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Health, United States, 2013 - Chartbook

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Chartbook: Figure 1-19

 

Mortality: Life Expectancy at Birth

Figure 1. Life expectancy at birth, by selected characteristics: United States, 1980–2010

Figure 1 consists of a line graph showing trends in life expectancy at birth, by race and sex, for 1980 through 2010, and a bar chart showing life expectancy at birth for 2010, by sex and race and Hispanic origin.

NOTE: Life expectancy by Hispanic origin was available starting in 2006.
Source: CDC/NCHS, Health, United States, 2013, Table 18. Data from the National Vital Statistics System (NVSS).

The gap in life expectancy at birth between white persons and black persons persists but has narrowed since 1990.

Life expectancy is a measure often used to gauge the overall health of a population. Between 1980 and 2010, life expectancy at birth in the United States increased from 70.0 years to 76.2 years for males and from 77.4 years to 81.0 years for females. Racial disparities in life expectancy at birth persisted for both males and females in 2010 but have narrowed since 1990 (1). Life expectancy at birth was 8.2 years longer for white males than for black males in 1990, and 4.7 years longer for white males than for black males in 2010. In 1990, life expectancy at birth was 5.8 years longer for white females than for black females; by 2010, life expectancy at birth was 3.3 years longer for white females than for black females. In 2010, Hispanic males and females had longer life expectancy at birth than non-Hispanic white or non-Hispanic black males and females.

 

Mortality: Infant Mortality

Figure 2. Infant, neonatal, and postneonatal mortality rates: United States, 2000–2010

Figure 2 is a line graph showing infant, neonatal, and postneonatal mortality rates for 2000 through 2010.

Source: CDC/NCHS, Health, United States, 2013, Table 13 and reference 2. Data from the National Vital Statistics System (NVSS).

Infant, neonatal, and postneonatal mortality rates declined between 2000 and 2010.

The infant mortality rate is the risk of death during the first year of life. The 2010 infant mortality rate of 6.15 per 1,000 live births—a historically low value—was 11% lower than in 2000. During the same period, the neonatal mortality rate (death rate among infants under 28 days, a subset of infant mortality) decreased 13% to 4.05 per 1,000 live births, and the postneonatal mortality rate (death rate among infants 28 days through 11 months, a subset of infant mortality) declined 8% to 2.10 per 1,000 live births.

 

Mortality: Selected Causes of Death

Figure 3. Age-adjusted death rates for selected causes of death for all ages, by sex: United States, 2000–2010

Figure 3 consists of two line graphs, one for males and one for females, showing age-adjusted death rates for selected causes of death for all ages, for 2000 through 2010.

Source: CDC/NCHS, Health, United States, 2013, Table 20. Data from the National Vital Statistics System (NVSS).

Between 2000 and 2010, the all-cause age-adjusted death rate decreased 16% among males and 13% among females.

During this 10-year period, age-adjusted death rates among males declined 37% for stroke, 30% for heart disease, 16% for cancer, and 13% for chronic lower respiratory diseases, while the age-adjusted death rate for Alzheimer's disease increased 38%, and the age-adjusted death rate for unintentional injury was stable. Among females, age-adjusted death rates declined 35% for stroke, 32% for heart disease, and 12% for cancer while the age adjusted death rates increased 41% for Alzheimer's disease, and 16% for unintentional injuries. In 2010, age-adjusted death rates were higher for males than females for heart disease, cancer, chronic lower respiratory diseases, diabetes, and unintentional injuries; were similar for stroke; and were higher among females than males for Alzheimer's disease.

 

Mortality: Motor Vehicle-related Death Rates

Figure 4. Motor vehicle-related death rates among persons aged 15–24, by sex and age: United States, 2000–2010

Figure 4 is a line graph showing motor vehicle-related death rates among persons aged 15 to 24, by sex and age for 2000 through 2010.

Source: CDC/NCHS, Health, United States, 2013, Table 33. Data from the National Vital Statistics System (NVSS).

Between 2000 and 2010, motor vehicle-related death rates declined among males and females aged 15–19 and 20–24.

Motor vehicle-related deaths are a significant cause of preventable death, accounting for 35,332 deaths in the United States in 2010 across all ages (3). Motor vehicle-related death rates were higher for males and females aged 15–24 than for most other age groups (Table 33). For males and females aged 15–19, motor vehicle-related death rates declined 47% from 2000 to 2010. Motor vehicle-related death rates declined 31% for males aged 20–24 and 26% for females in the same age group during this 10-year period.

 

Natality: Teenage Childbearing

Figure 5. Teenage childbearing, by maternal age and race and Hispanic origin: United States, 2002–2012

Figure 5 consists of two line graphs, one for mothers aged 15 to 17 and one for mothers aged 18 to 19, showing childbearing rates, by race and Hispanic origin, for 2002 through 2012.

Source: CDC/NCHS, Health, United States, 2013, Table 3. Data from the National Vital Statistics System (NVSS).

Between 2002 and 2012, teenage birth rates declined among all racial and ethnic groups.

In 2012, 2.3% of births were to teenagers under age 18 and 5.5% were to women aged 18–19 (Table 4). Between 2002 and 2012, birth rates declined 39% for teenagers aged 15–17 and 29% for women aged 18–19 (Table 3). Birth rates were higher among Hispanic and non-Hispanic black teenagers than among other racial and ethnic groups. Since 2002, birth rates have decreased 48% for Hispanic teenagers aged 15–17 and 46% for non-Hispanic black teenagers in the same age group. Also during this period, birth rates for those aged 18–19 decreased 39% for Hispanic teenagers and 32% for non-Hispanic black teenagers.

 

Morbidity: Heart Disease Prevalence

Figure 6. Respondent-reported heart disease prevalence among adults aged 18 and over, by sex and age: United States, average annual, 2001–2002 through 2011–2012

Figure 6 consists of two line graphs, one for men and one for women, showing respondent-reported heart disease prevalence among adults aged 18 and over, by sex and age group, for 2001 to 2002 (average annual) through 2011 and 2012 (average annual).

SOURCE: CDC/NCHS, Health, United States, 2013, Table 44.
Data from the National Health Interview Survey (NHIS).

During 2001–2002 through 2011–2012, heart disease prevalence remained stable among men and women in most age groups.

Heart disease is the leading cause of death in the United States for both males and females, accounting for 307,384 deaths among males and 290,305 deaths among females across all ages in 2010 (Table 22). During 2001–2002 through 2011–2012, heart disease prevalence remained stable among men and women in all age groups except among women aged 65 and over, where the prevalence declined. In 2011–2012, the prevalence of respondent-reported heart disease among adults aged 18–54 was similar for men and women; among adults aged 55 and over, the prevalence was higher for men than for women. In 2011–2012, 43.5% of men aged 75 and over reported having ever been told by a physician that they had heart disease, compared with 31.5% of women in the same age group.

 

Disability Measures: Basic Actions Difficulty and Complex Activity Limitation

Figure 7. Basic actions difficulty and complex activity limitation among adults aged 18 and over, by sex and age: United States, 2002–2012

Figure 7 consists of two line graphs, one showing basic actions difficulty and one showing complex activity limitation, among adults aged 18 and over, by sex and age group, for 2002 through 2012.


SOURCE: CDC/NCHS, Health, United States, 2013, Table 49. Data from the National Health Interview Survey (NHIS).

During 2002 through 2012, the percentage of the noninstitutionalized population with basic actions difficulty and the percentage of the noninstitutionalized population with complex activity limitation increased with age.

Basic actions difficulty and complex activity limitation are two constructs for defining and measuring disability status (4). Basic actions difficulty captures limitations in movement, emotional, sensory, or cognitive functioning associated with a health problem. Complex activity limitation is the inability to function successfully in certain social roles, such as working, maintaining a household, living independently, or participating in community activities. In 2012, the prevalence of each disability measure was higher for women than men in the same age group, with the exception of complex activity limitation among those aged 18–64, where the prevalence was similar for men and women (12.0%–12.9%).

 

Health Risk Factors: Current Cigarette Smoking

Figure 8. Current cigarette smoking among high school seniors and adults aged 18 and over, by sex and age: United States, 2002–2012

Figure 8 consists of two line graphs, one for males and one for females, showing current cigarette smoking among high school seniors and adults aged 18 and over, by sex and age group, for 2002 through 2012.

SOURCE: CDC/NCHS, Health, United States, 2013, Tables 56 and 61. Data from the National Health Interview Survey (NHIS) and the Monitoring the Future (MTF) Study.

During 2002 through 2012, cigarette smoking prevalence declined among high school seniors
and among adults aged 18–44 and women aged 45–64.

Smoking is associated with an increased risk of heart disease, stroke, lung and other types of cancers, and chronic lung diseases (5). Between 2002 and 2012, cigarette smoking among students in grade 12 decreased from 27.4% to 19.3% for male students and from 25.5% to 14.5% for female students. During 2002 through 2012, the percentage of adults who smoked cigarettes declined for men and women aged 18–44 and for women aged 45–64, while remaining stable for men aged 45–64 and for men and women aged 65 and over. In 2012, 20.5% of adult men aged 18 and over and 15.8% of adult women were current cigarette smokers (Table 56).

 

Health Risk Factors: Uncontrolled High Blood Pressure

Figure 9. Uncontrolled high blood pressure among adults aged 20 and over with hypertension, by sex and age: United States, 1988–1994 through 2009–2012

Figure 9 consists of two line graphs, one for men and one for women, showing uncontrolled high blood pressure among adults aged 20 and over for adults with hypertension, by age group, for four time periods: 1988 to 1994, 1999 to 2002, 2003 to 2006, and 2009 to 2012.

SOURCE: CDC/NCHS, Health, United States, 2013, Table 65. Data from the National Health and Nutrition Examination Survey (NHANES).

Although control of high blood pressure has improved since 1988–1994, nearly one-half of adults with hypertension had uncontrolled high blood pressure in 2009–2012.

Hypertension increases the risk for cardiovascular disease, including heart attack and stroke (6). Between 1988–1994 and 2009–2012, the prevalence of uncontrolled high blood pressure (defined as an average systolic blood pressure of 140 mm Hg or higher, or an average diastolic pressure of 90 mm Hg or higher, among those with hypertension) declined for all age groups of men and women. However, nearly one-half (47.4%) of adults aged 20 and over with hypertension continued to have uncontrolled high blood pressure in 2009–2012 (Table 65).

 

Health Risk Factors: Obesity Among Children

Figure 10. Obesity among children and adolescents, by age: United States, 2003–2004 through 2011–2012

Figure 10 is a line graph showing trends in obesity among children and adolescents, by age group, for 2003 to 2004 through 2011 to 2012.


SOURCE: CDC/NCHS, Health, United States, 2013, Table 65. Data from the National Health and Nutrition Examination Survey (NHANES).

Between 2003–2004 and 2011–2012, the prevalence of obesity among children aged 2–5 decreased, while the prevalence of obesity among older children and adolescents remained stable.

