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.
|Date of creation:||Sep 2013|
|Publication status:||published as Modeling Area-Level Health Rankings Charles Courtemanche Ph.D.1,3,*, Samir Soneji Ph.D.2 andRusty Tchernis Ph.D.1, Health Services Research Volume 50, Issue 5, pages 1413–1431, October 2015|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
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- Hogan J.W. & Tchernis R., 2004. "Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 314-324, January.
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