Abstract
BackgroundMetrics based on self-reports of health status have been proposed for tracking population health and making comparisons among different populations. While these metrics have been used in the US to explore disparities by sex, race/ethnicity, and socioeconomic position, less is known about how self-reported health varies geographically. This study aimed to describe county-level trends in the prevalence of poor self-reported health and to assess the face validity of these estimates.MethodsWe applied validated small area estimation methods to Behavioral Risk Factor Surveillance System data to estimate annual county-level prevalence of four measures of poor self-reported health (low general health, frequent physical distress, frequent mental distress, and frequent activity limitation) from 1995 and 2012. We compared these measures of poor self-reported health to other population health indicators, including risk factor prevalence (smoking, physical inactivity, and obesity), chronic condition prevalence (hypertension and diabetes), and life expectancy.ResultsWe found substantial geographic disparities in poor self-reported health. Counties in parts of South Dakota, eastern Kentucky and western West Virginia, along the Texas-Mexico border, along the southern half of the Mississippi river, and in southern Alabama generally experienced the highest levels of poor self-reported health. At the county level, there was a strong positive correlation among the four measures of poor self-reported health and between the prevalence of poor self-reported health and the prevalence of risk factors and chronic conditions. There was a strong negative correlation between prevalence of poor self-reported health and life expectancy. Nonetheless, counties with similar levels of poor self-reported health experienced life expectancies that varied by several years. Changes over time in life expectancy were only weakly correlated with changes in the prevalence of poor self-reported health.ConclusionsThis analysis adds to the growing body of literature documenting large geographic disparities in health outcomes in the United States. Health metrics based on self-reports of health status can and should be used to complement other measures of population health, such as life expectancy, to identify high need areas, efficiently allocate resources, and monitor geographic disparities.
Highlights
Metrics based on self-reports of health status have been proposed for tracking population health and making comparisons among different populations
We examined the correlation between change in prevalence of low general health, frequent physical distress, frequent mental distress, and frequent activity limitation, and change in life expectancy between 1995 and 2012
The standard deviation of county-level prevalence of frequent physical distress, frequent mental distress, and frequent activity limitation increased over this same period
Summary
Metrics based on self-reports of health status have been proposed for tracking population health and making comparisons among different populations. They fail to take into account individuals’ own assessment of and satisfaction with their health and functioning In response to these limitations, metrics based on self-reported health status have been proposed as a complement to objective measures for use in tracking levels of population health over time and for evaluating disparities in health [4, 5]. Since 1993, the BRFSS has included four core “Healthy Days” questions in which respondents are asked to rate their overall health and to report the number of days in the past month that they experienced poor physical health, poor mental and emotional health, or were unable to participate in their usual activities These questions are designed to elicit respondents’ self-assessment of and satisfaction with their health generally and with their recent physical health, mental and emotional health, and functional limitations [4]. Health metrics based on these and similar questions have been shown to be highly correlated with metrics based on lengthier survey instruments [8, 9], health behaviors and risk factors [10,11,12], chronic health conditions [13], health care utilization [14], and mortality risk [15]
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