Excess body weight in children is associated with excess morbidity in childhood and adulthood (7,8). Obesity among children is defined as a body mass index at or above the sex-and age-specific 95th percentile of the CDC growth charts. The percentage of children aged 2–5 who were obese decreased from 14.0% in 2003–2004 to 8.4% in 2011–2012. The prevalence of obesity among children aged 6–11 and adolescents aged 12–19 was stable between 2003–2004 and 2011–2012. In 2011–2012, 17.7% of children aged 6–11 and 20.5% of adolescents aged 12–19 were obese.

 

Health Risk Factors: Overweight and Obesity Among Adults

Figure 11. Overweight and obesity among adults aged 20 and over, by sex: United States, 1988–1994 through 2009–2012

Figure 11 consists of two area graphs, one for men and one for women, showing overweight but not obese, and grade 1, grade 2, and grade 3 obesity, among adults aged 20 and over, for four time periods: 1988 to 1994, 1999 to 2002, 2003 to 2006 and 2009-2012.

Source: CDC/NCHS, Health, United States, 2013, Table 69. Data from the National Health and Nutrition Examination Survey (NHANES).

In 2009–2012, the percentage of adults aged 20 and over with Grade 1 obesity was higher for men than women, and the percentage with Grade 2 or Grade 3 obesity was higher for women than men.

Reducing the prevalence of obesity is a public health priority because obesity is correlated with excess morbidity and mortality (9–12). In particular, Grade 2 or higher obesity [a body mass index (BMI) of 35 or higher] significantly increases the risk of death (13). Between 1988–1994 and 2009–2012, the percentage of men and women aged 20 and over who were overweight but not obese (BMI greater than or equal to 25 but less than 30) was stable. During this period, the percentage of adults aged 20 and over with Grade 1 obesity (BMI greater than or equal to 30 but less than 35), Grade 2 obesity (BMI greater than or equal to 35 but less than 40), and Grade 3 obesity (BMI of 40 or higher) increased among both men and women. In 2009–2012, 4.4% of men and 8.2% of women aged 20 and over had Grade 3 obesity.

 

Prevention: Influenza and Pneumococcal Vaccination

Figure 12. Influenza and pneumococcal vaccination among noninstitutionalized adults aged 18 and over, by type of vaccination and age: United States, 2002–2012

Figure 12 consists of two line graphs, one showing influenza vaccination in the past 12 months and one showing pneumococcal vaccination ever, among adults aged 18 and over, by age group, for 2002 through 2012.

NOTE: See Table 82 for a definition of the high-risk category for pneumococcal vaccination.
SOURCE: CDC/NCHS, Health, United States, 2013, Tables 81 and 82. Data from the National Health Interview Survey (NHIS).

During 2002 through 2012, influenza vaccination in the past 12 months increased among adults under age 65, while remaining stable among those aged 65 and over. The percentage of adults aged 65 and over who had ever received a pneumococcal vaccination increased during this period.

Vaccination of persons at risk for complications from influenza and invasive pneumococcal disease is an important public health strategy (14). During 2002 through 2012, influenza vaccination in the past 12 months for noninstitutionalized adults increased among those aged 18–49 and 50–64 but was stable among those aged 65 and over. Decreases in influenza vaccination coverage in 2005 were related to a vaccine shortage (15). During 2002 through 2012, the percentage of noninstitutionalized adults who had ever received pneumococcal vaccination was stable among high-risk persons aged 18–64, and increased among those aged 65–74 and 75 and over.

 

Prevention: Vaccination Coverage Among Adolescents Aged 13–17

Figure 13. Vaccination coverage among adolescents aged 13–17, by type of vaccine: United States, 2012

Figure 13 consists of a bar chart showing vaccination coverage among adolescents aged 13 to 17 years, by type of vaccine for 2012.

1The HPV vaccination series was recommended for males in October 2011.
SOURCE: CDC/NCHS, Health, United States, 2013, Table 80. Data from the National Center for Immunization and Respiratory Diseases, National Immunization Survey–Teen.

Vaccination coverage for adolescents aged 13–17 varied by type of vaccine.

Early adolescence (ages 11–12) is the recommended time for adolescents to catch up on missed childhood vaccinations and to receive three vaccines specifically recommended for them—Tetanus and diphtheria toxoids (Tdap), Meningococcal conjugate (MenACWY), and Human papillomavirus (HPV) (16,17). In 2012, among adolescents aged 13–17 who had time to obtain the recommended vaccinations, 84.6% had received Tdap vaccine and 74.0% had received MenACWY vaccine (17). The HPV vaccination series was recommended for females starting in June 2006 and for males in October 2011 (18,19). In 2012, 33.4% of females aged 13–17 had received three or more doses of HPV vaccine. Among males, 6.8% of those aged 13–17 had completed the HPV series in 2012—the first year following the recommendation for males.

 

Health Insurance: Coverage Among Adults Aged 18–64

Figure 14. Health insurance coverage among adults aged 18–64, by age and type of coverage: United States, 2002–2012

Figure 14 consists of two line graphs, one for adults aged 18 to 44 and one for adults aged 45 to 64, showing health insurance coverage by type of coverage, for 2002 through 2012.

NOTES: The Medicaid category includes the Children's Health Insurance Program (CHIP). Adults categorized as having Medicaid or private coverage may have additional types of health insurance coverage.
SOURCE: CDC/NCHS, Health, United States, 2013, Tables 122, 124, and 125. Data from the National Health Interview Survey (NHIS).

During 2002 through 2012, the percentage of adults aged 18–44 and 45–64 with private health insurance coverage decreased, while the percentage with Medicaid and the percentage uninsured increased.

Health insurance is a major determinant of access to health care (20). Among adults aged 18–44, the percentage with private coverage declined from 68.7% in 2002 to 61.4% in 2012, while the percentage with Medicaid coverage increased from 7.1% to 11.6%. The percentage of adults aged 18–44 who were uninsured increased from 23.0% to 24.8% during this period (also see Figure 15). Similarly, the percentage of adults aged 45–64 with private coverage declined from 77.3% in 2002 to 70.0% in 2012. The percentage of adults aged 45–64 with Medicaid coverage increased from 5.3% to 8.0%, and the percentage uninsured increased from 13.1% to 15.6%.

 

Health Insurance: Coverage Among Adults Aged 19–25

Figure 15. Health insurance coverage among adults aged 19–25, by type of coverage: United States, 2002–2012

Figure 15 is a line graph showing trends in health insurance coverage among adults aged 19 to 25, by type of coverage, for 2002 through 2012.

NOTE: Adults categorized as having Medicaid or private coverage may have additional types of health insurance coverage.
SOURCE: CDC/NCHS, Health, United States, 2013, Tables 122, 124, and 125. Data from the National Health Interview Survey (NHIS).

Between 2010 and 2012, the percentage of adults aged 19–25 who were uninsured decreased from 33.8% to 26.3%.

Historically, adults aged 19–25 have experienced high levels of uninsurance (Table 125).The percentage of adults aged 19–25 with private coverage declined from 59.3% in 2002 to 51.8% in 2010 and then rose to 58.1% in 2012. Between 2002 and 2010, the percentage of adults aged 19–25 who were uninsured fluctuated between 30.5% and 33.8%, and then decreased from 33.8% in 2010 to 26.3% in 2012. The section of the Patient Protection and Affordable Care Act (ACA) that allows most young adults to remain on their parent's coverage until age 26 came into effect with the policy year that began after September 23, 2010 (21–23). The percentage of adults aged 19–25 with Medicaid coverage [a category that includes the Children's Health Insurance Program (CHIP)] increased from 9.0% in 2002 to 12.6% in 2010 and was 13.4% in 2012.

 

Utilization and Access: Emergency Department Use

Figure 16. One or more emergency department visits in the past 12 months, by age and type of coverage: United States, 2002–2012

Figure 16 consists of two line graphs, one for children under age 18 and one for adults aged 18 to 64, showing one or more emergency department visits in the past 12 months, by type of coverage, for 2002 to 2012.

SOURCE: CDC/NCHS, Health, United States, 2013, Tables 86 and 87. Data from the National Health Interview Survey (NHIS).

Children and adults aged 18–64 with Medicaid coverage were more likely to have at least one emergency department visit in the past year, compared with the uninsured and those with private coverage.

During 2002 through 2012, the percentage of children under age 18 with at least one emergency department visit in the past year declined for those with private coverage and for children with Medicaid coverage, while remaining stable for uninsured children. In 2012, 24.8% of children with Medicaid, 15.6% of uninsured children, and 13.0% of children with private coverage had an emergency department visit in the past year. During 2002 through 2012, the percentage of adults aged 18–64 with at least one emergency department visit was stable for those with Medicaid and for the uninsured. For adults with private coverage, the percentage with an emergency department visit declined during 2002 through 2012.

 

Utilization and Access: Usual Source of Care Among Children

Figure 17. No usual source of care among children under age 18, by type of coverage: United States, average annual, 2001–2002 through 2011–2012

Figure 17 is a line graph showing no usual source of care among children under age 18, by health insurance status, for 2001 to 2002 (average annual) through 2011 to 2012 (average annual).

NOTE: Persons who reported the emergency department as their usual source of care were classified as not having a usual source of care.
SOURCE: CDC/NCHS, Health, United States, 2013, Table 72. Data from the National Health Interview Survey (NHIS).

Uninsured children under age 18 were more likely than those with Medicaid and private coverage to lack a usual source of care.

Children benefit from having a usual source of health care for the provision of preventive services and treatment of acute and chronic conditions (24). During 2001–2002 through 2011–2012, the percentage of children without a usual source of care was stable for uninsured children and for those with private coverage, and decreased for those with Medicaid coverage. Throughout this period, uninsured children were more likely to lack a usual source of care than those with Medicaid or private coverage. In 2011–2012, 28.4% of uninsured children, 3.1% of children with Medicaid coverage, and 1.7% of those with private coverage lacked a usual source of care.

 

Utilization and Access: Delay or Nonreceipt of Medical Care or Nonreceipt of Dental Care Due to Cost

Figure 18. Delay or nonreceipt of needed medical care or nonreceipt of needed dental care in the past 12 months due to cost among adults aged 18–64, by percent of poverty level: United States, 2002–2012

Figure 18 consists of two line graphs for adults aged 18 to 64, one showing delay or nonreceipt of needed medical care due to cost and one showing nonreceipt of needed dental care due to cost, during the past 12 months, by percent of poverty level, for 2002 through 2012.

SOURCE: CDC/NCHS, Health, United States, 2013, Table 74. Data from the National Health Interview Survey (NHIS).

During 2002 through 2012, the percentage of adults aged 18–64 who delayed or did not receive needed medical care in the past 12 months due to cost increased for those living below 400% of the poverty level; the percentage of adults who did not receive needed dental care due to cost increased for all family income groups.

During 2002–2012, the percentage of adults aged 18–64 who delayed or did not receive medical care in the past 12 months due to cost was higher for adults living below 200% of the poverty level than for those with higher family income (22.4% and 22.3%, compared with 13.2% for those at 200%–399% and 5.3% for those at 400% or more of the poverty level in 2012). Also during 2002 through 2012, nonreceipt of dental care due to cost was higher for adults living below 200% of the poverty level than for those with higher family income (26.9% and 25.3%, compared with 13.8% for those at 200%–399% and 5.5% for those at 400% or more of the poverty level in 2012).

