Early trends of socio-economic and health indicators influencing case fatality rate of COVID-19 pandemic.

Shahir Asfahan, Aneesa Shahul, Gopal Chawla, Naveen Dutt, Ram Niwas, Neeraj Gupta
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引用次数: 20

Abstract

Coronavirus disease 2019, i.e. COVID-19, started as an outbreak in a district of China and has engulfed the world in a matter of 3 months. It is posing a serious health and economic challenge worldwide. However, case fatality rates (CFRs) have varied amongst various countries ranging from 0 to 8.91%. We have evaluated the effect of selected socio-economic and health indicators to explain this variation in CFR. Countries reporting a minimum of 50 cases as on 14th March 2020, were selected for this analysis. Data about the socio-economic indicators of each country was accessed from the World bank database and data about the health indicators were accessed from the World Health Organisation (WHO) database. Various socioeconomic indicators and health indicators were selected for this analysis. After selecting from univariate analysis, the indicators with the maximum correlation were used to build a model using multiple variable linear regression with a forward selection of variables and using adjusted R-squared score as the metric. We found univariate regression results were significant for GDP (Gross Domestic Product) per capita, POD 30/70 (Probability Of Dying Between Age 30 And Exact Age 70 From Any of Cardiovascular Disease, Cancer, Diabetes or Chronic Respiratory Disease), HCI (Human Capital Index), GNI(Gross National Income) per capita, life expectancy, medical doctors per 10000 population, as these parameters negatively corelated with CFR (rho = -0.48 to -0.38 , p<0.05). Case fatality rate was regressed using ordinary least squares (OLS) against the socio-economic and health indicators. The indicators in the final model were GDP per capita, POD 30/70, HCI, life expectancy, medical doctors per 10,000, median age, current health expenditure per capita, number of confirmed cases and population in millions. The adjusted R-squared score was 0.306. Developing countries with a poor economy are especially vulnerable in terms of COVID-19 mortality and underscore the need to have a global policy to deal with this on-going pandemic. These trends largely confirm that the toll from COVID-19 will be worse in countries ill-equipped to deal with it. These analyses of epidemiological data are need of time as apart from increasing situational awareness, it guides us in taking informed interventions and helps policy-making to tackle this pandemic.

影响COVID-19大流行病死率的社会经济和卫生指标的早期趋势
2019冠状病毒病,即COVID-19,从中国一个地区开始爆发,在3个月内席卷了全球。它在全球范围内构成了严重的健康和经济挑战。然而,各国的病死率(CFRs)各不相同,从0到8.91%不等。我们评估了选定的社会经济和健康指标的影响,以解释CFR的这种变化。选择截至2020年3月14日报告至少50例病例的国家进行分析。关于每个国家的社会经济指标的数据来自世界银行数据库,关于健康指标的数据来自世界卫生组织(卫生组织)数据库。该分析选择了各种社会经济指标和健康指标。从单因素分析中选取相关性最大的指标,采用正向选择变量的多变量线性回归方法,以调整后的r平方评分为度量标准,构建模型。我们发现,人均GDP(国内生产总值)、POD 30/70(30岁至70岁之间死于心血管疾病、癌症、糖尿病或慢性呼吸系统疾病的概率)、HCI(人力资本指数)、人均GNI(国民总收入)、预期寿命、每10000人的医生数量)的单变量回归结果显著,因为这些参数与CFR呈负相关(rho = -0.48至-0.38,p . 1)
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