Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study.
dc.contributor.author | Walker, PGT | en_US |
dc.contributor.author | Griffin, JT | en_US |
dc.contributor.author | Ferguson, NM | en_US |
dc.contributor.author | Ghani, AC | en_US |
dc.date.accessioned | 2020-10-01T09:42:33Z | |
dc.date.available | 2016-04-18 | en_US |
dc.date.issued | 2016-07 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/67340 | |
dc.description.abstract | BACKGROUND: Reducing the burden of malaria is a global priority, but financial constraints mean that available resources must be allocated rationally to maximise their effect. We aimed to develop a model to estimate the most efficient (ie, minimum cost) ordering of interventions to reduce malaria burden and transmission. We also aimed to estimate the efficiency of different spatial scales of implementation. METHODS: We combined a dynamic model capturing heterogeneity in malaria transmission across Africa with financial unit cost data for key malaria interventions. We combined estimates of patterns of malaria endemicity, seasonality in rainfall, and mosquito composition to map optimum packages of these interventions across Africa. Using non-linear optimisation methods, we examined how these optimum packages vary when control measures are deployed and assessed at national, subnational first administrative (provincial), or fine-scale (5 km(2) pixel) spatial scales. FINDINGS: The most efficient package in a given setting varies depending on whether disease reduction or elimination is the target. Long-lasting insecticide-treated nets are generally the most cost-effective first intervention to achieve either goal, with seasonal malaria chemoprevention or indoor residual spraying added second depending on seasonality and vector species. These interventions are estimated to reduce malaria transmission to less than one case per 1000 people per year in 43·4% (95% CI 40·0-49·0) of the population at risk in Africa. Adding three rounds of mass drug administration per year is estimated to increase this proportion to 90·9% (95% CI 86·9-94·6). Further optimisation can be achieved by targeting policies at the provincial level, achieving an estimated 32·1% (95% CI 29·6-34·5) cost saving relative to adopting country-wide policies. Nevertheless, we predict that only 26 (95% CI 22-29) of 41 countries could reduce transmission to these levels with these approaches. INTERPRETATION: These results highlight the cost-benefits of carefully tailoring malaria interventions to the ecological landscape of different areas. However, novel interventions are necessary if malaria eradication is to be achieved. FUNDING: Bill & Melinda Gates Foundation, UK Medical Research Council. | en_US |
dc.format.extent | e474 - e484 | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Lancet Glob Health | en_US |
dc.rights | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.subject | Africa | en_US |
dc.subject | Antimalarials | en_US |
dc.subject | Humans | en_US |
dc.subject | Insecticide-Treated Bednets | en_US |
dc.subject | Malaria, Falciparum | en_US |
dc.subject | Models, Statistical | en_US |
dc.subject | Mosquito Control | en_US |
dc.subject | Plasmodium falciparum | en_US |
dc.title | Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study. | en_US |
dc.type | Article | |
dc.rights.holder | © 2016 The Author(s). Published by Elsevier Ltd. | |
dc.identifier.doi | 10.1016/S2214-109X(16)30073-0 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/27269393 | en_US |
pubs.issue | 7 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 4 | en_US |
dcterms.dateAccepted | 2016-04-21 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Synthesising data from multiple spatial scales and levels of detail to improve malaria transmission model predictions::Medical Research Council (MRC) | en_US |
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Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.