Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries
Language English Country England, Great Britain Media print
Document type Journal Article
PubMed
33479720
PubMed Central
PMC7928985
DOI
10.1093/eurpub/ckaa226
PII: 6106197
Knihovny.cz E-resources
- MeSH
- COVID-19 mortality MeSH
- Adult MeSH
- HIV Infections epidemiology MeSH
- Gross Domestic Product MeSH
- Population Density MeSH
- Incidence MeSH
- Comorbidity MeSH
- Smoking epidemiology MeSH
- Middle Aged MeSH
- Humans MeSH
- Urban Population statistics & numerical data MeSH
- Adolescent MeSH
- Overweight epidemiology MeSH
- Bed Occupancy MeSH
- Pandemics prevention & control MeSH
- Delivery of Health Care organization & administration MeSH
- Prevalence MeSH
- SARS-CoV-2 * MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Socioeconomic Factors MeSH
- Temperature MeSH
- Tuberculosis epidemiology MeSH
- Age Distribution MeSH
- Public Health MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe epidemiology MeSH
BACKGROUND: The numbers of coronavirus disease 2019 (COVID-19) deaths per million people differ widely across countries. Often, the causal effects of interventions taken by authorities are unjustifiably concluded based on the comparison of pure mortalities in countries where interventions consisting different strategies have been taken. Moreover, the possible effects of other factors are only rarely considered. METHODS: We used data from open databases (European Centre for Disease Prevention and Control, World Bank Open Data, The BCG World Atlas) and publications to develop a model that could largely explain the differences in cumulative mortality between countries using non-interventional (mostly socio-demographic) factors. RESULTS: Statistically significant associations with the logarithmic COVID-19 mortality were found with the following: proportion of people aged 80 years and above, population density, proportion of urban population, gross domestic product, number of hospital beds per population, average temperature in March and incidence of tuberculosis. The final model could explain 67% of the variability. This finding could also be interpreted as follows: less than a third of the variability in logarithmic mortality differences could be modified by diverse non-pharmaceutical interventions ranging from case isolation to comprehensive measures, constituting case isolation, social distancing of the entire population and closure of schools and borders. CONCLUSIONS: In particular countries, the number of people who will die from COVID-19 is largely given by factors that cannot be drastically changed as an immediate reaction to the pandemic and authorities should focus on modifiable variables, e.g. the number of hospital beds.
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