup equivalents per 1,000 kilocalories of intake. YRBSS data are used to track increased frequency of fruit consumption, a key recommendation of the Dietary Guidelines for Americans.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective NWS-15.1: Increase the contribution of total vegetables to the diets of the population aged ≥2 years.

Related chronic disease indicator topics

None.


References

  1. US Department of Agriculture, US Department of Health and Human Services. Dietary guidelines for Americans, 2010. 7th ed. Washington, DC: US Government Printing Office; 2010.
  2. US Department of Health and Human Services. Healthy people 2020. Atlanta, GA: US Department of Health and Human Services, CDC. Available at http://www.healthypeople.gov.
  3. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 6: U.S. census tracts with healthier food retailers within 1/2 mile of boundary

Demographic group

All residents.

Numerator

The number of U.S. census tracts with at least one healthier food retailer (at least one supermarket, supercenter, larger grocery store, warehouse club, or fruit and vegetable specialty stores) located within the tract or within 1/2 mile. Two separate national directories on retail food stores were used to develop a comprehensive list of healthier food retailers in the United States: InfoUSA, a proprietary source of individual store listings, current as of June 2011 and a list of stores authorized to accept Supplemental Nutrition Assistance Program (SNAP) benefits as of January 2012. The following InfoUSA stores were defined as healthier food retailers using several criteria including 2007 North American Industry Classification Codes (NAICS), annual sales volume, and annual employees on payroll: larger grocery stores and supermarkets (stores classified as NAICS 445110 with ≥10 annual payroll employees or ≥$2 million in annual sales); other chain supermarkets, supercenters, and warehouse clubs (NAICS 445, 452112, and 452910 whose company names matched a name on a list of national supermarket/supercenter chains); and fruit and vegetable specialty food stores (NAICS 445230). (NAICS descriptions are available at http://www.census.gov/eos/www/naics.) The following stores identified through the SNAP application process were defined as healthier food retailers: supermarkets, supercenters, warehouse clubs, large grocery stores, or fruit and vegetable specialty stores.

Denominator

Total number of U.S. census tracts (1).

Measures of frequency

Percentage of U.S. census tracts.

Time period of case definition

Current year.

Background

One measure of access to fruits and vegetables is the percentage of census tracts in states that have a typical healthier food retailer (at least one supermarket, supercenter, larger grocery store, warehouse club, or fruit and vegetable specialty stores) located within the tract or within 1/2 mile. A census tract is a small and relatively permanent subdivision of counties that is similar in population and economic characteristics and living conditions. On average supermarkets, supercenters, larger grocery stores, warehouse clubs, and fruit and vegetable specialty stores stock a wide selection of affordable, high quality fruits and vegetables. In 2011, 70% of U.S. census tracts had a healthy food retailer within 1/2 mile of the boundary (2).

Significance

Having access to stores that sell fruits and vegetables and other healthier foods might increase consumption of fruits and vegetables and other healthier foods among adults (3).

Limitations of indicator

Neighborhoods identified as not having at least one healthier food retailer might still have access to healthier foods if smaller stores (e.g., convenience stores and corner stores) that provide a wide selection and adequate quantity of affordable produce and other items. However, because no systematic way exists to identify smaller retailers offering healthier foods at a national level, they are not included as a healthier food retailer in this metric. Residents might have additional access to produce in their neighborhoods through farmers' markets and farm stands. However, these venues are not included in this analysis because they might not be available year round.

Data resources

InfoUSA; US Department of Agriculture list of SNAP-authorized retailers; U.S. census tract boundaries (1).

Limitations of data resources

Evidence suggests that secondary data might only include 55%–68% of food outlets that actually exist in an area, and store misclassification is also common. However, two independent data sources were used to reduce inaccuracies in store operational status and store misclassification.

Related indicators or recommendations

Healthy People 2020 objective NWS-4 (Developmental): Increase the proportion of Americans who have access to a food retail outlet that sells various foods that are encouraged by the Dietary Guidelines for Americans.

Related chronic disease indicator topics

None.


References

  1. US Census Bureau. Census tract boundaries 2010. Washington, DC: US Census Bureau. Available at https://www.census.gov/geo/maps-data/maps/2010tract.html.
  2. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.
  3. Larson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the U.S. Am J Prev Med 2009;36:74–81.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 7.1: Farmers' markets that accept Women and Infant Children (WIC) Farmers' Market Nutrition Program coupons

Demographic group

Special Nutrition Program for Women, Infants, and Children (WIC) enrollees.

Numerator

Number of farmers' markets that accept WIC farmers market nutrition program coupons.

Denominator

Total number of farmers' markets.

Measures of frequency

Percentage of farmers' markets.

Time period of case definition

Current year.

Background

Farmers' markets are a mechanism for purchasing foods from local farms and can augment access to fruits and vegetables from typical retail stores or provide a retail venue for fruits and vegetables in areas lacking such stores. Increasing access to farmers' markets includes increasing access to persons with lower household incomes who are participating in the WIC program. In 2012, 25.8% of farmers' markets accepted WIC Farmers' Market Nutrition Program coupons (1).

Significance

Farmers' markets that accept nutrition assistance program benefits, such as Supplemental Nutrition Assistance Program (SNAP), WIC, FMNP coupons, and WIC cash value vouchers (CVV), improve access to fruits and vegetables for persons and families with lower incomes.

Limitations of indicator

None noted.

Data resources

US Department of Agriculture (USDA), Agricultural Marketing Service; USDA National Farmers' Market Directory.

Limitations of data resources

None identified.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Overarching conditions; reproductive health.


References

  1. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 7.2: Farmers' markets that accept Supplemental Nutrition Assistance Program (SNAP) benefits

Demographic group

Supplemental Nutrition Assistance Program (SNAP) participants.

Numerator

The number of farmers' markets that accept SNAP benefits.

Denominator

Total number of farmers' markets.

Measures of frequency

Percentage of farmers' markets.

Time period of case definition

Current year.

Background

Farmers' markets are a mechanism for purchasing foods from local farms and can augment access to fruits and vegetables from typical retail stores or provide a retail venue for fruits and vegetables in areas lacking such stores. Increasing access to farmers' markets includes increasing access to persons with lower household incomes. In 2012, 21% of farmers' markets accepted SNAP benefits (1).

Significance

Farmers' markets that accept nutrition assistance program benefits, such as SNAP, Special Supplemental Nutrition Program for Women, Infants, and Children Farmers Market Nutrition Program coupons, and WIC cash value vouchers, improve access to fruits and vegetables for individuals and families with lower incomes.

Limitations of indicator

None noted.

Data resources

United States Department of Agriculture (USDA), Agricultural Marketing Service; USDA National Farmers' Market Directory.

Limitations of data resources

None identified.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Overarching conditions.


References

  1. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 8: Number of farmers' markets per 100,000 residents

Demographic group

All residents.

Numerator

The number of farmers' markets.

Denominator

Total population estimate divided by 100,000.

Measures of frequency

Number of farmers' markets per 100,000 residents.

Time period of case definition

Current year.

Background

Farmers' markets are a mechanism for purchasing foods from local farms and can augment access to fruits and vegetables from typical retail stores or provide a retail venue for fruits and vegetables in areas lacking such stores. In 2012, a total of 2.5 farmers' markets per 100,000 U.S. population were available (1).

Significance

The number of farmers' markets per 100,000 state residents provides a broad estimate of the availability of fruits and vegetables from farmers' markets adjusted for variation in state population.

Limitations of indicator

None noted.

