Human Capital and Lifetime Income Gains of Scaling-up Small-Quantity Lipid Nutrient Supplements Among Children Under 2 Years: A Modelling Analysis

Résumé fourni par la source

Undernutrition in early childhood is associated with adverse health and developmental outcomes later in life and remains a persistent global public health problem. Providing small-quantity lipid nutrient supplements (SQ-LNS) to children aged 6-24 months improves child growth and neurodevelopmental outcomes, but the potential long-term benefits to human capital have not been previously estimated. We estimated the potential returns to schooling and lifetime income attributable to increasing coverage of SQ-LNS for children < 2 years of age from 0% to 50% or 90% per five-year birth cohort in five countries (Bangladesh, Burkina Faso, Ethiopia, Pakistan, and Uganda) with a high burden of undernutrition. Random-effects meta-analyses were used to estimate the effect of SQ-LNS on child development using evidence from randomized controlled trials, and to estimate the returns to lifetime income as a function of change in development based on a de novo meta-analysis of observational economic studies. Gains in school years attributable to scaling-up SQ-LNS to 90% coverage ranged from 0.14 million school years (95% uncertainty interval [UI]: 0.064, 0.25) in Burkina Faso to 1.18 million school years (95%UI: 0.54, 2.11) in Pakistan per five-year birth cohort. With an effect size of 18% return in income per one standard deviation increase in development, the estimated gains in lifetime income ranged from $US 0.41 billion (95% UI: 0.20, 0.68) in Burkina Faso to $US 6.91 billion (95% UI: 3.32, 11.4) in Pakistan per five-year birth cohort. Returns in income per child were above the estimated per child cost of providing SQ-LNS. These findings demonstrate that scaling-up SQ-LNS among children aged 6-24 months may lead to substantial human capital gains in countries with a high-burden of child undernutrition. Longitudinal studies on the long-term effects of SQ-LNS are needed to refine model parameters and to better characterize the impacts on broader health and human capital outcomes.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.