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Bayesian Analysis of Population Health Data

Author

Listed:
  • Dorota Młynarczyk

    (Departament de Matemàtiques, Edifici C, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), 08193 Barcelona, Spain)

  • Carmen Armero

    (Departament d’Estadística i Investigació Operativa, Facultat de Ciències Matemàtiques, Universitat de València, Burjassot, 46100 València, Spain)

  • Virgilio Gómez-Rubio

    (Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, 02071 Albacete, Spain)

  • Pedro Puig

    (Departament de Matemàtiques, Edifici C, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), 08193 Barcelona, Spain
    Centre de Recerca Matemàtica (CRM), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain)

Abstract

The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and survival models are considered for analyzing the individual probabilities of stroke and the times to the occurrence of an ischemic stroke event. Demographic and socioeconomic variables as well as drug prescription information are available at an individual level. Spatial variation is considered by means of region-level random effects.

Suggested Citation

  • Dorota Młynarczyk & Carmen Armero & Virgilio Gómez-Rubio & Pedro Puig, 2021. "Bayesian Analysis of Population Health Data," Mathematics, MDPI, vol. 9(5), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:577-:d:513243
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    References listed on IDEAS

    as
    1. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    2. van Niekerk, J. & Bakka, H. & Rue, H., 2021. "A principled distance-based prior for the shape of the Weibull model," Statistics & Probability Letters, Elsevier, vol. 174(C).
    3. Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
    4. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    5. Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
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