Data resources

United States Department of Agriculture (USDA), Agricultural Marketing Service; USDA National Farmers' Market Directory.

Limitations of data resources

None noted.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Overarching conditions.


References

  1. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 9.1: Presence of regulations pertaining to serving fruit in early care and education settings

Demographic group

Children aged 0–5 years in child care settings.

Numerator

The numerator for the U.S. measure is the number of states with child care regulations serving children aged 0–5 years in child care that support serving fruits of several varieties, especially whole fruits at each meal. (For states with separate regulations for large and small homes and centers, language in all sets of regulations should fully include national guidelines.) Individual states have a yes/no response to this indicator.

Denominator

The denominator for the U.S. measure is 50 states.

Measures of frequency

The measure of frequency for the U.S. data is the percent of states with language that supports serving fruits of several varieties, especially whole fruits, in state child care regulations. Individual states have a yes/no response to this indicator.

Time period of case definition

Current year.

Background

In 2011, 8% of states had language in state child care regulations that supported serving fruits of several varieties, especially whole fruits, at each meal (1).

Significance

Caring for Our Children: National Health and Safety Performance Standards (third edition) recommends a set of national standards based on evidence-based best practices in nutrition, physical activity, and screen time for all types of early care and education programs (2). Current research supports a diet based on various nutrient-dense foods that provide substantial amounts of essential nutrients (3). To ensure that child care programs are offering various foods children should be offered items from each food group, including eating various fruit, especially whole fruits.

Limitations of indicator

Indicator does not capture compliance with regulation.

Data resources

CDC State Indicator Report on Fruits and Vegetables (4).

Limitations of data resources

The organization of and language used in state documents vary substantially.

Related indicators or recommendations

Healthy People 2020 objective NWS-1: Increase the number of states with nutrition standards for foods and beverages provided to preschool-aged children in child care.

Related chronic disease indicator topics

Reproductive health; school health.


References

  1. National Resource Center for Health and Safety in Child Care and Early Education. Achieving a state of healthy weight: 2011 update. Aurora, CO: University of Colorado Denver; 2012. Available at http://nrckids.org.
  2. American Academy of Pediatrics, American Public Health Association, National Resource Center for Health and Safety in Child Care and Early Education. Caring for our children: National health and safety performance standards; Guidelines for early care and education programs. 3rd ed. Elk Grove Village, IL: American Academy of Pediatrics; Washington, DC: American Public Health Association; 2011. Available at http://nrckids.org.
  3. US Departments of Agriculture and Health and Human Services. Dietary guidelines for Americans, 2010. 7th ed. Washington, DC: US Government Printing Office; 2010.
  4. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 9.2: Presence of regulations pertaining to serving vegetables in early care and education settings

Demographic group

Children in child care aged 0–5 years.

Numerator

The numerator for the U.S. measure is the number of states with child care regulations serving children aged 0–5 years in child care that support serving vegetables, specifically dark green, orange, deep yellow, and root vegetables, at each meal. (For states with separate regulations for large and small homes and centers, language in all sets of regulations should include the full national guidelines.) Individual states have a yes/no response to this indicator.

Denominator

The denominator for the U.S. measure is 50 states.

Measures of frequency

The measure of frequency for the U.S. data is the percentage of states with language that supports serving vegetables, specifically dark green, orange, deep yellow, and root vegetables in state child care regulations. Individual states have a yes/no response to this indicator.

Time period of case definition

Current year.

Background

In 2011, 8% of states had language in child care regulations supporting serving vegetables, specifically dark green, orange, deep yellow, and root vegetables, at each meal (1).

Significance

Caring for Our Children: National Health and Safety Performance Standards (third edition) recommends a set of national standards based on evidence-based best practices in nutrition, physical activity, and screen time for all types of early care and education programs (2). Current research supports a diet based on various nutrient-dense foods that provide substantial amounts of essential nutrients (3). To ensure that child care programs are offering various foods, children should be offered items from each food group, including dark green, orange, and deep yellow vegetables.

Limitations of indicator

Indicator does not capture compliance with regulation.

Data resources

CDC State Indicator Report on Fruits and Vegetables (4).

Limitations of data resources

The organization of and language used in state documents vary substantially.

Related indicators or recommendations

Healthy People 2020 objective NWS-1: Increase the number of states with nutrition standards for foods and beverages provided to preschool-aged children in child care.

Related chronic disease indicator topics

Reproductive health; school health.


References

  1. National Resource Center for Health and Safety in Child Care and Early Education. Achieving a state of healthy weight: 2011 update. Aurora, CO: University of Colorado Denver; 2012. Available at http://nrckids.org.
  2. American Academy of Pediatrics, American Public Health Association, National Resource Center for Health and Safety in Child Care and Early Education. Caring for our children: National health and safety performance standards; Guidelines for early care and education programs. 3rd ed. Elk Grove Village, IL: American Academy of Pediatrics; Washington, DC: American Public Health Association; 2011. Available at http://nrckids.org.
  3. US Departments of Agriculture and Health and Human Services. Dietary guidelines for Americans, 2010. 7th ed. Washington, DC: US Government Printing Office; 2010.
  4. CDC. State indicator report on fruits and vegetables, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; US Department of Health and Human Services; 2013.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 10: No leisure-time physical activity among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Respondents who answered "no" to the following question: "During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?"

Denominator

Number of adults aged ≥18 years who reported any or no physical activity in the past month (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence (crude and age adjusted) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past month.

Background

The 2008 Physical Activity Guidelines for Americans states that all adults should avoid inactivity (1). In 2011, nationwide (50 states and the District of Columbia), 25.4% of adults participated in no leisure-time physical activity in the past month (2).

Significance

Regular physical activity can improve the health and quality of life of persons in the United States of all ages, regardless of the presence of a chronic disease or disability (1). Among adults and older adults, physical activity can lower the risk for early death, coronary heart disease, stroke, high blood pressure, type 2 diabetes, breast and colon cancer, falls, and depression (3). The 2008 guidelines state that some physical activity is better than none, and adults who participate in any amount of physical activity gain some health benefits (1).

Limitations of indicator

Indicator captures information only about nonoccupational physical activity. The National Health Interview Survey is the national data source for Healthy People 2020, and BRFSS is the state data source. Because the questions from each data source and the survey administration are different, data cannot be compared.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective PA-1: Reduce the proportion of adults who engage in no leisure-time physical activity.

Related chronic disease indicator topics

Cancer; cardiovascular disease; diabetes; older adults.


References

  1. US Department of Health and Human Services. 2008 physical activity guidelines for Americans. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/pdf/paguide.pdf.
  2. CDC. Behavioral Risk Factor Surveillance System survey data. Atlanta, GA: US Department of Health and Human Services, CDC; 2011. Available at http://apps.nccd.cdc.gov/brfss.
  3. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 11.1: Meeting aerobic physical activity guidelines for substantial health benefits among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Number of adults aged ≥18 years who reported at least 150 minutes per week of moderate-intensity physical activity or at least 75 minutes per week of vigorous-intensity physical activity or a combination of moderate-intensity and vigorous-intensity physical activity (multiplied by two) totaling at least 150 minutes per week.

Denominator

Number of adults aged ≥18 years who report any or no moderate or vigorous physical activity in the past month (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence (crude and age adjusted) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past month.

Background

The 2008 Physical Activity Guidelines for Americans states that for substantial health benefits, adults should do at least 150 minutes (2 hours and 30 minutes) per week of moderate-intensity or 75 minutes (1 hour and 15 minutes) per week of vigorous-intensity aerobic physical activity or an equivalent combination of moderate- and vigorous-intensity aerobic activity (1). In 2011, nationwide (50 states and the District of Columbia), 51.6% of adults participated in 150 minutes or more of moderate-intensity equivalent aerobic physical activity per week (2).