 

Personal Health Care Expenditures: Major Source of Funds

Figure 19. Personal health care expenditures, by source of funds: United States, 2001–2011

Figure 19 is a line graph showing trends in personal health care expenditures, by source of funds, for 2001 through 2011.

NOTE: Average annual percent change computed from estimates shown in Table 115.
SOURCE: CDC/NCHS, Health, United States, 2013, Table 115. Data from the Centers for Medicare & Medicaid Services, National Health Expenditure Accounts (NHEA).

Out-of-pocket spending for personal health care expenditures grew less rapidly than Medicare, federal and state Medicaid, and private insurance spending between 2001 and 2011.

Between 2001 and 2011, total personal health care expenditures grew from $1.3 trillion to $2.3 trillion (Table 115). During this period, the average annual growth in Medicare expenditures was 8.1%; for Medicaid (federal) it was 6.4%, for Medicaid (state) it was 5.5%, for private health insurance 5.8%, and for out-of-pocket spending 3.9%. In 2011, private health insurance spending for personal health care expenditures was $786.1 billion; Medicare spending was $521.6 billion, out-of-pocket spending was $307.7 billion, Medicaid (federal) spending was $229.0 billion, and Medicaid (state) spending was $145.5 billion (Table 115).

 

Chartbook: Figures 20-29, Special Feature on Prescription Drugs

 

Introduction

Prescription drugs play an important role in U.S. health care. For millions of Americans, prescription drugs have saved lives, prevented or delayed the onset of chronic disease and disability, controlled or cured disease, and provided relief from pain (25). In 2007–2010, almost one-half of the U.S. population took at least one prescription drug in the preceding month and 1 in 10 reported taking five or more drugs (Table 93; data are for the civilian noninstitutionalized U.S. population only). Americans' use of prescription drugs has grown over the past half-century due to many factors, including the development of new and innovative drug therapies to treat infectious and chronic conditions, the expansion of prescription drug coverage by public and private payers, and the growth of marketing by pharmaceutical companies (25–27).

The introduction and widespread use of vaccines in the 20th century contributed to the control of infectious diseases such as measles, polio, and diphtheria, and the discovery of antibiotics led to significant declines in mortality from bacterial infections (28). By 2010, only pneumonia and influenza remained among the leading causes of death, accounting for just 2.0% of all U.S. deaths (Table 22). Newer drugs also help in the control of infectious disease. With the adoption of antiretroviral therapies, the death rate from human immunodeficiency virus (HIV) disease has decreased almost 80% since 1996 (Table 31 and Figure 24).

With the decline of infectious disease morbidity and mortality in the United States, prescription drug development and investment in the second half of the 20th century focused on chronic diseases such as cancer, heart disease, diabetes, and mental health. Drugs to treat these chronic conditions were among the most commonly used by adults (Figure 21) (29). The widespread use of chemotherapy and other biologics contributed to raising the 5-year, all-sites cancer survival rates to 67% in 2009 (30). Drug research has also led to better treatment and control of the risk factors for heart disease, such as hypertension, high cholesterol, and diabetes (6,31–34). The percentage of Americans with poor control of blood pressure, cholesterol, and diabetes is down since 1988–1994 (Tables 46, 65, and 66). Prescription drugs are an important component in the treatment of mental health disorders and have helped many patients avoid hospitalization (35,36). About 85% of people who received treatment for mental health conditions in 2009 received prescription drugs.

Several other factors have contributed to Americans' greater use of prescription drugs. These include the growth of third-party insurance coverage over the past few decades, which has made drugs more affordable (37,38). In 2006, Medicare Part D was introduced, offering a drug benefit as part of the insurance program relied on by most persons aged 65 and over. Discounts and other savings under the Affordable Care Act have already helped more than 6 million Medicare Part D enrollees save over $6 billion on prescription drugs since its introduction in 2010 (39). Another factor increasing the demand for drugs is more drug marketing to physicians and consumers since companies began promoting their prescription drug products directly to consumers by means of direct-to-consumer advertising in the 1980s. Although the vast majority of promotional spending for all drugs is targeted toward physicians, spending on direct-to-consumer advertising for all drugs more than tripled between 1996 and 2005, to $4.2 billion (40–42).

The greater role of prescription drugs in U.S. health care is reflected in the amount spent on drugs: $263 billion in 2011. This was 9.7% of all national health expenditures, up from 5.6% in 1990 (Table 114). From 1990 to 2000, prescription drug spending grew 11.6%—much faster than spending for hospital (5.2%) and physician and clinic (6.2%) care (Table 114). In response, many insurers instituted cost control efforts, including copays, cost-sharing, formularies, tiered pricing, and mail order pharmacies (43). These efforts, along with other factors including the ending of patent protection for a number of popular drugs, has led to slower growth in prescription drug spending in recent years (44). During 2009 through 2011, spending on prescription drugs remained flat (Table 114).

Although prescription drugs have been instrumental in improving health outcomes, misuse of some prescription drugs has resulted in serious public health problems. For example, antibiotics continue to be prescribed to treat viral infections, even though they are ineffective for this purpose. This misuse contributes to the development of antibiotic-resistant bacterial infections (45,46). Educational outreach to physicians and patients has helped decrease the use of antibiotics for colds and other viral conditions (Figure 26). Opioid analgesic pain relievers play an important role in appropriate pain management, but their misuse is a growing public health problem (47). Opioid analgesic consumption increased 300% between 1999 and 2010 (48), and death rates for poisoning involving opioid analgesics more than tripled between 2000 and 2010 (Table 32 and Figure 28) (49,50).

This Special Feature examines the use of prescription drugs in the United States. Data are presented on the number and classes of drugs used by Americans. Access problems—those who did not get prescription drugs in the past 12 months due to cost—are presented by insurance and poverty status. The impact of specific groups of drugs used to control chronic disease (i.e., antiretrovirals to treat HIV disease and antidepressant drugs) is presented. Quality issues are examined by looking at the misuse of antibiotics to treat cold symptoms; deaths from misuse of opioid analgesic drugs; and the adoption of electronic health record systems by providers, which may be used for ordering prescription drugs, providing warnings of drug interactions or contraindications, and other functions intended to improve safety. And finally, the growth in national spending on prescription drugs is shown. This group of charts provides an overview of the role of prescriptions drugs in the United States.

 

Prescription Drug Use

In 2007–2010, almost one-half of all Americans reported taking one or more prescription drugs in the past 30 days; use increased with age, from 1 in 4 children to 9 in 10 persons aged 65 and over.

Drugs are a frequently used therapy for reducing morbidity and mortality and improving the quality of life of Americans (29,51). In the past half-century, Americans' use of prescription drugs has increased (26,27). Prescription drug use is related to many factors, including health status, prescription drug coverage, and the availability of drug therapies. For many conditions, such as high cholesterol, high blood pressure, diabetes, and asthma, emphasis on treatment with evidence-based medications has increased (6,32–34,37,38).

Between 1988–1994 and 2007–2010, the percentage of Americans who reported taking no prescription drugs in the past 30 days decreased from 60.9% to 52.5%. The percentage taking five or more drugs in the past month increased from 4.0% in 1988–1994 to 10.1% in 2007–2010 (age-adjusted).

In 2007–2010, prescription drug use was higher among older age groups. About one-quarter of children (23.2%) reported taking one to four drugs in the past 30 days, compared with one-half (49.4%–50.0%) of adults aged 45 and over. The percentage taking five or more drugs in the past 30 days increased with age, from less than 1.0% of children to 39.7% of adults aged 65 and over.

Figure 20. Prescription drug use in the past 30 days, by number of drugs taken and age: United States, 1988–1994 through 2007–2010

Figure 20 consists of a line graph for all ages showing prescription drug use in the past 30 days, by number of drugs, for four time periods: 1988 to 1994, 1999-2002, 2003-2006, and 2007 to 2010, and a bar chart showing prescription drug use in the past 30 days, by age group and number of drugs, for 2007 to 2010.

NOTES: Except for age group estimates, percentages are age-adjusted. See data table for Figure 20.
SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Prescription Drug Use by Drug Class

In 2007–2010, cardiovascular agents (used to treat high blood pressure, heart disease, or kidney disease) and cholesterol-lowering (antihyperlipidemic) drugs were two of the most commonly used classes of prescription drugs among adults aged 18–64 and 65 and over.

Drugs increasingly play a role in the long-term treatment and control of chronic conditions, including hypertension, high cholesterol, and diabetes, which are major risk factors for heart disease (6,31–34). In 2007–2010, 17.7% of adults aged 18–64 took at least one cardiovascular agent in the past 30 days (29). Other commonly used prescription drug classes among this age group were cholesterol-lowering drugs, analgesics, and antidepressants. The use of cholesterol-lowering drugs among those aged 18–64 has increased more than six-fold since 1988–1994, due in part to the introduction and acceptance of statin drugs to lower cholesterol.

Among adults aged 65 and over, 70.2% took at least one cardiovascular agent and 46.7% took a cholesterol-lowering drug in the past 30 days in 2007–2010. Other commonly used classes for this age group include anti-acid reflux, antidiabetics, anticoagulants, and analgesics. The use of cholesterol-lowering drugs by this age group has increased more than seven-fold since 1988–1994. The use of antidepressants (4.6 times greater), anticoagulants (3.0 times greater), and anti-acid reflux drugs (2.9 times greater) also increased substantially between 1988–1994 and 2007–2010.

Figure 21. Prescription drug use in the past 30 days among adults aged 18 and over, by age and selected drug class: United States, 1988–1994 and 2007–2010

Figure 21 consists of two bar charts, one for adults aged 18 to 64 and one for adults aged 65 years and over showing prescription drug use in the past 30 days, by selected drug class, for 1988 to 1994 and 2007 to 2010.

NOTES: Cardiovascular agents include drug classes such as angiotensin-converting enzyme (ACE) inhibitors, beta blockers, calcium channel blockers, and diuretics. See data table for Figure 21 for definitions of drug classes included in this chart. Also, data for children under age 18 are shown in the data table.
SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Polypharmacy

In 2007–2010, adults taking five or more drugs in the past 30 days were more likely to be aged 65 and over and in fair or poor health than those taking one to four drugs.

Drugs offer the opportunity to prevent, treat, and control many acute and chronic conditions. As Americans rely more on prescription drugs, some are taking multiple drugs each month. This is known as polypharmacy, which may increase the likelihood of drug interactions, adverse effects, and dosing and compliance issues. In some cases, multiple physicians may be prescribing for the patient and be unaware of all drugs the patient is taking. Polypharmacy is important because patients taking multiple drugs are more likely to confuse medication, dose, and timing (52,53). Polypharmacy is of particular concern for the elderly, who may be more at risk for significant side effects with some commonly prescribed medicines (54). In 2007–2010, 13.9% of Americans aged 18 and over took five or more prescription drugs in the past 30 days (Figure 20) (55).

In 2007–2010, adults taking five or more drugs in the past 30 days were older, with 10.8% aged 18–44, 41.7% aged 45–64, and 47.5% aged 65 and over. Among those taking one to four drugs, 40.7% were aged 18–44, 39.8% were 45–64, and 19.5% were 65 and over. Adults taking five or more drugs were more likely to classify themselves as in fair or poor health (39.6%) compared with those taking one to four drugs (14.3%). Adults taking no drugs in the past 30 days were younger (69.2% were aged 18–44, 26.9% were 45–64, and 3.9% were 65 and over) and reported better health status (12.5% reported fair or poor health and 49.7% reported excellent or very good health) (see data table for Figure 22).

