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

dc.contributor.authorCassy, Sheyla Rodrigues
dc.contributor.authorManda, S.O.M. (Samuel)
dc.contributor.authorMarques, Filipe
dc.contributor.authorMartins, Maria do Rosario Oliveira
dc.contributor.emailsamuel.manda@up.ac.zaen_US
dc.date.accessioned2022-12-12T05:42:42Z
dc.date.available2022-12-12T05:42:42Z
dc.date.issued2022-05-23
dc.descriptionDATA AVAILABILITY STATEMENT : The datasets used in this study are publicly available and can be downloaded at https://dhsprogram.com/ (accessed on 21 July 2021).en_US
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_US
dc.description.departmentStatisticsen_US
dc.description.sponsorshipThe 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.en_US
dc.description.urihttps://www.mdpi.com/journal/ijerphen_US
dc.identifier.citationCassy, S.R.; Manda, S.; Marques, F.; Martins, M.d.R.O. 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. International Journal of Environmental Research and Public Health 2022, 19, 6319. https://doi.org/10.3390/ijerph19106319.en_US
dc.identifier.issn1660-4601 (online)
dc.identifier.other10.3390/ijerph19106319
dc.identifier.urihttps://repository.up.ac.za/handle/2263/88729
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.subjectBayesian spatial smoothingen_US
dc.subjectSurvey sampling weightsen_US
dc.subjectDisease mappingen_US
dc.subjectChild malnutritionen_US
dc.subjectSub-Saharan Africa (SSA)en_US
dc.subjectDeviance information criteria (DIC)en_US
dc.subjectMalawi
dc.subjectMozambique
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_US
dc.typeArticleen_US

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