Significance

Regular physical activity can improve the health and quality of life of persons in the United States of all ages, regardless of the presence of a chronic disease or disability (1). Among adults and older adults, physical activity can lower the risk for early death, coronary heart disease, stroke, high blood pressure, type 2 diabetes, breast and colon cancer, falls, and depression (3).

Limitations of indicator

Indicator captures information only about nonoccupational physical activity. The questions only collect information about the two types of physical activities that the respondent spent the most time doing in the past month. The National Health Interview Survey is the national data source for Healthy People 2020, and the Behavioral Risk Factor Surveillance System is the state data source. Because the questions from each data source and the survey administration are different, data cannot be compared.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective PA–2: Increase the proportion of adults who meet current federal physical activity guidelines for aerobic physical activity and for muscle-strengthening activity.
  • Healthy People 2020 objective PA-2.1: Increase the proportion of adults who engage in aerobic physical activity of at least moderate intensity for at least 150 minutes per week or of vigorous intensity for at least 75 minutes per week or an equivalent combination.

Related chronic disease indicator topics

Cancer; cardiovascular disease; diabetes; older adults.


References

  1. US Department of Health and Human Services. 2008 physical activity guidelines for Americans. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/pdf/paguide.pdf.
  2. CDC. Adult participation in aerobic and muscle-strengthening physical activities—United States, 2011. MMWR 2013;62:326–30.
  3. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 11.2: Meeting aerobic physical activity guidelines for substantial health benefits and for muscle-strengthening activity among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Number of adults aged ≥18 years who reported both of the following:

  • At least 150 minutes per week of moderate-intensity physical activity or at least 75 minutes per week of vigorous-intensity physical activity or a combination of moderate-intensity and vigorous-intensity physical activity (multiplied by two) totaling at least 150 minutes per week.
  • Muscle-strengthening activities on ≥2 days per week.

Denominator

Number of adults aged ≥18 years who report any or no moderate or vigorous physical activity and who report any or no muscle-strengthening activity in the past month (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence (crude and age adjusted) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past month.

Background

The 2008 Physical Activity Guidelines for Americans state that for substantial health benefits, adults should do at least 150 minutes (2 hours and 30 minutes) per week of moderate-intensity or 75 minutes (1 hour and 15 minutes) per week of vigorous-intensity aerobic physical activity or an equivalent combination of moderate- and vigorous-intensity aerobic activity (1). In addition, the 2008 guidelines state that adults should do muscle-strengthening activities that are moderate or high intensity and involve all major muscle groups on ≥2 days per week (1). In 2011, nationwide (50 states and DC) 20.6 percent of adults participated in 150 minutes or more of moderate-intensity equivalent aerobic physical activity per week and performed muscle-strengthening activities on ≥2 days per week (2).

Significance

Regular physical activity can improve the health and quality of life of persons in the United States of all ages, regardless of the presence of a chronic disease or disability (1). Among adults and older adults, physical activity can lower the risk for early death, coronary heart disease, stroke, high blood pressure, type 2 diabetes, breast and colon cancer, falls, and depression (1). Muscle-strengthening activities provide additional benefits not found with aerobic activity (1). The benefits of muscle-strengthening activity include increased bone strength and muscular fitness (1).

Limitations of indicator

Indicator captures information only about nonoccupational physical activity. The questions assessing aerobic physical activity only collect information about the two types of physical activities that the respondent spent the most time doing in the past month. The question assessing muscle-strengthening activities does not specify the intensity nor does it specify that the activities should involve all major muscle groups. The National Health Interview Survey is the national data source for Healthy People 2020, and the Behavioral Risk Factor Surveillance System is the state data source. Because the questions from each data source and the survey administration are different, data cannot be compared.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

  • Healthy People 2020 objective PA-2: Increase the proportion of adults who meet current federal physical activity guidelines for aerobic physical activity and for muscle-strengthening activity.
  • Healthy People 2020 objective PA–2.4: Increase the proportion of adults who meet the objectives for aerobic physical activity and for muscle-strengthening activity.

Related chronic disease indicator topics

Cancer; cardiovascular disease; diabetes; older adults.


References

  1. US Department of Health and Human Services. 2008 physical activity guidelines for Americans. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/pdf/paguide.pdf.
  2. CDC. Adult participation in aerobic and muscle-strengthening physical activities–United States, 2011. MMWR 2013;62:326–30.
  3. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 11.3: Meeting aerobic physical activity guidelines for additional and more extensive health benefits among adults aged ≥18 years

Demographic group

Resident adults aged ≥18 years.

Numerator

Number of adults aged ≥18 years who reported at least 300 minutes per week of moderate-intensity physical activity, or at least 150 minutes per week of vigorous-intensity physical activity, or a combination of moderate-intensity and vigorous-intensity physical activity (multiplied by two) totaling at least 300 minutes per week.

Denominator

Number of adults aged ≥18 years who report any or no moderate or vigorous physical activity in the past month (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Annual prevalence (crude and age adjusted) with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past month.

Background

The 2008 Physical Activity Guidelines for Americans states that for substantial health benefits, adults should do at least 150 minutes (2 hours and 30 minutes) per week of moderate-intensity, or 75 minutes (1 hour and 15 minutes) per week of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- and vigorous intensity aerobic activity (1). For additional and more extensive health benefits, adults should increase their aerobic physical activity to 300 minutes (5 hours) per week of moderate-intensity or 150 minutes per week (2.5 hours) of vigorous-intensity aerobic physical activity or an equivalent combination of moderate- and vigorous-intensity activity (1). In 2011, nationwide (50 states and the District of Columbia), 31.8% of adults participated in ≥300 minutes of moderate-intensity equivalent aerobic physical activity per week (2).

Significance

Regular physical activity can improve the health and quality of life of persons in the United States of all ages, regardless of the presence of a chronic disease or disability (1). Among adults and older adults, physical activity can lower the risk for early death, coronary heart disease, stroke, high blood pressure, type 2 diabetes, breast and colon cancer, falls, and depression (3).

Limitations of indicator

Indicator captures information only about nonoccupational physical activity. The questions only collect information about the two types of physical activities that the respondent spent the most time doing in the past month. The National Health Interview Survey is the national data source for Healthy People 2020, and the Behavioral Risk Factor Surveillance System is the state data source. Because the questions from each data source and the survey administration are different, the data cannot be compared.

Data resources

Behavioral Risk Factor Surveillance System (BRFSS).

Limitations of data resources

As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage (e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). To address some of these potential concerns, BRFSS began including cellular telephone–only users in the 2011 data collection. Because of changes in sampling and weighting methods, 2011 is a new baseline for BRFSS, and comparisons with previous years' data are inappropriate.

Related indicators or recommendations

Healthy People 2020 objective PA-2.2: Increase the proportion of adults who engage in aerobic physical activity of at least moderate intensity for >300 minutes per week or of vigorous intensity for >150 minutes per week or an equivalent combination.

Related chronic disease indicator topics

Cancer; cardiovascular disease; diabetes; older adults.


References

  1. US Department of Health and Human Services. 2008 physical activity guidelines for Americans. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/pdf/paguide.pdf.
  2. CDC. Behavioral Risk Factor Surveillance System Survey Data. Atlanta, GA: US Department of Health and Human Services, CDC; 2011. Available at http://apps.nccd.cdc.gov/brfss.
  3. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 11.4: Meeting aerobic physical activity guidelines among high school students

Demographic group

Students in grades 9–12.

