Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.11/8691 |
Resumo: | The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including, ordinary kriging, and clustering, was used to generate national distribution maps for several factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers. |
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Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-makingHealth dataInformation theoryBayesian learningOrdinary krigingGclustersThe health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including, ordinary kriging, and clustering, was used to generate national distribution maps for several factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers.SpringerRepositório Científico do Instituto Politécnico de Castelo BrancoPazo, MaríaBoente, CarlosAlbuquerque, M.T.D.Gerassis, SakiRoque, N.Taboada, Javier2023-10-27T14:27:33Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/8691engPazo, Maria [et al] (2023) - Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making. Mathematical Geosciences. DOI: 10.1007/s11004-023-10087-510.1007/s11004-023-10087-5info: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:RCAAP2023-11-11T01:45:51Zoai:repositorio.ipcb.pt:10400.11/8691Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:26:02.831837Repositório 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 |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making |
title |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making |
spellingShingle |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making Pazo, María Health data Information theory Bayesian learning Ordinary kriging Gclusters |
title_short |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making |
title_full |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making |
title_fullStr |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making |
title_full_unstemmed |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making |
title_sort |
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making |
author |
Pazo, María |
author_facet |
Pazo, María Boente, Carlos Albuquerque, M.T.D. Gerassis, Saki Roque, N. Taboada, Javier |
author_role |
author |
author2 |
Boente, Carlos Albuquerque, M.T.D. Gerassis, Saki Roque, N. Taboada, Javier |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Castelo Branco |
dc.contributor.author.fl_str_mv |
Pazo, María Boente, Carlos Albuquerque, M.T.D. Gerassis, Saki Roque, N. Taboada, Javier |
dc.subject.por.fl_str_mv |
Health data Information theory Bayesian learning Ordinary kriging Gclusters |
topic |
Health data Information theory Bayesian learning Ordinary kriging Gclusters |
description |
The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including, ordinary kriging, and clustering, was used to generate national distribution maps for several factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-27T14:27:33Z 2023 2023-01-01T00:00:00Z |
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/10400.11/8691 |
url |
http://hdl.handle.net/10400.11/8691 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pazo, Maria [et al] (2023) - Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making. Mathematical Geosciences. DOI: 10.1007/s11004-023-10087-5 10.1007/s11004-023-10087-5 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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