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Using small-area analysis to estimate county-level racial disparities in obesity demonstrating the necessity of targeted interventions.

D'Agostino-McGowan L, Gennarelli RL, Lyons SA, Goodman MS - Int J Environ Res Public Health (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA. dagostinol@wudosis.wustl.edu.

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.

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Race-stratified within-state variability of obesity prevalence rates.
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ijerph-11-00418-f003: Race-stratified within-state variability of obesity prevalence rates.

Mentions: Within-state variance was then calculated again for each state with race-specific prevalence rates included for each county. This race-stratified within-state variability aims to quantify the racial disparity among obesity rates in each state. The calculated race-stratified within-state variances were higher than both the un-stratified within-state variance and the overall between-state variance for all 26 states included in the analysis (Figure 3). This graph plots the variance of race-stratified county-level obesity prevalence by state along with 95% upper (UCL) and lower (LCL) confidence limits as compared to the variance of obesity prevalence of overall state-level estimates (Between-State). The variance estimates for all 26 states included in the analysis fall outside of the confidence bounds for the overall between-state variance, although the same cannot be said for the state-specific confidence bounds. In 12 (46%) of the 26 states, the variance estimate and its entire confidence bound fell completely outside of the overall between-state’s confidence bounds. Again, this highlights the variability within states and the necessity of tailored, culturally competent, and region-specific community based interventions to address the obesity problem in our nation.


Using small-area analysis to estimate county-level racial disparities in obesity demonstrating the necessity of targeted interventions.

D'Agostino-McGowan L, Gennarelli RL, Lyons SA, Goodman MS - Int J Environ Res Public Health (2013)

Race-stratified within-state variability of obesity prevalence rates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924452&req=5

ijerph-11-00418-f003: Race-stratified within-state variability of obesity prevalence rates.
Mentions: Within-state variance was then calculated again for each state with race-specific prevalence rates included for each county. This race-stratified within-state variability aims to quantify the racial disparity among obesity rates in each state. The calculated race-stratified within-state variances were higher than both the un-stratified within-state variance and the overall between-state variance for all 26 states included in the analysis (Figure 3). This graph plots the variance of race-stratified county-level obesity prevalence by state along with 95% upper (UCL) and lower (LCL) confidence limits as compared to the variance of obesity prevalence of overall state-level estimates (Between-State). The variance estimates for all 26 states included in the analysis fall outside of the confidence bounds for the overall between-state variance, although the same cannot be said for the state-specific confidence bounds. In 12 (46%) of the 26 states, the variance estimate and its entire confidence bound fell completely outside of the overall between-state’s confidence bounds. Again, this highlights the variability within states and the necessity of tailored, culturally competent, and region-specific community based interventions to address the obesity problem in our nation.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA. dagostinol@wudosis.wustl.edu.

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.

Show MeSH
Related in: MedlinePlus