Numerator

Students in grades 9–12 who answered "7 days" to the following question: "During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day? (Add up all the time you spend in any kind of physical activity that increases your heart rate and makes you breathe hard some of the time.)"

Denominator

Students in grades 9–12 who reported doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time for a total of at least 60 minutes per day on ≥0 days during the 7 days before the survey.

Measures of frequency

Biennial (odd years) prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 7 days.

Background

The 2008 Physical Activity Guidelines for Americans states that children and adolescents should do ≥60 minutes (≥1 hour) of physical activity daily (1). In 2011, 28.7% of high school students had been physically active doing any kind of physical activity that increased their heart rate and made them breathe hard some of the time for a total of least 60 minutes per day on each of the 7 days before the survey (i.e., physically active at least 60 minutes on all 7 days) (2).

Significance

Among children and adolescents, physical activity can improve bone health, improve cardiorespiratory and muscular fitness, decrease levels of body fat, and reduce symptoms of depression (3). Physical activity patterns established during adolescence might extend into adulthood and affect future chronic disease risk.

Limitations of indicator

The indicator might not be measuring the accurate amount of physical activity because the respondent must calculate each day's activities and then consider this across the week. The indicator also does not capture the full guideline for children and adolescents, which includes the following specifications:

  • Aerobic: Most of the ≥60 minutes per day should be either moderate- or vigorous-intensity aerobic physical activity and should include vigorous-intensity physical activity at least 3 days per week.
  • Muscle-strengthening: As part of their ≥60 minutes of daily physical activity, children and adolescents should include muscle-strengthening physical activity on at least 3 days of the week.
  • Bone-strengthening: As part of their ≥60 minutes of daily physical activity, children and adolescents should include bone-strengthening physical activity on at least 3 days of the week.

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (2). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

  • Healthy People 2020 objective PA-3: Increase the proportion of adolescents who meet current federal physical activity guidelines for aerobic physical activity and for muscle-strengthening activity.
  • Healthy People 2020 objective PA-3.1: Increase the proportion of adolescents who meet current federal physical activity guidelines for aerobic physical activity.

Related chronic disease indicator topics

School health.


References

  1. US Department of Health and Human Services. 2008 physical activity guidelines for Americans. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/pdf/paguide.pdf.
  2. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).
  3. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 12.1: Participation in daily school physical education classes among high school students

Demographic group

Students in grades 9–12.

Numerator

Respondents who answered "5 days" to the following question: "In an average week in school when you go to school, how many days do you attend physical education (PE) classes?"

Denominator

Students surveyed in grades 9–12. Respondents with missing data were excluded.

Measures of frequency

Biennial (odd years) prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

An average week in school.

Background

Physical education is an effective strategy to increase physical activity among young persons. In 2011, 31.5% of students went to physical education classes 5 days in an average week when they were in school (i.e., attended physical education classes daily) (1).

Significance

Among children and adolescents, physical activity can improve bone health, improve cardiorespiratory and muscular fitness, decrease levels of body fat, and reduce symptoms of depression (2). Physical activity patterns established during adolescence might extend into adulthood and affect future chronic disease risk (2). The 2008 Physical Activity Guidelines for Americans states that children and adolescents should do ≥60 minutes (≥1 hour) of physical activity daily (2).

Limitations of indicator

The indicator does not assess time spent in physical education class nor does it assess time spent physically active in class.

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (1). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

  • Healthy People 2020 objective PA-4: Increase the proportion of the nation's public and private schools that require daily physical education for all students.
  • Healthy People 2020 objective PA-4.3: Increase the proportion of the nation's public and private senior high schools that require daily physical education for all students.
  • Healthy People 2020 objective PA-5: Increase the proportion of adolescents who participate in daily school physical education.

Related chronic disease indicator topics

School health.


References

  1. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).
  2. US Department of Health and Human Services. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/Report/Default.aspx.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 12.2: Soda consumption among high school students

Demographic group

Students in grades 9–12.

Numerator

Students in grades 9–12 who report consuming one or more cans, bottles, or glasses of soda per day.

Denominator

Students in grades 9–12 who report consuming any cans, bottles, or glasses of soda, including zero, per day (excluding those who refused to answer, had a missing answer, or answered "don't know/not sure").

Measures of frequency

Biennial (odd years) prevalence per day with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

Past 7 days.

Background

In 2011, 27.8% students in grades 9–12 drank one or more cans, bottles, or glasses of soda or pop per day (1).

Significance

Sugar-sweetened beverage intake has been associated with obesity (2), dental caries (3), type 2 diabetes (4), displacement of nutrient-rich foods (e.g., dairy) (5), disruptive behaviors (6,7), and poor mental health (e.g., psychological distress) (8).

Limitations of indicator

It does not include all sources of sugar-sweetened beverages.

Data resources

Youth Risk Behavior Surveillance System (YRBSS).

Limitations of data resources

As with all data from self-reported sample surveys, YRBSS data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). YRBSS data only apply to youths who are attending school and therefore might not be representative of all persons in this age group (1). Results are not available from every state because some states do not participate in YRBSS. Moreover, some states that do participate do not achieve a high enough overall response rate to receive weighted data and are therefore not included in the results.

Related indicators or recommendations

None.

Related chronic disease indicator topics

Diabetes; oral health; school health.


References

  1. CDC. Youth Risk Behavior Surveillance—United States, 2011. MMWR 2012;61(No. SS-4).
  2. Malik VS, et al. Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am J Clin Nutr 2013;98:1084–102.
  3. Sohn W, Burt BA, Sowers MR. Carbonated soft drinks and dental caries in the primary dentition. J Dent Res 2006;85:262–6.
  4. Malik VS, Popkin BM, Bray GA, et al. Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis. Diabetes Care 2010;33:2477–83.
  5. Frary CD, Johnson RK, Wang MQ. Children and adolescents' choices of foods and beverages high in added sugars are associated with intakes of key nutrients and food groups. J Adolesc Health 2004;34:56–63.
  6. Park S, et al. Problem behavior, victimization, and soda intake in high school students. Am J Health Behav 2013;37:414–21.
  7. Lien L, Lien N, Heyerdahl S, et al. Consumption of soft drinks and hyperactivity, mental distress, and conduct problems among adolescents in Oslo, Norway. Am J Public Health 2006;96:1815–20.
  8. Shi Z, Taylor AW, Wittert G, et al. Soft drink consumption and mental health problems among adults in Australia. Public Health Nutr 2010;13:1073–9.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 13.1: Secondary schools that allow community-sponsored use of physical activity facilities by youths outside of normal school hours

Demographic group

Secondary schools.

Numerator

Number of secondary schools that had a "yes" response to the following question: "Does your school, either directly or through the school district, have a joint use agreement for shared use of school or community physical activity facilities?"

Denominator

Number of secondary schools.

Measures of frequency

Percentage.

Time period of case definition

Current year.

Background

This question measures the extent to which schools and communities share physical activity facilities. School spaces and facilities should be available to youths before, during, and after the school day; on weekends; and during summer and other vacations. Access to these facilities increases visibility of schools; provides youths, their families, and community members a safe place for physical activity; and might increase partnerships with community-based physical activity programs. Community resources can expand existing school programs by providing program staff as well as intramural and club activities on school grounds. For example, community agencies and organizations can use school facilities for after-school physical fitness programs for children and adolescents, weight management programs for overweight or obese young persons, and sports and recreation programs for young persons with disabilities or chronic health conditions (14).

