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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 Mozambique

2022-05-01Artigo científico Acesso aberto
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dc.contributor.authorCassy, Sheyla Rodrigues
dc.contributor.authorManda, Samuel
dc.contributor.authorMarques, Filipe
dc.contributor.authorMartins, Maria Do Rosário Oliveira
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.institutionPopulation health, policies and services (PPS)
dc.contributor.institutionGlobal Health and Tropical Medicine (GHTM)
dc.contributor.institutionInstituto de Higiene e Medicina Tropical (IHMT)
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2022-09-23T22:26:13Z
dc.date.available2022-09-23T22:26:13Z
dc.date.issued2022-05-01
dc.descriptionFunding 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.
dc.description.abstractMost 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent2642682
dc.identifier.doi10.3390/ijerph19106319
dc.identifier.issn1661-7827
dc.identifier.otherPURE: 46703251
dc.identifier.otherPURE UUID: 914ef92a-964c-4c6a-b4f8-a8281a35f0b3
dc.identifier.otherScopus: 85130313109
dc.identifier.otherPubMed: 35627854
dc.identifier.otherORCID: /0000-0002-7941-0285/work/119585168
dc.identifier.urihttp://hdl.handle.net/10362/144003
dc.identifier.urlhttps://www.scopus.com/pages/publications/85130313109
dc.language.isoeng
dc.peerreviewedyes
dc.subjectBayesian spatial smoothing
dc.subjectchild malnutrition, fever and diarrhea
dc.subjectdisease mapping
dc.subjectsub-Saharan Africa
dc.subjectsurvey sampling weights
dc.subjectPublic Health, Environmental and Occupational Health
dc.subjectStatistics and Probability
dc.subjectSDG 3 - Good Health and Well-being
dc.subjectSDG 10 - Reduced Inequalities
dc.titleAccounting 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 Mozambiqueen
dc.typejournal article
degois.publication.issue10
degois.publication.titleInternational Journal of Environmental Research and Public Health
degois.publication.volume19
dspace.entity.typePublication
rcaap.rightsopenAccess

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