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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

机译:严重疟疾感染及其在布基纳法索卫生区孕妇的结果:分层贝叶斯时空模型应用于2013年至2018年的经常收集的数据

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摘要

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.
机译:细尺热点检测对于在布基纳法索妊娠(MIP)和死亡病例中减少严重疟疾的最佳锻炼服务至关重要。 本研究使用了层次结构 - 贝叶斯时空建模,探讨了时空模式和定位健康区,具有严重的MIP发病率和死亡率的概率。 研究还评估了健康区服务交付(准备)对严重MIP成果的影响。

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