Background: India is strongly committed to reducing the burden of child malnutrition, which has remained a persistent issue. Findings from recent surveys indicate co-existence of child undernutrition, micronutrient deficiency and overweight/obesity, i.e. the triple burden of malnutrition in children below five years. While considerable efforts are being made to address this challenging issue, 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, 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). Through the use of maps and spatial tools, we gauged the existence of neighbourhood dependency, the formation of clusters, within and across states.
Result: MANUSH method succeeds over its counterparts – linear aggregation and geometric mean, by fulfilling additional conditions of Shortfall and Hiatus Sensitivity, implicitly penalising when, improvement in worst-off dimension is less or not proportionate to improvement in best-off dimension, or when, even with overall improvement, the gap between dimensions remain same. MANUSH scores helped in revealing the changing paradigm in the improvement of nutrition outcomes 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. The usefulness of MANUSH index in context-specific planning and prioritising actions is also brought out using the case of the National Nutrition Mission.
Conclusion: MANUSH indexing depicts balanced development effectively, hence finds relevance in bringing out inequality in a diverse and developing economy like India.