Abstract
BackgroundChild stunting due to chronic malnutrition is a major problem in low- and middle-income countries due, in part, to inadequate nutrition-related practices and insufficient access to services. Limited budgets for nutritional interventions mean that available resources must be targeted in the most cost-effective manner to have the greatest impact. Quantitative tools can help guide budget allocation decisions.MethodsThe Optima approach is an established framework to conduct resource allocation optimization analyses. We applied this approach to develop a new tool, ‘Optima Nutrition’, for conducting allocative efficiency analyses that address childhood stunting. At the core of the Optima approach is an epidemiological model for assessing the burden of disease; we use an adapted version of the Lives Saved Tool (LiST). Six nutritional interventions have been included in the first release of the tool: antenatal micronutrient supplementation, balanced energy-protein supplementation, exclusive breastfeeding promotion, promotion of improved infant and young child feeding (IYCF) practices, public provision of complementary foods, and vitamin A supplementation. To demonstrate the use of this tool, we applied it to evaluate the optimal allocation of resources in 7 districts in Bangladesh, using both publicly available data (such as through DHS) and data from a complementary costing study.ResultsOptima Nutrition can be used to estimate how to target resources to improve nutrition outcomes. Specifically, for the Bangladesh example, despite only limited nutrition-related funding available (an estimated $0.75 per person in need per year), even without any extra resources, better targeting of investments in nutrition programming could increase the cumulative number of children living without stunting by 1.3 million (an extra 5%) by 2030 compared to the current resource allocation. To minimize stunting, priority interventions should include promotion of improved IYCF practices as well as vitamin A supplementation. Once these programs are adequately funded, the public provision of complementary foods should be funded as the next priority. Programmatic efforts should give greatest emphasis to the regions of Dhaka and Chittagong, which have the greatest number of stunted children.ConclusionsA resource optimization tool can provide important guidance for targeting nutrition investments to achieve greater impact.
Highlights
Child stunting due to chronic malnutrition is a major problem in low- and middle-income countries due, in part, to inadequate nutrition-related practices and insufficient access to services
Both Lives Saved Tool (LiST) and the Optima approach have been described in detail elsewhere [24, 30], but we provide a short summary of them both, and describe how they were integrated to develop Optima Nutrition
Epidemiological model structure The core epidemiological model used within Optima Nutrition is a dynamic, deterministic, compartmental model which tracks the number of children in a population from birth until 5 years of age across five age bands: < 1 month, 1–5 months, 6–11 months, 12–23 months and 24–59 months
Summary
Child stunting due to chronic malnutrition is a major problem in low- and middle-income countries due, in part, to inadequate nutrition-related practices and insufficient access to services. Reduced linear growth, affects around 200 million children younger than 5 years of age – mostly in Asia and Africa [1, 2]. Sustainable Development Goal 2 calls for an end to malnutrition by 2030 and significant reductions in stunting by 2025. Research suggests that this goal can be partly met through a set of evidence-based interventions [9] in the period from conception to 5 years of age [10]. For children aged 6 months to 5 years, providing high quality and nutritionally diverse complementary foods, and supplementing diets with micro-nutrients including vitamin A and zinc, if needed, can reduce the risk of stunting [14,15,16]. The quality and use of health, social protection, and sanitation service delivery systems are further elements affecting nutrition outcomes
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