Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom; Sanaria Institute of Global Health and Tropical Medicine, Rockville, United States; Fogarty International Center, National Institutes of Health, Bethesda, United States
Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
Andrew J Tatem
Fogarty International Center, National Institutes of Health, Bethesda, United States; Flowminder Foundation, Stockholm, Sweden; Department of Geography and the Environment, University of Southampton, Southampton, United Kingdom
Michael Lynch
Global Malaria Programme, World Health Organization, Geneva, Switzerland
Cristin A Fergus
Global Malaria Programme, World Health Organization, Geneva, Switzerland
Joshua Yukich
Center for Applied Malaria Research and Evaluation, Department of Global Health Systems and Development, Tulane University School of Public Health and Tropical Medicine, New Orleans, United States
Adam Bennett
Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, San Francisco, United States
Thomas P Eisele
Center for Applied Malaria Research and Evaluation, Department of Global Health Systems and Development, Tulane University School of Public Health and Tropical Medicine, New Orleans, United States
Jan Kolaczinski
Strategy, Investment and Impact Division, The Global Fund to Fight AIDS, Tuberculosis and Malaria, Geneva, Switzerland
Richard E Cibulskis
Global Malaria Programme, World Health Organization, Geneva, Switzerland
Fogarty International Center, National Institutes of Health, Bethesda, United States; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
Peter W Gething
Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
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%.
