ObjectiveRank county health using a Bayesian factor analysis model.Data SourcesSecondary county data from the National Center for Health Statistics (through 2007) and Behavioral Risk Factor Surveillance System (through 2009).Study DesignOur model builds on the existing county health rankings (CHRs) by using data‐derived weights to compute ranks from mortality and morbidity variables, and by quantifying uncertainty based on population, spatial correlation, and missing data. We apply our model to Wisconsin, which has comprehensive data, and Texas, which has substantial missing information.Data Collection MethodsThe data were downloaded fromwww.countyhealthrankings.org.Principal FindingsOur estimated rankings are more similar to theCHRs for Wisconsin than Texas, as the data‐derived factor weights are closer to the assigned weights for Wisconsin. The correlations between theCHRs and our ranks are 0.89 for Wisconsin and 0.65 for Texas. Uncertainty is especially severe for Texas given the state's substantial missing data.ConclusionsThe reliability of comprehensiveCHRs varies from state to state. We advise focusing on the counties that remain among the least healthy after incorporating alternate weighting methods and accounting for uncertainty. Our results also highlight the need for broader geographic coverage in health data.