Published: Lung-Chang Chien & Ge Lin Kan

Lung-Chang Chien and Ge Lin Kan (both Environmental and Occupational Health) co-authored "Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S." to assess concordance and inconsistency among three small area estimation methods — multi-level logistic regression, spatial logistic regression, and spatial Poison regression — that currently are providing county-level health indicators in the U.S. They used diabetes prevalence at the county level from the 2012 sample of Behavioral Risk Factor Surveillance System as an example. The mapping results show that all three methods displayed elevated diabetes prevalence in the South, while point estimates are apparently different among different methods. This study provides the evidence about the need of building up a unified small area estimate method with necessary clusters and confounding variables to prevent possible inconsistencies in prevalence estimates. Both Chien and Kan are with the epidemiology and biostatistics section of their department.

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