41a5ef85740ec5076416c66d000639f7 Mat_a2021v9n5p577.pdf e0e160eb0f85e4f80c0419d28763f8372bf26836 Mat_a2021v9n5p577.pdf 3f2ad2ae8d8db5770f5506afdb3b57fd1d33286dce07cc98788e5bdae69318db Mat_a2021v9n5p577.pdf Title: Bayesian Analysis of Population Health Data Subject: 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 Author: Dorota Młynarczyk, Carmen Armero, Virgilio Gómez-Rubio and Pedro Puig Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Mar 9 09:17:15 2021 CET ModDate: Tue Mar 9 09:20:34 2021 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 15 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2007290 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- KUTYBX+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 PIVFWF+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 VXLEHR+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 DVTHKG+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 27 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 49 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 53 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 57 0 XLNSDE+URWPalladioL-Roma-Slant_167 Type 1 Custom yes yes no 69 0 WMELZR+CMEX10 Type 1 Builtin yes yes no 76 0 TLRYUO+PazoMath-Italic Type 1 Builtin yes yes no 80 0 JUBGRC+CMSY10 Type 1 Builtin yes yes no 84 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 88 0 FMHRLD+PazoMath-BoldItalic Type 1 Builtin yes yes no 92 0 KGCAVY+URWPalladioL-BoldItal Type 1 Custom yes yes no 96 0 DJEYPD+PazoMath Type 1 Builtin yes yes no 103 0 SYFPBV+CMMI10 Type 1 Builtin yes yes no 110 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2023-12-05 02:35:25 CET RepresentationInformation: Mat_a2021v9n5p577.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2023-12-04 16:23:16 CET Size: 2007290 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 276 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Statistical Models Destination: section.2 Children: Item: Title: Logistic Regression Destination: subsection.2.1 Item: Title: Accelerated Failure Time Survival Models Destination: subsection.2.2 Item: Title: Bayesian Inference and the Integrated Nested Laplace Approximation Destination: subsection.2.3 Item: Title: Analysis of Ischemic Stroke and Risk Factors in Poland Destination: section.3 Children: Item: Title: Bayesian Logistic and Survival Modeling Destination: subsection.3.1 Item: Title: Discussion Destination: section.4 Item: Title: References Destination: section.4 Info: Title: Bayesian Analysis of Population Health Data Author: Dorota Młynarczyk, Carmen Armero, Virgilio Gómez-Rubio and Pedro Puig Subject: 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. 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