背景:疟疾死亡率受几个因素的影响,包括气候和环境因素,干预措施,社会经济地位(SES)和获得卫生系统的机会。这里,我们利用肯尼亚西部卫生和人口监测系统(HDSS)的数据,在不同空间尺度调查了气候和非气候因素对5岁以下儿童疟疾死亡率的联合影响.
方法:我们将贝叶斯时空(零膨胀)负二项模型拟合到按村庄尺度和HDSS内卫生设施集水区汇总的每月死亡率数据,2008年至2019年。包括一阶自回归时间和条件自回归空间过程作为随机效应,以考虑时间和空间变化。遥感气候和环境变量,蚊帐使用,SES,前往卫生设施的时间,模型中包括与水体/溪流和海拔高度的接近度,以评估它们与疟疾死亡率的关系。
结果:降雨量增加(死亡率比率(MRR)=1.12,95%贝叶斯可信区间(BCI):1.04-1.20),归一化植被指数(MRR=1.16,95%BCI:1.06-1.28),作物覆盖率(MRR=1.17,95%BCI:1.11-1.24)和前往医院的时间(MRR=1.09,95%BCI:1.04-1.13)与死亡率增加有关,而蚊帐使用增加(MRR=0.84,95%BCI:0.70-1.00),到最近流的距离(MRR=0.89,95%BCI:0.83-0.96),SES(MRR=0.95,95%BCI:0.91-1.00)和海拔(MRR=0.86,95%BCI:0.81-0.90)与较低的死亡率相关。当在卫生设施集水区一级汇总数据时,旅行时间和SES的影响不再显着。
结论:尽管HDSS的尺寸相对较小,疟疾死亡率存在空间差异,每年5月至6月达到峰值。疟疾死亡率的迅速下降与蚊帐有关,和更精细的空间尺度分析确定了额外的重要变量。时间和空间针对性的控制干预可能会有所帮助,当数据可用时,应考虑精细的空间尺度。
BACKGROUND: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya.
METHODS: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality.
RESULTS: Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level.
CONCLUSIONS: Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.