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Severe-malaria infection and its outcomes among pregnant women in Burkina Faso health-districts: Hierarchical Bayesian space-time models applied to routinely-collected data from 2013 to 2018.

Rouamba T., Samadoulougou S., Tinto H., Alegana VA., Kirakoya-Samadoulougou F.

Fine-scale hotspots detection is crucial for optimum delivery of essential health-services for reducing severe malaria in pregnancy (MiP) and death cases in Burkina Faso. This study used hierarchical-Bayesian Spatio-temporal modeling to explore space-time patterns and pinpoint health-districts with an exceedance probability of severe MiP incidence and fatality rate. Study also assessed effect of health-district service delivery (readiness) on severe-MiP outcomes. Severe-MiP fatality rate declined considerably while its incidence rate remained unchanged between January-2013 and December-2018. Severe-MiP cases persisted throughout the year with peaks between August and November. These peaks increased 2.5-fold the fatality rate. Furthermore, severe-MiP fatality was higher in health-districts classified as low-readiness (IRR = 2.469, 95%CrI: 1.632-3.738). However, the fatality rate decreased significantly with proper coverage with three doses for intermittent-preventive-treatment with sulphadoxine-pyrimethamine. Severe-MiP burden was heterogeneous spatially and temporally. The study suggested that health-programs should increase health-districts readiness and optimize resource allocation in high burden areas and months.

More information Original publication

DOI

10.1016/j.sste.2020.100333

Type

Journal article

Publication Date

2020-06-01T00:00:00+00:00

Volume

33

Addresses

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Keywords

Humans, Pregnancy Complications, Parasitic, Malaria, Sulfadoxine, Pyrimethamine, Drug Combinations, Antimalarials, Treatment Outcome, Severity of Illness Index, Incidence, Bayes Theorem, Pregnancy, Adult, Burkina Faso, Female, Young Adult, Spatial Analysis