Department of Statistical Science, University College London, London, United Kingdom.
Centre de Recherche Entomologique de Cotonou, Quartier DONATIN/AKPAKPA, Benin.
PLoS One. 2022 Mar 24;17(3):e0263446. doi: 10.1371/journal.pone.0263446. eCollection 2022.
Prospective malaria public health interventions are initially tested for entomological impact using standardised experimental hut trials. In some cases, data are collated as aggregated counts of potential outcomes from mosquito feeding attempts given the presence of an insecticidal intervention. Comprehensive data i.e. full breakdowns of probable outcomes of mosquito feeding attempts, are more rarely available. Bayesian evidence synthesis is a framework that explicitly combines data sources to enable the joint estimation of parameters and their uncertainties. The aggregated and comprehensive data can be combined using an evidence synthesis approach to enhance our inference about the potential impact of vector control products across different settings over time.
Aggregated and comprehensive data from a meta-analysis of the impact of Pirimiphos-methyl, an indoor residual spray (IRS) product active ingredient, used on wall surfaces to kill mosquitoes and reduce malaria transmission, were analysed using a series of statistical models to understand the benefits and limitations of each.
Many more data are available in aggregated format (N = 23 datasets, 4 studies) relative to comprehensive format (N = 2 datasets, 1 study). The evidence synthesis model had the smallest uncertainty at predicting the probability of mosquitoes dying or surviving and blood-feeding. Generating odds ratios from the correlated Bernoulli random sample indicates that when mortality and blood-feeding are positively correlated, as exhibited in our data, the number of successfully fed mosquitoes will be under-estimated. Analysis of either dataset alone is problematic because aggregated data require an assumption of independence and there are few and variable data in the comprehensive format.
We developed an approach to combine sources from trials to maximise the inference that can be made from such data and that is applicable to other systems. Bayesian evidence synthesis enables inference from multiple datasets simultaneously to give a more informative result and highlight conflicts between sources. Advantages and limitations of these models are discussed.
前瞻性疟疾公共卫生干预措施最初使用标准化实验棚试验来测试其对昆虫学的影响。在某些情况下，由于存在杀虫干预措施，会汇总潜在蚊虫叮咬结果的计数数据。更罕见的是，能够获得全面的数据，即蚊虫叮咬尝试的可能结果的完整细分。贝叶斯证据综合是一个框架，它明确地结合了数据源，从而能够联合估计参数及其不确定性。可以使用证据综合方法来综合汇总和全面的数据，以增强我们对不同时间和不同环境中控制媒介产品潜在影响的推断。
使用一系列统计模型分析汇总和全面数据，对使用壁面室内滞留喷洒（IRS）产品活性成分扑灭司林（Pirimiphos-methyl）的影响进行荟萃分析，以了解每种方法的优缺点。
汇总格式的数据（N = 23 个数据集，4 项研究）比全面格式的数据（N = 2 个数据集，1 项研究）多得多。证据综合模型在预测蚊虫死亡或存活和吸血的概率方面具有最小的不确定性。从相关伯努利随机样本生成优势比表明，当死亡率和吸血率呈正相关时，如我们的数据所示，成功吸血的蚊虫数量将被低估。仅分析一个数据集是有问题的，因为汇总数据需要独立性假设，而全面格式的数据很少且变量很大。
我们开发了一种方法来合并来自试验的来源，以最大限度地从这些数据中得出可以得出的推断，并且该方法适用于其他系统。贝叶斯证据综合使我们能够同时从多个数据集进行推断，从而提供更有信息的结果并突出来源之间的冲突。讨论了这些模型的优缺点。
