Source document: https://docs-lawep.s3.us-east-2.amazonaws.com/1694834265553.pdf (India)
Understanding determinants of the burden of stunting in India Phuong Hong Nguyen, Derek Headey, Rasmi Avula, Purnima Menon International Food Policy Research Institute Stunting Conference- London, September 7-8, 2017 Background  Several studies have documented various factors associated with stunting: oInfant feeding (Menon et al.)
oSanitation (Spears et al) oMaternal factors (Coffey and others)  Limited evidence exists on spatial analysis to identify hotspots of stunting and to explain the factors that contribute to geographical inequalities in child stunting  This analysis is important in a policy context that aims to close gaps with a focus on the worst-off districts.
Stunting varies considerably across Indian states and districts - Stunting ranges from 12% to 65% - Heavily clustered in the north and center of the country Low prevalence (<20%) Medium prevalence (20-30%) High prevalence (30-40%) Very high prevalence (≥ 40% 29 170 202 239 Source: NFHS-4 80% of India’s stunting burden is concentrated in larger northern states Differences in immediate and underlying determinants across groups of districts Low prevalence (<20%) Medium prevalence (20- <30%) High prevalence (30- <40%) Very high prevalence (≥ 40% Immediate determinants Women with BMI <18.5 Early initiation of breastfeeding Exclusive breastfeeding1 Timely introduction of foods2 Adequate diet Underlying determinants Women with ≥10 years education Girls married after age of 18 Asset score Water within premises Open defecation density per sq km 12.5 52.5 48.7 29.5 17.3 56.1 90.3 61.0 60.7 128.1 15.3* 49.7 59.0 63.2** 15.2 44.0*** 83.0** 52.8 50.4* 180.5 21.9*** 47.1 59.7 46.9 28.6*** 39.4*** 53.3 34.2 9.8*** 6.9*** 33.9*** 76.5*** 39.9*** 40.1*** 257.6* 25.7*** 68.5*** 25.0*** 36.3*** 508.7*** Significant difference from districts with low level of stunting: *p<0.05, **p<0.01, ***p<0.001 1Data for EBF are available for 425 districts only 2Data for timely introduction of foods are available for 186 districts only 3Data for ORS during diarrhea are available for 328 districts only Differences in coverage of nutrition-specific interventions across groups of districts Low prevalence (<20%) Medium prevalence (20- <30%) High prevalence (30- <40%) Very high prevalence (≥ 40% Nutrition-specific interventions ANC first trimester 4+ ANC visits Consumed 100+ IFA during pregnancy Full immunization Received vitamin A in the last 6 mos ORS during diarrhea3 Other factors SC population Household size 77.2 74.4 46.8 75.7 72.4 91.4 13.4 4.5 67.9** 62.3*** 50.7*** 67.5 42.4 67.7* 66.0 65.7* 14.7 4.6 54.5*** 35.9*** 33.1*** 20.2*** 62.6*** 56.7*** 58.7*** 55.5*** 57.0** 48.5*** 14.4 4.9* 15.5 5.4*** Significant difference from districts with
 low level of stunting: *p<0.05, **p<0.01, ***p<0.001 Determinants of stunting and district variability: Reducing stunting requires pulling all districts up on determinants 100 ) % ( e g a t n e c r e P 80 60 40 20 0 Immediate determinants Underlying determinants Nutrition-specific interventions Factors contributing to the differences in stunting prevalence between very high burden (stunting>40%) and low burden districts (stunting<20%) Adequate diet, 9% 4+ ANC visit, 6% Low BMI women, 19% Unexplained, 29% HH size , 5% Asset score, 7% Open defecation , 7% Married after 18 y, 7% 10+ years schooling, 12% Conclusion  Our findings emphasize the variability in stunting across India, reinforce the multifactorial determinants that contribute to stunting, and highlight that inter-district differences in stunting are not explained by any single factor, but rather by a multitude of economic, health, hygiene and demographic factors.
 Strategies to address these multiple determinants, with enhanced targeting towards higher burden districts, could help to reduce inequalities in childhood stunting in India.

