Mapping of variations in child stunting, wasting and underweight within the states of India: the Global Burden of Disease Study 2000–2017

• Main Authors: Rajkumar Hemalatha, Anamika Pandey, Damaris Kinyoki, Siddarth Ramji, Rakesh Lodha, G. Anil Kumar, Nicholas J. Kassebaum, Elaine Borghi, Deepti Agrawal, Subodh S. Gupta, Avula Laxmaiah, Anita Kar, Matthews Mathai, Chris M. Varghese, Shally Awasthi, Priyanka G. Bansal, Joy K. Chakma, Michael Collison, Supriya Dwivedi, Mahaveer J. Golechha, Zaozianlungliu Gonmei, Suparna G. Jerath, Rajni Kant, Ajay K. Khera, Rinu P. Krishnankutty, Anura V. Kurpad, Laishram Ladusingh, Ridhima Malhotra, Raja S. Mamidi, Helena Manguerra, Joseph L. Mathew, Parul Mutreja, Arlappa Nimmathota, Ashalata Pati, Manorama Purwar, Kankipati V. Radhakrishna, Neena Raina, Mari J. Sankar, Deepika S. Saraf, Megan Schipp, R.S. Sharma, Chander Shekhar, Anju Sinha, V. Sreenivas, K. Srinath Reddy, Hendrik J. Bekedam, Soumya Swaminathan, Stephen S. Lim, Rakhi Dandona, Christopher J.L. Murray, Simon I. Hay, G.S. Toteja, Lalit Dandona
• Format: Article
• Language: English
• Published: Elsevier 2020-05-01
• Series: EClinicalMedicine
• Subjects: Child growth failure; District-level; Geospatial mapping; Inequality; National Nutrition Mission; Prevalence
• Online Access: http://www.sciencedirect.com/science/article/pii/S2589537020300614

Background: To inform actions at the district level under the National Nutrition Mission (NNM), we assessed the prevalence trends of child growth failure (CGF) indicators for all districts in India and inequality between districts within the states. Methods: We assessed the trends of CGF indicators (stunting, wasting and underweight) from 2000 to 2017 across the districts of India, aggregated from 5 × 5 km grid estimates, using all accessible data from various surveys with subnational geographical information. The states were categorised into three groups using their Socio-demographic Index (SDI) levels calculated as part of the Global Burden of Disease Study based on per capita income, mean education and fertility rate in women younger than 25 years. Inequality between districts within the states was assessed using coefficient of variation (CV). We projected the prevalence of CGF indicators for the districts up to 2030 based on the trends from 2000 to 2017 to compare with the NNM 2022 targets for stunting and underweight, and the WHO/UNICEF 2030 targets for stunting and wasting. We assessed Pearson correlation coefficient between two major national surveys for district-level estimates of CGF indicators in the states. Findings: The prevalence of stunting ranged 3.8-fold from 16.4% (95% UI 15.2–17.8) to 62.8% (95% UI 61.5–64.0) among the 723 districts of India in 2017, wasting ranged 5.4-fold from 5.5% (95% UI 5.1–6.1) to 30.0% (95% UI 28.2–31.8), and underweight ranged 4.6-fold from 11.0% (95% UI 10.5–11.9) to 51.0% (95% UI 49.9–52.1). 36.1% of the districts in India had stunting prevalence 40% or more, with 67.0% districts in the low SDI states group and only 1.1% districts in the high SDI states with this level of stunting. The prevalence of stunting declined significantly from 2010 to 2017 in 98.5% of the districts with a maximum decline of 41.2% (95% UI 40.3–42.5), wasting in 61.3% with a maximum decline of 44.0% (95% UI 42.3–46.7), and underweight in 95.0% with a maximum decline of 53.9% (95% UI 52.8–55.4). The CV varied 7.4-fold for stunting, 12.2-fold for wasting, and 8.6-fold for underweight between the states in 2017; the CV increased for stunting in 28 out of 31 states, for wasting in 16 states, and for underweight in 20 states from 2000 to 2017. In order to reach the NNM 2022 targets for stunting and underweight individually, 82.6% and 98.5% of the districts in India would need a rate of improvement higher than they had up to 2017, respectively. To achieve the WHO/UNICEF 2030 target for wasting, all districts in India would need a rate of improvement higher than they had up to 2017. The correlation between the two national surveys for district-level estimates was poor, with Pearson correlation coefficient of 0.7 only in Odisha and four small north-eastern states out of the 27 states covered by these surveys. Interpretation: CGF indicators have improved in India, but there are substantial variations between the districts in their magnitude and rate of decline, and the inequality between districts has increased in a large proportion of the states. The poor correlation between the national surveys for CGF estimates highlights the need to standardise collection of anthropometric data in India. The district-level trends in this report provide a useful reference for targeting the efforts under NNM to reduce CGF across India and meet the Indian and global targets.