Factors affecting COVID-19 mortality: an exploratory study
| Autor: | Ashish Upadhyaya, Sushant Koirala, Rand Ressler, Kamal Upadhyaya |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Health Research, Vol 36, Iss 1, Pp 166-175 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0857-4421 2586-940X |
| DOI: | 10.1108/JHR-09-2020-0448/full/pdf?title=factors-affecting-covid-19-mortality-an-exploratory-study |
| Popis: | Purpose – The purpose of this paper is to study the factors affecting COVID-19 mortality. Design/methodology/approach – An empirical model is developed in which the mortality rate per million is the dependent variable, and life expectancy at birth, physician density, education, obesity, proportion of population over the age of 65, urbanization (population density) and per capita income are explanatory variables. Crosscountry data from 184 countries are used to estimate the quantile regression that is employed. Findings – The estimated results suggest that obesity, the proportion of the population over the age of 65 and urbanization have a positive and statistically significant effect on COVID-19 mortality. Not surprisingly, per capita income has a negative and statistically significant effect on COVID-19 death rate. Research limitations/implications – The study is based on the COVID-19 mortality data from June 2020, which have constantly being changed. What data reveal today may be different after two or three months. Despite this limitation, it is expected that this study will serve as the basis for future research in this area. Practical implications – Since the findings suggest that obesity, population over the age of 65 and density are the primary factors affecting COVID-19 death, the policy-makers should pay particular attention to these factors. Originality/value – To the authors’ knowledge, this is first attempt to estimate the factors affecting the COVID-19 mortality rate. Its novelty also lies in the use of quantile regressions, which is more efficient in estimating empirical models with heterogeneous data. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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