Bayesian Analysis of Population Health Data

Dorota Młynarczyk, Carmen Armero, Virgilio Gómez-Rubio and Pedro Puig
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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

Mathematics, 2021, vol. 9, issue 5, 1-15

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.

Keywords: bayesian inference; disease mapping; integrated nested Laplace approximation; spatial models; survival models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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