Asfahan Shahir, Shahul Aneesa, Chawla Gopal, Dutt Naveen, Niwas Ram, Gupta Neeraj
Department of Pulmonary Medicine, All India Institute of Medical Sciences, Jodhpur.
Department of Paediatrics, All India Institute of Medical Sciences, Jodhpur.
Monaldi Arch Chest Dis. 2020 Jul 22;90(3). doi: 10.4081/monaldi.2020.1388.
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.
2019冠状病毒病，即COVID-19，始于中国一个地区的疫情爆发，并在短短3个月内席卷全球。它在全球范围内构成了严峻的健康和经济挑战。然而，各国的病死率（CFR）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的这种差异。选取截至2020年3月14日报告至少50例病例的国家进行此次分析。各国社会经济指标的数据来自世界银行数据库，健康指标的数据来自世界卫生组织（WHO）数据库。本次分析选取了各种社会经济指标和健康指标。在单变量分析的基础上进行筛选后，选取相关性最高的指标，采用向前选择变量的多元线性回归方法，以调整后的R平方得分作为衡量指标建立模型。我们发现，单变量回归结果显示，人均国内生产总值（GDP）、30/70岁死亡概率（30岁至70岁之间因心血管疾病、癌症、糖尿病或慢性呼吸道疾病等任何一种疾病死亡的概率）、人力资本指数（HCI）、人均国民总收入（GNI）、预期寿命、每万人口中的医生数量等参数与CFR呈负相关（rho=-0.48至-0.38，p<0.05）。使用普通最小二乘法（OLS）对病死率与社会经济和健康指标进行回归分析。最终模型中的指标包括人均GDP、30/70岁死亡概率、人力资本指数、预期寿命、每万人口中的医生数量、中位数年龄、人均当前卫生支出、确诊病例数和以百万计的人口数。调整后的R平方得分为0.306。经济欠发达的发展中国家在COVID-19死亡率方面尤其脆弱，这突出表明需要制定一项全球政策来应对这场持续的大流行。这些趋势在很大程度上证实，在应对能力不足的国家，COVID-19造成的损失将更为惨重。对流行病学数据进行这些分析很有必要，因为除了提高态势感知能力外，它还能指导我们采取明智的干预措施，并有助于制定应对这场大流行的政策。