In 2012, the percentage of secondary schools that had a joint use agreement for shared of school or community physical activity facilities ranged from 40.9% to 86.6% (median: 65.2%) (5).

Significance

Among children and adolescents, physical activity can improve bone health, improve cardiorespiratory and muscular fitness, decrease levels of body fat, and reduce symptoms of depression (6). Physical activity patterns established during adolescence might extend into adulthood and protect against future chronic disease risk (6).

Limitations of indicator

As with any study that relies on self-report, the data might reflect some degree of overreporting or underreporting and lack of knowledge.

Data resources

School Health Profiles Principal Survey. Data are only available for states with >70% response rate; data are weighted.

Limitations of data resources

National data (other than median of state estimates) are not available. Data presented in this report apply only to secondary schools and are limited to the school populations. As with all sample surveys, data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias).

Related indicators or recommendations

Healthy People 2020 objective PA-10: Increase the proportion of the Nation's public and private schools that provide access to their physical activity spaces and facilities for all persons outside of normal school hours (that is, before and after the school day, on weekends, and during summer and other vacations).

Related chronic disease indicator topics

School health.


References

  1. CDC. School health guidelines to promote healthy eating and physical activity. MMWR 2011;60:1–75.
  2. Sallis JF, Conway TL, Prochaska JJ, et al. The association of school environments with youth physical activity. Am J Public Health 2001;1:618–20.
  3. Evenson KR, McGinn AP. Availability of school physical activity facilities to the public in four U.S. communities. Am J Health Promot 2004;18:243–50.
  4. Choy LB, McGurk MD, Tamashiro R, Nett B, Maddock JE. Increasing access to places for physical activity through a joint use agreement: a case study in urban Honolulu. Prev Chronic Dis 2008;5:A91.
  5. Demissie Z, Brener ND, McManus T, Shanklin SL, Hawkins J, Kann L. School health profiles 2012: characteristics of health programs among secondary schools. Atlanta, GA: CDC; 2013. Available at http://www.cdc.gov/healthyyouth/profiles/2012/profiles_report.pdf.
  6. US Department of Health and Human Services. 2008 physical activity guidelines for Americans. Washington, DC: US Department of Health and Human Services; 2008. Available at http://www.health.gov/paguidelines/pdf/paguide.pdf.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 13.2: Secondary schools that allow students to purchase soda or fruit drinks

Demographic group

Secondary schools.

Numerator

Number of secondary schools that allowed students to purchase soda pop or fruit drinks that are not 100% juice from vending machines or at the school store, canteen, or snack bar.

Denominator

Number of secondary schools.

Measures of frequency

Percentage.

Time period of case definition

2012.

Background

Many schools offer foods and beverages in after-school programs, school stores, snack bars, or canteens (1), and these foods sold in competition with school meals (competitive foods) often are relatively low in nutrient density and relatively high in fat, added sugar, and calories (2). Competitive foods are widely available in many elementary schools, in most middle schools, and in almost all secondary schools (1,35). Given that schools offer numerous and diverse opportunities for young persons to learn and make consumption choices about healthful eating, schools should provide a consistent environment that is conducive to healthful eating behaviors (6). To help improve dietary behavior and reduce overweight among youths, schools should offer appealing and nutritious foods in school snack bars and vending machines and discourage sale on school grounds of foods that are high in fat, sodium, and added sugar and beverages and foods containing caffeine (711). Restricting the availability of high-calorie, energy-dense foods in schools while increasing the availability of healthful foods might be an effective strategy for promoting more healthful choices among students at school (6,12). In 2012, the percentage of secondary schools that allowed students to purchase soda pop or fruit drinks at the school store, canteen, or snack bar ranged from 4.2% to 56.1% (median: 30.1%) (13).

Significance

Calorically sweetened beverage intake has been associated with dental caries and cardiovascular disease risk factors (14–21). These data are included in the CDC School Health Profiles summary report and were used as an indicator in the Children's Food Environment Indicator Report (22).

Limitations of indicator

Indicator does not include data on access outside of the school setting. As with any study that relies on self-report, the data might reflect some amount of overreporting or underreporting and actual lack of knowledge.

Data resources

School Heath Profiles principal survey. Data are only available for states with >70% response rate; data are weighted.

Limitations of data resources

National data (other than median of state estimates) are not available. National data (other than median of state estimates) are not available. Data presented in this report apply only to secondary schools and are limited to school populations. As with all sample surveys, data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias).

Related indicators or recommendations

Healthy People 2020 objective NWS-2.1: Increase the proportion of schools that do not sell or offer calorically sweetened beverages to students.

Related chronic disease indicator topics

Diabetes; oral health; school health.


References

  1. O'Toole T, Anderson S, Miller C, Guthrie J. Nutrition services and foods and beverages available at school: results from the School Health Policies and Programs Study. J Sch Health 2007;77:500–21.
  2. US Department of Agriculture. Foods sold in competition with USDA school meal programs: a report to Congress. Alexandria, VA: Food and Nutrition Service; 2001.
  3. Brener ND, Kann L, O'Toole TP, Wechsler H, Kimmons J. Competitive foods and beverages available for purchase in secondary schools—selected sites, United States, 2006. MMWR 2008;57:935–8.
  4. US Government Accountability Office. School meal programs: competitive foods are widely available and generate substantial revenues for schools. Report to Congressional Requesters GAO-05-563. Washington, DC: US Government Accountability Office; 2005. Available at http://www.gao.gov/new.items/d05563.pdf.
  5. Fox MK, Gordon A, Nogales R, Wilson A. Availability and consumption of competitive foods in U.S. public schools. J Am Diet Assoc 2009;109:S57–66.
  6. Committee on Prevention of Obesity of Children and Youth, Food and Nutrition Board, Board on Health Promotion and Disease Prevention; Institute of Medicine. Schools [Chapter 7]. In: JP Koplan, CT Liverman, VI Kraak, eds. Preventing childhood obesity: health in the balance. Washington, DC: National Academies Press; 2005:237–84. Available at http://books.nap.edu/openbook.php?record_id=11015&page=237.
  7. Wechsler H, McKenna ML, Lee SM, Dietz WH. The role of schools in preventing childhood obesity. The State Education Standard 2004;5:4–12.
  8. Bergman EA, Gordon RW; American Dietetic Association. Position of the American Dietetic Association: local support for nutrition integrity in schools. J Am Diet Assoc 2010;110:1244–54.
  9. Institute of Medicine. Nutrition standards for foods in schools: leading the way toward healthier youth. Washington, DC: Institute of Medicine of the National Academies; 2007.
  10. Fox MK, Dodd AH, Wilson A, Gleason PM. Association between school food environment and practices and body mass index of U.S. public school children. J Am Diet Assoc 2009b;109(suppl):S108–17.
  11. Briefel RR, Crepinsek MK, Cabili C, Wilson A, Gleason PM. School food environments and practices affect dietetic behaviors of U.S. public school children. J Am Diet Assoc 2009;109(Suppl 1):S91–107.
  12. Story M, Nanney MS, Schwartz MB. Schools and obesity prevention: creating school environments and policies to promote healthy eating and physical activity. Milbank Q 2009;87:71–100.
  13. Demissie Z, Brener ND, McManus T, Shanklin SL, Hawkins J, Kann L. School health profiles 2012: characteristics of health programs among secondary schools. Atlanta, GA: CDC; 2013. Available at http://www.cdc.gov/healthyyouth/profiles/2012/profiles_report.pdf.
  14. Welsh JA, Sharma A, Abramson JL, Vaccarino V, Gillespie C, Vos MB. Caloric sweetener consumption and dyslipidemia among U.S. adults. JAMA 2010;303:1490–7.
  15. Welsh JA, Sharma A, Cunningham SA, Vos MB. Consumption of added sugars and indicators of cardiovascular disease risk among U.S. adolescents. Circulation 2011;123:249–57.
  16. Marshall TA, Levy SM, Broffitt B, et al. Dental caries and beverage consumption in young children. Pediatrics 2003;112:e184–91.
  17. Ismail AI, Burt BA, Eklund SA. The cariogenicity of soft drinks in the United States. J Am Dent Assoc 1984;109:241–5.
  18. Heller KE, Burt BA, Eklund SA. Sugared soda consumption and dental caries in the United States. J Dent Res 2001;80:1949–53.
  19. Malik VS, Hu FB. Sweeteners and risk of obesity and type 2 diabetes: the role of sugar-sweetened beverages. Curr Diab Rep 2012;12:195–203.
  20. Malik V, Popkin BM, Bray GA, Despres JP, Hu FB. Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation 2010;121:1356–64.
  21. Kit B, Fakhouri TH, Park S, Nielson SJ, Ogen C. Trends in sugar-sweetened beverage consumption among youth and adults in the united states: 1999–2010. Am J Clin Nutr 2013;98:180–8.
  22. CDC. Children's food environment state indicator report, 2011. Atlanta, GA: US Department of Health and Human Services, CDC; 2011. Available at http://www.cdc.gov/obesity/downloads/childrensfoodenvironment.pdf.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 13.3: Secondary schools that allow students to purchase sports drinks

Demographic group

Secondary schools.

Numerator

Number of secondary schools that allow students to purchase sports drinks from vending machines or at the school store, canteen, or snack bar.

Denominator

Number of secondary schools.

Measures of frequency

Percentage.

Time period of case definition

2012.

Background

Many schools offer foods and beverages in after-school programs, school stores, snack bars, or canteens (1), and these foods sold in competition with school meals (competitive foods) often are relatively low in nutrient density and relatively high in fat, added sugar, and calories (2). Competitive foods are widely available in many elementary schools, in most middle schools, and in almost all secondary schools (1,35). Given that schools offer numerous and diverse opportunities for young persons to learn and make consumption choices about healthful eating, schools should provide a consistent environment that is conducive to healthful eating behaviors (6). To help improve dietary behavior and reduce overweight among youths, schools should offer appealing and nutritious foods in school snack bars and vending machines and discourage sale on school grounds of foods that are high in fat, sodium, and added sugar and beverages and foods containing caffeine (711). Restricting the availability of high-calorie, energy-dense foods in schools while increasing the availability of healthful foods might be an effective strategy for promoting more healthful choices among students at school (6,12). In 2012, the percentage of secondary schools allowed students to purchase sports drinks at the school store, canteen, or snack bar ranged from 6.7% to 73.8% (median: 46.0%) (13).

Significance

Calorically sweetened beverage intake has been associated with dental caries and cardiovascular disease risk factors (1421). These data are included in the CDC School Health Profiles report (13).

Limitations of indicator

Indicator does not include data on access outside of the school setting.

Data resources

School Heath Profiles principal survey. Data are only available for those states with >70% response rate; data are weighted.

Limitations of data resources

National data (other than median of state estimates) are not available. National data (other than the median of state estimates) are not available. Data presented in this report apply only to secondary schools and are limited to the school populations. As with all sample surveys, data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias).

Related indicators or recommendations

Healthy People 2020 objective NWS-2.1: Increase the proportion of schools that do not sell or offer calorically sweetened beverages to students.

Related chronic disease indicator topics

Diabetes; oral health; school health.


References

  1. O'Toole T, Anderson S, Miller C, Guthrie J. Nutrition services and foods and beverages available at school: results from the School Health Policies and Programs Study. J Sch Health 2007;77:500–21.
  2. US Department of Agriculture. Foods sold in competition with USDA school meal programs: a report to Congress. Alexandria, VA: Food and Nutrition Service; 2001.
  3. Brener ND, Kann L, O'Toole TP, Wechsler H, Kimmons J. Competitive foods and beverages available for purchase in secondary schools—selected sites, United States, 2006. MMWR 2008;57:935–8.
  4. US Government Accountability Office. School meal programs: competitive foods are widely available and generate substantial revenues for schools. Report to Congressional Requesters GAO-05-563. Washington, DC: US Government Accountability Office; 2005. Available at http://www.gao.gov/new.items/d05563.pdf.
  5. Fox MK, Gordon A, Nogales R, Wilson A. Availability and consumption of competitive foods in U.S. public schools. J Am Diet Assoc 2009;109:S57–66.
  6. Committee on Prevention of Obesity of Children and Youth, Food and Nutrition Board, Board on Health Promotion and Disease Prevention; Institute of Medicine. Schools [Chapter 7]. In: JP Koplan, CT Liverman, VI Kraak, eds. Preventing childhood obesity: health in the balance. Washington, DC: National Academies Press; 2005:237–84. Available at http://books.nap.edu/openbook.php?record_id=11015&page=237.
  7. Wechsler H, McKenna ML, Lee SM, Dietz WH. The role of schools in preventing childhood obesity. The State Education Standard 2004;5:4–12.
  8. Bergman EA, Gordon RW; American Dietetic Association. Position of the American Dietetic Association: local support for nutrition integrity in schools. J Am Diet Assoc 2010;110:1244–54.
  9. Institute of Medicine. Nutrition standards for foods in schools: leading the way toward healthier youth. Washington, DC: Institute of Medicine of the National Academies; 2007.
  10. Fox MK, Dodd AH, Wilson A, Gleason PM. Association between school food environment and practices and body mass index of U.S. public school children. J Am Diet Assoc 2009;109(suppl):S108–17.
  11. Briefel RR, Crepinsek MK, Cabili C, Wilson A, Gleason PM. School food environments and practices affect dietetic behaviors of U.S. public school children. J Am Diet Assoc 2009;109(Suppl 1):S91–107.
  12. Story M, Nanney MS, Schwartz MB. Schools and obesity prevention: creating school environments and policies to promote healthy eating and physical activity. Milbank Q 2009;87:71–100.
  13. Demissie Z, Brener ND, McManus T, Shanklin SL, Hawkins J, Kann L. School health profiles 2012: characteristics of health programs among secondary schools. Atlanta, GA: CDC; 2013. Available at http://www.cdc.gov/healthyyouth/profiles/2012/profiles_report.pdf.
  14. Welsh JA, Sharma A, Abramson JL, Vaccarino V, Gillespie C, Vos MB. Caloric sweetener consumption and dyslipidemia among U.S. adults. JAMA 2010;303:1490–7.
  15. Welsh JA, Sharma A, Cunningham SA, Vos MB. Consumption of added sugars and indicators of cardiovascular disease risk among U.S. adolescents. Circulation 2011;123:249–57.
  16. Marshall TA, Levy SM, Broffitt B, et al. Dental caries and beverage consumption in young children. Pediatrics 2003;112:e184–91.
  17. Ismail AI, Burt BA, Eklund SA. The cariogenicity of soft drinks in the United States. J Am Dent Assoc 1984;109:241–5.
  18. Heller KE, Burt BA, Eklund SA. Sugared soda consumption and dental caries in the United States. J Dent Res 2001;80:1949–53.
  19. Malik VS, Hu FB. Sweeteners and risk of obesity and type 2 diabetes: the role of sugar-sweetened beverages. Curr Diab Rep 2012; Epub ahead of print.
  20. Malik V, Popkin BM, Bray GA, Despres JP, Hu FB. Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation 2010;121:1356–64.
  21. Kit B, Fakhouri TH, Park S, Nielson SJ, Ogen C. Trends in sugar-sweetened beverage consumption among youth and adults in the united states: 1999–2010. Am J Clin Nutr 2013;98:180–8.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 13.4: Secondary schools that offer less healthy foods as competitive foods

Demographic group

Secondary schools.

