Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017

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%.

Keywords: Bayesian compatment model; ITN; LLIN; Malaria; epidemiology; global health; human.

Figure 1.. Time series of ITN delivery, distribution, and estimated net crop in sub-Saharan households 2000–2013 for (A) LLINs and (B) cITNs.

Manufacturer data on deliveries were available for LLINs only. cITNs, conventional insecticide-treated nets; HHs, households; ITNs, insecticide-treated nets; LLINs, long-lasting insecticidal nets; NMCP, National Malaria Control Programme. DOI: http://dx.doi.org/10.7554/eLife.09672.003

Figure 2.. Continental-level time series of estimated ITN coverage indicators for the years 2000–2013.

(A) % households with at least one ITN; (B) % households with at least one ITN for every two people; (C) % population with access to an ITN within their household; (D) % population who slept under an ITN the previous night; (E) 'ownership gap', the % of ITN-owning households with insufficient ITNs for one-between-two. Black circles are the annual estimates; pink envelopes denote the 95% posterior credible interval. ITNs, insecticide-treated nets. DOI: http://dx.doi.org/10.7554/eLife.09672.004

Figure 3.. Country-level time series of estimated ITN coverage indicators 2000–2013.

Each plot shows the four ITN coverage indicators: % households with at least one ITN (black); % households with at least one ITN for every two people (red); % population with access to an ITN within their household (green); % population who slept under an ITN the previous night (blue). CAR = Central African Republic; DRC = Democratic Republic of Congo; ITNs, insecticide-treated nets; HH = household. DOI: http://dx.doi.org/10.7554/eLife.09672.005

Figure 4.. Time series of over-allocation for the combined set of 40 sub-Saharan endemic countries, 2000–2013.

Over-allocation refers to insecticide-treated nets distributed to households already owning enough nets for one-between-two, measured as the percentage of over-allocated nets among all nets in households. DOI: http://dx.doi.org/10.7554/eLife.09672.006

Figure 5.. Insecticide treated netretention.

Estimated long-lasting insecticidal net retention curves for each country individually (blue lines) and combined (red line), in both cases relating to the average of the most recent 3 years, 2011–2013. Also shown for reference are the rate of loss recommended in the Roll Back Malaria Harmonization Working Group needs assessment exercise (green line) and the loss rate fitted by Flaxman et al. (orange line). DOI: http://dx.doi.org/10.7554/eLife.09672.007

Figure 6.. Projected 2017 coverage for sub-Saharan Africa in relation to number of LLINs delivered over 2014–2017 period.

(A) % households owning at least one ITN; (B) % households owning enough ITN for one between two; (C) % population with access to ITN within the household; (D) % population sleeping under an ITN the previous night; (E) 'ownership gap', the % of ITN-owning households with insufficient ITNs for one-between-two. For each indicator, we project likely coverage under four scenarios: current levels of over-allocation and net loss (i.e. 'business as usual'); with minimised over-allocation; with longer average net retention (3-yr median); and with both minimised over-allocation and longer net retention. The vertical dashed lines indicate the number of LLINs calculated as required over the period under the country programmatic needs assessment supported by Roll Back Malaria Harmonization Working Group. LLINs, long-lasting insecticidal nets; ITNs, insecticide-treated nets. DOI: http://dx.doi.org/10.7554/eLife.09672.008

Figure 7.. Schematic showing overall analytical framework linking data, model components, and outputs.

HH = household; ITN, insecticide-treated net; NMCP = National Malaria Control Programme. DOI: http://dx.doi.org/10.7554/eLife.09672.009

Figure 8.. Distribution of national survey data on ITN net crop and household ownership, access and use used in this study, by country and year.

The different types of survey are shown in the key: CAR = Central African Republic; DHS = Demographic and Health Survey; DRC = Democratic Republic of Congo; ITN, insecticide-treated net; MICS = Multiple Indicator Cluster Survey; MIS = Malaria Indicator Survey; STP = São Tomé and Príncipe. DOI: http://dx.doi.org/10.7554/eLife.09672.010

Appendix figure 1.. Number of household surveys by country: Each country is shaded to represent the number of household surveys datasets and reports acquired with data on insecticide-treated net ownership and use.

DOI: http://dx.doi.org/10.7554/eLife.09672.012

Appendix figure 2.. Compartment model dynamics for LLINs.

The compartment model predicts the total number of nets in a country (thick purple line). For a given country at given years there will be LLINs delivered to the country from manufacturers (green arrows), and LLINs distributed at some point in that or subsequent years within the country (blue arrows). Country-wide survey reports give a cross-sectional measure of the total number of nets in households (red line). Evidence synthesis of three data sources can then be inferred by estimating a rate of loss (via a loss function). LLINs, long-lasting insecticidal nets. DOI: http://dx.doi.org/10.7554/eLife.09672.013

Appendix figure 3.. Parameter fits for the hurdle exponential NMCP prior.

(A,C) are the exponential rate priors on the per capita rates of cITN (A) and LLIN (C) distributions in a given year (see Equations 10 and 11). (B,D) are the Bernoulli hurdle probabilities that zero cITNs (B) or LLINs (D) are distributed in a given year (see Equations 12 and 13). Red points are the data, and blue line are optimised spline fits. cITNs, conventional insecticide-treated nets; LLINs, long-lasting insecticidal nets; NMCP, National Malaria Control Programme. DOI: http://dx.doi.org/10.7554/eLife.09672.014

Appendix figure 4.. Histogram of #ITNs per household as observed in the Nigeria 2010 Malaria Indicator Survey.

Red bars represent the number of households with one or more nets, and the grey bar represents households with no nets. ITNs, insecticide-treated nets. DOI: http://dx.doi.org/10.7554/eLife.09672.015

Appendix figure 5.. Nigeria 2010 Malaria Indicator Survey household survey summary stratified by household size.

ITNs, insecticide-treated nets. DOI: http://dx.doi.org/10.7554/eLife.09672.016

Appendix figure 6.. Calculation of indicators 1–3 from two-dimensional histograms for Nigeria 2010 Malaria Indicator Survey.

(A) % HHs with one or more net, obtained by dividing the sum of red bars (HHs with one or more ITN) by the sum of all bars. (B) % HHs owning enough nets for one-between-two, obtained by dividing sum of red bars (i.e. those HHs meeting or exceeding the one-between-two criteria) by the sum of all bars. (C) % population with access to ITN in the HH, obtained by first converting bars to represent people rather than HHs, then dividing sum of individuals that would be able to access an ITN sharing with one other (red bars in right-hand histogram) with the sum of all individuals (grey bars in left-hand histogram). HHs, households; ITNs, insecticide-treated nets. DOI: http://dx.doi.org/10.7554/eLife.09672.017

Appendix figure 7.. The regression relationship between the proportion of people who slept under an ITN and the proportion of people with access to an ITN.

ITN, insecticide-treated net. DOI: http://dx.doi.org/10.7554/eLife.09672.018

Author response image 1.

DOI: http://dx.doi.org/10.7554/eLife.09672.019

Author response image 2.

DOI: http://dx.doi.org/10.7554/eLife.09672.020