Dataset Information

Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors.

ABSTRACT: The impact of insecticide resistance in malaria vectors is poorly understood and quantified. Here a series of geospatial datasets for insecticide resistance in malaria vectors are provided, so that trends in resistance in time and space can be quantified, and the impact of resistance found in wild populations on malaria transmission in Africa can be assessed. Specifically, data have been collated and geopositioned for the prevalence of insecticide resistance, as measured by standard bioassays, in representative samples of individual species or species complexes. Data are provided for the Anopheles gambiae species complex, the Anopheles funestus subgroup, and for nine individual vector species. Data are also given for common genetic markers of resistance to support analyses of whether these markers can improve the ability to monitor resistance in low resource settings. Allele frequencies for known resistance-associated markers in the Voltage-gated sodium channel (Vgsc) are provided. In total, eight analysis-ready, standardised, geopositioned datasets encompassing over 20,000 African mosquito collections between 1957 and 2017 are released.

SUBMITTER: Moyes CL

PROVIDER: S-EPMC6629700 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors.

Moyes Catherine L CL Wiebe Antoinette A Gleave Katherine K Trett Anna A Hancock Penelope A PA Padonou Germain Gil GG Chouaïbou Mouhamadou S MS Sovi Arthur A Abuelmaali Sara A SA Ochomo Eric E Antonio-Nkondjio Christophe C Dengela Dereje D Kawada Hitoshi H Dabire Roch K RK Donnelly Martin J MJ Mbogo Charles C Fornadel Christen C Coleman Michael M

Scientific data 20190715 1

The impact of insecticide resistance in malaria vectors is poorly understood and quantified. Here a series of geospatial datasets for insecticide resistance in malaria vectors are provided, so that trends in resistance in time and space can be quantified, and the impact of resistance found in wild populations on malaria transmission in Africa can be assessed. Specifically, data have been collated and geopositioned for the prevalence of insecticide resistance, as measured by standard bioassays, i ...[more]

PMID: 31308378

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Project description:Insecticide resistance has emerged as one of the major challenges facing National Malaria Control Programmes in Africa. A well-coordinated national database on insecticide resistance (IRBase) can facilitate the development of effective strategies for managing insecticide resistance and sustaining the effectiveness of chemical-based vector control measures. The aim of this study was to assemble a database on the current status of insecticide resistance among malaria vectors in Kenya.Data was obtained from published literature through PubMed, HINARI and Google Scholar searches and unpublished literature from government reports, research institutions reports and malaria control programme reports. Each data source was assigned a unique identification code and entered into Microsoft Excel 2010 datasheets. Base maps on the distribution of insecticide resistance and resistance mechanisms among malaria vectors in Kenya were generated using ArcGIS Desktop 10.1 (ESRI, Redlands, CA, USA).Insecticide resistance status among the major malaria vectors in Kenya was reported in all the four classes of insecticides including pyrethroids, carbamates, organochlorines and organophosphates. Resistance to pyrethroids has been detected in Anopheles gambiae (s.s.), An. arabiensis and An. funestus (s.s.) while resistance to carbamates was limited to An. gambiae (s.s.) and An. arabiensis. Resistance to the organochlorine was reported in An. gambiae (s.s.) and An. funestus (s.s.) while resistance to organophosphates was reported in An. gambiae (s.l.) only. The mechanisms of insecticide resistance among malaria vectors reported include the kdr mutations (L 1014S and L 1014F) and elevated activity in carboxylesterase, glutathione S-transferases (GST) and monooxygenases. The kdr mutations L 1014S and L 1014F were detected in An. gambiae (s.s.) and An. arabiensis populations. Elevated activity of monooxygenases has been detected in both An. arabiensis and An. gambiae (s.s.) populations while the elevated activity of carboxylesterase and GST has been detected only in An. arabiensis populations.The geographical maps show the distribution of insecticide resistance and resistance mechanisms among malaria vectors in Kenya. The database generated will provide a guide to intervention policies and programmes in the fight against malaria.

