Department of Geography, Geo-informatics and Climatic Sciences, Makerere University, P.O Box 7062, Kampala, Uganda.
Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, 2055 Mowry Rd, Gainesville, FL, 32610, USA.
Malar J. 2023 Oct 4;22(1):297. doi: 10.1186/s12936-023-04735-8.
Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria's transmission complexity, control, and integrated modelling, with no available evidence on Uganda's refugee settlements. Using the 2018-2019 Uganda's Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda.
In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance.
Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model's spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises).
Ranking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.
已知家庭层面的疟疾风险因素复杂、不确定、随机、非线性且多维。这些因素相互作用，使得针对疟疾的干预措施和资源分配具有挑战性。然而，很少有研究能够证明疟疾传播的复杂性、控制和综合建模，乌干达难民定居点也没有这方面的证据。本研究使用 2018-2019 年乌干达疟疾指标调查（UMIS）的数据，采用替代贝叶斯信念网络（BBN）建模方法，分析、预测、排名并说明儿童疟疾感染风险因素之间的概念推理和复杂因果关系在乌干达难民定居点。
在 UMIS 中，使用标准化问卷获取家庭层面的信息，共有 675 名 5 岁以下儿童接受了疟疾检测。从该数据集创建了一个病例文件，其中包含疟疾检测结果、人口统计学、社会经济和环境信息。病例文件分为培训（80%，n=540）和测试（20%，n=135）数据集。根据既定指南，使用培训数据集开发 BBN 模型。使用测试数据集评估模型性能。
模型准确率为 91.11%，接收者操作特征曲线下面积为 0.95。模型的球形收益为 0.91，对数和二次损失分别为 0.36 和 0.16，表明模型具有很强的预测和分类能力。难民儿童疟疾检测呈阳性和阴性的概率分别为 48.1%和 51.9%。基于敏感性分析的排名最高的疟疾风险因素包括：（1）儿童年龄；（2）屋顶材料（即茅草屋顶）；（3）墙壁材料（即带泥和茅草的杆和墙壁）；（4）儿童是否睡在经杀虫剂处理的蚊帐中；（5）使用的厕所设施类型（即无厕所设施和带石板的坑式厕所）；（6）到水源的步行时间距离（0-10 分钟）；（7）饮用水源（即露天水源和房内自来水）。
对疟疾风险因素进行排名，而不是对其进行统计学意义上的分析，是一种应用研究方法，因为它可以帮助利益相关者确定如何在难民定居点资源有限的情况下为有针对性的疟疾干预措施分配资源。
