UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Green, Nathan; Agossa, Fiacre; Yovogan, Boulais; Oxborough, Richard; Kitau, Jovin; Muller, Pie; Constant, Edi; ... Sherrard-Smith, Ellie; + view all (2022) An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying. PloS One , 17 (3) , Article e0263446. 10.1371/journal.pone.0263446. Green open access

[thumbnail of Green_An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying_VoR.pdf]
Preview
Text
Green_An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying_VoR.pdf - Published Version

Download (1MB) | Preview

Abstract

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 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. RESULTS: 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. CONCLUSIONS: 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.

Type: Article
Title: An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0263446
Publisher version: https://doi.org/10.1371/journal.pone.0263446
Language: English
Additional information: © 2022 Green et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10153188
Downloads since deposit
16Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item