Figure 22. Number of prescription drugs taken in the past 30 days among adults aged 18 and over, by selected characteristics: United States, 2007–2010

Figure 22 consists of two bar charts, one for taking 1 to 4 prescription drugs in the past 30 days and one for taking 5 or more prescription drugs in the past 30 days, showing selected characteristics of adults aged 18 and over, for 2007 to 2010.

*Estimate is considered unreliable. Data preceded by an asterisk have a relative standard error of 20%–30%.
NOTES: Race and Hispanic origin estimates do not sum to 100% because of respondents in other racial and ethnic groups. See data table for Figure 22.
SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Nonreceipt of Needed Prescription Drugs Due to Cost

In 2012, adults aged 18–64 who were uninsured for all or part of the past year were more than four times as likely to report not getting needed prescription drugs due to cost as adults who were insured for the whole year.

Uninsured adults are more likely to delay or forego needed care, are less likely to receive needed medical care and prescription drugs due to cost, and are less likely to seek preventive care than the insured (20,56). Evidence suggests that underuse of medications due to cost concerns is associated with poorer health and increased use of other health care services (57,58).

During 2002 through 2012, the percentage of adults aged 18–64 who did not get prescription drugs in the past 12 months due to cost was at least four times as high for those who were uninsured for all or part of the past year as for those who were insured for the whole year. In 2012, 22.4% of uninsured adults aged 18–64 reported not getting needed prescription drugs due to cost, compared with 5.0% of adults who were insured for the whole year.

In 2011–2012, prescription drug access problems due to cost decreased as family income increased for both the insured and uninsured. Among adults insured for the whole year, those with family income levels below 200% of the poverty level were more likely to report problems getting needed prescription drugs due to cost than those with higher incomes. Among those uninsured for any part of the past year, access problems due to cost declined as family income increased. Of those living below the poverty level, 30.7% reported not getting needed prescription drugs due to cost, compared with 23.2% of those at 100%–199% of the poverty level, 19.4% of those at 200%–399% of the poverty level, and 14.9% of uninsured adults with incomes at 400% or more of the poverty level. The uninsured with high incomes (400% or more of the poverty level) were more likely to report prescription drug access problems (14.9%) than the insured with low incomes (below 200% of the poverty level) (10.8%–11.5%).

Figure 23. Nonreceipt of needed prescription drugs in the past 12 months due to cost among adults aged 18–64, by insurance status and percent of poverty level: United States, 2002–2012

Figure 23 consists of a line graph showing nonreceipt of needed prescription drugs due to cost among adults aged 18 to 64, by insurance status for the past 12 months, for 2002 through 2012, and a bar chart showing nonreceipt of needed prescription drugs due to cost among adults aged 18 to 64, by insurance status for the past 12 months and by percent of poverty level, for 2011 to 2012.

NOTE: See data table for Figure 23.
SOURCE: CDC/NCHS, National Health Interview Survey. See Appendix I, National Health Interview Survey (NHIS).

 

Deaths from HIV Disease

The introduction of highly active antiretroviral therapy (HAART) led to substantial declines in mortality from HIV disease, including a 73% decline among non-Hispanic white males and a 54% decline among non-Hispanic black males between 1995 and 1997.

Human immunodeficiency virus (HIV) disease, and the related acquired immunodeficiency syndrome (AIDS), emerged as a leading cause of death among adults aged 25–44 in the United States in the 1980s (59), and the death rate for HIV disease among this age group increased steadily through the early 1990s (60). During the early years of HIV, there were few treatment options and mortality was high (61,62). The first antiretroviral medication to treat HIV disease was approved in 1987 (62) and was soon followed by the introduction of other antiretroviral drugs. The health of individuals living with HIV improved when clinicians began to treat individuals with combinations of multiple antiretroviral drugs that act at different stages of the HIV disease cycle (63)—regimens known as HAART.

After HAART became the standard of care in 1996, there were marked reductions in morbidity and mortality associated with HIV disease (63–67). Between 1995 and 1997, the death rate from HIV disease among males declined by two-thirds, from 27.3 deaths per 100,000 population in 1995 to 9.6 in 1997. The decline ranged from 54% for non-Hispanic black males, to 66% for Hispanic males, to 73% for non-Hispanic white males and Asian or Pacific Islander males (see data table for Figure 24). Declines in HIV death rates also were seen for females in each of the racial and ethnic groups examined. After 1997, the rate of decline for HIV mortality slowed across all groups, although gender and racial and ethnic differences in HIV mortality persist.

Figure 24. Age-adjusted death rates for human immunodeficiency virus (HIV) disease for all ages, by sex and race and Hispanic origin: United States, 1990–2010

Figure 24 is a line graph showing death rates for human immunodeficiency virus (HIV) disease for all ages, by sex and race and Hispanic origin, for 1990 to 2010.

NOTES: HAART is highly active antiretroviral therapy. See data table for Figure 24 for rates for additional racial groups.
SOURCE: CDC/NCHS, National Vital Statistics System. See Appendix I, National Vital Statistics System (NVSS).

 

Use of Antidepressants

Between 1988–1994 and 2007–2010, among adults aged 18 and over, the use of antidepressants increased more than four-fold, from 2.4% to 10.8%.

Depression is a common and serious illness that takes a toll on functional status, productivity, quality of life, and physical health (35,68–70). In 2009, 7% of adults had a major depressive episode in the past year (35). The increased use of prescription antidepressants may be the result of several factors, including the introduction of a new class of drugs known as selective serotonin reuptake inhibitors (SSRIs) in 1988, improved public attitudes about seeking care for mental health issues, increased direct-to-consumer marketing of antidepressants, and expanded recommendations for the use of antidepressants for conditions other than depression (71). In addition to depression, antidepressants are used to treat obsessive-compulsive disorder, panic disorder, anxiety disorders, and perimenopausal and menopausal symptoms (71).

The use of antidepressants increased more than four-fold for men (from 1.6% to 6.6%, age-adjusted) and women (from 3.2% to 14.8%, age-adjusted) between 1988–1994 and 2007–2010. Increased use of antidepressants during this time period was seen for each of the age groups examined: 18–44, 45–64, and 65 and over.

In 2007–2010, the use of prescription antidepressants was higher among women than among men overall, and for each age group. For both men and women, antidepressant use was higher for those aged 45 and over compared with younger adults. Among men, adults aged 45 and over (8.9%–9.4%) were twice as likely to take antidepressants as younger adults aged 18–44 (4.4%). Among women, those aged 45 and over (17.0%–19.6%) were about 1.5 times more likely to take antidepressants than younger adults aged 18–44 (11.3%).

Figure 25. Use of prescription antidepressants in the past 30 days among adults aged 18 and over, by sex and age: United States, 1988–1994 through 2007–2010

Figure 25 consists of two lines graphs, one for men and one for women, showing use of prescription antidepressants in the past 30 days among adults aged 18 and over, by age group, for four time periods: 1988 to 1994, 1999 to 2002, 2003 to 2006, and 2007 to 2010.

NOTE: The 1988–1994 estimates for men are considered unreliable because the estimates have relative standard errors of 20%–30%. See data table for Figure 25.
SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Antibiotics Prescribed for Colds

Between 1995–1996 and 2009–2010, the prescribing of antibiotics during ambulatory care visits for cold symptoms declined 39%.

Antibiotics are a mainstay of treating bacterial infections, and the control of infectious diseases using antibiotics is considered one of the major public health achievements of the 20th century (28,46). But unnecessary antibiotic use can lead to adverse effects and contributes to antibiotic resistance, which may lead to longer hospital stays and unnecessary deaths (45,46,72). Of particular concern is the prescribing of antibiotics for colds and viral respiratory infections, because antibiotics are ineffective in treating these conditions (72).

Between 1995–1996 and 2009–2010, the prescribing of antibiotics during ambulatory care visits (to physician offices and hospital outpatient and emergency departments) for the sole diagnosis of cold symptoms has declined by two-fifths, from 44.7% of cold symptom visits to 27.1%. Significant declines were seen for both children and adults over this time frame.

Throughout the time period, prescribing of antibiotics for ambulatory care visits for the sole diagnosis of cold symptoms was higher for visits by adults aged 18 and over than for children. In 2009–2010, 21.5% of ambulatory care visits for cold symptoms among children aged 18 and under had antibiotics prescribed or ordered, compared with 39.5% for adults.

Figure 26. Antibiotics ordered or provided during emergency department, outpatient, and physician visits for cold symptom diagnoses, by age: United States, average annual, 1995–1996 through 2009–2010

Figure 26 is a line graph showing antibiotics ordered or provided during emergency department, outpatient, and physician visits for cold symptom diagnoses, by age, for 1995 to 1996 through 2009 to 2010.

NOTE: See data table for Figure 26.
SOURCE: CDC/NCHS, National Ambulatory Medical Care Survey, National Hospital Ambulatory Medical Care Survey: Emergency Department and Outpatient Components. See Appendix I, National Ambulatory Medical Care Survey (NAMCS); National Hospital Ambulatory Medical Care Survey (NHAMCS).

 

Computerized Systems for Prescription Drugs

In 2010, 53.7% of physician offices, 50.3% of hospital outpatient departments (OPDs), 58.1% of hospital emergency departments (EDs), and 19.7% of residential care facilities (RCFs) reported having computerized prescription ordering systems, a key element of electronic health records (EHRs).

EHRs and e-prescription software are thought to improve caregivers' decisions, coordination of care, health care safety, and patients' outcomes, and to make health care delivery systems more efficient (73,74).

To promote health care providers' adoption of EHRs, the Health Information Technology for Economic and Clinical Health Act (HITECH) authorized incentive payments through Medicare and Medicaid to providers who implement EHRs with specific elements that are thought to improve processes and outcomes (73,75). Several of these elements relate to prescription drugs, and in 2010, physician offices, OPDs, EDs, and RCFs were surveyed about their EHR systems, including questions about specific elements related to prescription drugs.

In 2010, about one-half of physician offices and OPDs, 58.1% of EDs, and one-fifth of RCFs reported having a computerized system for ordering prescription drugs. Almost one-half of physician offices (46.0%) and EDs (45.9%), 38.7% of OPDs, and 17.9% of RCFs had a computerized system for warning of drug interactions or contraindications. Almost one-half of physician offices (44.6%), 30.3% of OPDs, 19.7% of EDs, and 8.2% of RCFs could submit prescriptions to the pharmacy electronically. More than one-half of physician offices (51.0%), 56.4% of OPDs, 66.0% of EDs, and 28.3% of RCFs had a computerized system of clinical notes, including a list of patient medications and allergies. In 2010, 33.3% of physician offices, 24.3% of OPDs, 15.7% of EDs, and 3.3% of RCFs had all four of these elements of EHRs.

Figure 27. Computerized systems for prescription drugs, by provider and system type: United States, 2010

Figure 27 is a bar chart showing computerized systems for prescription drugs, by provider and system type, for 2010.

