Assessing health insurance coverage in Florida using the behavioral risk factor surveillance system

We use data from the Behavioral Risk Factor Surveillance System, BRFSS, to investigate the important topic of health insurance coverage. Here, our investigation is about coverage in Florida at the county level and for important subpopulations defined by age, gender, and race. As large US government administered surveys are designed to provide reliable estimates of finite population characteristics for large geographical areas such as the entire US or for individual states, they are not designed to make direct inferences for small geographical regions and/or subpopulations. Given the importance of this topic, we use Bayesian predictive inference for the finite population quantities of interest, thus avoiding approximations necessary in other approaches. There are careful diagnostic checks of the model that we propose, including residual checks and cross‐validation, together with formal tests of the concordance between the observed data and model. We check whether there is a selection bias investigating, in particular, the possible role of the conventional survey weights in correcting for selection bias and in improving inferences. We display our results in choropleth maps, together with displays of map variation. The latter maps can be used to assess the visual appearance of the “mean map”, ie, the one usually presented, relative to a sequence of possible maps. Finally, we compare our county estimates of health insurance coverage with estimates from the BRFSS produced by the Centers for Disease Control and Prevention and from the US Census Bureau under their SAHIE program.