Abstract
20 min readFull text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods Appendix 1. Supplementary information on data and methods References Decision letter Author response Article and author information Metrics 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%. https://doi.org/10.7554/eLife.09672.001 eLife digest Malaria is a major cause of death in many parts of the world, especially in sub-Saharan Africa. Recently, there has been a renewed emphasis on using preventive measures to reduce the deaths and illnesses caused by malaria. Insecticide-treated nets are the most prominent preventive measure used in areas where malaria is particularly common. However, despite huge international efforts to send enough nets to the regions that need them, the processes of delivering and distributing the nets are inefficient. This problem is compounded by the fact that little information is available on how many nets people actually own and use within each country.` Bhatt et al. have now created a mathematical model that describes the use and distribution of nets across Africa since 2000. This is based on data collected from national surveys and reports on the delivery and distribution of the nets. The model estimates that in 2013, only 43% of people at risk of malaria slept under a net. Furthermore, 21% of new nets were allocated to households that already had enough nets, an inefficiency that has worsened over the years. Nets are also lost from households much more rapidly than previously thought. It's currently estimated that 920 million additional nets are required to ensure that everyone at risk from malaria in Africa is adequately protected. However, Bhatt et al.'s model suggests that given the current inefficiencies in net distribution, the extra nets would in reality protect a much smaller proportion of the population. Taking measures to more effectively target the nets to the households that need them could improve this coverage level to 95% of the population. The next challenge is to devise distribution strategies to send nets to where they are most needed. https://doi.org/10.7554/eLife.09672.002 Introduction Insecticide-treated nets (ITNs), which comprise conventional (cITNs) and long-lasting insecticidal nets (LLINs), are the single most widely used intervention for malaria control in Africa, proven to significantly reduce morbidity and mortality via direct protection and community-wide reductions in transmission (Lim et al., 2011; Lengeler and Lengeler, 2004; Eisele et al., 2010; Killeen et al., 2007). The World Health Organization (WHO) promotes a target of universal coverage for all populations at risk with either ITNs or indoor residual spraying (IRS), with the former representing the primary vector control tool in nearly all endemic African countries (WHO, 2013a). The international community has invested billions of dollars in the provision of at least 700 million LLINs since 2004 (WHO, 2013a). While these investments have led to enormous scale up in population access to ITNs (Noor et al., 2009; Monasch et al., 2004), the target of universal coverage remains distant and millions of African households at risk remain unprotected (WHO, 2013a). Bridging this gap is a key component of future strategies to reduce further the burden of malaria in Africa (WHO, 2014), and will require sustained commitment from donors, policy makers and national programmes. Central to these efforts is the capacity to monitor reliably current levels of ITN coverage in populations at risk and evaluate the systems that give rise to this coverage. This, in turn, enables progress towards international goals to be tracked and opportunities for efficiency gains to be identified. Such information is essential for evaluating the existing commodity and financing shortfalls and assessing future requirements if the target of universal coverage is to be achieved. Modelling coverage To facilitate standardised and comparable monitoring of ITN coverage through time, WHO and the Roll Back Malaria Monitoring and Evaluation Reference Group (RBM-MERG) has over the past decade defined a series of indicators to capture two different aspects of ITN coverage: access and use. Gold standard measurements of these indicators are provided by nationally representative household surveys such as Demographic and Health Surveys (DHS) (Measure, 2014), Multiple Indicator Cluster Surveys (MICS) (UNICEF, 2012), and Malaria Indicator Surveys (MIS) (RBM, 2014a). These surveys are carried out relatively infrequently, however, meaning they cannot be used directly for evaluating year-on-year coverage trends or for generating timely estimates of continent-wide coverage levels. In contrast, programmatic data such as the number of ITNs delivered and distributed within countries, while not describing coverage directly, are available for most countries and years (WHO, 2013a ). In a 2009 study, Flaxman and colleagues (Flaxman et al., 2010) used a compartmental modelling approach to link these programmatic and survey data, generating annual estimates of the two ITN indicators recommended at that time on access (% households with at least one ITN) and use (% children < 5 years old who slept under an ITN the previous night). Since that study, there has been increasing recognition that a richer set of indictors is required to identify the complex nature of ITN coverage (Kilian et al., 2013). An intra-household 'ownership gap' may exist whereby many households with some nets may not have enough for one net between two occupants (the recommended minimum level of protection (WHO, 2013b). Similarly, a 'usage gap' may exist whereby individuals with access to a net do not sleep under it. In response, the measurement