Penalized composite link mixed models for two-dimensional count data

Mortality data provide valuable information for the study of the spatial distribution of mortality risk, in disciplines such as spatial epidemiology, medical demography, and public health. However, they are often available in an aggregated form over irregular geographical units, hindering the visual...

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Detalles Bibliográficos
Autores: Ayma, D., Durban, M., Lee, D.J., Eilers, P.H.C.
Tipo de recurso: informe técnico
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/363
Acceso en línea:http://hdl.handle.net/20.500.11824/363
Access Level:acceso abierto
Palabra clave:Penalized composite link models
Mixed Models
Mortality rates
Spatial disaggregation
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spelling Penalized composite link mixed models for two-dimensional count dataAyma, D.Durban, M.Lee, D.J.Eilers, P.H.C.Penalized composite link modelsMixed ModelsMortality ratesSpatial disaggregationMortality data provide valuable information for the study of the spatial distribution of mortality risk, in disciplines such as spatial epidemiology, medical demography, and public health. However, they are often available in an aggregated form over irregular geographical units, hindering the visualization of the underlying mortality risk and the detection of meaningful patterns. Also, it could be of interest to obtain mortality risk estimates on a finer spatial resolution, such that they can be linked with potential risk factors — in a posterior correlation analysis — that are usually measured in a different spatial resolution than mortality data. In this paper, we propose the use of the penalized composite link model and its representation as a mixed model to deal with these issues. This model takes into account the nature of mortality rates by incorporating the population size at the finest resolution, and allows the creation of mortality maps at a desirable scale, reducing the visual bias resulting from the spatial aggregation within original units. We illustrate our proposal with the analysis of several datasets related with deaths by respiratory diseases, cardiovascular diseases, and lung cancer.MTM2011-28285-C02-02 MTM2014-52184-P201720172015info:eu-repo/semantics/reportinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/363reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttp://e-archivo.uc3m.es/handle/10016/20672info:eu-repo/grantAgreement/MINECO//SEV-2013-0323info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2014-2017Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/3632026-06-19T12:47:47Z
dc.title.none.fl_str_mv Penalized composite link mixed models for two-dimensional count data
title Penalized composite link mixed models for two-dimensional count data
spellingShingle Penalized composite link mixed models for two-dimensional count data
Ayma, D.
Penalized composite link models
Mixed Models
Mortality rates
Spatial disaggregation
title_short Penalized composite link mixed models for two-dimensional count data
title_full Penalized composite link mixed models for two-dimensional count data
title_fullStr Penalized composite link mixed models for two-dimensional count data
title_full_unstemmed Penalized composite link mixed models for two-dimensional count data
title_sort Penalized composite link mixed models for two-dimensional count data
dc.creator.none.fl_str_mv Ayma, D.
Durban, M.
Lee, D.J.
Eilers, P.H.C.
author Ayma, D.
author_facet Ayma, D.
Durban, M.
Lee, D.J.
Eilers, P.H.C.
author_role author
author2 Durban, M.
Lee, D.J.
Eilers, P.H.C.
author2_role author
author
author
dc.subject.none.fl_str_mv Penalized composite link models
Mixed Models
Mortality rates
Spatial disaggregation
topic Penalized composite link models
Mixed Models
Mortality rates
Spatial disaggregation
description Mortality data provide valuable information for the study of the spatial distribution of mortality risk, in disciplines such as spatial epidemiology, medical demography, and public health. However, they are often available in an aggregated form over irregular geographical units, hindering the visualization of the underlying mortality risk and the detection of meaningful patterns. Also, it could be of interest to obtain mortality risk estimates on a finer spatial resolution, such that they can be linked with potential risk factors — in a posterior correlation analysis — that are usually measured in a different spatial resolution than mortality data. In this paper, we propose the use of the penalized composite link model and its representation as a mixed model to deal with these issues. This model takes into account the nature of mortality rates by incorporating the population size at the finest resolution, and allows the creation of mortality maps at a desirable scale, reducing the visual bias resulting from the spatial aggregation within original units. We illustrate our proposal with the analysis of several datasets related with deaths by respiratory diseases, cardiovascular diseases, and lung cancer.
publishDate 2015
dc.date.none.fl_str_mv 2015
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/report
info:eu-repo/semantics/publishedVersion
format report
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/363
url http://hdl.handle.net/20.500.11824/363
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://e-archivo.uc3m.es/handle/10016/20672
info:eu-repo/grantAgreement/MINECO//SEV-2013-0323
info:eu-repo/grantAgreement/Gobierno Vasco/BERC/BERC.2014-2017
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
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