Project description:BackgroundThere is a paucity of data about the susceptibility status of malaria vectors to Public Health insecticides along the Thailand-Myanmar border. This lack of data is a limitation to guide malaria vector-control in this region. The aim of this study was to assess the susceptibility status of malaria vectors to deltamethrin, permethrin and DDT and to validate a simple molecular assay for the detection of knock-down resistance (kdr) mutations in the study area.MethodsAnopheles mosquitoes were collected in four sentinel villages during August and November 2014 and July 2015 using human landing catch and cow bait collection methods. WHO susceptibility tests were carried out to measure the mortality and knock-down rates of female mosquitoes to deltamethrin (0.05%), permethrin (0.75%) and DDT (4%). DNA sequencing of a fragment of the voltage-gated sodium channel gene was carried out to identify knock-down resistance (kdr) mutations at position 1014 in mosquitoes surviving exposure to insecticides.ResultsA total of 6295 Anopheles belonging to ten different species were bioassayed. Resistance or suspected resistance to pyrethroids was detected in An. barbirostris (s.l.) (72 and 84% mortality to deltamethrin (n = 504) and permethrin (n = 493) respectively), An. hyrcanus (s.l.) (33 and 48% mortality to deltamethrin (n = 172) and permethrin (n = 154), respectively), An. jamesii (87% mortality to deltamethrin, n = 111), An. maculatus (s.l.) (85 and 97% mortality to deltamethrin (n = 280) and permethrin (n = 264), respectively), An. minimus (s.l.) (92% mortality, n = 370) and An. vagus (75 and 95% mortality to deltamethrin (n =148) and permethrin (n = 178), respectively). Resistance or suspected resistance to DDT was detected in An. barbirostris (s.l.) (74% mortality, n = 435), An. hyrcanus (s.l.) (57% mortality, n = 91) and An. vagus (97% mortality, n = 133). The L1014S kdr mutation at both heterozygous and homozygous state was detected only in An. peditaeniatus (Hyrcanus Group).ConclusionResistance to pyrethroids is present along the Thailand-Myanmar border, and it represents a threat for malaria vector control. Further investigations are needed to better understand the molecular basis of insecticide resistance in malaria vectors in this area.

Project description:BackgroundMalaria is one of the most devastating tropical diseases, resulting in loss of lives each year, especially in children under the age of 5 years. Malaria burden, related deaths and stall in the progress against malaria transmission is evident, particularly in countries that have moderate or high malaria transmission. Hence, mitigating malaria spread requires information on the distribution of vectors and the drivers of insecticide resistance (IR). However, owing to the impracticality in establishing the critical need for real-world information at every location, modelling provides an informed best guess for such information. Therefore, this review examines the various methodologies used to model spatial, temporal and spatio-temporal patterns of IR within populations of malaria vectors, incorporating pest-biology parameters, adopted ecological principles, and the associated modelling challenges.MethodsThe review focused on the period ending March 2023 without imposing restrictions on the initial year of publication, and included articles sourced from PubMed, Web of Science, and Scopus. It was also limited to publications that deal with modelling of IR distribution across spatial and temporal dimensions and excluded articles solely focusing on insecticide susceptibility tests or articles not published in English. After rigorous selection, 33 articles met the review's elibility criteria and were subjected to full-text screening.ResultsResults show the popularity of Bayesian geostatistical approaches, and logistic and static models, with limited adoption of dynamic modelling approaches for spatial and temporal IR modelling. Furthermore, our review identifies the availability of surveillance data and scarcity of comprehensive information on the potential drivers of IR as major impediments to developing holistic models of IR evolution.ConclusionsThe review notes that incorporating pest-biology parameters, and ecological principles into IR models, in tandem with fundamental ecological concepts, potentially offers crucial insights into the evolution of IR. The results extend our knowledge of IR models that provide potentially accurate results, which can be translated into policy recommendations to combat the challenge of IR in malaria control.

Dataset Information

Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors.

Publications

Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors.

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