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

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

Version 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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  1. Thomas S Churcher
  2. Natalie Lissenden
  3. Jamie T Griffin
  4. Eve Worrall
  5. Hilary Ranson
(2016)
The impact of pyrethroid resistance on the efficacy and effectiveness of bednets for malaria control in Africa
eLife 5:e16090.
https://doi.org/10.7554/eLife.16090

Further reading

  1. Modelling the effectiveness of bednets against mosquitoes and malaria.

    1. Epidemiology and Global Health
    Charumathi Sabanayagam, Feng He ... Ching Yu Cheng
    Research Article Updated

    Background:

    Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).

    Methods:

    We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40–80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004–2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).

    Results:

    ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847–0.856), which was 7.0% relatively higher than by LR 0.795 (0.790–0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies.

    Conclusions:

    Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites.

    Funding:

    This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.