BACKGROUND Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. METHODS Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. RESULTS Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. CONCLUSION The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

中文翻译：

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2004 年至 2017 年马拉维儿童疟疾病例发病率：气候和非气候因素的时空模型。

背景技术疟疾传播受到包括气候、社会经济、环境因素和干预措施在内的复杂相互作用的影响。整个非洲的疟疾控制工作所产生的影响好坏参半。气候驱动因素可能在气候变化中发挥越来越重要的作用。加强非洲各地基于常规设施的每月疟疾数据收集的努力，为解释负担趋势和监测控制计划进展创造了越来越有价值的数据源。更好地了解疟疾发病率随时间和空间的变化与其他气候和非气候驱动因素的关联可能有助于指导和解释干预措施的影响。方法 收集马拉维 27 个地区 2004 年至 2017 年间每月例行的儿科门诊临床疟疾病例数据，并结合气候、环境、社会经济和干预因素以及地区人口估计数据进行分析。使用贝叶斯推理拟合时空广义线性混合模型，以量化马拉维各种风险因素与地区级临床疟疾发病率变化的关联强度，并使用地图进行可视化。结果 2004 年至 2017 年间，报告的儿童临床疟疾病例率略有上升，从每 1000 人 50 例增加到 53 例，全国各地气候带之间的差异很大。气候和环境因素，包括月平均气温和降雨量异常、归一化植被指数（NDVI）和RDT用于诊断显示与疟疾发病率存在显着关系。当月和前 3 个月的气温与疾病发病率存在显着相关性，而降雨异常则仅与 3 个月前的疟疾发病率相关。估计风险地图显示马拉维湖泊和夏尔河谷地区的风险相对较高。结论 建模方法可以识别马拉维疟疾发病风险可能异常高或低的地点，并区分可通过测量的风险因素和无法解释的残余空间变化解释的风险贡献。此外，应用于现成常规数据的空间统计方法提供了一种替代信息源，可以补充政策制定和实施中的调查数据，以指导监测和干预工作。