of two additional indicators was recommended: % households with at least one ITN for every two people and % population with access to an ITN within their household (assuming each net was used by two people) (RBM, UNICEF, WHO, 2013; RBM, 2011). In addition, the indicator on usage was extended to include the entire population rather than only children under 5 years old. This updated set of four indicators, used individually and in combination, has the potential to provide a nuanced picture of ITN access and use patterns that can directly guide operational decision making (Kilian et al., 2013). To achieve this, there is a need to develop modelling frameworks to allow all four to be tracked through time. Evaluating efficiency Countries have an ongoing struggle to maintain high LLIN coverage in the face of continuous loss of nets from households due to damage, repurposing, or movement away from target areas. In response, systems need to be responsive to emerging coverage gaps by ensuring nets are distributed to households that need them and avoiding over-allocation (i.e. distribution of nets to those that already have them). Together, the rate of net loss and the degree of over-allocation of new nets play a key role in determining how efficiently delivery to countries will translate into household coverage levels. These factors are not currently well understood but triangulation of survey and programmatic data allows new insights into both. Estimating future needs The WHO define universal access to ITNs on the basis that two people can share one net. Using the working assumptions of a 3-year ITN lifespan and a 1.8 person-per-net ratio (one-between-two but allowing for odd-numbered households), a simple calculation yields an indicative estimate of 150 million new nets required each year to provide universal coverage to an African population at risk of around 810 million (WHO, 2013a). To support country planning and donor application processes (RBM-HWG, 2014), a more elaborate needs assessment approach has been developed by the RBM Harmonization Working Group (RBM-HWG) and implemented by 41 of the 47 endemic African countries (RBM, 2014; Paintain et al., 2013). The tool takes into account the size and structure of national target populations, a 1.8 person-per-net ratio for mass campaigns, additional routine distribution mechanisms employed by countries, and volumes of previously distributed nets and their likely rates of loss through time. Countries have used these inputs to calculate requirements for new nets to achieve national coverage targets, leading to an estimated continent-wide need for 920 million ITNs over the 2014–2017 period (approximately 230 million per year) (RBM, 2014). This tool provides a transparent, intuitive and standardised mechanism for comparing forecasted needs against current financing levels and identifying likely shortfalls. However, calculated needs are sensitive to assumptions about how a given volume of new nets will translate into population coverage, and inefficiencies in the system such as such as over-allocation and rate of net loss are not accounted for explicitly in the current needs assessment exercise. The purpose of this study is to define a new dynamic modelling approach, triangulating all available data on ITN delivery, distribution and coverage in sub-Saharan Africa in order to (i) provide validated and data-driven time-series estimates for all four internationally recommended ITN indicators; (ii) explore and quantify different aspects of system efficiency and how these contribute to reduced coverage levels; and (iii) estimate future LLIN needs to achieve universal access by 2017 under different efficiency scenarios and how these compare to existing needs assessment estimates. Results Net stock estimates Figure 1A summarises the main inputs to and outputs from the stock-and-flow model for LLINs when aggregated at the continental level. Some 718 million LLINs have been delivered across the 40 endemic countries since their introduction in 2004. As is well documented (WHO, 2013a), annual LLIN deliveries increased year-on-year from 2004 to 2010, reaching 145 million in that year, but then declined dramatically in 2011 and 2012 to less than half that amount before rising again to 143 million in 2013 (green line). Taking into account rates of loss in households, these LLIN deliveries led to a continental net crop shown by the red line. We estimate that there were 252 million LLINs in sub-Saharan households by the end of 2013, with that net crop growing approximately linearly from 2004, with the exception of a slow-down resulting from the reduced supply of nets in 2011–2012. Figure 1B shows equivalent distribution and resulting net crop estimates for cITNs, which constituted nearly all ITNs prior to 2005 but diminished rapidly in importance following the introduction of LLINs thereafter. Figure 1 Download asset Open asset 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. https://doi.org/10.7554/eLife.09672.003 Coverage estimates Figure 2 shows continent-level time-series estimates of the four internationally recommended ITN indicators, along with the 'access gap' indicator. All four indicators show a similar temporal trend: very low coverage levels and modest year-on-year increases for the first 5 years from 2000, with a marked inflexion point in 2005 and much more rapid gains thereafter. Importantly, however, all four indicators show that the pace of increase has, overall, slowed since 2005. By the end of 2013, we estimate that around two-thirds (66%, 95% CI 62%–71%) of households at risk owned at least one ITN. However, less than one-third (31%, 29%–34%) owned enough for one ITN between two people. This much lower