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Int. J. Environ. Res. Public Health 2014, 11(1), 418-428; doi:10.3390/ijerph110100418

Using Small-Area Analysis to Estimate County-Level Racial Disparities in Obesity Demonstrating the Necessity of Targeted Interventions

Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
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Received: 12 November 2013 / Revised: 19 December 2013 / Accepted: 19 December 2013 / Published: 27 December 2013
(This article belongs to the Special Issue Economics of the Prevention and Treatment of Obesity)
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Abstract

Data on the national and state levels is often used to inform policy decisions and strategies designed to reduce racial disparities in obesity. Obesity-related health outcomes are realized on the individual level, and policies based on state and national-level data may be inappropriate due to the variations in health outcomes within and between states. To examine county-level variation of obesity within states, we use a small-area analysis technique to fill the void for county-level obesity data by race. Five years of Behavioral Risk Factor Surveillance System data are used to estimate the prevalence of obesity by county, both overall and race-stratified. A modified weighting system is used based on demographics at the county level using 2010 census data. We fit a multilevel reweighted regression model to obtain county-level prevalence estimates by race. We compare the distribution of prevalence estimates of non-Hispanic Blacks to non-Hispanic Whites. For 25 of the 26 states included in our analysis there is a statistically significant difference between within-state county-level average obesity prevalence rates for non-Hispanic Whites and non-Hispanic Blacks. This study provides information needed to target disparities interventions and resources to the local areas with greatest need; it also identifies the necessity of doing so.
Keywords: obesity; disparities; small-area analysis; multilevel regression; targeted interventions obesity; disparities; small-area analysis; multilevel regression; targeted interventions
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

D'Agostino-McGowan, L.; Gennarelli, R.L.; Lyons, S.A.; Goodman, M.S. Using Small-Area Analysis to Estimate County-Level Racial Disparities in Obesity Demonstrating the Necessity of Targeted Interventions. Int. J. Environ. Res. Public Health 2014, 11, 418-428.

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