Article ; Online: Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries? Evidence from India.
2021 Volume 41, Page(s) 100990
Abstract: This paper studies BMI as a correlate of the early spatial distribution and intensity of Covid-19 ... of respiratory cases, does not diminish the predictive power of BMI in influencing the spatial incidence and ... to identify and protect especially at-risk populations in developing countries like 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. |
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| MeSH term(s) | Adult ; Age Factors ; Aged ; Body Mass Index ; COVID-19/epidemiology ; COVID-19/mortality ; Comorbidity ; Developing Countries/statistics & numerical data ; Female ; Humans ; Incidence ; India/epidemiology ; Male ; Middle Aged ; Residence Characteristics ; Risk Factors ; SARS-CoV-2 ; Sex Factors ; Socioeconomic Factors ; Spatial Analysis ; Urban Population |
| Language | English |
| Publishing date | 2021-02-17 |
| Publishing country | Netherlands |
| Document type | Journal Article |
| ZDB-ID | 2099749-8 |
| ISSN | 1873-6130 ; 1570-677X |
| ISSN (online) | 1873-6130 |
| ISSN | 1570-677X |
| DOI | 10.1016/j.ehb.2021.100990 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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