Volume 50 | Number 5 | October 2015

Abstract List

Charles Courtemanche Ph.D., Samir Soneji Ph.D., Rusty Tchernis Ph.D.


Rank county health using a Bayesian factor analysis model.

Data Sources

Secondary county data from the National Center for Health Statistics (through 2007) and Behavioral Risk Factor Surveillance System (through 2009).

Study Design

Our model builds on the existing county health rankings (s) 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 Methods

The data were downloaded from .

Principal Findings

Our estimated rankings are more similar to the s for Wisconsin than Texas, as the data‐derived factor weights are closer to the assigned weights for Wisconsin. The correlations between the s 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.


The reliability of comprehensive s 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.