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Community Indicators of Health-Related Quality of Life -- United States, 1993-1997

It is known that persons' longevity is affected by the environmental and population characteristics of their community (1-3). Studies that identify community-level characteristics associated with the health-related quality of life (HRQOL) of residents could help guide local health planning. Data from the Behavioral Risk Factor Surveillance System (BRFSS) for 1993-1997 indicate that HRQOL differs among U.S. counties according to county population size. In addition, socioeconomic and health status indicators, such as poverty, noncompletion of high school, unemployment, number of persons with severe work disabilities, mortality, and births to adolescents, also might affect county-level HRQOL differences. This report examines initial findings on the relation between selected community health status indicators (CHSIs) and the mean number of days that persons aged >18 years reported ill health (i.e., unhealthy days), a surveillance measure of population HRQOL (4-6). The findings suggest that CHSIs may be useful in the public health planning process.

Since 1993, CDC and participating state health departments have tracked the number of days persons aged >18 years have reported feeling unhealthy through BRFSS, an ongoing, state-based, random-digit-dialed telephone survey of the civilian, noninstitutionalized U.S. population aged >18 years. Unhealthy days were measured using the sum of the responses to two questions about the estimated number of days during the 30 days preceding the survey when the respondent's physical health (i.e., "physical illness and injury") or mental health (i.e., "stress, depression, and problems with emotions") was not good, with the restriction that unhealthy days for an individual could not exceed 30 days (6). The mean number of unhealthy days was estimated for each U.S. county after each response was weighted to the age, race, and sex distribution of the state in which the county was located. Data from 1993 through 1997 were combined to increase the precision of the estimates of the mean number of unhealthy days per county. Data from 2450 (80%) of 3081 U.S. counties were analyzed; Alaska and 631 counties with <20 BRFSS respondents were excluded from the analysis.

Potential county indicators of HRQOL were selected from preliminary CHSI data provided for this analysis by the Public Health Foundation (PHF)* based on recognized associations with HRQOL (6) or on their possible relation to population HRQOL (i.e., mortality rate and births to adolescents). Socioeconomic and health status indicators (specifically, rates of poverty, high school education, unemployment, severe work disability, mortality, and proportion of births to adolescents) were analyzed for mean population HRQOL differences among counties categorized by population size and the prevalence level of each indicator. Multiple linear regression was used to estimate the percentage of variability in the mean number of unhealthy days per county explained by these indicators after weighting county records by the square root of the BRFSS sample size to allow use of county data with smaller BRFSS sample sizes and to reflect the increased precision of HRQOL estimates in counties with larger sample sizes. A maximum relative weight of 6.32 (i.e., the square root of 800 divided by the square root of 20) was assigned to counties with >800 respondents.

Overall, persons aged >18 years reported an average of 5.3 unhealthy days (range: 0.7-12.7 days) during the 30 days preceding the survey (Table 1). The most unhealthy days were reported by persons in the most populous counties (i.e., 5.6 unhealthy days for counties of >1,000,000); the least unhealthy days were reported by persons in counties with populations of 500,000-999,999 (5.1 days). Compared with the latter group, persons in smaller and larger counties were estimated to have 1.3 million excess unhealthy years of life. For each CHSI indicator, counties in the lowest third (i.e., the one third that had the lowest rates for poverty, noncompletion of high school education, unemployment, severe work disability, mortality, and proportion of births to adolescents) had the lowest mean number of unhealthy days overall and for almost all county sizes. Taking all tested indicators together, the variability in county unhealthy days predicted was approximately 11%. Socioeconomic and health-related factors accounted for almost all of the predicted variability; age and population size and density accounted for only 0.4%.

Reported by: N Kanarek, PhD, D Sockwell, MSPH, Public Health Foundation, Washington, DC. H Jia, PhD, Univ of Tennessee, Knoxville. The following BRFSS coordinators: S Reese, MPH, Alabama; P Owen, Alaska; B Bender, MBA, Arizona; G Potts, MBA, Arkansas; B Davis, PhD, California; M Leff, MSPH, Colorado; M Adams, MPH, Connecticut; F Breukelman, Delaware; I Bullo, District of Columbia; S Hoecherl, Florida; L Martin, MS, Georgia; F Reyes-Salvail, MS, Hawaii; J Aydelotte, MA, Idaho; B Steiner, MS, Illinois; L Stemnock, Indiana; J Igbokwe, PhD, Iowa; C Hunt, MPH, Kansas; T Sparks, Kentucky; B Bates, MSPH, Louisiana; D Maines, Maine; A Weinstein, MA, Maryland; D Brooks, MPH, Massachusetts; H McGee, MPH, Michigan; N Salem, PhD, Minnesota; D Johnson, MS, Mississippi; T Murayi, PhD, Missouri; P Feigley, PhD, Montana; L Andelt, PhD, Nebraska; E DeJan, MPH, Nevada; Larry Powers, MA, New Hampshire; G Boeselager, MS, New Jersey; W Honey, MPH, New Mexico; C Baker, New York; Z Gizlice, PhD, North Carolina; L Shireley, MPH, North Dakota; P Pullen, Ohio; K Baker, MPH, Oklahoma; K Pickle, MS, Oregon; L Mann, Pennsylvania; Y Cintron, MPH, Puerto Rico; J Hesser, PhD, Rhode Island; M Wu, MD, South Carolina; M Gildemaster, South Dakota; D Ridings, Tennessee; K Condon, Texas; K Marti, Utah; C Roe, MS, Vermont; K Carswell, MPH, Virginia; K Wynkoop-Simmons, PhD, Washington; F King, West Virginia; K Pearson, Wisconsin; M Futa, MA, Wyoming. Health Care and Aging Studies Br, Div of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, CDC.