Thank you EXTRA ANALYSIS SLIDES 80% of India’s stunting burden is concentrated in larger northern states No.
Share of Stunting Stunted Share of stunted districts districts (%) Rate (%) children children (%) Stunting burden categories Low prevalence (<20%) Medium prevalence (20-30%) High prevalence (30-40%) Very high prevalence (≥ 40% Total Spatial categories North-east and Islands South & Smaller Northern States Larger Northern States Association of stunting with various immediate and underlying determinants β= 0·73*** β= -0·18*** β= -0·08** β= -0·50*** β= -0·81*** β= -0·42*** β= -0·41*** β= -0.32*** *p<0.05, **p<0.01, ***p<0.001 0204060Stunting (%)01020304050Women with BMI <18.50204060Stunting (%)20406080100Early initiation of breastfeeding0204060Stunting (%)020406080100Exclusive breastfeeding0204060Stunting (%)01020304050Adequate diet0204060Stunting (%)1.522.53Asset score, log0204060Stunting (%)020406080More than 10 years education0204060Stunting (%)20406080100Married after age of 180204060Stunting (%)02468Open defecation density, log Association of stunting with various coverage indicators β= -0·27*** β= -0·22*** β= -0·27*** β= -0·19*** β= -0·17*** β= -0·24*** *p<0.05, **p<0.01, ***p<0.001 0204060Stunting (%)020406080100ANC first trimester0204060Stunting (%)0204060801004+ ANC visits0204060Stunting020406080100Consumed 100+ IFA during pregnancy0204060Stunting020406080100Full immunization0204060Stunting (%)20406080100Received vitamin A in the last 6 mos0204060Stunting (%)20406080100ORS during diarrhea Least squares regression models of stunting among children 0- 5 years of age against its underlying determinants1 Model 12 Model 2 Model 3 Full model Women with BMI <18.5 Early initiation of breastfeeding Adequate diet 4+ ANC visits, log Consumed 100+ IFA during pregnancy Full immunization Received vitamin A in the last 6 mos Women with ≥10 years education Girls married after age of 18 Water within premises Asset score, log Open defecation density per sq km, log SC population Household size R-square N 0.63*** 0.02 -0.23*** -0.67*** -0.09** -0.04 -0.02 0.68 640 0.61 635 0.31*** 0.01 -0.22*** -0.22* 0.03 -0.01 -0.03 -0.13** -0.10*** -0.01 -0.16* 0.10* -0.06 1.63** 0.74 635 -0.16*** -0.11*** -0.02 -0.41*** 0.11** -0.06 1.64** 0.72 640 1 All models included state fixed effects and are weighted by the number of children 0-5 years in each district.
2 Model 1 included immediate determinants only; model 2 included nutrition sensitive intervention only; model 3 included underlying determinants only; full model included all factors.

*p<0.05, **p<0.01, ***p<0.001 Summary  Despite a significant decline the national prevalence of stunting between 2006 and 2016 (from 48 to 38.4%), stunting in India remains high, but varies by district.

 Some 239 of the 640 districts have stunting levels above 40% and a further 202 have rates of 30-40%.
 High stunting districts are heavily clustered in the north and center of the country where the bulk of India’s population resides.

 Regression models reveal several significant factors associated with stunting  A decomposition model explains 71% of the observed difference in stunting rates.

This explained share is accounted for by differences in maternal low BMI (19%), education (12%), child adequate diets (9%), sanitation, household assets and others (5-7% each).
Discussion  Our study results are consistent with previous literature, and add insights from this most recent dataset  A focus on addressing women’s nutrition emerges as a key priority area in our analyses: maternal nutrition, education levels, age at marriage, and use/access to antenatal care services account for nearly half of differences  Maternal BMI explain 20% difference in low and high burden districts  Indian account for 1/6 the global population with 42% of low BMI prepregnant  Early marriage are common  Investments in girls and women are likely to have both biological and social pathways to better nutrition for children Limitations  Interpret these findings as suggestive of causality, but not as indicative of strictly causal impacts.
 The regression model is ecological in nature, and could still be hampered by confounding factors  The limited data included in the district-level summary data from the NFHS-4 preclude the inclusion of additional variables available in the unit-level data but not yet released.