NOTE: See data table for Figure 27.
SOURCE: CDC/NCHS, National Ambulatory Medical Care Survey, National Hospital Ambulatory Medical Care Survey: Emergency Department and Outpatient Components, and National Survey of Residential Care Facilities. See Appendix I, National Ambulatory Medical Care Survey (NAMCS); National Hospital Ambulatory Medical Care Survey (NHAMCS); and National Survey of Residential Care Facilities (NSRCF).

 

Deaths Involving Opioid Analgesics

Drug poisoning deaths involving opioid analgesics among those aged 15 and over more than tripled in the past decade, from 1.9 deaths per 100,000 population in 1999–2000 to 6.6 in 2009–2010 (age-adjusted).

Opioid analgesics are prescription pain relievers, such as oxycodone and hydrocodone, and they play an important role in the appropriate management of both acute and chronic pain, which are often difficult to treat (76). Opioid analgesic consumption increased 300% between 1999 and 2010 (48). Misuse of opioid analgesics is increasingly seen as a significant public health concern because poisoning death rates involving opioid analgesics more than tripled between 2000 and 2010 (Table 32) (47,49,50).

Between 1999–2000 and 2009–2010, among those aged 15 and over, the age-adjusted death rate for poisoning involving opioid analgesics increased from 1.9 deaths per 100,000 population to 6.6. Poisoning death rates involving opioid analgesics in the past decade increased for both males and females, for all age groups aged 15 and over, and for all racial and Hispanic origin groups examined.

In 2009–2010, among racial and Hispanic origin groups, the death rate for non-Hispanic white persons aged 15 and over was highest, at 8.9 deaths per 100,000, followed by the rate among the American Indian and Alaska Native population (7.1). Opioid analgesic poisoning death rates among the non-Hispanic black (2.7) and Hispanic populations (2.4) were about one-third that of the non-Hispanic white population. Among those aged 15 and over, the death rate involving opioid analgesics for males was 8.1 deaths per 100,000, compared with 5.1 for females. Those aged 35–54 had the highest death rate for opioid analgesics (9.9), followed by those aged 25–34 (8.1) and 55–64 (6.0).

Figure 28. Drug poisoning deaths involving opioid analgesics among persons aged 15 and over, by race and Hispanic origin, sex, and age: United States, 1999–2000 through 2009–2010

Figure 28 shows three line graphs, one for race, one for sex, and one for age group, showing drug poisoning deaths involving opioid analgesics among persons aged 15 and over for 1999 to 2000 through 2009 to 2010.

NOTES: Rates are age-adjusted, except for age group data. Drug poisoning deaths with the drug type unspecified (up to 25% of the total) are not included. See data table for Figure 28.
SOURCE: CDC/NCHS, National Vital Statistics System. See Appendix I, National Vital Statistics System (NVSS).

 

Spending on Prescription Drugs

The annual growth in spending on retail prescription drugs slowed from 14.7% in 2001 to 2.9% in 2011.

In 2011, spending on prescription drugs was $263 billion. Although hospital care (31.5%) and physician and clinical services (20.0%) accounted for greater shares of national health expenditures, the share for prescription drugs increased from 4.7% in 1980 to 9.7% of all national health spending in 2011 (Table 114).

The growth in spending on prescription drugs was in the double digits from the mid-1990s through the mid-2000s, when it fell below 10% (Table 115). Between 2001 and 2011, the annual percent change in spending on retail prescription drugs slowed from 14.7% to 2.9%. This recent slowdown is the result of a variety of factors, including cost control efforts introduced by insurers, such as copays, formularies, tiered pricing, generic substitution, and the use of mail order pharmacies. In addition, several popular drugs ended their patent protection during this time frame (43,44).

Spending on prescription drugs is projected to grow slowly through 2012 and 2013 (77–79). Starting in 2014, spending is expected to pick up due to expanded insurance coverage as a result of the Affordable Care Act (ACA) and because fewer drugs are expected to lose patent protection in 2013 compared with 2012 (21,78).

In 2011, private health insurance, out-of-pocket spending, and Medicare paid for almost 90% of all prescription drug spending. A decade earlier, in 2001, private health insurance, out-of-pocket spending, and Medicaid paid the biggest share of all prescription drug spending. The shift in spending from Medicaid to Medicare between 2001 and 2011 is largely the result of the introduction of Medicare Part D in 2006 (39,80). Dual eligibles (people with both Medicare and Medicaid) who enrolled in a Medicare Part D plan had much of their drug spending paid for in 2011 by Medicare instead of Medicaid.

Figure 29. Retail prescription drug expenditures, annual percent change, and spending by payer: United States, 2001–2011

Figure 29 consists of a line graph showing retail prescription drug expenditures annual percent change, for 2001 through 2011 and two pie charts showing spending by payer, for 2001 and 2011.

NOTES: Medicaid includes Children's Health Insurance Program (CHIP) expenditures. See data table for Figure 29.
SOURCE: Centers for Medicare & Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Expenditure Accounts. See Appendix I, National Health Expenditure Accounts (NHEA).

 

Data Tables for Special Feature: Figures 20-29

 

Data table for Figure 20. Prescription drug use in the past 30 days, by number of drugs taken and age: United States, 1988–1994 through 2007–2010

Excel and PowerPoint

Characteristic 1988–1994 1999–2002 2003–2006 2007–2010
Percent Standard error Percent Standard error Percent Standard error Percent Standard error
Number of prescription drugs in past 30 days
Total, crude:
No drugs 62.2 0.5 55.0 1.1 52.7 0.8 51.5 0.9
1–4 drugs 34.2 0.5 37.6 0.6 37.1 0.5 37.9 0.7
5 or more drugs 3.6 0.2 7.4 0.3 10.1 0.5 10.6 0.5
Total, age-adjusted:1
No drugs 60.9 0.5 54.8 0.9 53.1 0.6 52.5 0.7
1–4 drugs 35.2 0.5 37.7 0.8 36.9 0.4 37.3 0.6
5 or more drugs 4.0 0.1 7.5 0.3 10.0 0.3 10.1 0.4

 

2007–2010 Under 18 years 18–44 years 45–64 years 65 years and over
Percent Standard error Percent Standard error Percent Standard error Percent Standard error
No drugs 76.0 0.7 61.3 1.2 33.8 1.2 10.3 0.6
1–4 drugs 23.2 0.7 35.6 1.0 49.4 1.1 50.0 1.1
5 or more drugs 0.8 0.1 3.1 0.5 16.8 0.9 39.7 1.2

 

1 Estimates are age-adjusted to the year 2000 standard population using four age groups: under 18 years, 18-44 years, 45-64 years, and 65 years and over.

NOTES: Data are for the civilian noninstitutionalized population. Only prescriptions the respondent themselves took are included. Prescriptions administered in other health care settings, such as physician offices and hospital outpatient departments, are not collected. See Appendix II, Age adjustment; Drug. See related Table 92.

SOURCE: CDC/NCHS. National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Data table for Figure 21. Prescription drug use in the past 30 days, by age and selected drug class: United States, 1988–1994 and 2007–2010

Excel and PowerPoint

Age and drug class (common indications for use) 1988–1994 2007–2010
Percent Standard error Percent Standard error
Under 18 years
Antiasthmatics (asthma, allergies, breathing)1 3.2 0.4 6.8 0.4
Antibiotics (bacterial infections)2 10.1 0.6 6.1 0.5
Central nervous system stimulants (attention deficit disorder)3 *0.8 0.2 4.2 0.4
Analgesics (pain relief)4 1.2 0.2 1.3 0.2
Antidepressants (depression and related disorders)5 * * 1.3 0.2
18–64 years
Cardiovascular agents (high blood pressure, heart disease, kidney disease)6 10.0 0.4 17.7 0.7
Cholesterol-lowering drugs (high cholesterol)7 1.6 0.2 10.7 0.5
Anti-acid reflux drugs (gastric reflux, ulcers)8 3.0 0.2 9.0 0.7
Antidiabetic agents (diabetes)9 2.4 0.2 5.3 0.4
Anticoagulants (blood clot prevention)10 0.7 0.1 1.8 0.2
Analgesics (pain relief)4 8.6 0.4 10.5 0.7
Antidepressants (depression and related disorders)5 2.2 0.2 10.6 0.6
65 years and over
Cardiovascular agents (high blood pressure, heart disease, kidney disease)6 51.5 0.9 70.2 1.2
Cholesterol-lowering drugs (high cholesterol)7 5.9 0.5 46.7 1.1
Anti-acid reflux drugs (gastric reflux, ulcers)8 7.5 0.7 21.5 1.2
Antidiabetic agents (diabetes)9 9.0 0.6 18.4 0.9
Anticoagulants (blood clot prevention)10 6.1 0.5 18.1 0.7
Analgesics (pain relief)4 13.8 0.7 17.5 1.1
Antidepressants (depression and related disorders)5 3.0 0.4 13.7 0.8

*Estimates are considered unreliable. Data preceded by an asterisk have a relative standard error (RSE) of 20%-30%. Estimates not shown have an RSE greater than 30%.

1Includes one or more asthma drugs, including bronchodilators, mast cell stabilizers, inhaled corticosteroids, leukotriene modifiers, and antiasthmatic combinations (level 2, class 125, 130, 131, or 243). For a full list of drug classes included, see Technical Notes.

2Includes one or more antibiotic drugs, including penicillins, tetracyclines, cephalosporins, and macrolide derivatives (level 2, class 6, 8-18, 240, 315, or 406). For a full list of drug classes included, see Technical Notes.

3Includes one or more central nervous system stimulants (level 2, class 71).

4Includes one or more analgesic drugs (level 2, class 58).

5Includes one or more antidepressant drugs (level 2, class 249).

6Includes one or more cardiovascular agents, including drug classes such as ACE inhibitors, beta blockers, calcium channel blockers, and diuretics (level 1, class 40). For a full list of drug classes included, see Technical Notes.

7Includes one or more cholesterol-lowering (antihyperlipidemic) drugs (level 2, class 19).

8Includes one or more anti-acid reflux (proton pump inhibitors or H2 antagonists) drugs (level 2, class 94 or 272).

9Includes one or more antidiabetic drugs (level 2, class 99).

10Includes one or more anticoagulants or antiplatelet agents (level 2, class 82 or 83).

NOTES: Data are for the civilian noninstitutionalized population. Only prescriptions the respondent themselves took are included. Prescriptions administered in other health care settings, such as physician offices and hospital outpatient departments, are not collected. Drug classes are from Lexicon Plus (Cerner Multum, Denver, CO), a proprietary comprehensive database of all prescription and some nonprescription drug products available in the U.S. drug market. For more information on the drug classes in each category, see Technical Notes. See Appendix II, Drug; Multum Lexicon Plus therapeutic class. See related Table 93.

