Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017
- University of Oxford, United Kingdom
- National Institutes of Health, United States
- World Health Organization, Switzerland
- Tulane University School of Public Health and Tropical Medicine, United States
- University of California, San Francisco, United States
- The Global Fund to Fight AIDS, Tuberculosis and Malaria, Switzerland
Abstract
Insecticide-treated nets (ITNs) for malaria control are widespread but coverage remains inadequate. We developed a Bayesian model using data from 102 national surveys, triangulated against delivery data and distribution reports, to generate year-by-year estimates of four ITN coverage indicators. We explored the impact of two potential 'inefficiencies': uneven net distribution among households and rapid rates of net loss from households. We estimated that, in 2013, 21% (17%-26%) of ITNs were over-allocated and this has worsened over time as overall net provision has increased. We estimated that rates of ITN loss from households are more rapid than previously thought, with 50% lost after 23 (20-28) months. We predict that the current estimate of 920 million additional ITNs required to achieve universal coverage would in reality yield a lower level of coverage (77% population access). By improving efficiency, however, the 920 million ITNs could yield population access as high as 95%.
Article and author information
Author details
Reviewing Editor
- Catherine Kyobutungi, Africa Population Health Research Center, Kenya
Version history
- Received: June 25, 2015
- Accepted: November 26, 2015
- Accepted Manuscript published: December 29, 2015 (version 1)
- Version of Record published: February 8, 2016 (version 2)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Metrics
-
- 3,186
- views
-
- 663
- downloads
-
- 127
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017
eLife 4:e09672.
https://doi.org/10.7554/eLife.09672
Further reading
-
- Epidemiology and Global Health
We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
-
- Epidemiology and Global Health
- Microbiology and Infectious Disease
Systematically tracking and analysing reproductive loss in livestock helps with efforts to safeguard the health and productivity of food animals by identifying causes and high-risk areas.
