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Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries? Evidence from India.

ABSTRACT: This paper studies BMI as a correlate of the early spatial distribution and intensity of Covid-19 across the districts of India and finds that conditional on a range of individual, household and regional characteristics, adult BMI significantly predicts the likelihood that the district is a hotspot, the natural log of the confirmed number of cases, the case fatality rate, and the propensity that the district is a red zone. Controlling for air-pollution, rainfall, temperature, demographic factors that measure population density, the proportion of the elderly, and health infrastructure including per capita health spending and the proportion of respiratory cases, does not diminish the predictive power of BMI in influencing the spatial incidence and spread of the virus. The association between adult BMI and measures of spatial outcomes is especially pronounced among educated populations in urban settings, and impervious to conditioning on differences in testing rates across states. We find that among women, BMI proxies for a range of comorbidities (hemoglobin, high blood pressure and high glucose levels) that affects the severity of the virus while among men, these health indicators are also important, as is exposure to risk of contracting the virus as measured by work propensities. We conduct sensitivity checks and control for differences that may arise due to variations in timing of onset. Our results provide a readily available health marker that may be used to identify and protect especially at-risk populations in developing countries like India.

SUBMITTER: Menon N

PROVIDER: S-EPMC7886627 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Climate change is an important and urgent challenge facing the world, affecting the survival and development of human beings from multiple dimensions such as environment and economy. Carbon emissions are a barometer of climate change, and energy consumption is closely related to carbon emissions. Reducing energy consumption can reduce carbon emissions, thereby optimizing the human living environment. Compared with developed countries, the carbon emissions and energy consumption of developing countries are still growing strongly, so it's significant to study the energy consumption of developing countries. Energy intensity can be reduced through reducing energy intensity, and financial development can have an impact on energy intensity. So, using panel data from 67 developing countries from 1995 to 2018, this study selected six financial development indicators and uses the system Generalized Method of Moments (sys-GMM for short) establishes a dynamic panel model to study the impact of financial development on energy intensity from the six aspects of access, depth and efficiency of financial institutions and financial markets, which are more implementable and more realistic for formulating relevant financial policies to reduce energy intensity. We introduced the index of industrial structure upgrading and studied the interaction between financial development and financial development in the impact of financial development on energy intensity for the first time. The findings revealed that improving financial institutions' access, depth, and efficiency, as well as financial markets' access, depth, and efficiency, might dramatically lower developing countries' energy intensity. The improvement of industrial structure resulted in a reduction in the energy intensity of financial development. Furthermore, the 67 developing countries were grouped from five different perspectives, and the influence of financial development on energy intensity reduction was found to be robust. Therefore, the research results significantly reduce energy intensity for financial development in developing countries.

Project description:BackgroundOvernutrition increases the risk of diabetes. Evidence on the causal impact of overnutrition on diabetes is scarce for India. Considering a representative sample from India, this study examines the causal effect of a rise in the Body Mass Index (BMI) of an individual on the likelihood of being diabetic while addressing the issue of unobserved endogeneity between overnutrition and diabetes.MethodsThe study considers individual level data from Demographic and Health Surveys (DHS) of India, namely, National Family Health Survey (NFHS) for the year 2015-16. The NFHS is a large-scale, multi-round survey conducted in a representative sample of households throughout India. The survey covers females having age 15-49 years and males having age 15-54 years. The instrument variable approach is used to address the potential endogeneity in the relationship between BMI and diabetes. We instrument BMI of an individual by BMI of a non-biologically related household member. Ordered Probit, Probit and IV-Probit models are estimated using two alternative definitions for measuring diabetes-self-reported diabetes status and blood glucose levels (ordinal measure).ResultsThe coefficients obtained from the Ordered Probit and Probit models are much smaller than those estimated by an IV-Probit model. The latter estimates the causal impact of a rise in BMI on diabetes by taking into account the effect of the unobserved genetic and other related factors. The likelihood of being diabetic is twice or more among the overweight and obese individuals as compared to non-overweight individuals in all the specifications. With a unit increase in BMI the probability of being diabetic increases by about 1.5% among overweight and obese individuals and by 0.5% among the non-overweight individuals in the IV-Probit model. Similar results from the Ordered Probit model show that on average, the overweight and obese individuals experience about 0.2% increase in the probability of being diabetic and about 0.4% increase in the probability of being prediabetic.ConclusionOur study demonstrates that the likelihood of being both prediabetic and diabetic is higher among the overweight and obese individuals as compared to the non-overweight individuals. We also find that the level of risk of being prediabetic or diabetic differs across gender, wealth quintiles and regions and the effects are more severe among population in the urban areas, belonging to the richest wealth quintile and men. Our findings have significant implications for the policy formulation as diabetes has a substantial health and economic burden associated with it. Future studies may investigate the effect of abdominal obesity on diabetes.

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Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries? Evidence from India.

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