Assessing inequalities and regional disparities in child nutrition outcomes in India using MANUSH – a more sensitive yardstick

BACKGROUND: India is strongly committed to reducing the burden of child malnutrition, which has remained a persistent concern. Findings from recent surveys indicate co-existence of child undernutrition, micronutrient deficiency and overweight/obesity, i.e. the triple burden of malnutrition among children below 5 years. While considerable efforts are being made to address this challenge, and several composite indices are being explored to inform policy actions, the methodology used for creating such indices, i.e., linear averaging, has its limitations. Briefly put, it could mask the uneven improvement across different indicators by discounting the ‘lagging’ indicators, and hence not incentivising a balanced improvement. Signifying negative implications on policy discourse for improved nutrition. To address this gap, we attempt to develop a composite index for estimating the triple burden of malnutrition in India, using a more sensitive measure, MANUSH. METHODOLOGY: Data from publicly available nation-wide surveys - National Family Health Survey (NFHS) and Comprehensive National Nutrition Survey (CNNS), was used for this study. First, we addressed the robustness of MANUSH method of composite indexing over conventional aggregation methods. Second, using MANUSH scores, we assessed the triple burden of malnutrition at the subnational level over different periods NHFS- 3(2005–06), NFHS-4 (2015–16) and CNNS (2106–18). Using mapping and spatial analysis tools, we assessed neighbourhood dependency and formation of clusters, within and across states. RESULT: MANUSH method scores over other aggregation measures that use linear aggregation or geometric mean. It does so by fulfilling additional conditions of Shortfall and Hiatus Sensitivity, implicitly penalising cases where the improvement in worst-off dimension is lesser than the improvement in best-off dimension, or where, even with an overall improvement in the composite index, the gap between different dimensions does not reduce. MANUSH scores helped in revealing the gaps in the improvement of nutrition outcomes among different indicators and, the rising inequalities within and across states and districts in India. Significant clusters (p < 0.05) of high burden and low burden districts were found, revealing geographical heterogeneities and sharp regional disparities. A MANUSH based index is useful in context-specific planning and prioritising different interventions, an approach advocated by the newly launched National Nutrition Mission in India. CONCLUSION: MANUSH based index emphasises balanced development in nutritional outcomes and is hence relevant for diverse and unevenly developing economy like India.