Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S
Data Science Journal
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This paper assesses concordance and inconsistency among three small area estimation methods that are currently providing county-level health indicators in the United States. The three methods are multi-level logistic regression, spatial logistic regression, and spatial Poison regression, all proposed since 2010. Diabetes prevalence is estimated for each county in the continental United States from the 2012 sample of Behavioral Risk Factor Surveillance System. The mapping results show that all three methods displayed elevated diabetes prevalence in the South. While the Pearson correlation coefficients among three model-based estimates were all above 0.60, the highest one was 0.80 between the multilevel and spatial logistic methods. While point estimates are apparently different among the three small area estimate methods, their top and bottom of quintile distributions are fairly consistent based on Bangdiwala’s B-statistic, suggesting that outputs from each method would support consistent policy making in terms of identifying top and bottom percent counties.
Small area estimate; Diabetes prevalence; Multi-level logistic regression; Spatial logistic regression; Spatial Poisson regression
Community Health and Preventive Medicine | Public Health
Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S.
Data Science Journal, 17