Bayesian analysis of population health data

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...

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Autores: Młynarczyk, Dorota|||0000-0002-7957-2567, Armero, Carmen|||0000-0001-9839-6442, Gómez-Rubio, Virgilio|||0000-0002-4791-3072, Puig i Casado, Pere|||0000-0002-6607-9642
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:271752
Acceso en línea:https://ddd.uab.cat/record/271752
https://dx.doi.org/urn:doi:10.3390/math9050577
Access Level:acceso abierto
Palabra clave:Bayesian inference
Disease mapping
Integrated nested Laplace approximation
Spatial models
Survival models
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spelling Bayesian analysis of population health dataMłynarczyk, Dorota|||0000-0002-7957-2567Armero, Carmen|||0000-0001-9839-6442Gómez-Rubio, Virgilio|||0000-0002-4791-3072Puig i Casado, Pere|||0000-0002-6607-9642Bayesian inferenceDisease mappingIntegrated nested Laplace approximationSpatial modelsSurvival modelsThe 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. 22021-01-0120212021-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/271752https://dx.doi.org/urn:doi:10.3390/math9050577reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2019-106341GB-I00Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-096072-B-I00open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2717522026-06-06T12:50:31Z
dc.title.none.fl_str_mv Bayesian analysis of population health data
title Bayesian analysis of population health data
spellingShingle Bayesian analysis of population health data
Młynarczyk, Dorota|||0000-0002-7957-2567
Bayesian inference
Disease mapping
Integrated nested Laplace approximation
Spatial models
Survival models
title_short Bayesian analysis of population health data
title_full Bayesian analysis of population health data
title_fullStr Bayesian analysis of population health data
title_full_unstemmed Bayesian analysis of population health data
title_sort Bayesian analysis of population health data
dc.creator.none.fl_str_mv Młynarczyk, Dorota|||0000-0002-7957-2567
Armero, Carmen|||0000-0001-9839-6442
Gómez-Rubio, Virgilio|||0000-0002-4791-3072
Puig i Casado, Pere|||0000-0002-6607-9642
author Młynarczyk, Dorota|||0000-0002-7957-2567
author_facet Młynarczyk, Dorota|||0000-0002-7957-2567
Armero, Carmen|||0000-0001-9839-6442
Gómez-Rubio, Virgilio|||0000-0002-4791-3072
Puig i Casado, Pere|||0000-0002-6607-9642
author_role author
author2 Armero, Carmen|||0000-0001-9839-6442
Gómez-Rubio, Virgilio|||0000-0002-4791-3072
Puig i Casado, Pere|||0000-0002-6607-9642
author2_role author
author
author
dc.subject.none.fl_str_mv Bayesian inference
Disease mapping
Integrated nested Laplace approximation
Spatial models
Survival models
topic Bayesian inference
Disease mapping
Integrated nested Laplace approximation
Spatial models
Survival models
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2
2021-01-01
2021
2021-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/271752
https://dx.doi.org/urn:doi:10.3390/math9050577
url https://ddd.uab.cat/record/271752
https://dx.doi.org/urn:doi:10.3390/math9050577
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2019-106341GB-I00
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-096072-B-I00
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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