An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying

Background 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. Methods Aggregated and comprehensive data from a meta-analysis of the impact of Actellic® 300CS (Syngenta), an indoor residual spray (IRS) product, 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. Results Many more data are available in aggregated format ( N = 23 datasets, 5 studies) relative to comprehensive format ( N = 3 datasets, 2 studies). The evidence synthesis model was most robust 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. Conclusions We developed an approach to combine sources from trials to maximise the inference that can be made from such data. 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. Key messages Statistical models are developed to best infer key mosquito IRS statistics from disparate data sources. Naïve methods, previously adopted, of combining different data sources can create bias and must be explicitly considered. Mosquitoes are highly likely to feed in experimental hut trials using indoor residual spraying of insecticides so the assumption of independence in the probability of mosquitoes dying is reflective of trial data. We provide code for practitioners to implement this approach themselves in both BUGS and Stan languages running from R.