Editorial Note:

Local health agencies play a major role in promoting health and quality of life, and community indicators of HRQOL can help to guide planning programs to improve community health. This initial study of community indicators of HRQOL predicted approximately 11% of the variability in unhealthy days among counties. Although no similar county-based HRQOL studies are known, the amount of variability explained was similar to that found in efforts to predict health-care costs of various populations using socioeconomic and health-related indicators (7). Although counties with populations of 500,000-999,999 residents reported better HRQOL than the other counties, this study indicates that counties of all sizes might be able to address factors to reduce adult unhealthy days.

The findings in this report are subject to at least five limitations. First, BRFSS reaches only persons who have a telephone and are able and willing to participate in the survey; therefore, results may underestimate the number of unhealthy days experienced by persons living at home and do not reflect persons living in long-term-care facilities or other institutions. Second, unhealthy days may be overestimated for some persons who report both physical and mental unhealthy days. Third, the county indicators explored in this study were few, cross-sectional, and not necessarily the most valid and sensitive indicators of population HRQOL. Fourth, the analysis was limited by the small BRFSS sample size available at the county level, and BRFSS data are weighted to reflect their state's population characteristics, which may differ from population characteristics of the county. Finally, although one scheme for weighting counties in the regression analysis was used, others should be explored.

Using a validated HRQOL measure, this study represents an initial effort to quantify certain factors that contribute to the well-being of populations in U.S. counties (8). However, to improve county health planning, additional factors that contribute directly to HRQOL, such as access to health care and preventive services, environmental factors, workplace safety, public safety, and health behaviors, should be assessed. Also, county health departments should use local HRQOL data and associated community indicators to identify health issues and guide their community health improvement process (9,10).


  1. Dever GEA. Community health analysis: global awareness at the local level. Gaithersburg, Maryland: Aspen Publishers, 1991.
  2. Murray CJ, Michaud CM, McKenna MT, Marks JS. U.S. patterns of mortality by county and race: 1965-1994. Cambridge, Massachusetts: Harvard Center for Population and Development Studies; Atlanta, Georgia: US Department of Health and Human Services, CDC, 1998.
  3. Yen IH, Syme SL. The social environment and health: a discussion of the epidemiological literature. Annu Rev Public Health 1999;20:287-308.
  4. US Department of Health and Human Services. Healthy people 2010 (Conference ed., vol 1 and 2). Washington, DC: US Department of Health and Human Services, January 2000. Available at Accessed March 20, 2000.
  5. Hennessy CH, Moriarty DG, Zack MM, Scherr PA, Brackbill R. Measuring health-related quality of life for public health surveillance. Public Health Rep 1994;109:665-72.
  6. CDC. State differences in reported healthy days among adults -- United States, 1993-1996. MMWR 1998;47:239-44.
  7. Ettner SL, Frank RG, McGuire TG, Newhouse JP, Notman EH. Risk adjustment of mental health and substance abuse payments. Inquiry 1998;35:223-39.
  8. Moriarty D, Zack M. Validation of the Centers for Disease Control and Prevention's healthy days measures [Abstract]. In: Quality of Life Research, Abstracts Issue, Sixth Annual Conference of the International Society for Quality of Life Research, Barcelona, Spain, 1999.
  9. Durch JS, Bailey LA, Stoto MA. Improving health in the community: a role for performance monitoring. Washington, DC: National Academy of Sciences Press, 1997. Available at Accessed March 20, 2000.
  10. Last J. Public health and human ecology. Stamford, Connecticut: Appleton and Lange, 1998.

* County data for age distribution, population size and density, poverty, high school graduation, unemployment, severe work disabilities, all-cause mortality, and births to adolescents were obtained from the Health Resources and Services Administration-funded Community Health Status Indicator Project Health Status Reports, which were created by the CHSI Project partners (Association of State and Territorial Health Officials, National Association of County and City Health Officials, and PHF). The CHSI Project is described by PHF at References to sites of non-CDC organizations on the Internet are provided as a service to MMWR readers and do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S. Department of Health and Human Services. CDC is not responsible for the content of pages found at these sites.

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