Exercising in India: An Exploratory Analysis Using The Time Use Survey, 2019

In this paper, we use the nationally representative Time Use Survey (TUS) data from India to estimate the proportion of people that spend any time of the day exercising. We found that overall, less than 7% of the adult population (age ≥18 Years) spent any time of the day exercising. Our estimates also revealed that the proportion of population exercising varied Across states, by rural and urban sectors, and by social and religious groups. We also estimated logistic regressions to Model the probability of people exercising. We found that males had three times higher odds of exercising than females. Relative to less educated people (primary school and below), those with educational level of graduate and above had almost 2.5 times higher odds of exercising. People in the higher strata of consumption class, the top 10%, had 1.7 times higher odds of exercising relative to the bottom 50%. From a public policy perspective, the low level of exercise across all geographies and social, economic, and demographic characteristics indicates the need for population-wide interventions in India to encourage exercise

Public health spending and infant and child mortality in India: a state-year panel analysis

Background: To investigate the association between public health spending and probability of infant and child death in India. Methods: We used data from the three rounds of National Family Health Survey (NFHS) conducted in India during 1992-93, 1998-99 and 2005-06 to investigate the association between public health spending and probability of infant and child death. We used data from the birth history of three NFHS rounds to create state-year panels of births, infant and child deaths, state-level public finance variables, food grain production, household and individual variables for the period 1980-2005. Two-stage probit regression model is used to investigate the association. State-level per capita gross fiscal deficit is used as an instrument for estimating two-stage probit model. Findings: Findings suggest association between public health spending and infant and child mortality in India. A 10% increase in per capita public health spending is likely to reduce the probability of infant and child deaths by 0•005 (95% CI: 0•003, 0•007) and 0•003 (95% CI: 0•002, 0•004) respectively. The second and third lags of public health spending were also statistically significant. Other factors affecting infant and child death were sex of the child, birth order, mother’s age at birth of the index child, mother’s schooling and urban-rural residence. Interpretation: Public health spending was associated with probability of infant and child death in India. Our findings lend support to the government’s initiative to increase public health spending in India

Public health spending and infant and child mortality in India: a state-year panel analysis

Background: To investigate the association between public health spending and probability of infant and child death in India. Methods: We used data from the three rounds of National Family Health Survey (NFHS) conducted in India during 1992-93, 1998-99 and 2005-06 to investigate the association between public health spending and probability of infant and child death. We used data from the birth history of three NFHS rounds to create state-year panels of births, infant and child deaths, state-level public finance variables, food grain production, household and individual variables for the period 1980-2005. Two-stage probit regression model is used to investigate the association. State-level per capita gross fiscal deficit is used as an instrument for estimating two-stage probit model. Findings: Findings suggest association between public health spending and infant and child mortality in India. A 10% increase in per capita public health spending is likely to reduce the probability of infant and child deaths by 0•005 (95% CI: 0•003, 0•007) and 0•003 (95% CI: 0•002, 0•004) respectively. The second and third lags of public health spending were also statistically significant. Other factors affecting infant and child death were sex of the child, birth order, mother’s age at birth of the index child, mother’s schooling and urban-rural residence. Interpretation: Public health spending was associated with probability of infant and child death in India. Our findings lend support to the government’s initiative to increase public health spending in India

Association of maternal height with child mortality, anthropometric failure and anemia in India

CONTEXT: Prior research on the determinants of child health has focused on contemporaneous risk factors such as maternal behaviors, dietary factors, and immediate environmental conditions. Research on intergenerational factors that might also predispose a child to increased health adversity remains limited. OBJECTIVE: To examine the association between maternal height and child mortality, anthropometric failure, and anemia. DESIGN, SETTING, AND POPULATION: We retrieved data from the 2005–2006 National Family Health Survey in India (released in 2008). The study population constitutes a nationally representative cross-sectional sample of singleton children aged 0 to 59 months and born after January 2000 or January 2001 (n=50 750) to mothers aged 15 to 49 years from all 29 states of India. Information on children was obtained by a face-to-face interview with mothers, with a response rate of 94.5%. Height was measured with an adjustable measuring board calibrated in millimeters. Demographic and socioeconomic variables were considered as covariates. Modified Poisson regression models that account for multistage survey design and sampling weights were estimated. MAIN OUTCOME MEASURES: Mortality was the primary end point; underweight, stunting, wasting, and anemia were included as secondary outcomes. RESULTS: In adjusted models, a 1-cm increase in maternal height was associated with a decreased risk of child mortality (relative risk [RR], 0.978; 95% confidence interval [CI], 0.970–0.987; P<.001), underweight (RR, 0.971; 95% CI, 0.968–0.974; P<.001), stunting (RR, 0.971; 95% CI, 0.968–0.0973; P<.001), wasting (RR, 0.989; 95% CI, 0.984–0.994; P<.001), and anemia (RR, 0.998; 95% CI, 0.997–0.999; P=.02). Children born to mothers who were less than 145 cm in height were 1.71 times more likely to die (95% CI, 1.37–2.13) (absolute probability, 0.09; 95% CI, 0.07–0.12) compared with mothers who were at least 160 cm in height (absolute probability, 0.05; 95% CI, 0.04–0.07). Similar patterns were observed for anthropometric failure related to underweight and stunting. Paternal height was not associated with child mortality or anemia but was associated with child anthropometric failure. CONCLUSION: In a nationally representative sample of households in India, maternal height was inversely associated with child mortality and anthropometric failure

The poorer stay thinner: stable socioeconomic gradients in body mass index among women in lower and middle income countries

An elderly couple stands along South High Street at the intersection of Frankfort Street in the German Village neighborhood of Columbus, Ohio. The High Street Photograph Collection is comprised of over 400 photographs of High Street in Columbus, Ohio, taken in the early 1970s. These photographs were taken primarily at street level and document people and the built environment from the Pontifical College Josephinum on North High Street in Worthington through Clintonville, the University District and Short North, Downtown and South Columbus. The photographs were used in a television photo documentary that aired on WOSU called "High Street." Photographers that were involved in this project were Alfred Clarke, Carol Hibbs Kight, Darrell Muething, Clayton K. Lowe, and Julius Foris, Jr

A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA).

Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata.bSpecific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses

Validation of a geospatial aggregation method for congressional districts and other US administrative geographies

Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap