Literature DB >> 32696629

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

Shahir Asfahan1, Aneesa Shahul2, Gopal Chawla3, Naveen Dutt4, Ram Niwas5, Neeraj Gupta6.   

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.

Entities:  

Mesh:

Year:  2020        PMID: 32696629     DOI: 10.4081/monaldi.2020.1388

Source DB:  PubMed          Journal:  Monaldi Arch Chest Dis        ISSN: 1122-0643


  4 in total

1.  COVID-19: Combined supply-side and demand-side shocks, so lift restrictions (carefully) lest GPD declines ultimately kill more than COVID-19.

Authors:  Victor Grech; Peter Grech
Journal:  Early Hum Dev       Date:  2020-10-01       Impact factor: 2.079

2.  Relationship between Influenza Vaccination Coverage Rate and COVID-19 Outbreak: An Italian Ecological Study.

Authors:  Mauro Amato; José Pablo Werba; Beatrice Frigerio; Daniela Coggi; Daniela Sansaro; Alessio Ravani; Palma Ferrante; Fabrizio Veglia; Elena Tremoli; Damiano Baldassarre
Journal:  Vaccines (Basel)       Date:  2020-09-16

3.  Socioeconomic inequalities associated with mortality for COVID-19 in Colombia: a cohort nationwide study.

Authors:  Myriam Patricia Cifuentes; Laura Andrea Rodriguez-Villamizar; Maylen Liseth Rojas-Botero; Carlos Arturo Alvarez-Moreno; Julián Alfredo Fernández-Niño
Journal:  J Epidemiol Community Health       Date:  2021-03-04       Impact factor: 3.710

4.  Socio-cultural Correlates of the COVID-19 Outcomes.

Authors:  Timo Lajunen; Esma Gaygısız; Ümmügülsüm Gaygısız
Journal:  J Epidemiol Glob Health       Date:  2022-08-23
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.