Comparison of small-area analysis techniques for estimating prevalence by race

Abstract

Introduction: The Behavioral Risk Factor Surveillance System (BRFSS) is commonly used for estimating the prevalence of chronic disease. One limitation of the BRFSS is that valid estimates can only be obtained for states and larger geographic regions. Limited health data are available on the county level and, thus, many have used small-area analysis techniques to estimate the prevalence of disease on the county level using BRFSS data.

Methods: This study compared the validity and precision of 4 small-area analysis techniques for estimating the prevalence of 3 chronic diseases (asthma, diabetes, and hypertension) by race on the county level. County-level reference estimates obtained through local data collection were compared with prevalence estimates produced by direct estimation, synthetic estimation, spatial data smoothing, and regression. Discrepancy statistics used were Pearson and Spearman correlation coefficients, mean square error, mean absolute difference, mean relative absolute difference, and rank statistics.

Results: The regression method produced estimates of the prevalence of chronic disease by race on the county level that had the smallest discrepancies for a large number of counties.

Conclusion: Regression is the preferable method when applying small-area analysis techniques to obtain county-level prevalence estimates of chronic disease by race using a single year of BRFSS data.