title: Modeling Area-Level Health Rankings.
abstract: OBJECTIVE
  Rank county health using a Bayesian factor analysis model.
  
  
  METHODS
  Secondary county data from the National Center for Health Statistics (through 2007) and Behavioral Risk Factor Surveillance System (through 2009).
  
  
  METHODS
  Our 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.
  
  
  METHODS
  The data were downloaded from www.countyhealthrankings.org.
  
  
  RESULTS
  Our estimated rankings are more similar to the CHRs for Wisconsin than Texas, as the data-derived factor weights are closer to the assigned weights for Wisconsin. The correlations between the CHRs 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.
  
  
  CONCLUSIONS
  The reliability of comprehensive CHRs 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.
authors.affiliation: Department of Economics, Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA.|National Bureau of Economic Research, Institute for the Study of Labor (IZA), Atlanta, GA.
authors.forename: Charles
authors.initials: C
authors.lastname: Courtemanche
authors.affiliation: Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Norris Cotton Cancer Center, Lebanon, NH.
authors.forename: Samir
authors.initials: S
authors.lastname: Soneji
authors.affiliation: Department of Economics, Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA.|National Bureau of Economic Research, Institute for the Study of Labor (IZA), Atlanta, GA.
authors.forename: Rusty
authors.initials: R
authors.lastname: Tchernis
mesh_terms: D001499:Bayes Theorem; D040343:Behavioral Risk Factor Surveillance System; D001724:Birth Weight; D003627:Data Interpretation, Statistical; D015438:Health Behavior; D006304:Health Status; D006801:Humans; D009111:Local Government; D008603:Mental Health; D020380:Needs Assessment; D062486:Public Health Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D013781:Texas; D014481:United States; D014922:Wisconsin
keywords: Bayesian; County; factor analysis; health; rank