Disease mapping models for data with weak spatial dependence or spatial discontinuities

Detalhes bibliográficos
Autor(a) principal: Baptista, Helena
Data de Publicação: 2020
Outros Autores: Congdon, Peter, Mendes, Jorge M., Maria Rodrigues, Ana, Canhão, Helena, Dias, Sara Simões
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/108167
Resumo: Baptista, H., Congdon, P., Mendes, J. M., Rodrigues, A. M., Canhão, H., & Dias, S. S. (2020). Disease mapping models for data with weak spatial dependence or spatial discontinuities. Epidemiologic Methods, 9(1), [20190025]. https://doi.org/10.1515/em-2019-0025
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spelling Disease mapping models for data with weak spatial dependence or spatial discontinuitiesbayesian modellingbody mass index(BMI)limiting health problemssimilarity-based and adaptive modelsspatial epidemiologyEpidemiologyApplied MathematicsSDG 3 - Good Health and Well-beingBaptista, H., Congdon, P., Mendes, J. M., Rodrigues, A. M., Canhão, H., & Dias, S. S. (2020). Disease mapping models for data with weak spatial dependence or spatial discontinuities. Epidemiologic Methods, 9(1), [20190025]. https://doi.org/10.1515/em-2019-0025Recent advances in the spatial epidemiology literature have extended traditional approaches by including determinant disease factors that allow for non-local smoothing and/or non-spatial smoothing. In this article, two of those approaches are compared and are further extended to areas of high interest from the public health perspective. These are a conditionally specified Gaussian random field model, using a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a spatially adaptive conditional autoregressive prior model. The methods are specially design to handle cases when there is no evidence of positive spatial correlation or the appropriate mix between local and global smoothing is not constant across the region being study. Both approaches proposed in this article are producing results consistent with the published knowledge, and are increasing the accuracy to clearly determine areas of high- or low-risk.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)Comprehensive Health Research Centre (CHRC) - pólo NMSNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Centro de Estudos de Doenças Crónicas (CEDOC)RUNBaptista, HelenaCongdon, PeterMendes, Jorge M.Maria Rodrigues, AnaCanhão, HelenaDias, Sara Simões2020-12-04T00:18:50Z2020-01-012020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/108167eng2194-9263PURE: 26637243https://doi.org/10.1515/em-2019-0025info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:52:46Zoai:run.unl.pt:10362/108167Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:41:07.985479Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Disease mapping models for data with weak spatial dependence or spatial discontinuities
title Disease mapping models for data with weak spatial dependence or spatial discontinuities
spellingShingle Disease mapping models for data with weak spatial dependence or spatial discontinuities
Baptista, Helena
bayesian modelling
body mass index(BMI)
limiting health problems
similarity-based and adaptive models
spatial epidemiology
Epidemiology
Applied Mathematics
SDG 3 - Good Health and Well-being
title_short Disease mapping models for data with weak spatial dependence or spatial discontinuities
title_full Disease mapping models for data with weak spatial dependence or spatial discontinuities
title_fullStr Disease mapping models for data with weak spatial dependence or spatial discontinuities
title_full_unstemmed Disease mapping models for data with weak spatial dependence or spatial discontinuities
title_sort Disease mapping models for data with weak spatial dependence or spatial discontinuities
author Baptista, Helena
author_facet Baptista, Helena
Congdon, Peter
Mendes, Jorge M.
Maria Rodrigues, Ana
Canhão, Helena
Dias, Sara Simões
author_role author
author2 Congdon, Peter
Mendes, Jorge M.
Maria Rodrigues, Ana
Canhão, Helena
Dias, Sara Simões
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
Comprehensive Health Research Centre (CHRC) - pólo NMS
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
Centro de Estudos de Doenças Crónicas (CEDOC)
RUN
dc.contributor.author.fl_str_mv Baptista, Helena
Congdon, Peter
Mendes, Jorge M.
Maria Rodrigues, Ana
Canhão, Helena
Dias, Sara Simões
dc.subject.por.fl_str_mv bayesian modelling
body mass index(BMI)
limiting health problems
similarity-based and adaptive models
spatial epidemiology
Epidemiology
Applied Mathematics
SDG 3 - Good Health and Well-being
topic bayesian modelling
body mass index(BMI)
limiting health problems
similarity-based and adaptive models
spatial epidemiology
Epidemiology
Applied Mathematics
SDG 3 - Good Health and Well-being
description Baptista, H., Congdon, P., Mendes, J. M., Rodrigues, A. M., Canhão, H., & Dias, S. S. (2020). Disease mapping models for data with weak spatial dependence or spatial discontinuities. Epidemiologic Methods, 9(1), [20190025]. https://doi.org/10.1515/em-2019-0025
publishDate 2020
dc.date.none.fl_str_mv 2020-12-04T00:18:50Z
2020-01-01
2020-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/108167
url http://hdl.handle.net/10362/108167
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2194-9263
PURE: 26637243
https://doi.org/10.1515/em-2019-0025
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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