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. 2020 Nov 23;42(4):681-687.
doi: 10.1093/pubmed/fdaa119.

Factors determining different death rates because of the COVID-19 outbreak among countries

Konstantinos N Fountoulakis  1 , Nikolaos K Fountoulakis  2 , Sotirios A Koupidis  3 , Panagiotis E Prezerakos  4
Affiliations

Factors determining different death rates because of the COVID-19 outbreak among countries

Konstantinos N Fountoulakis et al. J Public Health (Oxf). .

Abstract

Background: During the coronavirus disease 2019 (COVID-19) pandemic, all European countries were hit, but mortality rates were heterogenous. The aim of the current paper was to identify factors responsible for this heterogeneity.

Methods: Data concerning 40 countries were gathered, concerning demographics, vulnerability factors and characteristics of the national response. These variables were tested against the rate of deaths per million in each country. The statistical analysis included Person correlation coefficient and Forward Stepwise Linear Regression Analysis (FSLRA).

Results: The FSLRA results suggested that 'days since first national death for the implementation of ban of all public events' was the only variable significantly contributing to the final model, explaining 44% of observed variability.

Discussion: The current study suggests that the crucial factor for the different death rates because of COVID-19 outbreak was the fast implementation of public events ban. This does not necessarily mean that the other measures were useless, especially since most countries implemented all of them as a 'package'. However, it does imply that this is a possibility and focused research is needed to clarify it, and is in accord with a model of spreading where only a few superspreaders infect large numbers through prolonged exposure.

Keywords: COVID-19; death rate; measures; public events ban.

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Figures

Fig. 1
Fig. 1
Deaths per million population (vertical axis) versus latency days for the implementation of public events ban since first national (left) and first European (right) death. The place of each country is pointed in the scatterplot and four groups of countries emerge (lucky versus unlucky and fast versus slow). Unlucky are the countries first stricken by the outbreak (e.g. Italy) whereas lucky those stricken last (e.g. Latvia). Fast were the countries implementing measures early (e.g. Greece) whereas slow where those implementing measures later or not at all (e.g. Sweden).
Fig. 2
Fig. 2
Timeline map of first death occurrence in the various countries of Europe.
See this image and copyright information in PMC

References

    1. Boccia S, Ricciardi W, Ioannidis JPA. What other countries can learn from Italy during the COVID-19 pandemic. JAMA Intern Med 2020;180(7):927–28. - PubMed
    1. Abrams EM, Szefler SJ. COVID-19 and the impact of social determinants of health. Lancet Respir Med 2020;8:659–61. - PMC - PubMed
    1. Ahmed F, Ahmed N, Pissarides C, Stiglitz J. Why inequality could spread COVID-19. Lancet Public Health 2020;5:e240. - PMC - PubMed
    1. Tsai J, Wilson M. COVID-19: a potential public health problem for homeless populations. Lancet Public Health 2020;5:e186–e7. - PMC - PubMed
    1. Vardavas CI, Nikitara K. COVID-19 and smoking: a systematic review of the evidence. Tob Induc Dis 2020;18:20. - PMC - PubMed