PANEL 5.1 MEASURING COVERAGE OF PROGRAMS TO TREAT SEVERE ACUTE MALNUTRITION
JOSE LUIS ALVAREZ O
ur capacity to identify, rehabilitate, and cure children experiencing severe acute malnutrition (SAM) has improved dramat- ically in recent years, resulting in robust, cost-effective models of care (Bhutta et al. 2013a). These developments have not only led to consistently high cure rates, but greatly increased the number of SAM cases identi- fied and receiving treatment. According to UNICEF, more than 2.6 million children with SAM worldwide were treated in 2012 (UNICEF 2012). Nevertheless, it remains difficult to measure the coverage, or proportion of cases that are receiving treatment. Joint estimates from UNICEF, Action Against Hunger, and the Coverage Monitoring Network suggest that less than 15 percent of the global SAM population is currently receiving treatment (UNICEF, CMN, and ACF International 2013). At the national level, only a handful of coun- tries are able to report reliable, direct esti- mates of coverage. Why is this? Part of the challenge is that measuring treatment coverage requires time and techni- cal capacity. New tools (including the SQUEAC, SLEAC, and S3M methods) provide ways of monitoring program coverage practi- cally, regularly, and easily (Myatt et al. 2012).
These methods can provide not only direct coverage estimates, but also valuable insights into the spatial distribution of coverage and the barriers preventing potential beneficia- ries from accessing services. This information has helped SAM treatment services adapt, improve, and provide national authorities with guidelines for scaling up treatment. But national-level time and technical capacity— to design, implement, and analyze coverage surveys—remain in short supply. Collabora- tive platforms like the international Coverage Monitoring Network1 these gaps.
are helping to address
Another difficulty is that coverage data are not currently collected as part of existing national, formal, and periodic surveys such as the Demographic and Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS). This is in part because the target population for coverage assessment, which consists of the people eligible for treatment, is different from the target populations for these periodic surveys. Instead, coverage data on SAM treatment are generated through more ad hoc stand-alone surveys that do not link up with standardized DHS/MICS surveys and rarely have national coverage. The resulting
data are difficult to compare across countries. The new methods for measuring coverage are less resource intensive than their pre- decessors, can be more easily implemented frequently, and can be better integrated into more regular data collection processes, includ- ing periodic surveys.
Successfully integrating coverage into these systems will take time, but plenty can be done now to start bridging and linking these data sets. UNICEF, Action Against Hun- ger, Food and Nutrition Technical Assistance (FANTA), and the Coverage Monitoring Net- work are working together to develop ways of using existing administrative data (admis- sions and exits, stock accounts, and screen- ing) to identify determinants of coverage and bottlenecks affecting coverage. It will also be important to explore options for including coverage questions in periodic surveys such as DHS and MICS. Such approaches will not replace coverage surveys altogether, but they would enable nutrition services to better use existing information to generate strategies for improving access to and coverage of SAM treatment services.
in Bhutta et al. (2013a). By scaling up the 12 interventions, the model estimates reductions in the prevalence of stunting of 17 percent, 21 percent, and 18 percent from 2013 to 2025 in Bangladesh, Ethiopia, and Pakistan, respectively. Predicted reductions in the prevalence of severe wasting are estimated at 65 percent, 62 percent, and 58 percent in Bangladesh, Ethio-
pia, and Pakistan, respectively. The impacts on severe wasting are particularly noteworthy, whereas the estimated declines in stunting are modest and signal the need to increase both the coverage and quality of these interventions. Implementation research has the potential to play a particularly important role here (Menon et al. 2014).
DATA GAPS 1. The collection of intervention coverage data—in general—needs to be scaled up as interventions themselves are scaled up. 2. Data on folic-acid supplementation during the periconceptual period are lacking. 3. Recent efforts to collect data on coverage of MAD, MDD, and zinc treatment for diarrhea need to be sustained.
4. Further methodological work is required to develop viable real-time methods for generating information on SAM and MAM treat- ment programming and coverage.
ACTIONS & ACCOUNTABILITY TO ACCELERATE THE WORLD’S PROGRESS ON NUTRITION
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