Spatio-Temporal Bayesian Models for Malaria Risk Using Survey and Health Facility Routine Data in Rwanda
Bibliographic Data
| ID | 15500239 |
|---|---|
| Authors | Muhammed Semakula (0000-0002-9076-4922, Royal Tropical Institute, corresponding author), François Niragire (0000-0003-0473-387X, University of Rwanda), Christel Faes (0000-0002-1878-9869, Hasselt University) |
| Year | 2023 |
| Volume | 20 |
| Issue | 5 |
| Pages | 4283-4283 |
| Publication date | 2023-02-28 |
| 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/ijerph20054283 |
| PMID | 36901291 |
| OpenAlex | W4322629473 |
| Language | EN |
| References cited | 19 |
The findings of this analysis suggest that using DHS combined with routine health services data for active malaria surveillance may provide provide more precise estimates of the malaria burden, which can be used toward malaria elimination targets. We compared findings from geostatistical modeling of malaria prevalence among under-five-year old children using DHS 2019-2020 and findings from malaria relative risk spatio-temporal modeling using both DHS survey 2019-2020 and health facility routine data. The strength of routinely collected data at small scales and high-quality data from the survey contributed to a better understanding of the malaria relative risk at the subnational level in Rwanda
Environmental health · Geography · Health facility · Health services · Malaria · Population · Relative risk · Statistics · Survey data collection · Census and Population Estimation · Demography · Global Maternal and Child Health · Mathematics · Medicine · Statistical Methods and Bayesian Inference
| Highly cited | No |
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