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Modeling Area-Level Health Rankings

Author

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  • Courtemanche, Charles

    () (Georgia State University)

  • Soneji, Samir

    () (Geisel School of Medicine at Dartmouth College)

  • Tchernis, Rusty

    () (Georgia State University)

Abstract

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.

Suggested Citation

  • Courtemanche, Charles & Soneji, Samir & Tchernis, Rusty, 2013. "Modeling Area-Level Health Rankings," IZA Discussion Papers 7631, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp7631
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    References listed on IDEAS

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    1. Peppard, P.E. & Kindig, D.A. & Dranger, E. & Jovaag, A. & Remington, P.L., 2008. "Ranking community health status to stimulate discussion of local public health issues: The Wisconsin County health rankings," American Journal of Public Health, American Public Health Association, vol. 98(2), pages 209-212.
    2. Phillips, C.D. & McLeroy, K.R., 2004. "Health in rural America: Remembering the importance of place," American Journal of Public Health, American Public Health Association, vol. 94(10), pages 1661-1663.
    3. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(3), pages 409-431, August.
    4. Kindig, D.A. & Stoddart, G., 2003. "What is population health?," American Journal of Public Health, American Public Health Association, vol. 93(3), pages 380-383.
    5. Friedman, D.J. & Starfield, B., 2003. "Models of population health: Their value for US public health practice, policy, and research," American Journal of Public Health, American Public Health Association, vol. 93(3), pages 366-369.
    6. 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.
    7. Doorslaer, Eddy van & Jones, Andrew M., 2003. "Inequalities in self-reported health: validation of a new approach to measurement," Journal of Health Economics, Elsevier, vol. 22(1), pages 61-87, January.
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    Cited by:

    1. Qiu, Qihua & Sung, Jaesang & Davis, Will & Tchernis, Rusty, 2018. "Using spatial factor analysis to measure human development," Journal of Development Economics, Elsevier, vol. 132(C), pages 130-149.

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    More about this item

    Keywords

    county; rank; health; factor analysis; Bayesian;
    All these keywords.

    JEL classification:

    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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