Does BMI Predict the Early Spatial Variation and Intensity of Covid-19 in Developing Countries? Evidence from India
53 Pages Posted: 7 Jul 2020
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, the proportion of respiratory cases, and the number of viral disease outbreaks in the recent past, 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 less important and exposure to risk of contracting the virus as measured by work propensities is explanatory. We conduct heterogeneity and 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 especially at-risk populations in developing countries like India.
JEL Classification: I15, I18, O12, D83
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