Bayesian inference for asymptomatic COVID-19 infection rates.

To strengthen inferences meta-analyses are commonly used to summarize information from a set of independent studies. In some cases, though, the data may not satisfy the assumptions underlying the meta-analysis. Using three Bayesian methods that have a more general structure than the common meta-analytic ones, we can show the extent and nature of the pooling that is justified statistically. In this article, we reanalyze data from several reviews whose objective is to make inference about the COVID-19 asymptomatic infection rate. When it is unlikely that all of the true effect sizes come from a single source researchers should be cautious about pooling the data from all of the studies. Our findings and methodology are applicable to other COVID-19 outcome variables, and more generally.

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.

West Nile virus epidemic, northeast Ohio, 2002.

Serum samples and sociodemographic data were obtained from 1,209 Ohio residents. West Nile virus immunoglobulin M (IgM) and IgG antibodies were detected by enzyme-linked immunosorbent assay and confirmed. Children were 4.5 times more likely to become infected yet 110 times less likely to have neuroinvasive disease develop.