BACKGROUND Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. METHODS With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings. RESULTS Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. CONCLUSIONS Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.

中文翻译：

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使用医疗机构数据绘制马达加斯加疟疾季节性图。

背景技术由于按蚊和疟原虫的生命周期对环境条件的变化做出了反应，因此许多疟疾流行地区的发病率都有季节性波动。确定位置特定的季节性特征对于计划干预措施很有用。虽然大多数现有的疟疾季节性地图都使用固定的降雨，温度和/或植被指数阈值来确定合适的传播月份，但我们构建了统计建模框架来表征直接从每月卫生设施数据中得出的季节性模式。方法根据马达加斯加3247个卫生机构中2669个的数据，使用时空回归模型估算整个岛屿的季节性模式。在缺乏流域人口估计或无法汇总到地区级别的能力的情况下，这侧重于按卫生机构级别划分的年度病例总数中的每月比例。该模型由已知直接影响季节性疟疾趋势的动态环境协变量提供信息。为了确定与操作相关的特性，例如传输开始月份和相关的不确定性度量，开发了一种算法并将其应用于模型实现。季节性指数用于合并来自家庭患病率调查的负担信息，并总结“季节性”位置与周围环境的相对关系。结果发现，每月病例数与降雨和温度适宜性的时间滞后协变量之间存在正相关。与现有文献一致，模型估计表明，虽然马达加斯加的大部分地区在3月至4月附近经历了疟疾传播高峰，但东部沿海在2月前后经历了更早的高峰。估计从东南地区开始传播，先于西南地区开始传播，这表明室内残留喷涂应以相同顺序完成。在数据表明季节信号冲突或两个传播季节冲突的地区，季节特征的估计值偏差较大，因此确定性较低。结论每月的卫生设施数据可用于建立疟疾负担的季节性模式，并增加家庭患病率调查提供的信息。所提出的建模框架允许对基于位置的季节性特征进行基于证据的综合推断。