Numerator

Number of secondary schools that sell baked goods (e.g., cookies, crackers, cakes, pastries, or other baked goods), salty snacks, chocolate candy, other kinds of candy, soda pop or fruit drinks, and sports drinks in vending machines or at the school store, canteen, or snack bar.

Denominator

Number of secondary schools.

Measures of frequency

Percentage.

Time period of case definition

2012.

Background

Many schools offer foods and beverages in after-school programs, school stores, snack bars, or canteens (1), and these foods sold in competition with school meals (competitive foods) often are relatively low in nutrient density and relatively high in fat, added sugar, and calories (2). Competitive foods are widely available in many elementary schools, in most middle schools, and in almost all secondary schools (1,35). Given that schools offer numerous and diverse opportunities for young persons to learn and make consumption choices about healthful eating, schools should provide a consistent environment that is conducive to healthful eating behaviors (6). To help improve dietary behavior and reduce overweight among youths, schools should offer appealing and nutritious foods in school snack bars and vending machines and discourage sale on school grounds of foods that are high in fat, sodium, and added sugar and beverages and foods containing caffeine (711). Because students' food choices are influenced by the total food environment, the simple availability of healthful foods such as fruits and vegetables might not be sufficient to prompt the choice of fruits and vegetables when other high-fat or high-sugar foods are easily accessible (12,13). However, offering a wider range of healthful foods can be an effective way to promote better food choices among high school students (14). Restricting access to snack foods is associated with higher frequency of fruit and vegetable consumption in elementary school–aged children (15). Taken together, such findings suggest that restricting the availability of high-calorie, energy dense foods in schools while increasing the availability of healthful foods might be an effective strategy for promoting more healthful choices among students at school (6,16). In 2012, the percentage of secondary schools that did not sell any of the following six items (baked goods, salty snacks, candy, soda pop or fruit drinks, or sports drinks) at the school store, canteen, or snack bar ranged from 12.9% to 88.9% (median: 42.7%) (17).

Significance

Most foods and beverages that are sold in the school but outside of the school meals program are high in sugar, fat, and calories, including high-fat salty snacks, high-fat baked goods, and high-calorie sugar-sweetened beverages, such as soft drinks, sport drinks, and fruit drinks. The School Health Profiles survey includes this indicator in its annual reports.

Limitations of indicator

Indicator does not include data on access outside of the school setting.

Data resources

School Heath Profiles principal survey. Data are only available for those states with >70% response rate; data are weighted.

Limitations of data resources

National data (other than median of state estimates) are not available. National data (other than the median of state estimates) are not available. Data presented in this report apply only to secondary schools and are limited to the school populations. As with all sample surveys, data might be subject to systematic error resulting from noncoverage (e.g., no participation by certain schools), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias).

Related indicators or recommendations

Healthy People 2020 objective NWS-2: Increase the proportion of schools that offer nutritious food and beverages outside of school meals.

Related chronic disease indicator topics

Cardiovascular disease; diabetes; oral health; school health.


References

  1. O'Toole T, Anderson S, Miller C, Guthrie J. Nutrition services and foods and beverages available at school: results from the School Health Policies and Programs Study. J Sch Health 2007;77:500–21.
  2. US Department of Agriculture. Foods sold in competition with USDA school meal programs: a report to Congress. Alexandria, VA: Food and Nutrition Service; 2001.
  3. Brener ND, Kann L, O'Toole TP, Wechsler H, Kimmons J. Competitive foods and beverages available for purchase in secondary schools—selected sites, United States, 2006. MMWR 2008;57:935–8.
  4. US Government Accountability Office. School meal programs: competitive foods are widely available and generate substantial revenues for schools. Report to Congressional Requesters GAO-05-563. Washington, DC: US Government Accountability Office; 2005. Available at http://www.gao.gov/new.items/d05563.pdf.
  5. Fox MK, Gordon A, Nogales R, Wilson A. Availability and consumption of competitive foods in U.S. public schools. J Am Diet Assoc 2009;109:S57–66.
  6. Committee on Prevention of Obesity of Children and Youth, Food and Nutrition Board, Board on Health Promotion and Disease Prevention; Institute of Medicine. Schools [Chapter 7]. In: JP Koplan, CT Liverman, VI Kraak, eds. Preventing childhood obesity: health in the balance. Washington, DC: National Academies Press; 2005:237–84. Available at http://books.nap.edu/openbook.php?record_id=11015&page=237.
  7. Wechsler H, McKenna ML, Lee SM, Dietz WH. The role of schools in preventing childhood obesity. The State Education Standard 2004;5:4–12.
  8. Bergman EA, Gordon RW; American Dietetic Association. Position of the American Dietetic Association: local support for nutrition integrity in schools. J Am Diet Assoc 2010;110:1244–54.
  9. Institute of Medicine. Nutrition standards for foods in schools: leading the way toward healthier youth. Washington, DC: Institute of Medicine of the National Academies; 2007.
  10. Fox MK, Dodd AH, Wilson A, Gleason PM. Association between school food environment and practices and body mass index of U.S. public school children. J Am Diet Assoc 2009;109(suppl):S108–17.
  11. Briefel RR, Crepinsek MK, Cabili C, Wilson A, Gleason PM. School food environments and practices affect dietetic behaviors of U.S. public school children. J Am Diet Assoc 2009;109(Suppl 1):S91–107.
  12. Cullen KW, Eagan J, Baranowski T, Owens E, deMoor C. Effect of a la carte and snack bar foods at school on children's lunchtime intake of fruits and vegetables. J Am Diet Assoc 2000;100:1482–6.
  13. Kubik MY, Lytle LA, Hannan PJ, Perry CL, Story M. The association of the school food environment with dietary behaviors of young adolescents. Am J Public Health 2003;93:1168–73.
  14. French SA, Story M, Fulkerson JA, Hannan P. An environmental intervention to promote lower fat food choices in secondary schools. Outcomes of the TACOS study. Am J Public Health 2004;94:1507–12.
  15. Gonzalez W, Jones SJ, Frongillo EA. Restricting snacks in U.S. elementary schools is associated with higher frequency of fruit and vegetable consumption. J Nutr 2009;139:142–4.
  16. Story M, Nanney MS, Schwartz MB. Schools and obesity prevention: creating school environments and policies to promote healthy eating and physical activity. Milbank Q 2009;87:71–100.
  17. Demissie Z, Brener ND, McManus T, Shanklin SL, Hawkins J, Kann L. School health profiles 2012: characteristics of health programs among secondary schools. Atlanta, GA: CDC; 2013. Available at http://www.cdc.gov/healthyyouth/profiles/2012/profiles_report.pdf.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 14: Presence of regulations pertaining to screen time in early care and education settings

Demographic group

Children in child care aged 0–5 years.

Numerator

The numerator for the U.S. measure is the number of states with child care regulations that support limiting screen time. Individual states have a yes/no response to this indicator.