level of adequate ownership is reflected in the levels of access and use, with 48% (45%–51%) of people at risk having access to an ITN within their household (on a one-between-two basis) and 43% (39%–46%) sleeping under an ITN the previous night. Comparison of Figure 2A,B demonstrates that many households that own some ITNs do not own enough for one-between-two, and this is captured in the time-series for the 'ownership gap' (Figure 2E). Encouragingly, this gap has been narrowed from 77% (76%–78%) of net-owning households having insufficient nets in 2000 to 56% (54%–57%) in 2013. Analysis of the 'use gap' suggested a large majority (89%, 84%–93%) of those with access to an ITN in the household slept under it the previous night, and we found no evidence of significant change in this proportion through time. Figure 2 Download asset Open asset 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. https://doi.org/10.7554/eLife.09672.004 The relatively smooth temporal trends seen at continental level obscure a great deal of complexity in the patterns of ITN scale-up occurring at national level (Figure 3). Nearly all countries began with very low coverage levels in 2000 and display a marked inflection point towards the middle of the decade, although there was considerable variation in the timing of onset of concerted scale-up activities. Importantly, the monotonic increases in coverage seen at the aggregated continental level are often replaced at national level with pronounced periods of rise and fall, and in many cases, 2013 does not represent the peak year. Variation in contemporary levels of coverage remains stark. The population with access to ITNs within the household, for example, was at or below 15% in seven countries in 2013, while above 70% for the top four. Figure 3 Download asset Open asset 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. https://doi.org/10.7554/eLife.09672.005 Over-allocation Over the 14-year period since 2000, on average 15% (12%–18%) of all ITNs distributed to households were over-allocated (owned by households already owning sufficient nets for one-between-two). Figure 4 illustrates how these over-allocation rates have changed through time. Around 7% (6%–9%) of ITNs were over-allocated in 2000, and this has risen steadily to 27% (22%–32%) in 2013. The year-on-year increase in over-allocation is to some extent an expected consequence of the overall growth in ITN provision: we found that over-allocation increased approximately 15 percentage points for each one-ITN-per-capita increase in net crop. Over-allocation also varied substantially between countries, for example ranging in 2013 from 50% (36%–65%) in the Republic of the Congo to 11% (9%–15%) in Côte D'Ivoire. Figure 4 Download asset Open asset 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. https://doi.org/10.7554/eLife.09672.006 Net loss Averaged over all years and all countries, we found the median retention time for LLINs in households was 23 (20–28) months. We found no statistically significant evidence of continent-wide temporal trends in retention times, but substantial between-country variation. Figure 5 plots the LLIN loss function representing the most recent three years (2011–2013) for each country individually (blue lines), along with the aggregated continental-level curve (red line). For reference, we also overlay on Figure 5 some alternative loss functions that have been proposed. Flaxman et al. (orange line) fitted very small annual loss rates (5%) for years 1, 2 and 3 - with all LLINs then assumed lost after 3 years (Flaxman et al., 2010). The RBM-HWG proposed rate of loss (green line) is 8, 20 and 50% of LLINs to remain after 1, 2 and 3 years, respectively, with all nets being lost thereafter (Networks, 2014). As can be seen, we found rates of loss for the first 3 years to be greater than both these alternatives for all countries. Both alternatives impose a three-year maximum retention time and our decision not to do so meant that we modelled a small proportion of LLINs lasting some years beyond that point. Figure 5 Download asset Open asset 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). https://doi.org/10.7554/eLife.09672.007 ITN requirements to achieve universal coverage Figure 6 shows the projected levels of coverage that we estimate would be achieved by the end of 2017 with LLIN deliveries for the 2014–2017 period varying from zero to 2.5 billion and under a range of different efficiency scenarios. The most important characteristic of our results is the pronounced shallowing of the delivery-coverage curves: smaller gains are in coverage as more LLINs are delivered in an of This that under a where current levels of over-allocation and LLIN loss very large increases in LLIN delivery would be required to achieve high coverage. this we estimate that 1 billion LLINs (i.e. an average of million per year) would be required to achieve of the population with access to an LLIN in the household by the end of although this would only translate into 70% population use. Figure 6 Download asset Open asset 2017 coverage for sub-Saharan Africa in to number of LLINs delivered over 2014–2017 (A) % households owning at least one ITN; (B) % households owning enough ITN for one between (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 we likely coverage under four current levels of over-allocation and net loss (i.e. as with with average net retention and with both over-allocation and net The the number of LLINs calculated as required over the period under the country programmatic needs assessment by Roll Back Malaria Harmonization Working LLINs, long-lasting insecticidal nets; ITNs, insecticide-treated nets. The extent to which coverage gains as