1. Epidemiology and Global Health
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The impact of pyrethroid resistance on the efficacy and effectiveness of bednets for malaria control in Africa

  1. Thomas S Churcher  Is a corresponding author
  2. Natalie Lissenden
  3. Jamie T Griffin
  4. Eve Worrall
  5. Hilary Ranson
  1. Imperial College London, United Kingdom
  2. Liverpool School of Tropical Medicine, United Kingdom
Research Article
  • Cited 102
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Cite this article as: eLife 2016;5:e16090 doi: 10.7554/eLife.16090

Abstract

Long lasting pyrethroid treated bednets are the most important tool for preventing malaria. Pyrethroid resistant Anopheline mosquitoes are now ubiquitous in Africa though the public health impact remains unclear, impeding the deployment of more expensive nets. Meta-analyses of bioassay studies and experimental hut trials are used to characterise how pyrethroid resistance changes the efficacy of standard bednets, and those containing the synergist piperonyl butoxide (PBO), and assess its impact on malaria control. New bednets provide substantial personal protection until high levels of resistance though protection may wane faster against more resistant mosquito populations as nets age. Transmission dynamics models indicate that even low levels of resistance would increase the incidence of malaria due to reduced mosquito mortality and lower overall community protection over the life-time of the net. Switching to PBO bednets could avert up to 0.5 clinical cases per person per year in some resistance scenarios.

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Article and author information

Author details

  1. Thomas S Churcher

    MRC Centre for Outbreak Analysis and Modelling, Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    For correspondence
    thomas.churcher@imperial.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8442-0525

  2. Natalie Lissenden

    Liverpool School of Tropical Medicine, Liverpool, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  3. Jamie T Griffin

    MRC Centre for Outbreak Analysis and Modelling, Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  4. Eve Worrall

    Liverpool School of Tropical Medicine, Liverpool, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  5. Hilary Ranson

    Liverpool School of Tropical Medicine, Liverpool, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Funding

Medical Research Council

  • Thomas S Churcher

Department for International Development

  • Thomas S Churcher

European Research Council

  • Hilary Ranson

Innovative Vector Control Consortium

  • Thomas S Churcher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Simon I Hay, Institute for Health Metrics and Evaluation, United States

Publication history

  1. Received: March 16, 2016
  2. Accepted: August 18, 2016
  3. Accepted Manuscript published: August 22, 2016 (version 1)
  4. Version of Record published: September 15, 2016 (version 2)

Copyright

© 2016, Churcher et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Epidemiology and Global Health
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    Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries

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    Research Article Updated

    Global targets to reduce salt intake have been proposed, but their monitoring is challenged by the lack of population-based data on salt consumption. We developed a machine learning (ML) model to predict salt consumption at the population level based on simple predictors and applied this model to national surveys in 54 countries. We used 21 surveys with spot urine samples for the ML model derivation and validation; we developed a supervised ML regression model based on sex, age, weight, height, and systolic and diastolic blood pressure. We applied the ML model to 54 new surveys to quantify the mean salt consumption in the population. The pooled dataset in which we developed the ML model included 49,776 people. Overall, there were no substantial differences between the observed and ML-predicted mean salt intake (p<0.001). The pooled dataset where we applied the ML model included 166,677 people; the predicted mean salt consumption ranged from 6.8 g/day (95% CI: 6.8–6.8 g/day) in Eritrea to 10.0 g/day (95% CI: 9.9–10.0 g/day) in American Samoa. The countries with the highest predicted mean salt intake were in the Western Pacific. The lowest predicted intake was found in Africa. The country-specific predicted mean salt intake was within reasonable difference from the best available evidence. An ML model based on readily available predictors estimated daily salt consumption with good accuracy. This model could be used to predict mean salt consumption in the general population where urine samples are not available.