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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., Design Type(s)data integration objectiveMeasurement Type(s)insecticide resistanceTechnology Type(s)digital curationFactor Type(s)geographic location • temporal_intervalSample Characteristic(s)gambiae species complex • Africa • funestus subgroup • Anopheles gambiae • Anopheles arabiensis • Anopheles rivulorum • Anopheles pharoensis • Anopheles mascarensis • Anopheles funestus • Anopheles coluzzii • Anopheles melas • Anopheles quadriannulatus • Anophelinae Machine-accessible metadata file describing the reported data (ISA-Tab format)

Objectives: With increasing global interest in hospital antimicrobial stewardship (AMS) programmes, there is a strong demand for core elements of AMS to be clearly defined on the basis of principles of effectiveness and affordability. To date, efforts to identify such core elements have been limited to Europe, Australia, and North America. The aim of this study was to develop a set of core elements and their related checklist items for AMS programmes that should be present in all hospitals worldwide, regardless of resource availability. Methods: A literature review was performed by searching Medline and relevant websites to retrieve a list of core elements and items that could have global relevance. These core elements and items were evaluated by an international group of AMS experts using a structured modified Delphi consensus procedure, using two-phased online in-depth questionnaires. Results: The literature review identified seven core elements and their related 29 checklist items from 48 references. Fifteen experts from 13 countries in six continents participated in the consensus procedure. Ultimately, all seven core elements were retained, as well as 28 of the initial checklist items plus one that was newly suggested, all with >= 80% agreement; 20 elements and items were rephrased. Conclusions: This consensus on core elements for hospital AMS programmes is relevant to both high- and low-to-middle-income countries and could facilitate the development of national AMS stewardship guidelines and adoption by healthcare settings worldwide. (C) 2018 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Background Indoor residual spraying (IRS) is a key tool for controlling and eliminating malaria by targeting vectors. To support the development of effective intervention strategies it is important to understand the impact of vector control tools on malaria incidence and on the spread of insecticide resistance. In 2006, the World Health Organization (WHO) stated that countries should report on coverage and impact of IRS, yet IRS coverage data are still sparse and unspecific. Here, the subnational coverage of IRS across sub-Saharan Africa for the four main insecticide classes from 1997 to 2017 were estimated. Methods Data on IRS deployment were collated from a variety of sources, including the President’s Malaria Initiative spray reports and National Malaria Control Programme reports, for all 46 malaria-endemic countries in sub-Saharan Africa from 1997 to 2017. The data were mapped to the applicable administrative divisions and the proportion of households sprayed for each of the four main insecticide classes; carbamates, organochlorines, organophosphates and pyrethroids was calculated. Results The number of countries implementing IRS increased considerably over time, although the focal nature of deployment means the number of people protected remains low. From 1997 to 2010, DDT and pyrethroids were commonly used, then partly replaced by carbamates from 2011 and by organophosphates from 2013. IRS deployment since the publication of resistance management guidelines has typically avoided overlap between pyrethroid IRS and ITN use. However, annual rotations of insecticide classes with differing modes of action are not routinely used. Conclusion This study highlights the gaps between policy and practice, emphasizing the continuing potential of IRS to drive resistance. The data presented here can improve studies on the impact of IRS on malaria incidence and help to guide future malaria control efforts.

The development of insecticide resistance in African malaria vectors threatens the continued efficacy of important vector control methods that rely on a limited set of insecticides. To understand the operational significance of resistance we require quantitative information about levels of resistance in field populations to the suite of vector control insecticides. Estimation of resistance is complicated by the sparsity of observations in field populations, variation in resistance over time and space at local and regional scales, and cross-resistance between different insecticide types. Using observations of the prevalence of resistance in mosquito species from the Anopheles gambiae complex sampled from 1,183 locations throughout Africa, we applied Bayesian geostatistical models to quantify patterns of covariation in resistance phenotypes across different insecticides. For resistance to the three pyrethroids tested, deltamethrin, permethrin, and λ-cyhalothrin, we found consistent forms of covariation across sub-Saharan Africa and covariation between resistance to these pyrethroids and resistance to DDT. We found no evidence of resistance interactions between carbamate and organophosphate insecticides or between these insecticides and those from other classes. For pyrethroids and DDT we found significant associations between predicted mean resistance and the observed frequency of kdr mutations in the Vgsc gene in field mosquito samples, with DDT showing the strongest association. These results improve our capacity to understand and predict resistance patterns throughout Africa and can guide the development of monitoring strategies.

Additional file 1: Maps showing IRS coverage in sub-Saharan Africa from 19972017 for carbamates, organochlorines, organophosphates and pyrethroids.

Additional file 3:Plasmodium falciparum infection prevalence from Bhatt et al. [1].

Additional file 1: Maps showing IRS coverage in sub-Saharan Africa from 19972017 for carbamates, organochlorines, organophosphates and pyrethroids.

Additional file 3:Plasmodium falciparum infection prevalence from Bhatt et al. [1].

Additional file 3:Plasmodium falciparum infection prevalence from Bhatt et al. [1].

Additional file 3:Plasmodium falciparum infection prevalence from Bhatt et al. [1].

Additional file 2: Maps showing modelled insecticide-treated bed net (ITN) coverage from Bhatt and Gething [35].

Additional file 2: Maps showing modelled insecticide-treated bed net (ITN) coverage from Bhatt and Gething [35].

Additional file 2: Maps showing modelled insecticide-treated bed net (ITN) coverage from Bhatt and Gething [35].

Additional file 1: Maps showing IRS coverage in sub-Saharan Africa from 19972017 for carbamates, organochlorines, organophosphates and pyrethroids.

Additional file 1: Maps showing IRS coverage in sub-Saharan Africa from 19972017 for carbamates, organochlorines, organophosphates and pyrethroids.