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

Sheyla Rodrigues Cassy, Samuel Manda, Filipe Marques and Maria do Rosário Oliveira Martins
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Sheyla Rodrigues Cassy: Department of Mathematics and Informatics, Faculty of Sciences, Eduardo Mondlane University, Maputo 254, Mozambique
Samuel Manda: Department of Statistics, University of Pretoria, Pretoria 0028, South Africa
Filipe Marques: Centre for Mathematics and Applications, CMA, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Lisbon, Portugal
Maria do Rosário Oliveira Martins: Global Health and Tropical Medicine, GHTM, Instituto de Higiene e Medicina Tropical, IHMT, Universidade Nova de Lisboa, 1349-0008 Lisbon, Portugal

IJERPH, 2022, vol. 19, issue 10, 1-15

Abstract: 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.

Keywords: survey sampling weights; disease mapping; child malnutrition, fever and diarrhea; Bayesian spatial smoothing; sub-Saharan Africa (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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