Modeling Area-Level Health Rankings
We propose a Bayesian factor analysis model to rank the health of localities. Mortality and morbidity variables empirically contribute to the resulting rank, and population and spatial correlation are incorporated into a measure of uncertainty. We use county-level data from Texas and Wisconsin to compare our approach to conventional rankings that assign deterministic factor weights and ignore uncertainty. Greater discrepancies in rankings emerge for Texas than Wisconsin since the differences between the empirically-derived and deterministic weights are more substantial. Uncertainty is evident in both states but becomes especially large in Texas after incorporating noise from imputing its considerable missing data.
We thank Barry Hirsch, Dann Millimet, Patrick Remington, Chris Ruhm, Harold Sox, and audiences at the Georgia Health Econ Research Day, Eastern Economics Conference, Usery Workplace Research Group, and Universidad del Pacifico for helpful feedback. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.