Objective:(i) To assess diagnostic accuracy of mid-upper arm circumference (MUAC) for screening thinness and severe thinness in Indian adolescent girls aged 10–14 and 15–19 years compared with BMI-for-age Z-score (BAZ) <−2 and <−3 as the gold standard and (ii) to identify appropriate MUAC cut-offs for screening thinness and severe thinness in Indian girls aged 10–14 and 15–19 years.Design:Cross-sectional, conducted October 2016–April 2017.Setting:Four tribal blocks of two eastern India states, Chhattisgarh and Odisha.Participants:Girls (n 4628) aged 10–19 years. Measurements included height, weight and MUAC to calculate BAZ. Standard diagnostic accuracy tests, receiver–operating characteristic curves and Youden index helped arrive at MUAC cut-offs at BAZ < −2 and <−3, as gold standard.Results:Mean MUAC and BMI correlation was positive (0·78, P = 0·001 and r 2 = 0·61). Among 10–14 years, MUAC cut-off corresponding to BAZ < −2 and BAZ < −3 was ≤19·4 and ≤18·9 cm. Among 15–19 years, corresponding values were ≤21·6 and ≤20·7 cm. For both BAZ < −2 and BAZ < −3, specificity was higher in 15–19 v. 10–14 years. State-wise variations existed. MUAC cut-offs ranged from 17·7 cm (10 years) to 22·5 cm (19 years) for BAZ < −2, and from 17·0 cm (10 years) to 21·5 cm (19 years) for BAZ < −3. Single-age area under the curve range was 0·82–0·97.Conclusions:Study provides a case for use of year-wise and sex-wise context-specific MUAC-cut-offs for screening thinness/severe thinness in adolescents, rather than one MUAC cut-off across 10–19 years, depending on purpose and logistic constraints.

Objective To examine prevalence, risk factors, and consequences of maternal obesity; and provide evidence on current policies and programs to manage maternal obesity in India. Methods This is a mixed‐methods study. We analyzed the National Family Health Survey (NFHS)‐4 data (2015–16) to estimate the prevalence and risk factors of obesity, followed by a desk review of literature and stakeholder mapping with interviews to develop policy guidance. Results National prevalence of obesity (defined by WHO as body mass index ≥25) was comparable among pregnant (12%) and postpartum women (13%) ≥20 years of age. A high prevalence of obesity (>40%) was observed in over 30 districts in multiple states. Older maternal age, urban residence, increasing wealth quintile, and secondary education were associated with increased odds of obesity among pregnant and postpartum women; higher education increased odds among postpartum women only (OR 1.90; 95% CI, 1.44–2.52). Dietary variables were not associated with obesity. Several implementation challenges across healthcare system blocks were observed at policy level. Conclusion Overall prevalence of obesity in India during and after pregnancy is high, with huge variation across districts. Policy and programs must be state‐specific focusing on prevention, screening, and management of obesity among pregnant and postpartum women.

Household risk factors affecting child health, particularly malnutrition, are mainly basic amenities like drinking water, toilet facility, housing and fuel used for cooking. This paper considered the collective impact of basic amenities measured by an index specially constructed as the contextual factor of child malnutrition. The contextual factor operates at both the macro and micro levels namely the state level and the household level. The importance of local contextual factors is especially important when studying the nutritional status of children of indigenous people living in remote and inaccessible regions. This study has shown the contextual factors as potential factors of malnutrition among children in northeast India, which is home to the largest number of tribes in the country. In terms of macro level contextual factor it has been found that 8.9 per cent, 3.7 per cent and 3.6 per cent of children in high, medium and low risk households respectively, are severely wasted. Lower micro level household health risks, literate household heads, and scheduled tribe households have a negating effect on child malnutrition. Children who received colostrum feeding at the time of birth and those who were vaccinated against measles are also less subject to wasting compared to other children, and these differences are statistically significant.