deliveries increase is substantially when over-allocation and ITN loss rate are In a with over-allocation over is set to population access in 2017 would be with 700 million nets million per ITN loss rate to a 3-year median retention time would have a similar in to these two efficiency gains were however, access could be in 2017 with around million nets million per We found that the importance of the over-allocation and LLIN loss rates changed as more LLINs were LLIN retention was the most important at low levels of net delivery, but as more and more nets were over-allocation more This is intuitive since it to over-allocation as more households adequate of nets. For reference, we also plot on Figure 6 the 920 million additional LLINs calculated by countries as required for universal coverage of populations by 2017 under the RBM-HWG needs assessment exercise. current levels of over-allocation and net we estimate that by the end of 2017 this of new LLINs would translate into 77% access those populations by countries for ITN current patterns sleeping under an ITN. the combined efficiency with over-allocation and median ITN retention time, however, the 920 million nets would approach universal access over Discussion By and national survey data using a simple model the has been to provide a and intuitive mechanism for net and resulting household coverage that the data while a range of insights about the system In so we have been to (i) provide a new approach for past trends and contemporary levels of ITN (ii) explore the of uneven net distribution between households and the rates of net loss in households; and (iii) use these insights to estimate how many LLINs are likely to be required to achieve different coverage in sub-Saharan Africa. We for the first time, extended dynamic of ITN coverage to all four internationally indicators, along with the two results a simple while gains in ITN coverage have been there remains an enormous challenge if the of universal access is to be achieved and The importance of the new of indicators is also while an two-thirds of households now own at least one less than half of these have enough to protect everyone who This ownership gap is but the remains across nearly all countries. there is little evidence that of available nets substantially to low coverage levels. We that the to not sleeping under an ITN is of access rather than of use WHO, Eisele et al., 2009; 2011; and 2014; et al., 2014). may be important in and can support of areas where change may be to reduce it (Kilian et al., 2013). We found substantial over-allocation of nets to households already owning a sufficient and that this more pronounced as overall ownership levels increased through time. distribution in be to over-allocation and of nets allocated to households on the basis of households and nets. As have however, commodity achieved by such strategies be against the operational of these more complex distribution mechanisms et al., 2013). is is that over-allocation a major to universal coverage when levels of ITN provision are high most new nets are leading to in many households, while there remains a This may have a high impact if those nets are in households at and more households may be to available nets but are often in regions of lower transmission and 2009; et al., While beyond the of the study, the we have developed could be extended to these of in coverage risk in more of the most important in our study is that LLINs may be lost from households at a substantially rate than is currently Importantly, we loss by comparing inputs to countries to in households and so we measure rather for example, of nets between et al., 2014). retention of the in some are not by the of evidence we have provided by triangulating net and household survey This more rapid loss rate has important for existing RBM is for mass ITN to be every 3 years, by continuous distribution of nets via routine in order to maintain coverage levels between those However, levels of coverage are achieved by a given we estimate that of the nets on will not be in households 2 years coverage time-series for many countries that routine distribution are not for this rate of often pronounced in coverage levels between mass continuous coverage some of more campaigns, greater ongoing distribution between campaigns, or more nets and by that to overall retention We nets in households as or with no for their In of nets may be by households in our when they are or have diminished insecticidal As our estimates of would be if additional measures of net were model is to provide an estimate for every country and every year of the of ITNs in households. This the of the to modelled or data on average rates of net in different et al., to explore measures of coverage. developed to countries to calculate LLIN have to define need using a simple ratio to populations at risk as 1.8 people per and have for net loss from households using rates of We have been to show that LLIN requirements are likely to be when the more rapid rates of loss are into along with the additional of likely over-allocation This more not only provides the basis for more needs but the importance of these different factors in determining the coverage that can be achieved for a given delivery level. of future LLIN needs from the time to 2017 demonstrates how these factors to a pronounced of as more nets are to a increases in coverage with over-allocation a problem at high net provision levels. the number of nets required to approach coverage is however, current system inefficiencies and increasing net retention are not and already the of much by countries and international Over-allocation is the
Discussion(0)
No comments yet. Be the first to comment.