Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/10362/144003 |
Resumo: | Funding Information: Acknowledgments: Support from a doctoral Calouste Gulbenkian Foundation grant (135422 to S.R.C.) is acknowledged. Support from the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) (through the project UIDB/00297/2020 (Centro de Matemática e Aplicações) to S.R.C. and F.M.) is acknowledged. Support from the South Africa Medical Research Council (SAMRC) with funds from the National Treasury in terms of the SAMRC’s competitive Intramural Research Fund (SAMRC-RFA-IFF-02-2016 to S.M.) is acknowledged. We also extend thanks to DHS Measure for allowing us to use the 2015-16 MDHS and 2015 IMASIDA datasets for this study. Funding Information: Funding: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Funding Information: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
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Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and MozambiqueBayesian spatial smoothingchild malnutrition, fever and diarrheadisease mappingsub-Saharan Africasurvey sampling weightsPublic Health, Environmental and Occupational HealthStatistics and ProbabilitySDG 3 - Good Health and Well-beingSDG 10 - Reduced InequalitiesFunding Information: Acknowledgments: Support from a doctoral Calouste Gulbenkian Foundation grant (135422 to S.R.C.) is acknowledged. Support from the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) (through the project UIDB/00297/2020 (Centro de Matemática e Aplicações) to S.R.C. and F.M.) is acknowledged. Support from the South Africa Medical Research Council (SAMRC) with funds from the National Treasury in terms of the SAMRC’s competitive Intramural Research Fund (SAMRC-RFA-IFF-02-2016 to S.M.) is acknowledged. We also extend thanks to DHS Measure for allowing us to use the 2015-16 MDHS and 2015 IMASIDA datasets for this study. Funding Information: Funding: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Funding Information: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Most analyses of spatial patterns of disease risk using health survey data fail to adequately account for the complex survey designs. Particularly, the survey sampling weights are often ignored in the analyses. Thus, the estimated spatial distribution of disease risk could be biased and may lead to erroneous policy decisions. This paper aimed to present recent statistical advances in disease-mapping methods that incorporate survey sampling in the estimation of the spatial distribution of disease risk. The methods were then applied to the estimation of the geographical distribution of child malnutrition in Malawi, and child fever and diarrhoea in Mozambique. The estimation of the spatial distributions of the child disease risk was done by Bayesian methods. Accounting for sampling weights resulted in smaller standard errors for the estimated spatial disease risk, which increased the confidence in the conclusions from the findings. The estimated geographical distributions of the child disease risk were similar between the methods. However, the fits of the models to the data, as measured by the deviance information criteria (DIC), were different.CMA - Centro de Matemática e AplicaçõesFaculdade de Ciências e Tecnologia (FCT)Population health, policies and services (PPS)Global Health and Tropical Medicine (GHTM)Instituto de Higiene e Medicina Tropical (IHMT)RUNCassy, Sheyla RodriguesManda, SamuelMarques, FilipeMartins, Maria Do Rosário Oliveira2022-09-23T22:26:13Z2022-05-012022-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/144003eng1661-7827PURE: 46703251https://doi.org/10.3390/ijerph19106319info: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:RCAAP2024-03-11T05:23:05Zoai:run.unl.pt:10362/144003Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:16.365937Repositório 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 |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique |
title |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique |
spellingShingle |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique Cassy, Sheyla Rodrigues Bayesian spatial smoothing child malnutrition, fever and diarrhea disease mapping sub-Saharan Africa survey sampling weights Public Health, Environmental and Occupational Health Statistics and Probability SDG 3 - Good Health and Well-being SDG 10 - Reduced Inequalities |
title_short |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique |
title_full |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique |
title_fullStr |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique |
title_full_unstemmed |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique |
title_sort |
Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique |
author |
Cassy, Sheyla Rodrigues |
author_facet |
Cassy, Sheyla Rodrigues Manda, Samuel Marques, Filipe Martins, Maria Do Rosário Oliveira |
author_role |
author |
author2 |
Manda, Samuel Marques, Filipe Martins, Maria Do Rosário Oliveira |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
CMA - Centro de Matemática e Aplicações Faculdade de Ciências e Tecnologia (FCT) Population health, policies and services (PPS) Global Health and Tropical Medicine (GHTM) Instituto de Higiene e Medicina Tropical (IHMT) RUN |
dc.contributor.author.fl_str_mv |
Cassy, Sheyla Rodrigues Manda, Samuel Marques, Filipe Martins, Maria Do Rosário Oliveira |
dc.subject.por.fl_str_mv |
Bayesian spatial smoothing child malnutrition, fever and diarrhea disease mapping sub-Saharan Africa survey sampling weights Public Health, Environmental and Occupational Health Statistics and Probability SDG 3 - Good Health and Well-being SDG 10 - Reduced Inequalities |
topic |
Bayesian spatial smoothing child malnutrition, fever and diarrhea disease mapping sub-Saharan Africa survey sampling weights Public Health, Environmental and Occupational Health Statistics and Probability SDG 3 - Good Health and Well-being SDG 10 - Reduced Inequalities |
description |
Funding Information: Acknowledgments: Support from a doctoral Calouste Gulbenkian Foundation grant (135422 to S.R.C.) is acknowledged. Support from the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) (through the project UIDB/00297/2020 (Centro de Matemática e Aplicações) to S.R.C. and F.M.) is acknowledged. Support from the South Africa Medical Research Council (SAMRC) with funds from the National Treasury in terms of the SAMRC’s competitive Intramural Research Fund (SAMRC-RFA-IFF-02-2016 to S.M.) is acknowledged. We also extend thanks to DHS Measure for allowing us to use the 2015-16 MDHS and 2015 IMASIDA datasets for this study. Funding Information: Funding: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Funding Information: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-09-23T22:26:13Z 2022-05-01 2022-05-01T00:00:00Z |
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/10362/144003 |
url |
http://hdl.handle.net/10362/144003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1661-7827 PURE: 46703251 https://doi.org/10.3390/ijerph19106319 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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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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