Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/10174/29383 |
Resumo: | Tuberculosis (TB) is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). In 2017, the World Health Organization (WHO) estimated 10.0 million people developed TB and 1.3 million deaths (range, 1.2–1.4 million) among HIV-negative people with an additional 300 000 deaths from TB (range, 266 000–335 000) among HIVpositive people. Studies that understand the socio-demographic characteristics, time and spatial distribution of the disease are vital to allocating resources in order to improve National TB Programs. The database includes information from all confirmed Pulmonary TB (PTB) cases notified in Continental Portugal between 2000 and 2010. Following a descriptive analysis of the main risk factors of the disease, a Structured Additive Regression (STAR) model is presented exploring possible spatial and temporal correlations in PTB incidence rates in order to identify the regions of increased incidence rates. Three main regions are identified as statistically significant areas of increased PTB incidence rates in Continental Portugal. STAR models proved to be a valuable and effective approach in identifying PTB incidence rates and will be used in future research to identify the associated risk factors in Continental Portugal, yielding high-level information for decision-making in TB control. |
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Structured Additive Regression Modeling of Pulmonary Tuberculosis InfectionStructured Additive Regression ModelsPulmonary TuberculosisSpatialTemporal EpidemiologyFull BayesianEmpirical BayesianTuberculosis (TB) is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). In 2017, the World Health Organization (WHO) estimated 10.0 million people developed TB and 1.3 million deaths (range, 1.2–1.4 million) among HIV-negative people with an additional 300 000 deaths from TB (range, 266 000–335 000) among HIVpositive people. Studies that understand the socio-demographic characteristics, time and spatial distribution of the disease are vital to allocating resources in order to improve National TB Programs. The database includes information from all confirmed Pulmonary TB (PTB) cases notified in Continental Portugal between 2000 and 2010. Following a descriptive analysis of the main risk factors of the disease, a Structured Additive Regression (STAR) model is presented exploring possible spatial and temporal correlations in PTB incidence rates in order to identify the regions of increased incidence rates. Three main regions are identified as statistically significant areas of increased PTB incidence rates in Continental Portugal. STAR models proved to be a valuable and effective approach in identifying PTB incidence rates and will be used in future research to identify the associated risk factors in Continental Portugal, yielding high-level information for decision-making in TB control.Proceeding of the 62nd ISI World Statistical Congress2021-03-24T19:16:04Z2021-03-242020-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/29383http://hdl.handle.net/10174/29383engDepartment of Statistics Malaysia (DOSM). 2019. Proceeding of the 62nd ISI World Statistics Congress 2019: Contributed Paper Session: Volume 3, 2019. 444 pageshttps://2019.isiproceedings.org/Files/9.Contributed-Paper-Session(CPS)-Volume-3.pdfbruno.desousa@fpce.uc.ptnddmog@uevora.ptPatricia.Filipe@iscte-iul.ptana.costa@estesl.ipl.ptCNunes@ensp.unl.pt336de Sousa, BrunoPires, CarlosGomes, DulceFilipe, Patrícia A.Costa-Veiga, ANunes, Carlainfo: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:RCAAP2024-01-03T19:26:11Zoai:dspace.uevora.pt:10174/29383Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:18:55.674089Repositório 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 |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection |
title |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection |
spellingShingle |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection de Sousa, Bruno Structured Additive Regression Models Pulmonary Tuberculosis SpatialTemporal Epidemiology Full Bayesian Empirical Bayesian |
title_short |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection |
title_full |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection |
title_fullStr |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection |
title_full_unstemmed |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection |
title_sort |
Structured Additive Regression Modeling of Pulmonary Tuberculosis Infection |
author |
de Sousa, Bruno |
author_facet |
de Sousa, Bruno Pires, Carlos Gomes, Dulce Filipe, Patrícia A. Costa-Veiga, A Nunes, Carla |
author_role |
author |
author2 |
Pires, Carlos Gomes, Dulce Filipe, Patrícia A. Costa-Veiga, A Nunes, Carla |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
de Sousa, Bruno Pires, Carlos Gomes, Dulce Filipe, Patrícia A. Costa-Veiga, A Nunes, Carla |
dc.subject.por.fl_str_mv |
Structured Additive Regression Models Pulmonary Tuberculosis SpatialTemporal Epidemiology Full Bayesian Empirical Bayesian |
topic |
Structured Additive Regression Models Pulmonary Tuberculosis SpatialTemporal Epidemiology Full Bayesian Empirical Bayesian |
description |
Tuberculosis (TB) is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). In 2017, the World Health Organization (WHO) estimated 10.0 million people developed TB and 1.3 million deaths (range, 1.2–1.4 million) among HIV-negative people with an additional 300 000 deaths from TB (range, 266 000–335 000) among HIVpositive people. Studies that understand the socio-demographic characteristics, time and spatial distribution of the disease are vital to allocating resources in order to improve National TB Programs. The database includes information from all confirmed Pulmonary TB (PTB) cases notified in Continental Portugal between 2000 and 2010. Following a descriptive analysis of the main risk factors of the disease, a Structured Additive Regression (STAR) model is presented exploring possible spatial and temporal correlations in PTB incidence rates in order to identify the regions of increased incidence rates. Three main regions are identified as statistically significant areas of increased PTB incidence rates in Continental Portugal. STAR models proved to be a valuable and effective approach in identifying PTB incidence rates and will be used in future research to identify the associated risk factors in Continental Portugal, yielding high-level information for decision-making in TB control. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-01T00:00:00Z 2021-03-24T19:16:04Z 2021-03-24 |
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/10174/29383 http://hdl.handle.net/10174/29383 |
url |
http://hdl.handle.net/10174/29383 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Department of Statistics Malaysia (DOSM). 2019. Proceeding of the 62nd ISI World Statistics Congress 2019: Contributed Paper Session: Volume 3, 2019. 444 pages https://2019.isiproceedings.org/Files/9.Contributed-Paper-Session(CPS)-Volume-3.pdf bruno.desousa@fpce.uc.pt nd dmog@uevora.pt Patricia.Filipe@iscte-iul.pt ana.costa@estesl.ipl.pt CNunes@ensp.unl.pt 336 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Proceeding of the 62nd ISI World Statistical Congress |
publisher.none.fl_str_mv |
Proceeding of the 62nd ISI World Statistical Congress |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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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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1799136671262834688 |