Bayesian Small Area Estimates of Diabetes Incidence by United States County, 2009

Abstract

In the United States, diabetes is common and costly. Programs to prevent new cases of diabetes are often carried out at the level of the county, a unit of local government. Thus, efficient targeting of such programs requires county-level estimates of diabetes incidence-the fraction of the non-diabetic population who received their diagnosis of diabetes during the past 12 months. Previously, only estimates of prevalence-the overall fraction of population who have the disease-have been available at the county level. Counties with high prevalence might or might not be the same as counties with high incidence, due to spatial variation in mortality and relocation of persons with incident diabetes to another county. Existing methods cannot be used to estimate county-level diabetes incidence, because the fraction of the population who receive a diabetes diagnosis in any year is too small. Here, we extend previously developed methods of Bayesian small-area estimation of prevalence, using diffuse priors, to estimate diabetes incidence for all U.S. counties based on data from a survey designed to yield state-level estimates. We found high incidence in the southeastern United States, the Appalachian region, and in scattered counties throughout the western U.S. Our methods might be applicable in other circumstances in which all cases of a rare condition also must be cases of a more common condition (in this analysis, "newly diagnosed cases of diabetes" and "cases of diabetes"). If appropriate data are available, our methods can be used to estimate proportion of the population with the rare condition at greater geographic specificity than the data source was designed to provide.

Keywords: Bayesian estimates; diabetes; small area estimates.

Figure 1

Map of 2009 diabetes incidence for 3143 U.S. counties

Figure 2

Scatterplot of design-based state incidence versus model-based county incidence aggregated to the state level for 48 states and the District of Columbia

Figure 3

Scatterplot of root mean squared error of model-based incidence estimates over population value versus area. Results are averaged over 50 random samples from the test population

Figure 4

Box plots of difference in incidence, population minus model-based, by area. Each plot box represents 50 random samples from the test population