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10.1016/j.ehb.2021.100990 http://scihub22266oqcxt.onion/10.1016/j.ehb.2021.100990 33631439!7886627!33631439     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Econ+Hum+Biol 2021 ; 41 (ä): 100990 Nephropedia Template TP {{tp|p=33631439|t=2021. Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries? Evidence from India |pdf=|usr=}}{{33631439}} gab.com Text Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries Evidence from India JO: Econ Hum Biol http://www.kidney.de/pget.php?&tw=1&pmid=33631439 #FOAvip 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. Twit Text FOAVip Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries Evidence from India ????Econ Hum Biol http://www.kidney.de/pget.php?&tw=1&pmid=33631439 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33631439 #FOAvip English Wikipedia <ref>{{Cite pmid|33631439}}</ref> Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries? Evidence from India #MMPMID33631439 Menon N Econ Hum Biol 2021[May]; 41 (ä): 100990 PMID33631439show ga 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. |*Body Mass Index[MESH] |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |COVID-19/*epidemiology/mortality[MESH] |Comorbidity[MESH] |Developing Countries/*statistics & numerical data[MESH] |Female[MESH] |Humans[MESH] |Incidence[MESH] |India/epidemiology[MESH] |Male[MESH] |Middle Aged[MESH] |Residence Characteristics[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Sex Factors[MESH] |Socioeconomic Factors[MESH] |Spatial Analysis[MESH]Does BMI predict the early spatial variation and intensity of Covid-19(epmc).pdf DeepDyve Pubget Overpricing