SOURCE: CDC/NCHS. National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Data table for Figure 22. Number of prescription drugs taken in the past 30 days among adults aged 18 and over, by selected characteristics: United States, 2007–2010

Excel and PowerPoint

Characteristic Number of drugs in past 30 days
Total No drugs 1–4 drugs 5 or more drugs
Percent distribution Standard error Percent distribution Standard error Percent distribution Standard error Percent distribution Standard error
Sex
Male 48.3 0.4 56.3 0.8 42.4 0.7 41.8 1.4
Female 51.7 0.4 43.7 0.8 57.6 0.7 58.2 1.4
Race and Hispanic origin
White only, not Hispanic 68.4 2.5 56.9 3.0 76.8 1.9 78.3 2.3
Black only, not Hispanic 11.5 1.0 13.8 1.2 9.4 0.9 10.7 1.4
Mexican origin 8.6 1.3 13.5 1.8 5.2 0.9 *3.9 1.1
Age
18–44 years 48.9 0.9 69.2 1.0 40.7 1.1 10.8 1.4
45–64 years 34.5 0.7 26.9 1.0 39.8 0.9 41.7 1.1
65 years and over 16.6 0.5 3.9 0.3 19.5 0.7 47.5 1.5
Health status (respondent-assessed)
Excellent or very good 44.4 1.2 49.7 1.2 46.8 1.4 20.9 1.6
Good 38.5 0.8 37.8 0.9 38.9 1.2 39.6 1.5
Fair or poor 17.1 0.7 12.5 0.7 14.3 0.9 39.6 1.3

NOTES: Data are for the civilian noninstitutionalized population. In 2007–2010, 43.4% of adults took no drugs in the past 30 days, 42.8% took 1–4 drugs, and 13.9% took 5 or more drugs. Only prescriptions the respondent themselves took are included. Prescriptions administered in other health care settings, such as physician offices and hospital outpatient departments, are not collected. Estimates include all race and Hispanic origin groups not shown separately, except for the race and Hispanic origin-specific estimates. Race and Hispanic origin estimates do not sum to 100% because of respondents in other racial and ethnic groups. See Appendix II, Drug.

SOURCE: CDC/NCHS. National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Data table for Figure 23. Nonreceipt of needed prescription drugs in the past 12 months due to cost among adults aged 18–64, by insurance status and percent of poverty level: United States, 2002–2012

Excel and PowerPoint

Insurance status and poverty level 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Insurance status in the past 12 months Percent
Total 7.6 8.1 9.2 9.4 9.3 9.6 10.7 11.2 11.2 10.5 9.4
Insured all 12 months 3.9 3.8 4.9 5.0 4.6 4.8 5.6 6.0 6.2 5.9 5.0
Uninsured all or part of past 12 months 20.9 22.7 23.6 24.1 23.9 25.5 27.3 26.7 25.8 24.1 22.4
Standard error
Total 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.2
Insured all 12 months 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Uninsured all or part of past 12 months 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 0.7 0.6 0.7

 

Insurance status in the past 12 months and percent of poverty level, 2011–2012 Percent Standard error
Insured all 12 months 5.5 0.1
Below 100% 10.8 0.6
100%–199% 11.5 0.5
200%–399% 6.2 0.3
400% or more 2.2 0.1
Uninsured all or part of past 12 months 23.2 0.5
Below 100% 30.7 0.9
100%–199% 23.2 0.8
200%–399% 19.4 0.8
400% or more 14.9 1.3

NOTES: Data are based on household interviews of a sample of the civilian noninstitutionalized population. Based on adults responding to the question, "During the past 12 months was there any time when you needed prescription medicine but did not get it because [person] couldn't afford it?" Persons not covered by private insurance, Medicaid, Children's Health Insurance Program (CHIP), public assistance (through 1996), state-sponsored or other government-sponsored health plans (starting in 1997), Medicare, or military plans are considered to have no health insurance coverage. Persons with only Indian Health Service coverage are considered to have no health insurance coverage. Percent of poverty level is based on family income and family size and composition using U.S. Census Bureau poverty thresholds. Missing family income data were imputed. See Appendix II, Family income; Health insurance coverage; Poverty; Table VI. See related Table 74.

SOURCE: CDC/NCHS. National Health Interview Survey. See Appendix I, National Health Interview Survey (NHIS).

 

Data table for Figure 24. Age-adjusted death rates for human immunodeficiency virus (HIV) disease for all ages, by sex and race and Hispanic origin: United States, 1990–2010

Year Male White, not Hispanic male Black, not Hispanic male Hispanic male American Indian or Alaska Native male Asian or Pacific Islander male
Deaths per 100,000 population
1990 18.5 14.1 47.1 28.8 3.3 4.3
1991 21.0 15.5 55.6 31.9 6.6 4.3
1992 23.3 16.7 65.4 35.1 4.8 4.6
1993 25.1 17.4 74.5 35.2 7.9 5.3
1994 27.5 18.5 88.2 41.5 8.9 6.7
1995 27.3 17.9 90.6 40.8 10.5 6.0
1996 19.0 11.2 71.8 28.0 6.4 4.4
1997 9.6 4.8 41.5 14.0 3.3 1.6
1998 7.6 3.7 33.8 10.2 3.5 1.3
1999 8.2 4.0 36.5 10.9 4.2 1.4
2000 7.9 3.8 35.5 10.6 3.5 1.2
2001 7.6 3.6 34.3 9.8 4.1 1.2
2002 7.4 3.5 33.8 9.3 3.3 1.5
2003 7.1 3.4 31.8 9.4 3.4 1.1
2004 6.6 3.1 29.5 8.4 4.1 1.2
2005 6.3 3.0 28.4 7.7 3.7 1.0
2006 5.9 2.8 26.6 7.2 2.9 1.1
2007 5.4 2.5 24.6 6.5 3.3 0.8
2008 4.8 2.3 21.8 5.5 2.8 1.0
2009 4.4 2.0 20.1 5.0 2.4 0.7
2010 3.8 1.8 17.0 4.6 2.6 0.7
Year Female White, not Hispanic female Black, not Hispanic female Hispanic female American Indian or Alaska Native female Asian or Pacific Islander female
1990 2.2 0.7 10.3 3.8 * *
1991 2.7 0.9 12.2 4.9 * *
1992 3.2 1.0 14.9 5.7 * 0.5
1993 3.9 1.3 17.8 6.8 * 0.8
1994 4.8 1.6 23.0 8.0 * 0.7
1995 5.3 1.7 25.0 8.8 2.5 0.6
1996 4.2 1.3 21.4 6.3 * 0.5
1997 2.6 0.7 13.9 3.3 * *
1998 2.2 0.5 12.2 2.8 * *
1999 2.5 0.7 13.4 3.0 * *
2000 2.5 0.7 13.5 2.9 * *
2001 2.5 0.6 13.8 2.7 * *
2002 2.5 0.6 13.8 2.7 * *
2003 2.4 0.6 13.1 2.6 1.4 *
2004 2.4 0.6 13.3 2.4 1.4 *
2005 2.3 0.6 12.2 1.9 1.3 *
2006 2.2 0.6 12.4 1.9 1.3 *
2007 2.1 0.5 11.7 1.8 1.5 *
2008 1.9 0.5 10.0 1.7 * 0.3
2009 1.7 0.4 9.2 1.4 * *
2010 1.4 0.4 7.9 1.1 * *

* Rates based on fewer than 20 deaths are considered unreliable and are not shown.

NOTES: Rates are based on resident population. Rates are age-adjusted using 11 age groups. Age-adjusted rates are calculated using the year 2000 standard population with unrounded population numbers. Highly active antiretroviral therapy (HAART) was introduced in 1996. The lines in the data table around 1995 to 1997 denote the period from pre-HAART to widespread HAART use. Categories for the coding and classification of HIV disease were introduced in the United States in 1987. For the period 1990-1998, underlying cause of death was coded according to the 9th Revision of the International Classification of Diseases (ICD-9). Starting with 1999 data, cause of death is coded according to ICD-10. Persons of Hispanic origin may be of any race. See Appendix II, Age adjustment; Cause of death; Hispanic origin; Human immunodeficiency virus (HIV) disease; Table IV See related Table 31.

SOURCE: CDC/NCHS. National Vital Statistics System. See Appendix I, National Vital Statistics System (NVSS).

 

Data table for Figure 25. Use of prescription antidepressants in the past 30 days among adults aged 18 and over, by sex and age: United States, 1988–1994 through 2007–2010

Excel and PowerPoint

Sex and age 1988–1994 1999–2002 2003–2006 2007–2010
Percent Standard error Percent Standard error Percent Standard error Percent Standard error
Total, crude 2.3 0.2 8.0 0.4 10.9 0.4 11.1 0.5
Total, age-adjusted1 2.4 0.2 7.9 0.4 10.8 0.4 10.8 0.5
18–44 years 1.6 0.2 6.0 0.5 7.5 0.5 7.9 0.6
45–64 years 3.5 0.5 10.5 0.7 15.8 0.9 14.4 0.8
65 years and over 3.0 0.4 9.3 0.6 12.0 0.8 13.7 0.8
Male, crude 1.5 0.2 5.2 0.4 6.7 0.4 6.7 0.4
Male, age-adjusted1 1.6 0.2 5.2 0.4 6.7 0.4 6.6 0.4
18–44 years *1.0 0.2 3.6 0.5 3.5 0.4 4.4 0.6
45–64 years *2.3 0.5 7.0 0.7 10.5 1.0 8.9 0.7
65 years and over *2.3 0.5 7.2 0.8 9.8 1.0 9.4 0.7
Female, crude 3.1 0.3 10.5 0.6 14.8 0.6 15.2 0.8
Female, age-adjusted1 3.2 0.3 10.5 0.6 14.6 0.6 14.8 0.8
18–44 years 2.3 0.4 8.5 0.7 11.4 0.8 11.3 0.9
45–64 years 4.6 0.7 13.8 1.2 20.9 1.2 19.6 1.2
65 years and over 3.5 0.4 10.8 0.8 13.7 1.1 17.0 1.2

* Estimates are considered unreliable. Data preceded by an asterisk have a relative standard error (RSE) of 20%–30%. Data not shown have an RSE greater than 30%.

1Estimates are age-adjusted to the year 2000 standard population using three age groups: 18–44 years, 45–64 years, and 65 years and over.

NOTES: Data are for the civilian noninstitutionalized population. Antidepressant use includes one or more antidepressant drugs (level 2, class 249). Only prescriptions the respondent themselves took are included. Prescriptions administered in other health care settings, such as physician offices and hospital outpatient departments, are not collected. Drug classes are from Lexicon Plus (Cerner Multum, Denver, CO), a proprietary comprehensive database of all prescription and some nonprescription drug products available in the U.S. drug market. See Appendix II, Age adjustment; Drug; Multum Lexicon Plus therapeutic class. See related Table 93.

SOURCE: CDC/NCHS. National Health and Nutrition Examination Survey. See Appendix I, National Health and Nutrition Examination Survey (NHANES).

