Obesity as a driver of international differences in COVID-19 death rates.

To determine what proportion of the inter-country variation in death rates can be explained in terms of obesity rates and other known risk factors for coronavirus disease 2019 (COVID-19). COVID-19 death rates from 30 industrialized countries were analysed using linear regression models. Covariates modelled population density, the age structure of the population, obesity, population health, per capita gross domestic product (GDP), ethnic diversity, national temperature and the delay in the government imposing virus control measures. The multivariable regression model explained 63% of the inter-country variation in COVID-19 death rates. The initial model was optimized using stepwise selection. In descending order of absolute size of model coefficient, the covariates in the optimized model were the obesity rate, the hypertension rate, population density, life expectancy, the percentage of the population aged older than 65 years, the percentage of the population aged younger than 15 years, the diabetes rate, the delay in imposing national COVID-19 control measures, per capita GDP and mean temperature (with a negative coefficient indicating an association between higher national temperatures and lower death rates). A large proportion of the inter-country variation in COVID-19 death rates can be explained by differences in obesity rates, population health, population densities, age demographics, delays in imposing national virus control measures, per capita GDP and climate. Some of the unexplained variation is probably attributable to inter-country differences in the definition of a COVID-19 death and in the completeness of the recording of COVID-19 deaths. © 2021 University College London. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

Citation

Julian Gardiner, Jude Oben, Alastair Sutcliffe. Obesity as a driver of international differences in COVID-19 death rates. Diabetes, obesity & metabolism. 2021 Jul;23(7):1463-1470

Mesh Tags

PMID: 33620765

search → result