The global burden of child undernutrition remains high by all measures of child anthropometric failures (including stunting, underweight, and wasting).^1,2 In 2018, 21.9% of children (ie, 149 million children) were estimated to have stunting.¹ Immediate actions are needed to meet Sustainable Development Goal 2, ie, to end all forms of malnutrition by 2030,³ which in turn can contribute to other targets associated with child survival, educational achievements, and overall well-being. Several conceptual models have been developed to understand the causes of child undernutrition, most of which adopt multifactorial framework.^4,5 The United Nations Children’s Fund (UNICEF) framework outlines socioeconomic conditions and national and global contexts as the fundamental factors affecting food security, care for children, and healthy household environment, all of which in turn further shape dietary intake, disease occurrence and, consequently, children’s nutritional status and growth.⁵ However, the UNICEF framework does not explicitly account for the role of parental nutritional status (eg, height and body mass index [BMI], calculated as weight in kilograms divided by height in meters squared), which may have intergenerational associations via biologic (eg, genetic disposition) and psychosocial (eg, poor living conditions) channels.⁶

Most interventions to foster child growth and development in India focus on improving food quality and quantity. We aimed to assess the pattern in food consumption and dietary diversity by socioeconomic status (SES) among Indian children. The most recent nationally representative, cross-sectional data from the National Family Health Survey (NFHS-4, 2015–16) was used for analysis of 73,852–74,038 children aged 6–23 months. Consumption of 21 food items, seven food groups, and adequately diversified dietary intake (ADDI) was collected through mother’s 24-h dietary recall. Logistic regression models were conducted to assess the association between household wealth and maternal education with food consumption and ADDI, after controlling for covariates. Overall, the mean dietary diversity score was low (2.26; 95% CI:2.24–2.27) and the prevalence of ADDI was only 23%. Both household wealth and maternal education were significantly associated with ADDI (OR:1.28; 95% CI:1.18–1.38 and OR:1.75; 95% CI:1.63–1.90, respectively), but the SES gradient was not particularly strong. Furthermore, the associations between SES and consumption of individual food items and food groups were not consistent. Maternal education was more strongly associated with consumption of essential food items and all food groups, but household wealth was found to have significant influence on intake of dairy group only. Interventions designed to improve food consumption and diversified dietary intake among Indian children need to be universal in their targeting given the overall high prevalence of inadequate dietary diversity and the relatively small differentials by SES.

Exposure to ambient fine particulate matter (mass concentrations of particles ≤2.5 μm in diameter [PM_2.5]) is associated with increased mortality and morbidity and reduced life expectancy.^1-4 Key findings on the negative outcomes associated with long-term exposure to PM_2.5 have come from cohort studies that have followed fixed sets of individuals over time.^5-7 In most studies, these individuals are not representative of the national US population. Studies relying on administrative data that are more representative of the US population as a whole have also found overwhelming evidence of negative outcomes associated with long-term exposure to PM_2.5^8-11 and the public health benefits associated with the overall decline of PM_2.5 concentrations over the years.^12-14 Although ecological regression analyses relying on administrative data have several limitations, eg, they are unable to adjust for individual-level risk factors and confusion between ecological associations and individual-level associations may present an ecological fallacy, they still may allow us to draw conclusions about the outcomes associated with pollution at the area level, which are important for policy makers.¹⁵

Despite the broad consensus that investments in nutrition-sensitive programmes are required to reduce child undernutrition, in practice empirical studies and interventions tend to focus on few nutrition-specific risk factors in isolation. The 2015-16 National Family Health Survey provides the first opportunity in more than a decade to conduct an up-to-date comprehensive evaluation of the relative importance of various maternal and child health and nutrition (MCHN) factors in respect to child anthropometric failures in India. The primary analysis included 140,444 children aged 6-59 months with complete data on 20 MCHN factors, and the secondary analysis included a subset of 25,603 children with additional paternal data. Outcome variables were stunting, underweight and wasting. We conducted logistic regression models to first evaluate each correlate separately in age- and sex-adjusted models, and then jointly in a mutually adjusted model. For all anthropometric failures, indicators of past and present socioeconomic conditions showed the most robust associations. The strongest correlates for stunting were short maternal stature (OR: 4.39; 95%CI: 4.00, 4.81), lack of maternal education (OR: 1.74; 95%CI: 1.60, 1.89), low maternal BMI (OR: 1.64; 95%CI: 1.54, 1.75), poor household wealth (OR: 1.25; 95%CI: 1.15, 1.35) and poor household air quality (OR: 1.22; 95%CI: 1.16, 1.29). Weaker associations were found for other correlates, including dietary diversity, vitamin A supplementation and breastfeeding initiation. Paternal factors were also important predictors of anthropometric failures, but to a lesser degree than maternal factors. The results remained consistent when stratified by children's age (6-23 vs 24-59 months) and sex (girls vs boys), and when low birth weight was additionally considered. Our findings indicate the limitation of nutrition-specific interventions. Breaking multi-generational poverty and improving environmental factors are promising investments to prevent anthropometric failures in early childhood.

Significance: Current knowledge on which geographic scale matters the most for inequalities in longevity in the United States is largely confined to counties, which have been the smallest unit at which national data on life expectancy were available. Making the inference that “counties make a difference” simply because “counties differ” is potentially problematic. In this systematic investigation of ascertaining the relative importance of three geographic scales: states, counties, and census tracts (CTs), we find that more than three-fourths of the total variation in life expectancy is attributable to CTs. This indicates that population inequalities in longevity are primarily a local phenomenon, and there is a need for greater precision and targeting of local geographies in public policy discourse.