 

Data table for Figure 26. Antibiotics ordered or provided during emergency department, outpatient, and physician visits for cold symptom diagnoses, by age: United States, average annual, 1995–1996 through 2009–2010

Excel and PowerPoint

Year All ages Under 18 years 18 years and over
Percent of visits for colds Standard error Percent of visits for colds Standard error Percent of visits for colds Standard error
1995–1996 44.7 3.0 37.5 4.3 58.5 3.4
1997–1998 36.5 2.6 26.8 2.8 52.0 3.9
1999–2000 27.7 2.8 20.4 2.5 37.8 5.1
2001–2002 36.1 3.0 26.8 2.8 52.9 4.9
2003–2004 30.4 3.6 20.9 3.0 45.1 6.3
2005–2006 27.1 2.9 14.8 2.4 44.1 4.4
2007–2008 32.1 2.3 26.3 2.9 42.5 3.8
2009–2010 27.1 2.8 21.5 2.9 39.5 4.3

NOTES: Visits for cold symptoms are those with the 9th Revision of the International Classification of Diseases, Clinical Modification codes 460 [acute nasopharyngitis (common cold)] or 465 (acute upper respiratory infections) and no other diagnoses. Until 2002, up to six prescription and nonprescription medications were recorded on the patient record form (PRF). Starting with 2003 data, up to eight prescription and nonprescription medications are recorded on the PRF. To be consistent over time, only the first six medication fields were considered. Antibiotics were identified based on drug codes. For a list of drug codes included, see Technical Notes. Visits with unknown drug codes (99980 and 99999) were excluded.

SOURCE: CDC/NCHS. National Ambulatory Medical Care Survey, National Hospital Ambulatory Medical Care Survey: Emergency Department and Outpatient Components. See Appendix I, National Ambulatory Medical Care Survey (NAMCS); National Hospital Ambulatory Medical Care Survey (NHAMCS).

 

Data table for Figure 27. Computerized systems for prescription drugs, by provider and system type: United States, 2010

Excel and PowerPoint

System type Provider type
Physician offices Hospital outpatient departments Hospital emergency departments Residential care facilities1
Percent of providers Standard error Percent of providers Standard error Percent of providers Standard error Percent of providers Standard error
Ordering prescriptions 53.7 2.0 50.3 4.5 58.1 4.6 19.7 0.9
Warning of drug interactions and contraindications 46.0 2.0 38.7 4.5 45.9 4.6 17.9 0.9
Submitting prescriptions electronically to pharmacy 44.6 2.0 30.3 4.5 19.7 2.9 8.2 0.6
Including patient's allergies and current medications (in clinical notes system) 51.0 2.0 56.4 5.0 66.0 4.3 28.3 1.1
All four system types 33.3 1.8 24.3 4.3 15.7 2.5 3.3 0.4

1Includes residential care facilities, assisted living residences, board and care homes, and other licensed shared housing establishments that offer help with personal care or health-related services and other services. Residences licensed to serve exclusively persons with mental illness, mental retardation, or developmental disabilities are excluded.

NOTE: For variables used, see Technical Notes.

SOURCE: CDC/NCHS. National Ambulatory Medical Care Survey, National Hospital Ambulatory Medical Care Survey: Emergency Department and Outpatient Components, and National Survey of Residential Care Facilities. See Appendix I, National Ambulatory Medical Care Survey (NAMCS); National Hospital Ambulatory Medical Care Survey (NHAMCS); and National Survey of Residential Care Facilities (NSRCF).

 

Data table for Figure 28. Drug poisoning deaths involving opioid analgesics among persons aged 15 and over, by race and Hispanic origin, sex, and age: United States, 1999–2000 through 2009–2010

Excel and PowerPoint

Characteristic 1999–2000 2001–2002 2003–2004 2005–2006 2007–2008 2009–2010
Deaths per 100,000 population
Aged 15 and over, age-adjusted1 1.9 2.9 4.0 5.2 6.1 6.6
Aged 15 and over, crude 1.9 2.9 4.0 5.2 6.0 6.5
Age
15–24 years 0.7 1.5 2.4 3.2 3.8 3.7
25–34 years 1.9 2.8 4.1 6.1 7.2 8.1
35–54 years 3.4 4.9 6.6 8.2 9.2 9.9
55–64 years 1.0 1.6 2.4 3.5 4.8 6.0
65 years and over 0.3 0.5 0.6 0.9 1.0 1.2
Sex1
Male 2.6 3.7 5.0 6.6 7.5 8.1
Female 1.3 2.1 3.0 3.9 4.6 5.1
Race and Hispanic origin1,2
White, not Hispanic 2.2 3.5 5.1 6.7 8.0 8.9
Black, not Hispanic 1.1 1.5 1.7 2.8 2.4 2.7
Hispanic or Latino 1.8 1.7 2.0 2.3 2.6 2.4
American Indian or Alaska Native 1.7 2.6 4.2 5.4 6.1 7.1
Asian or Pacific Islander 0.2 0.3 0.3 0.6 0.5 0.7

1Rates are age-adjusted using eight age groups. Age-adjusted rates are calculated using the year 2000 standard population with unrounded population numbers. See Appendix II, Age adjustment.

2The race groups, Asian or Pacific Islander and American Indian or Alaska Native, include persons of Hispanic and non-Hispanic origin. Persons of Hispanic origin may be of any race. Death rates for the American Indian or Alaska Native, Asian or Pacific Islander, and Hispanic populations are known to be underestimated. Starting with 2003 data, some states allowed the reporting of more than one race on the death certificate. The multiple-race data for these states were bridged to the single-race categories of the 1977 Office of Management and Budget standards, for comparability with other states. See Appendix II, Race.

NOTES: Rates are based on resident population. Drug poisoning deaths with the drug type unspecified (up to 25% of the total) are not included. Drug poisoning deaths involving opioid analgesics among children under 15 is low, 0.1 per 100,000 population in 2010. Therefore this analysis is limited to those aged 15 and over. Opioid analgesics include pharmaceutical opioids such as hydrocodone, codeine, and methadone, and synthetic narcotics such as fentanyl, meperidine, and propoxyphene. Drug poisoning deaths involving opioid analgesics include those with an underlying cause of drug poisoning and with opioid analgesics mentioned in the 10th Revision of the International Classification of Diseases (ICD–10) multiple causes of death. See Appendix I, National Vital Statistics System (NVSS), Multiple Cause-of-death File, for information about tabulating cause-of-death data in this table. These deaths include all manners and intents. See Appendix II, Age adjustment; Cause of death; Hispanic origin; Table IV. See related Table 32.

SOURCE: CDC/NCHS. National Vital Statistics System. See Appendix I, National Vital Statistics System (NVSS).

 

Data table for Figure 29. Retail prescription drug expenditures, annual percent change, and spending by payer: United States, 2001–2011

Excel and PowerPoint

Year Annual percent change
2001 14.7
2002 14.0
2003 11.3
2004 9.2
2005 6.5
2006 9.5
2007 5.2
2008 2.8
2009 5.0
2010 0.4
2011 2.9

 

Payer 2001 2011
Percent
Out-of-pocket 26.4 17.1
Private health insurance 50.9 46.5
Medicare 1.8 24.2
Medicaid1 17.1 7.8
Other health insurance programs2 1.8 3.0
Other third-party payers3 2.0 1.4

 

1Includes both the state and federal portions. Also includes Children's Health Insurance Program (CHIP) and Medicaid CHIP expansions.

2Includes Department of Defense and Department of Veterans Affairs programs.

3Includes worksite health care, other private revenues, Indian Health Service, workers' compensation, general assistance, maternal and child health, vocational rehabilitation, other federal programs, Substance Abuse and Mental Health Services Administration, other state and local programs, and school health.

NOTES: See Appendix II, Health expenditures, national. See related Table 115.

SOURCE: Centers for Medicare & Medicaid Services. Office of the Actuary, National Health Statistics Group, National Health Expenditure Accounts. See Appendix I, National Health Expenditure Accounts (NHEA).

 

Technical Notes

Data Sources and Comparability   

Data for the Health, United States, 2013 Chartbook come from many surveys and data systems and cover a broad range of years. Detailed descriptions of the data sources included in the Chartbook are provided in Appendix I. Additional information clarifying and qualifying the data are included in the table notes and in Appendix II. Definitions and Methods. 

Data Presentation  

Many measures in the Chartbook are shown for people in specific age groups because of the strong effect of age on most health outcomes. Some estimates are age-adjusted using the age distribution of the 2000 standard population; where this has been done, it is noted in the data tables that accompany the charts. Age-adjusted rates are computed to eliminate differences in observed rates that result from age differences in population composition (see Appendix II, Age adjustment). For some charts, data years are combined to increase sample size and the reliability of the estimates. Some charts present time trends, and others focus on differences in estimates among population subgroups for the most recent time point available. Trends are generally shown on a linear scale to emphasize absolute differences over time. The time trends for the overall mortality measures are shown on a logarithmic (log) scale to emphasize the rate of change and to enable measures with large differences in magnitude to be shown on the same chart. Point estimates and standard errors for Figures 1-19 are available in the Trend Table and Excel spreadsheet specified in the Note below the chart. Data tables with point estimates and standard errors (when appropriate) accompany Figures 20-29. Some data tables contain additional data that were not graphed because of space considerations.

Statistical Testing  

Data trends can be described in many ways. For trend analyses presented in the Chartbook, increases or decreases in the estimates over time are measured by the annual percent change using the weighted least squares regression method. Statistically significant changes in the trend are assessed at the 0.05 level using the National Cancer Institute Joinpoint software. For analyses that compare two time periods, differences between the two periods were assessed for statistical significance at the 0.05 level using two-sided significance tests (z-tests).   Terms such as "similar," "stable," and "no difference" used in the text indicate that the statistics being compared were not significantly different. Lack of comment regarding the difference between statistics does not necessarily suggest that the difference was tested and found to be not significant. Because statistically significant differences or trends are partly a function of sample size (the larger the sample, the smaller the change that can be detected), they do not necessarily have public health significance (81). Testing and comparisons use the estimates and standard errors in the trend and data tables, not the rounded estimates shown in the charts. 

Overall estimates generally have relatively small sampling errors, but estimates for certain population subgroups may be based on small numbers and have relatively large sampling errors. Numbers of deaths obtained from the National Vital Statistics System represent complete counts and therefore are not subject to sampling error. They are, however, subject to random variation, which means that the number of events that actually occur in a given year may be considered as one of a large series of possible results that could have arisen under the same circumstances. When the number of events is small and the probability of such an event is small, considerable caution must be observed in interpreting the conditions described by the charts. Estimates that are unreliable because of large sampling errors or small numbers of events have been noted with an asterisk. The criteria used to designate or suppress unreliable estimates are indicated in the notes to the applicable tables or charts.  

For NCHS surveys, point estimates and their corresponding variances were calculated using the SUDAAN software package, which takes into consideration the complex survey design (82). Standard errors for other surveys or data sets were computed using the methodology recommended by the programs providing the data, or were provided directly by those programs. 

Survey Questions and Coding  

Additional information on data used in the Special Feature, including exact wording of questions and coding schemes, is detailed below.  

Figures 20-22 and 25. The National Health and Nutrition Examination Survey (NHANES) questionnaire administered to all participants included a question on whether they had taken a prescription drug in the past 30 days [RXDUSE]. Those who answered ``yes'' were asked to show the interviewer the medication containers for all the prescriptions. For each drug reported, the interviewer entered the product's complete name from the container. If no container was available, the interviewer asked the participant to verbally report the name of the drug. Only prescriptions the respondent themselves took are included. Prescriptions administered in other health care settings, such as physician offices and hospital outpatient departments, are not collected. Over-the-counter drugs play an important role in health care, but most of the analysis in this feature focuses only on prescription drugs.  

