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The absence of reliable registration of Covid-19 deaths in India has prevented proper assessment and monitoring of the coronavirus pandemic. In addition, India's relatively young age structure tends to conceal the severity of Covid-19 mortality, which is concentrated in older age groups. In this paper, we present four different demographic samples of Indian populations for which we have information on both their demographic structures and death outcomes. We show that we can model the age distribution of Covid-19 mortality in India and use this modeling to estimate Covid-19 mortality in the country. Our findings point to a death toll of approximately 3.2–3.7 million persons by early November 2021. Once India's age structure is factored in, these figures correspond to one of the most severe cases of Covid-19 mortality in the world. India has recorded after February 2021 the second outbreak of coronavirus that has affected the entire country. The accuracy of official statistics of Covid-19 mortality has been questioned, and the real number of Covid-19 deaths is thought to be several times higher than reported. In this paper, we assembled four independent population samples to model and estimate the level of Covid-19 mortality in India. We first used a population sample with the age and sex of Covid-19 victims to develop a Gompertz model of Covid-19 mortality in India. We applied and adjusted this mortality model on two other national population samples after factoring in the demographic characteristics of these samples. We finally derive from these samples the most reasonable estimate of Covid-19 mortality level in India and confirm this result using a fourth population sample. Our findings point to a death toll of about 3.2–3.7 million persons by late May 2021. This is by far the largest number of Covid-19 deaths in the world. Once standardized for age and sex structure, India's Covid-19 mortality rate is above Brazil and the USA. Our analysis shows that existing population samples allow an alternative estimation of deaths due to Covid-19 in India. The results imply that only one out of 7–8 deaths appear to have been recorded as a Covid-19 death in India. The estimates also point to a very high Covid-19 mortality rate, which is even higher after age and sex standardization. The magnitude of the pandemic in India requires immediate attention. In the absence of effective remedies, this calls for a strong response based on a combination of non-pharmaceutical interventions and the scale-up of vaccination to make them accessible to all, with an improved surveillance system to monitor the progression of the pandemic and its spread across India's regions and social groups. [ABSTRACT FROM AUTHOR]

Background: The prevalence of stunting among under- three Indian children though decreased, still it is considered to be alarmingly high. In most of the previous studies, traditional (linear and logistic) regression analyses were applied. They were limited to encapsulated cross-distribution variations. The objective of the current study was to examine how the different determinants were heterogeneous in various percentiles of height for age (HAZ) distribution. Methods and findings: This article examined the change in the HAZ distribution of children and examined the relationships between the key co-variate trends and patterns in HAZ among children aged <3 years over a period of 24 years. Four successive rounds of the National Family Health Survey data 1992–93, 1998–99, 2005–06, and 2015–16 were used for analysis. The final study included 206579 children aged <3 years (N = 106136 male, 100443 female). To explain and analyse differences in the HAZ distribution, the lambda-mu-sigma (LMS) method was used. Trends in height for age (HAZ) distribution over time were analysed using separate gender-stratified quantile regression (QR). The selected socio-economic, demographic and other predictors were considered for this analysis. The quantile regressions have indicated that mothers who have higher than primary level education were more proactive in mitigating malnutrition among children at the lower end of the distribution. The age, birth order, mother's body-mass-index (BMI) and economic status, among children, were some more determining factors for HAZ. Results of selected quantile regression were estimated at the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantiles. Conclusions: The outcome of various covariates working differently across the HAZ distribution was suggested by quantile regression. The major discrepancies in different aspects were underlined by socioeconomic and demographic aspects among the Indian population. The heterogeneity of this effect was shown using quantile regression. Policymakers may choose to concentrate on the most important factors when formulating policies to lessen the prevalence of stunting in India. [ABSTRACT FROM AUTHOR]

Background: National data on birthweight from birth certificates or medical records are not available in India. The third Indian National Family Health Survey included data on birthweight of children obtained from health cards and maternal recall. This study aims to describe the population that these data represent and compares the birthweight obtained from health cards with maternal recall data in terms of its socioeconomic patterning and as a risk factor for childhood growth failure. Methodology/Principal Findings: The analytic sample consisted of children aged 0 to 59 months with birthweight data obtained from health cards (n = 3227) and maternal recall (n = 16787). The difference between the card sample and the maternal recall sample in the distribution across household wealth, parental education, caste, religion, gender, and urban residence was compared using multilevel models. We also assessed the ability of birthweight to predict growth failure in infancy and childhood in the two groups. The survey contains birthweight data from a majority of household wealth categories (>5% in every category for recall), both genders, all age groups, all caste groups, all religion groups, and urban and rural dwellers. However, children from the lowest quintile of household wealth were under-represented (4.73% in card and 8.62% in recall samples). Comparison of data across health cards and maternal recall revealed similar social patterning of low birthweight and ability of birthweight to predict growth failure later in life. Children were less likely to be born with low birthweight if they had mothers with over 12 years of education compared to 1-5 years of education with relative risk (RR) of 0.79 (95% confidence interval [CI]: 0.52, 1.2) in the card sample and 0.70 (95% CI: 0.59, 0.84) in the recall sample. A 100 gram difference in a child's birthweight was associated with a decreased likelihood of underweight in both the card (RR: 0.95; 95% CI: 0.94, 0.96) and recall (RR: 0.96; 95% CI: 0.96, 0.97) samples. Conclusions: Our results suggest that in the absence of other sources, the data on birthweight in the third Indian National Family Health Survey is valuable for epidemiologic research. [ABSTRACT FROM AUTHOR]