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Modeling Area‐Level Health Rankings
Author(s) -
Courtemanche Charles,
Soneji Samir,
Tchernis Rusty
Publication year - 2015
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.12352
Subject(s) - missing data , small area estimation , statistics , weighting , behavioral risk factor surveillance system , reliability (semiconductor) , population , data mining , econometrics , computer science , geography , medicine , environmental health , mathematics , power (physics) , physics , quantum mechanics , estimator , radiology
Objective 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 ( CHR 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 www.countyhealthrankings.org . Principal Findings Our estimated rankings are more similar to the CHR s for Wisconsin than Texas, as the data‐derived factor weights are closer to the assigned weights for Wisconsin. The correlations between the CHR 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. Conclusions The reliability of comprehensive CHR 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.