Awine Timothy, Malm Keziah, Peprah Nana Yaw, Silal Sheetal P
Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa.
South African Department of Science and Technology/National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa.
PLoS One. 2018 Jan 29;13(1):e0191707. doi: 10.1371/journal.pone.0191707. eCollection 2018.
BACKGROUND: Malaria incidence is largely influenced by vector abundance. Among the many interconnected factors relating to malaria transmission, weather conditions such as rainfall and temperature are known to create suitable environmental conditions that sustain reproduction and propagation of anopheles mosquitoes and malaria parasites. In Ghana, climatic conditions vary across the country. Understanding the heterogeneity of malaria morbidity using data sourced from a recently setup data repository for routine health facility data could support planning. METHODS: Monthly aggregated confirmed uncomplicated malaria cases from the District Health Information Management System and average monthly rainfall and temperature records obtained from the Ghana Meteorological Agency from 2008 to 2016 were analysed. Univariate time series models were fitted to the malaria, rainfall and temperature data series. After pre-whitening the morbidity data, cross correlation analyses were performed. Subsequently, transfer function models were developed for the relationship between malaria morbidity and rainfall and temperature. RESULTS: Malaria morbidity patterns vary across zones. In the Guinea savannah, morbidity peaks once in the year and twice in both the Transitional forest and Coastal savannah, following similar patterns of rainfall at the zonal level. While the effects of rainfall on malaria morbidity are delayed by a month in the Guinea savannah and Transitional Forest zones those of temperature are delayed by two months in the Transitional forest zone. In the Coastal savannah however, incidence of malaria is significantly associated with two months lead in rainfall and temperature. CONCLUSION: Data captured on the District Health Information Management System has been used to demonstrate heterogeneity in the dynamics of malaria morbidity across the country. Timing of these variations could guide the deployment of interventions such as indoor residual spraying, Seasonal Malaria Chemoprevention or vaccines to optimise effectiveness on zonal basis.
背景：疟疾发病率在很大程度上受病媒数量的影响。在与疟疾传播相关的众多相互关联的因素中，降雨和温度等天气条件已知会创造适宜的环境条件，以维持按蚊和疟原虫的繁殖与传播。在加纳，全国各地的气候条件各不相同。利用从最近设立的常规卫生机构数据存储库获取的数据来了解疟疾发病率的异质性，可为规划提供支持。 方法：分析了2008年至2016年期间从地区卫生信息管理系统获取的每月汇总确诊非重症疟疾病例，以及从加纳气象机构获取的月平均降雨量和温度记录。对疟疾、降雨量和温度数据序列拟合单变量时间序列模型。在对发病率数据进行预白化处理后进行交叉相关分析。随后，针对疟疾发病率与降雨量和温度之间的关系建立传递函数模型。 结果：不同区域的疟疾发病模式各不相同。在几内亚草原，发病率一年出现一次高峰；在过渡森林和沿海草原则出现两次高峰，呈现出与区域层面降雨量相似的模式。虽然在几内亚草原和过渡森林地区，降雨对疟疾发病率的影响延迟一个月，但在过渡森林地区温度的影响延迟两个月。然而，在沿海草原地区疟疾发病率与降雨量和温度提前两个月显著相关。 结论：地区卫生信息管理系统记录的数据已被用于证明全国疟疾发病动态的异质性。这些变化的时间安排可指导室内滞留喷洒、季节性疟疾化学预防或疫苗接种等干预措施的部署，以便在区域基础上优化效果。
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