A quarter of 400 million urban Indian residents are poor. Urban poor women are as undernourished as or worse than rural women but urban averages mask this disparity. We present the spectrum of malnutrition and their determinants for more than 26,000 urban women who gave birth within 5 years from the last two rounds of Demographic Health Survey 2006 and 2016. Among urban mothers in the lowest quartile by wealth index (urban poor), 12.8% (95% CI [11.3%, 14.5%]) were short or with height < 145 cm; 20.6% (95% CI [19%, 22.3%]) were thin or with body mass index < 18.5 kg/m2; 57.4% (95% CI [55.5%, 59.3%]) had any anaemia (haemoglobin < 12 g/dL), whereas 32.4% (95% CI [30.5%, 34.3%]) had moderate to severe anaemia; and 21.1% (95% CI [19.3%, 23%]) were obese (body mass index ≥ 25 kg/m2). Decadal gains were significant for thinness reduction (17p.p.) but obesity increased by 12 p.p. Belonging to a tribal household increased odds of thinness by 1.5 (95% CI [1.06, 2.18]) times among urban poor mothers compared with other socially vulnerable groups. Secondary education reduced odds of thinness (0.61; 95% CI [0.48, 0.77]) and higher education of short stature (0.41; 95% CI [0.18, 0.940]). Consuming milk/milk products, pulses/beans/eggs/meats, and dark green leafy vegetables daily reduced the odds of short stature (0.52; 95% CI [0.35, 0.78]) and thinness (0.72; 95% CI [0.54, 0.98]). Urban poor mothers should be screened for nutritional risks due to the high prevalence of all forms of malnutrition and counselled or treated as per risk.

Objectives Anemia has been a severe public health problem in India for decades, owing to its multifactorial etiology. Large surveys investigating causal factors of anemia in both male and female adolescents 10–19 years have not been available, thus our understanding of optimal interventions has been limited. Using data from a recent national micronutrient survey, our aims were to 1) describe the prevalence of anemia and micronutrient deficiencies (MNDs) in Indian adolescents and 2) examine risk factors of anemia in this population. Methods Data were from India's Comprehensive National Nutrition Survey (CNNS, 2016–18). Analyses were run separately for females (F; n = 3966) and males (M; n = 3944) aged 10–19 years. CNNS used a multi-stage, stratified, probability proportion to size cluster sampling design. Prevalence of anemia and micronutrient deficiencies were estimated based on age- and gender-specific WHO cutoffs, using weights for biomarker data. We examined predictors covering socio-demography (age, sex, residence, religion, caste, schooling status, parental education), nutrition (diets, anthropometry, micronutrient status), hygiene, sanitation and access to school-based services. Multivariable logistic regression models were used to examine associations between these factors and anemia. Results Forty % of females and 18% of males were anemic. The prevalence of anemia was higher in adolescents aged 15–19 years (F: 48%, M: 18%) than those aged 10–14 years (F: 32%, M: 17%). Deficiencies of iron (F: 31%, M: 12%), vitamin B12 (F: 27%, M: 35%), folate (F: 34%, M: 39%), vitamin A (16% in both), vitamin D (F: 35%, M: 14%) and zinc (F: 28% girls, M: 35%) were also common. Iron deficiency was the strongest predictor of anemia (adjusted OR and 95% CI for F: 4.23, 2.99–5.99; M: 4.12, 2.78–6.12). Among females, other risk factors were being older (1.53, 1.09–2.15) and belonging to a disadvantaged caste (1.86, 1.12–3.09). Among males, other risk factors included being older (1.47, 1.02–2.13), being short for age (1.61, 1.12–2.31) and no mobile or internet access (1.59, 1.16–2.19). Conclusions Anemia and MNDs are highly prevalent in Indian adolescents. Iron deficiency is a major biological risk factor for anemia as are a range of social determinants. Program design and targeting should fully account for the range of risk factors. Funding Sources UNICEF, POSHAN.