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

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. Keywords Child growth failureDistrict-levelGeospatial mappingInequalityNational Nutrition MissionPrevalenceStuntingTime trendsUnder-fiveUndernutritionUnderweightWastingWHO/UNICEF target

Distribution of socio-demographic and biological characteristics of APCAPS participants (n = 1038), 2009–10.

<p>All values are means(SD) unless otherwise stated.</p><p>MVPA = Moderate or Vigorous Physical Activity; 25(OH)D = 25-hydroxyvitamin D₃, IMT = Intima-Media Thickness, HDL = High-density lipoprotein, LDL = Low-density lipoprotein</p><p>* We used a cut-off of ≤20ng/ml to define deficiency (equal to 50nmol/l) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129468#pone.0129468.ref018" target="_blank">18</a>].</p><p>^† p-values are based on unpaired t-tests for heterogeneity in means, with appropriate degrees of freedom.</p><p>^‡ non-normal distribution; median (inter-quartile range) presented, and p-values are based on Mann-Whitney rank-sum tests for equality.</p><p>^Ф Analysis of fasting glucose, insulin, HDL cholesterol and LDL cholesterol exclude participants who did not fast (n = 36)</p><p>^§ Smoking status; former user = ceased use >6 months ago; current user = used in the last 6 months.</p><p>^¶ Manual occupations include roles such as labourers, craftsmen, servants, postal staff and farmers; professional occupations include role such as teachers, accountants, clinicians, business owners and engineers.</p><p>Distribution of socio-demographic and biological characteristics of APCAPS participants (n = 1038), 2009–10.</p

Key characteristics of APCAPS participants with a serum vitamin D result (n = 1038) compared to APCAPS participants without a serum vitamin D result (n = 408); 2009–10.

<p>Values are means. P-values are based on unpaired t-tests for heterogeneity in means, with appropriate degrees of freedom</p><p>Key characteristics of APCAPS participants with a serum vitamin D result (n = 1038) compared to APCAPS participants without a serum vitamin D result (n = 408); 2009–10.</p

Association of serum vitamin D (25(OH)D)^† with cardiovascular risk factors in a sample of young Indian females from the Andhra Pradesh Children and Parents Study (n = 418)^Ф; 2009–10.

<p>25(OH)D = 25-hydroxyvitamin D3; DXA = Dual X-ray Absorptiometry; BP = Blood Pressure; IMT = Intima-Media Thickness; aPWV = Aortic Pulse Wave Velocity; HDL = High-density lipoprotein; LDL = Low-density lipoprotein</p><p>Model 1 adjusts for age and intervention status. Model 2, as in Model 1, plus further adjustment for lifestyle factors (standard of living index, occupation, time spent in moderate or vigorous physical activity, smoking status), body fat and month of test. Results are based on linear mixed effect regression models with robust standard errors to account for clustering at the household and village level, rounded to 2 decimal places.</p><p>* Model 2 excludes body fat</p><p>^Ф Analysis of fasting glucose, insulin, HDL cholesterol and LDL cholesterol exclude participants who did not fast (n = 36)</p><p>^†Logged values</p><p>Association of serum vitamin D (25(OH)D)^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129468#t004fn005" target="_blank">†</a>} with cardiovascular risk factors in a sample of young Indian females from the Andhra Pradesh Children and Parents Study (n = 418)^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129468#t004fn004" target="_blank">Ф</a>}; 2009–10.</p

Subnational mapping of under-5 and neonatal mortality trends in India: the Global Burden of Disease Study 2000-17

Background India has made substantial progress in improving child survival over the past few decades, but a comprehensive understanding of child mortality trends at disaggregated geographical levels is not available. We present a detailed analysis of subnational trends of child mortality to inform efforts aimed at meeting the India National Health Policy (NHP) and Sustainable Development Goal (SDG) targets for child mortality. Methods We assessed the under-5 mortality rate (U5MR) and neonatal mortality rate (NMR) from 2000 to 2017 in 5 × 5 km grids across India, and for the districts and states of India, using all accessible data from various sources including surveys with subnational geographical information. The 31 states and groups of union territories were categorised into three groups using their Socio-demographic Index (SDI) level, calculated as part of the Global Burden of Diseases, Injuries, and Risk Factors Study on the basis of per-capita income, mean education, and total fertility rate in women younger than 25 years. Inequality between districts within the states was assessed using the coefficient of variation. We projected U5MR and NMR for the states and districts up to 2025 and 2030 on the basis of the trends from 2000 to 2017 and compared these projections with the NHP 2025 and SDG 2030 targets for U5MR (23 deaths and 25 deaths per 1000 livebirths, respectively) and NMR (16 deaths and 12 deaths per 1000 livebirths, respectively). We assessed the causes of child death and the contribution of risk factors to child deaths at the state level. Findings U5MR in India decreased from 83·1 (95% uncertainty interval [UI] 76·7–90·1) in 2000 to 42·4 (36·5–50·0) per 1000 livebirths in 2017, and NMR from 38·0 (34·2–41·6) to 23·5 (20·1–27·8) per 1000 livebirths. U5MR varied 5·7 times between the states of India and 10·5 times between the 723 districts of India in 2017, whereas NMR varied 4·5 times and 8·0 times, respectively. In the low SDI states, 275 (88%) districts had a U5MR of 40 or more per 1000 livebirths and 291 (93%) districts had an NMR of 20 or more per 1000 livebirths in 2017. The annual rate of change from 2010 to 2017 varied among the districts from a 9·02% (95% UI 6·30–11·63) reduction to no significant change for U5MR and from an 8·05% (95% UI 5·34–10·74) reduction to no significant change for NMR. Inequality between districts within the states increased from 2000 to 2017 in 23 of the 31 states for U5MR and in 24 states for NMR, with the largest increases in Odisha and Assam among the low SDI states. If the trends observed up to 2017 were to continue, India would meet the SDG 2030 U5MR target but not the SDG 2030 NMR target or either of the NHP 2025 targets. To reach the SDG 2030 targets individually, 246 (34%) districts for U5MR and 430 (59%) districts for NMR would need a higher rate of improvement than they had up to 2017. For all major causes of under-5 death in India, the death rate decreased between 2000 and 2017, with the highest decline for infectious diseases, intermediate decline for neonatal disorders, and the smallest decline for congenital birth defects, although the magnitude of decline varied widely between the states. Child and maternal malnutrition was the predominant risk factor, to which 68·2% (65·8–70·7) of under-5 deaths and 83·0% (80·6–85·0) of neonatal deaths in India could be attributed in 2017; 10·8% (9·1–12·4) of under-5 deaths could be attributed to unsafe water and sanitation and 8·8% (7·0–10·3) to air pollution. Interpretation India has made gains in child survival, but there are substantial variations between the states in the magnitude and rate of decline in mortality, and even higher variations between the districts of India. Inequality between districts within states has increased for the majority of the states. The district-level trends presented here can provide crucial guidance for targeted efforts needed in India to reduce child mortality to meet the Indian and global child survival targets. District-level mortality trends along with state-level trends in causes of under-5 and neonatal death and the risk factors in this Article provide a comprehensive reference for further planning of child mortality reduction in India