University College London Great Ormond Street Institute of Child Health, London, UK.
Department of Education, University of Oxford, Oxford, UK.
Diabetes Obes Metab. 2021 Jul;23(7):1463-1470. doi: 10.1111/dom.14357. Epub 2021 Mar 15.
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.
确定在冠状病毒病 2019(COVID-19)的死亡率的国家间差异中,有多少可以用肥胖率和其他已知的 COVID-19 风险因素来解释。
使用线性回归模型分析了 30 个工业化国家的 COVID-19 死亡率。模型化的协变量包括人口密度、人口年龄结构、肥胖、人口健康、人均国内生产总值(GDP)、民族多样性、国家温度和政府实施病毒控制措施的延迟。
多变量回归模型解释了 COVID-19 死亡率的 63%的国家间差异。使用逐步选择对初始模型进行了优化。按模型系数绝对值大小降序排列,优化模型中的协变量为肥胖率、高血压率、人口密度、预期寿命、65 岁以上人口比例、15 岁以下人口比例、糖尿病率、实施国家 COVID-19 控制措施的延迟、人均 GDP 和平均温度(负系数表示国家温度与死亡率之间的关联)。
COVID-19 死亡率的国家间差异的很大一部分可以用肥胖率、人口健康、人口密度、年龄人口结构、实施国家病毒控制措施的延迟、人均 GDP 和气候的差异来解释。一些未解释的差异可能归因于 COVID-19 死亡的定义以及 COVID-19 死亡记录的完整性在国家间存在差异。