More information on prescription drug data collection and coding in NHANES is available. Also see Appendix I, National Health and Nutrition Examination Survey; Appendix II, Drug.   

 

Figure 21. Data are from NHANES and are based on prescription drugs the respondent reported taking in the past 30 days. For each drug reported, the interviewer entered the product's complete name from the container and the drug was categorized into therapeutic classes. Drug classes cited are from Lexicon Plus (Cerner Multum, Denver, CO), a proprietary comprehensive database of all prescription and some nonprescription drug products available in the U.S. drug market.     

  • The category antiasthmatics includes one or more asthma drugs, including bronchodilators, mast cell stabilizers, inhaled corticosteroids, mucolytics, inhaled anti-infectives, leukotriene modifiers, and antiasthmatic combinations (level 2, class 125, 130, 131, or 243).    
  • The category antibiotics includes one or more antibiotic drugs, including antituberculosis agents, cephalosporins, leprostatics, macrolide derivatives, miscellaneous antibiotics, penicillins, quinolones, sulfonamides, tetracyclines, urinary anti-infectives, aminoglycosides, lincomycin derivatives, and glycopeptide antibiotics (level 2, class 6, 8-18, 240, 315, or 406).   
  • The category central nervous system stimulants includes one or more central nervous system stimulants (level 2, class 71).    
  • The category analgesics includes one or more analgesic drugs (level 2, class 58).    
  • The category antidepressants includes one or more antidepressant drugs (level 2, class 249).    
  • The category cardiovascular agents includes one or more cardiovascular agents, agents for hypertensive emergencies, ACE inhibitors, peripherally acting antiadrenergic agents, centrally acting antiadrenergic agents, antianginal agents, antiarrhythmic agents, beta-adrenergic blocking agents, calcium channel blocking agents, diuretics, inotropic agents, miscellaneous cardiovascular agents, peripheral vasodilators, vasodilators, vasopressors, antihypertensive combinations, angiotensin II inhibitors, agents for pulmonary hypertension, aldosterone receptor antagonists, renin inhibitors, anticholinergic chronotropic agents, and catecholamines (level 1, class 40).    
  • The category cholesterol-lowering drugs includes one or more antihyperlipidemic drugs (level 2, class 19).    
  • The category anti-acid reflux drugs includes one or more proton pump inhibitors or H2 antagonists (level 2, class 94 or 272).    
  • The category antidiabetic agents includes one or more antidiabetic drugs (level 2, class 99).    
  • The category anticoagulants includes one or more anticoagulants or antiplatelet agents (level 2, class 82 or 83).   

 

Figure 23. Data are from National Health Interview Survey and are based on adults responding to the question, "During the past 12 months was there any time when you needed prescription medicine but didn't get it because [person] couldn't afford it?" [AHCAFYR1]. Survey respondents may be covered by health insurance at the time of interview but may have experienced one or more lapses in coverage during the 12 months prior to interview. To be consistent with the 12-month period used to determine prescription drug access issues, insurance status during the prior 12 months was used. Insurance status during the prior 12 months was determined using two questions: (a) all persons without a known comprehensive health insurance plan were asked, "About how long has it been since [person] last had health care coverage?" [HILAST]; and (b) all persons with known health insurance coverage were asked, "In the past 12 months, was there any time when [person] did NOT have ANY health insurance coverage?" [HINOTYR]. Also see Appendix II, Health insurance coverage.   

 

Figure 26. Data are from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey: Emergency Department and Outpatient Components. The following drugs codes were used to identify antibiotics: 00007, 00009, 00028, 00062, 00103, 00125, 00194, 00340, 00345, 00349, 00391, 01017, 01044, 01046, 01053, 01054, 01189, 01196, 01242, 01315, 01630, 01635, 01640, 01685, 01865, 02047, 02070, 02102, 02116, 02146, 02161, 02987, 03059, 03081, 03109, 03138, 03220, 03283, 03331, 03425, 03430, 03741, 04156, 04157, 04180, 04235, 04240, 04264, 04528, 04531, 04586, 05117, 05156, 05190, 05207, 05232, 05233, 05245, 05690, 05955, 05983, 05985, 05988, 05993, 05995, 06097, 06125, 06127, 06128, 06130, 06131, 06133, 06162, 06196, 06204, 06224, 06238, 06839, 06883, 06963, 07015, 07067, 07561, 07888, 08030, 08081, 08113, 08130, 08132, 08150, 08252, 08268, 08373, 08468, 08496, 08557, 08574, 08640, 09182, 09379, 09433, 09569, 09611, 09752, 09846, 09878, 09892, 10340, 10350, 10355, 10363, 10364, 10705, 10800, 10820, 10845, 10875, 10905, 11553, 11651, 11655, 11657, 11658, 11660, 11665, 11667, 11669, 11905, 12967, 13350, 13355, 15490, 15495, 16472, 16475, 16480, 16482, 16485, 17150, 17270, 18325, 18645, 19050, 19263, 19460, 19465, 19698, 20140, 20175, 20215, 20218, 20490, 21250, 21385, 21795, 22233, 22328, 22340, 22670, 22935, 23047, 23125, 23150, 23185, 23195, 23215, 23220, 23221, 23222, 23223, 23225, 23228, 23230, 23305, 23500, 23603, 23605, 24228, 24435, 24440, 24465, 24848, 25070, 25075, 25130, 25575, 26460, 26795, 26800, 26825, 26940, 26960, 27835, 27840, 28205, 28258, 28260, 28280, 28285, 28320, 29078, 29315, 29838, 29843, 29888, 29897, 30025, 30035, 30575, 30725, 30850, 31020, 31045, 31050, 31055, 31060, 31075, 31645, 31650, 31870, 32020, 32423, 32430, 33068, 33092, 33155, 33355, 33400, 33410, 33425, 33430, 33780, 33805, 34085, 34090, 34950, 34970, 34975, 34990, 35595, 40310, 41785, 50036, 60115, 60120, 60125, 60295, 60335, 60485, 60500, 60505, 60780, 61085, 61185, 61295, 61410, 61415, 61470, 89015, 89027, 89028, 89029, 89059, 89075, 89076, 91015, 91017, 91059, 91067, 91068, 91069, 91070, 91094, 92004, 92006, 92013, 92029, 92031, 92109, 92110, 92111, 92112, 92140, 93038, 93088, 93093, 93098, 93166, 93179, 93214, 93230, 93301, 93303, 93338, 93360, 93387, 93416, 93417, 94037, 94129, 94139, 94146, 94169, 95028, 95037, 95149, 95167, 95187, 96070, 96087, 97001, 97004, 97045, 97132, 97163, 98029, 98040, 98061, 98066, 98082, 99001, 99014, 99022, 99073, 99135.   

 

Figure 27. Data are from the National Ambulatory Medical Care Survey (NAMCS), the National Hospital Ambulatory Medical Care Survey: Outpatient (NHAMCS-OPD) and Emergency Department (NHAMCS-ED) Components, and the National Survey of Residential Care Facilities (NSRCF).  

Data from NAMCS, NHAMCS-OPD, and NHAMCS-ED were based on the following questions about the four types of computerized systems. The variable names are in this order: NAMCS, NHAMCS-OPD, NHAMCS-ED. If the provider reported that they had the system but it was turned off, they were classified as not having that computer system type. If the response was unknown, blank, or otherwise missing, the record was excluded from the analysis. Missing values can be handled in a variety of ways. Estimates in Health, United States may differ from other estimates based on the same data presented elsewhere if missing values were handled differently.     

  • Ordering prescriptions: "Does your practice have a computerized system for orders for prescriptions?" [ECPOE, ECPOEO, ECPOEE]. Six percent of physician offices, 6% of hospital OPDs, and 5% of hospital EDs had their ordering prescriptions systems turned off and were counted as not having an ordering prescriptions system.    
  • Warning of drug interactions and contraindications: "If practice has a computerized system for orders for prescriptions, are there warnings of drug interactions or contraindications provided?" Providers who did not have a system for ordering prescriptions or had it turned off (prior question) were not asked this question and were classified as not having a warning system [EWARN, EWARNO, EWARNE].    
  • Submitting prescriptions electronically to pharmacy: "If practice has a computerized system for orders for prescriptions, are prescriptions sent electronically to pharmacy?" Providers who did not have a system for ordering prescriptions or had it turned off (prior question) were not asked this question and were classified as not having a system to submit prescriptions electronically [ESCRIP, EESCRIPO, EESCRIPE].    
  • Including patient's allergies and current medications (in clinical notes system): Based on two questions, "If practice has a computerized system for clinical notes, do they include a comprehensive list of the patient's allergies (including allergies to medication)?" and "If practice has a computerized system for clinical notes, do they include a list of medications that the patient is taking?" Only providers with "Yes" to both questions were counted as having a system recording patient's allergies and medications. Providers who did not have a system for clinical notes or had it turned off (prior question) were not asked these questions and were classified as not having a system to record allergies and medications electronically [EMEDS, EMEDSO, EMEDSE, and EALLERG, EALLERGO, EALLERGE]. Five percent of physician offices, 4% of hospital OPDs, and less than 1% of hospital EDs had their clinical notes systems turned off and were counted as not having a patient's allergies or medications system.   

Data from NSRCF were based on the following questions about the four types of computerized systems. Providers were shown a card with a list of computer systems and asked which types their facility had. If the response was unknown, blank, or otherwise missing, the record was excluded from the analysis.     

  • Ordering prescriptions: "Does this facility have the following computerized capabilities? ORDERS FOR PRESCRIPTIONS?" [ITPRESC].    
  • Warning of drug interactions and contraindications: "Does this facility have the following computerized capabilities? WARNING OF DRUG INTERACTIONS OR CONTRAINDICATIONS?" [ITCONTRA].    
  • Submitting prescriptions electronically to pharmacy: "Does this facility's computerized system support electronic health information exchange with PHARMACY?" [ITPHARM].    
  • Including patient's allergies and current medications (in clinical notes system): Based on two questions: "Does this facility have the following computerized capabilities? MAINTAINING ACTIVE MEDICATION ALLERGY LIST?" and "Does this facility have the following computerized capabilities? MAINTAINING LIST OF RESIDENT'S MEDICATIONS?" [ITRXLIST and ITALLERG].   

 

Figure 28. Propoxyphene was withdrawn from the market in 2010. However, because the chart includes data back to 2000, propoxyphene is included in the list of synthetic drugs.

 

References

  1. Kochanek KD, Arias E, Anderson RN. How did cause of death contribute to racial differences in life expectancy in the United States in 2010? NCHS data brief, no 125. Hyattsville, MD: NCHS. 2013.
  2. Murphy SL, Xu J, Kochanek KD. Deaths: Final data for 2010. National vital statistics reports; vol 61 no 4. Hyattsville, MD: NCHS; 2012.   
  3. NCHS. 2010 mortality file unpublished analysis.  
  4. Altman B, Bernstein A. Disability and health in the United States, 2001-2005. Hyattsville, MD: NCHS; 2008.  
  5. U.S. Department of Health and Human Services. How tobacco smoke causes disease: The biology and behavioral basis for smoking-attributable disease: A report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2010.
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