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. 2018 Jan;37(1):22-29.
doi: 10.1377/hlthaff.2017.1125.

Evaluating Community-Based Health Improvement Programs

Carrie E Fry  1 , Sayeh S Nikpay  2 , Erika Leslie  3 , Melinda B Buntin  4
Affiliations

Evaluating Community-Based Health Improvement Programs

Carrie E Fry et al. Health Aff (Millwood). 2018 Jan.

Abstract

Increasingly, public and private resources are being dedicated to community-based health improvement programs. But evaluations of these programs typically rely on data about process and a pre-post study design without a comparison community. To better determine the association between the implementation of community-based health improvement programs and county-level health outcomes, we used publicly available data for the period 2002-06 to create a propensity-weighted set of controls for conducting multiple regression analyses. We found that the implementation of community-based health improvement programs was associated with a decrease of less than 0.15 percent in the rate of obesity, an even smaller decrease in the proportion of people reporting being in poor or fair health, and a smaller increase in the rate of smoking. None of these changes was significant. Additionally, program counties tended to have younger residents and higher rates of poverty and unemployment than nonprogram counties. These differences could be driving forces behind program implementation. To better evaluate health improvement programs, funders should provide guidance and expertise in measurement, data collection, and analytic strategies at the beginning of program implementation.

Keywords: Determinants Of Health; Health Promotion/Disease Prevention; Public Health; Tobacco/Smoking.

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EXHIBIT 1
EXHIBIT 1. Numbers of community-based health improvement programs and their health outcome foci, 2007–12
SOURCE Authors’ analysis of selected community-based health improvement program data. NOTES The number of programs is cumulative over time. Programs with more than one focus are counted in each of their foci. Over this period, four programs were implemented at fifty-two sites that collectively contained 396 counties. The first program was implemented in 2007.
EXHIBIT 3
EXHIBIT 3. County-level changes in selected health outcomes after program implementation, by methodological approach
SOURCE Authors’ analysis of selected community-based health improvement program data and Behavioral Risk Factor Surveillance System Selected Metropolitan/Micropolitan Area Risk Trends data for 2002–12. NOTES The error bars indicate 95 percent confidence intervals. Models labeled “ARRA” include only counties that received funding via from the American Recovery and Reinvestment Act’s Communities Putting Prevention to Work grant. Models labeled “non-ARRA” include counties that did not receive funding from that grant. Standard difference-in-differences models are labeled “OLS” (ordinary least squares). Inverse propensity treatment score weighted models are labeled “IPW.” Statistical methods are described in the text, technical appendix, and appendix exhibits 2 and 3 (see note 15 in text).
EXHIBIT 4
EXHIBIT 4. County-level changes in selected health outcomes after program implementation, by focus of program
SOURCE Authors’ analysis of selected community-based health improvement program data and Behavioral Risk Factor Surveillance System Selected Metropolitan/Micropolitan Area Risk Trends data for 2002–12. NOTES The error bars indicate 95 percent confidence intervals. “Any smoking” includes people who reported smoking daily and people who reported smoking some. “Obese or overweight” includes people with a body mass index of ≥25 to ≤40. Standard errors are clustered at the county (FIPS) level. Programs labeled as “obesity program” or “tobacco program” may also focus on additional health outcomes. All models are inverse propensity treatment weighted. Statistical methods are detailed in the text, technical appendix, and appendix exhibit 3 (see note 15 in text).
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