Overlooked Burden of Undernutrition Among Older Adults in India.

The underweight status among older adults in India and its association with other health risks has received little attention. Using nationally representative data from the first wave of the Longitudinal Aging Study in India (LASI, 2017-18), this study examined the underweight status among the Indian older adults and investigated its relationship with various health outcomes. The effective sample size of this study was 28,050 older adults aged 60 years or above. Multivariable logistic and linear regression models were employed to meet the objectives of the study. Underweight status was found to be negatively associated functional health, cognitive function, and grip strength. However, no significant association was observed between underweight status and depressive symptoms. Moreover, socioeconomic, demographic, and geographic factors were identified as strong determinants of underweight status among Indian older adults. India has implemented a wide range of nutrition policies that primarily focus on children, mothers, and adolescents. While these policies are important, it is equally crucial to develop interventions specifically tailored for adults and older adults.

Inequalities in the prevalence of double burden of malnutrition among mother-child dyads in India.

In the midst of rapid urbanization and economic shifts, the global landscape witnesses a surge in overweight and obese individuals, even as child malnutrition persists as a formidable public health challenge in low- and middle-income countries (LMICs). This study seeks to unravel the prevalence of the double burden of malnutrition (DBM) within the context of India and delve into the associated disparities rooted in wealth. This study leverages data from the fifth wave of the National Family and Health Survey (NFHS-5), a nationally representative survey conducted in the year 2019-21 in India. This study focuses on mother-child dyads with children under the age of 3 years. Descriptive, bivariate and logistic regression analysis is used to decipher the intricate web of DBM's prevalence and risk factors, as underscored by socio-demographic attributes. Wagstaff decomposition analysis is applied to quantify the contribution of each inequality in the social determinants on the observed income-related inequality in the DBM. Result from bivariate and logistic regression indicated a heightened risk of DBM within households marked by C-section births, affluence, ongoing breastfeeding practices, advanced maternal age, and larger household sizes. Additionally, households harbouring women with abdominal obesity emerge as hotspots for elevated DBM risk. Notably, the interplay of abdominal obesity and geographical disparities looms large as drivers of substantial inequality in DBM prevalence, whereas other factors exert a comparably milder influence. As India grapples with the burgeoning burden of DBM, a conspicuous imbalance in its prevalence pervades, albeit inadequately addressed. This juncture warrants the formulation of dual-purpose strategies, and a slew of innovative actions to deftly navigate the complex challenges poised by the dual burden of malnutrition. Amidst these exigencies, the imperative to forge a holistic approach that encompasses both sides of the malnutrition spectrum remains a beacon guiding the quest for equitable health and nutrition outcomes.

In-utero exposure to multiple air pollutants and childhood undernutrition in India.

BACKGROUND: Several studies have been conducted to understand the impact of socioeconomic and maternal factors on child undernutrition. However, the past literature has not directly examined the joint impacts of fuel use and ambient pollution and have primarily focused on PM2.5. OBJECTIVE: This study explored the individual and community-level associations of both indoor (cooking fuel type) and ambient air pollution (PM2.5, NO2 and SO2) during maternal gestation on child undernutrition. METHODS: This study analysed stunting, being underweight, and anaemia of children aged 0-59 months (n = 259,627) using the National Family Health Survey. In-utero exposures to ambient PM2.5, NO2, and SO2 were measured using satellite data and self-reported fuel type was a marker of indoor pollution exposure. The study used univariate and bivariate Moran's I, spatial lag model and multivariable logistic regression models after adjusting for other covariates to understand the effect of pollution on in-utero exposure and child health status at the individual and community-levels. RESULTS: Higher concentration of indoor and ambient air pollution was found in the Northern and parts of Central regions of India. Estimates of spatial modelling show that each 1 µg/m-3 increase in maternal exposure to ambient PM2.5 across the clusters of India was associated with a 0.11, 9 and 19 percentage points increase in the prevalence of stunting, underweight and anaemia, respectively. The results of multi-pollutant model show that a higher ambient PM2.5 exposure during pregnancy was linked to higher odds of stunting (AOR:1.38; 95% CI:1.32-1.44), underweight (AOR:1.59; 95% CI:1.51-1.67) and anaemia (AOR:1.61; 95% CI:1.52-1.69) in children. Weaker but similar associations were observed for NO2, but not with SO2. Indoor pollution exposure during in-utero periods was also significantly associated with childhood undernutrition and this association was modified by ambient PM2.5 levels, where exposure to both indoor and ambient air pollution had even greater odds of being undernourished. IMPACT STATEMENT: Our research on multi-pollutant models has revealed the initial proof of the individual impacts of indoor and outdoor pollution (PM2.5, NO2, and SO2) exposure during fetal development on children's nutrition.

Prevalence and Predictors of Combined Body Mass Index and Waist Circumference Among Indian Adults.

Objective: To determine the prevalence and predictors of combined BMI-WC disease risk categories among Indian adults. Methods: The study utilizes data from Longitudinal Ageing Study in India (LASI Wave 1) with an eligible sample of 66, 859 individuals. Bivariate analysis was done to get the proportion of individuals in different BMI-WC risk categories. Multinomial logistic regression was used to identify the predictors of BMI-WC risk categories. Results: Poor self-rated health, female sex, urban place of residence, higher educational status, increasing MPCE quintile, and cardio-vascular disease increased with increasing BMI-WC disease risk level while increasing age, tobacco consumption, and engagement in physical activities was negatively associated with BMI-WC disease risk. Conclusion: Elderly persons in India have a considerable higher prevalence of BMI-WC disease risk categories which make them vulnerable to developing several disease. Findings emphasize the need of using combined BMI categories and waist circumference to assess the prevalence of obesity and associated disease risk. Finally, we recommend that intervention programs with an emphasis on urbanites wealthy women and those with a higher BMI-WC risk categories be implemented.