Comparison of small-area analysis techniques for estimating county-level outcomes Review Article

Background Since many health data are unavailable at the county level, policymakers sometimes rely on state-level datasets to understand the health needs of their communities. This can be accomplished using small-area estimation techniques. However, it is unknown which small- area technique produces the most valid and precise results. Methods The reliability and accuracy of three methods used in small-area analyses were examined, including the synthetic method, spatial smoothing, and regression. To do this, severe work disability measures were first validated by comparing the 2000 Behavioral Risk Factor Surveillance System (BRFSS) and Census 2000 measures (used as the gold standard). The three small-area analysis methods were then applied to 2000 BRFSS data to examine how well each technique predicted county-level disability prevalence. Results The regression method produces the most valid and precise estimates of county-level disability prevalence over a large number of counties when a single year of data is used. Conclusions Local health departments and policymakers who need to track trends in behavioral risk factors and health status within their counties should utilize the regression method unless their county is large enough for direct estimation of the outcome of interest.