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
Bibliographic Data
| ID | 15466602 |
|---|---|
| Authors | Sheyla Rodrigues Cassy (0000-0002-5377-8786, University of Lisbon), Samuel Manda (0000-0002-9672-3312, South African Medical Research Council, corresponding author), Filipe J Marques (0000-0001-6453-6558, University of Lisbon), Maria do Rosário Oliveira Martins (0000-0002-7941-0285, Universidade Nova de Lisboa) |
| Year | 2022 |
| Volume | 19 |
| Issue | 10 |
| Pages | 6319-6319 |
| Publication date | 2022-05-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph19106319 |
| PMID | 35627854 |
| OpenAlex | W4281397113 |
| Language | EN |
| References cited | 20 |
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
Bayesian probability · Econometrics · Environmental health · Estimation · Geography · Population · Sampling (signal processing · Sampling design · Spatial analysis · Statistics · Stratified sampling · Survey data collection · Child Nutrition and Water Access · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Mathematics · Medicine
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| Citation velocity | historical |
|---|---|
| Highly cited | No |