An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2022
2022
Historique:
received:
07
05
2021
accepted:
19
01
2022
entrez:
24
3
2022
pubmed:
25
3
2022
medline:
22
4
2022
Statut:
epublish
Résumé
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.
Sections du résumé
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.
Identifiants
pubmed: 35324929
doi: 10.1371/journal.pone.0263446
pii: PONE-D-21-15152
pmc: PMC8947499
doi:
Substances chimiques
Insecticides
0
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0263446Subventions
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/T041986/1
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Stat Med. 2009 Nov 10;28(25):3049-67
pubmed: 19630097
Influenza Other Respir Viruses. 2014 Jan;8(1):33-41
pubmed: 24209610
Parassitologia. 1996 Dec;38(3):481-9
pubmed: 9257337
J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):21-47
pubmed: 19381328
BMJ. 2019 Jan 30;364:k4597
pubmed: 30700442
Med Decis Making. 2015 Feb;35(2):148-61
pubmed: 23886677
J Clin Epidemiol. 2010 Mar;63(3):235-7
pubmed: 19595573
Malar J. 2011 Jul 07;10:183
pubmed: 21736735
Malar J. 2016 Mar 15;15:165
pubmed: 26979404
Res Synth Methods. 2015 Dec;6(4):293-309
pubmed: 26287812
Parasit Vectors. 2012 Nov 13;5:256
pubmed: 23148718
Trans R Soc Trop Med Hyg. 2014 Feb;108(2):84-91
pubmed: 24463582
J Am Stat Assoc. 2019 Apr 30;114(528):1436-1449
pubmed: 32165869
Stat Med. 2000 Dec 30;19(24):3417-32
pubmed: 11122505
Res Synth Methods. 2010 Jan;1(1):2-19
pubmed: 26056090
Med Vet Entomol. 1995 Jul;9(3):316-24
pubmed: 7548951
PLoS One. 2013 Jul 23;8(7):e69516
pubmed: 23936033
Malar J. 2014 Jan 29;13:37
pubmed: 24476070
Malar J. 2014 Aug 23;13:330
pubmed: 25149656
Cochrane Database Syst Rev. 2019 May 23;5:CD012688
pubmed: 31120132
Nat Commun. 2018 Nov 26;9(1):4982
pubmed: 30478327
J Med Entomol. 1997 May;34(3):285-9
pubmed: 9151491
Malar J. 2014 Aug 25;13:332
pubmed: 25152326
J Clin Epidemiol. 2002 Jan;55(1):86-94
pubmed: 11781126
Nature. 2015 Oct 8;526(7572):207-211
pubmed: 26375008
Cochrane Database Syst Rev. 2010 Apr 14;(4):CD006657
pubmed: 20393950
Res Synth Methods. 2013 Sep;4(3):220-9
pubmed: 26053842
J Clin Epidemiol. 2007 May;60(5):431-9
pubmed: 17419953
Stat Med. 2002 Feb 15;21(3):371-87
pubmed: 11813224
BMJ. 2017 Jan 5;356:i6460
pubmed: 28057641
Biostatistics. 2008 Oct;9(4):715-34
pubmed: 18349037
Parasite. 2004 Mar;11(1):75-82
pubmed: 15071831
Parasit Vectors. 2017 Sep 19;10(1):432
pubmed: 28927465