Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Ratchathewi, Bangkok, 10400, Thailand.
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
BMC Med Res Methodol. 2021 Dec 20;21(1):287. doi: 10.1186/s12874-021-01480-x.
In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases.
In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand.
From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence.
A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.
在大湄公河次区域（GMS）的许多地区，疟疾流行地区已缩小到以低传播为主的斑块。随着 2030 年消除疟疾的区域目标，重要的是使用适当的方法来分析和预测这些剩余传播焦点中发病率的趋势，以为规划工作提供信息。气候变量在全球范围内与疟疾发病率存在不同程度的关联，但在 GMS 中的关系不太明确，标准方法可能不适用于考虑气候与发病率之间的滞后以及发病率低的地区。
在这项研究中，开发了一种在贝叶斯框架内估计气候因素对泰国疟疾发病率的时空滞后效应的方法。根据代表我们研究人群中稀疏疟疾病例所见延迟关系的滞后效应曲线的真实数据进行了模拟。使用泰国西部一个省的细分地区的细地理尺度上的疟疾发病率进行了案例研究，以估计环境变量的滞后效应。
从模拟研究中可以看出，适应延迟效应和过多零值的模型假设在评估指标和场景方面似乎具有最佳的整体性能。案例研究表明，该建模的提出具有实际数据的滞后气候效应估计。这些模型似乎可用于估计与疟疾发病率的关联形状。
提出了一种估计低传播环境中气候对疟疾趋势的时空影响的新方法。所开发的方法有可能提高对过去和未来疟疾发病率趋势的理解和估计。随着进一步的发展，这可以帮助决策者决定如何更有效地分配资源并规划消除疟疾的策略。