Denominator

The denominator for the U.S. measure is 50 states.

Measures of frequency

The measure of frequency for the U.S. data is the percentage of states with child care regulations. Individual states have a yes/no response to this indicator.

Time period of case definition

Annual.

Background

In 2006, 65% percent of center-based programs and 11% of home-based programs watched no television during the early care and education day (1).

Significance

Excess screen time is associated with language delay among infants and with attention problems, less healthy diets, and obesity-related behaviors among children (27). Infants and children should have positive interactions with others and not be engaged in screen time that takes away from social interaction (8). To ensure that child care programs are promoting the healthy development of children, Caring for Our Children: National Health and Safety Performance Standards (third edition) recommends that screen time (television, video, DVD, and computer use) not be permitted for children aged <2 years and limited to ≤30 minutes once per week (for educational or physical activity use only) for children aged ≥2 years (9).

Limitations of indicator

Does not include screen time outside of the child care setting. In addition, this indicator does not capture compliance with regulation.

Data resources

Achieving a State of Healthy Weight: A National Assessment of Obesity Prevention Terminology in Child Care Regulations 2011; CDC State Indicator Report on Physical Activity (10).

Limitations of data resources

There is much variability in the way states' documents are organized and the language used within the states' documents.

Related indicators or recommendations

Healthy People 2020 objective PA-8.2: Increase the proportion of children and adolescents 2 years old through 12th grade who view television, videos, or play video games for no more than 2 hours a day.

Related chronic disease indicator topics

Reproductive health; school health.


References

  1. Christakis DA, Zimmerman FJ, Garrison MM. Television viewing in child care programs: a national survey. Commun Rep 2006;19:111–20.
  2. Zimmerman FJ, Christakis DA, Meltzoff A. Associations between media viewing and language development in children under age 2 years. J Pediatr 2007;151:364–8.
  3. Zimmerman FJ, Christakis DA. Children's television viewing and cognitive outcomes. Arch Pediatr Adolesc Med 2005;159:619–25.
  4. Reilly JJ, Armstrong J, Dorosty AR. Early life risk factors for obesity in childhood: cohort study. BMJ 2005;330:1357.
  5. Lumeng JC, Rahnama S, Appugliese D, Kaciroti N, Bradley RH. Television exposure and overweight risk in preschoolers. Arch Pediatr Adolesc Med 2006;160:417–22.
  6. Levin S, Martin MW, Riner WF. TV viewing habits and body mass index among South Carolina Head Start children. Ethn Dis 2004;14:336–9.
  7. Miller SA, Taveras EM, Rifas-Shiman SL, Gillman MW. Association between television viewing and poor diet quality in young children. Int J Pediatr Obes 2008;3:168–76.
  8. American Academy of Pediatrics, Council on Communications and Media. Policy statement: media violence. Pediatrics 2009;124:1495–503.
  9. American Academy of Pediatrics, American Public Health Association, National Resource Center for Health and Safety in Child Care and Early Education. Caring for our children: National health and safety performance standards; Guidelines for early care and education programs. 3rd ed. Elk Grove Village, IL: American Academy of Pediatrics; Washington, DC: American Public Health Association; 2011. Available at http://nrckids.org.
  10. CDC. State indicator report on physical activity, 2010. Atlanta, GA: CDC, US Department of Health and Human Services; 2010. Available at http://www.cdc.gov/physicalactivity/downloads/PA_State_Indicator_Report_2010.pdf.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 15: Infants breastfed at 6 months

Demographic group

Infants.

Numerator

Number of caregivers of children born in a cohort year who indicate their child was breastfed any amount at age 6 months.

Denominator

Number of children aged 19–35 months born in the same cohort year.

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

At age 6 months.

Background

Among U.S. infants born in 2010, 49.0% were breastfed (to any extent) at 6 months and 16.4% were exclusively breastfed for 6 months; 27.0% were breastfed (to any extent) for 12 months (1).

Significance

Breastfeeding is associated with health benefits for mother and infant (2). Mothers who breastfeed have a reduced risk for developing breast and ovarian cancer, and infants who are breastfed might be less likely to experience various infections and develop chronic conditions, including obesity, during childhood (2). The American Academy of Pediatrics recommends exclusive breastfeeding for approximately the first 6 months of life with continued breastfeeding for at least the first year (3).

Limitations of indicator

None identified.

Data resources

CDC National Immunization Survey. Breastfeeding estimates are released by CDC each August in the CDC Breastfeeding Report Card (1) and on the CDC website (available at http://www.cdc.gov/breastfeeding/data/NIS_data/index.htm).

Limitations of data resources

As with data from all self-reported sample surveys, the National Immunization Survey data might be subject to systematic error resulting from noncoverage (e.g., e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). Although sociodemographic and state-specific rates are available each year on the national sample, sample size per state does not permit for calculation of yearly rates by sociodemographic strata within states. However, CDC combines multiple birth years to report sociodemographic specific rates within a state.

Related indicators or recommendations

Healthy People 2020 objective MICH-21.2: Increase the proportion of infants who are breastfed at 6 months.

Related chronic disease indicator topics

Reproductive health.


References

  1. CDC. Breastfeeding report card: United States, 2013. Atlanta, GA: CDC, US Department of Health and Human Services; 2013.
  2. Ip S, Chung M, Raman G, et al. Breastfeeding and maternal and infant health outcomes in developed countries. Evid Rep Technol Assess (Full Rep) 2007;153:1–186.
  3. American Academy of Pediatrics. Breastfeeding and the use of human milk. Pediatrics 2012;129:e827–41.

Indicator Group: Nutrition, Physical Activity, and Weight Status

Indicator 16: Receiving formula supplementation within the first 2 days of life among breastfed infants

Demographic group

Infants.

Numerator

Breastfed infants who received formula supplementation before age 2 days.

Denominator

Infants born during the specified year and breastfeeding at age 2 days.

Measures of frequency

Annual prevalence with 95% confidence intervals and by demographic characteristics when feasible.

Time period of case definition

At age 2 days.

Background

Among U.S. infants born in 2010, 24.2% received formula before age 2 days (1). Furthermore, 49.0% were breastfed for 6 months, and 16.4% were exclusively breastfed for 6 months; 27.0% were breastfed for 12 months (1).

Significance

Supplementation of newborn breastfed infants with formula is associated with a shortened breastfeeding duration after hospital discharge (2) The Joint Commission, an organization that accredits hospitals, recently added a performance measure for which hospitals report the proportion of newborns who leave the hospital having had nothing but breast milk (3).

Limitations of indicator

Indicator does not account for rare situations in which supplementation to breast milk feeding is medically indicated.

Data resources

CDC National Immunization Survey. Estimates are released by CDC each August in the CDC Breastfeeding Report Card (1) and on the CDC website (available at http://www.cdc.gov/breastfeeding/data/NIS_data/index.htm).

Limitations of data resources

As with data from all self-reported sample surveys, the National Immunization Survey data might be subject to systematic error resulting from noncoverage (e.g., e.g., on college campuses or in the military), nonresponse (e.g., refusal to participate in the survey or to answer specific questions), or measurement (e.g., social desirability or recall bias). Although sociodemographic and state-specific rates are available each year on the national sample, sample size per state does not permit for calculation of yearly rates by sociodemographic strata within states. However, CDC combines multiple birth years to report sociodemographic specific rates within a state. These estimates will be released on the CDC website in 2014.

Related indicators or recommendations

Healthy People 2020 objective MICH-23: Reduce the proportion of breastfed newborns who receive formula supplementation within the first 2 days of life.