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

Fountoulakis, Konstantinos Ν.; Fountoulakis, Nikolaos K.; Koupidis, Sotirios A; Prezerakos, Panagiotis

doi:10.1093/pubmed/fdaa119

Cited by 26 publications

(29 citation statements)

References 8 publications

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“…The ecological study including time to implementation of restriction found that this could be the most important factor. 21 However, this explains only 44% of the model, while our model explains 67% of the variability. This number was verging to the results of important study, which used individual data from nine countries showing that the age distribution of the population explains 66% of the variation across countries.…”

Section: Discussionmentioning

confidence: 58%

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

Hradský

Komárek

2021

European Journal of Public Health

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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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Section: Discussionmentioning

confidence: 58%

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

Hradský

Komárek

2021

European Journal of Public Health

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“…Zhang et al 20 analysed another relatively long study period until August, and several authors performed an analysis until July 2020 21 , 22 , 23 . Some authors standardised the start and/or end of the study period in order to be able to compare the effectiveness of NPIs across units of analysis at similar stages of the epidemic 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 .…”

Section: Resultsmentioning

confidence: 99%

Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19

Méndez-Brito

Bcheraoui

Pozo-Martin

2021

Journal of Infection

210

151

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Objectives To evaluate which non-pharmaceutical interventions (NPIs) have been more and less effective in controlling the COVID-19 pandemic. Methods We performed a systematic review of published and unpublished empirical studies, either observational or interventional, analysing the comparative effectiveness of NPIs against the COVID-19 pandemic. We searched Embase/Medline and medRxiv to identify the relevant literature. Results We identified 34 studies. During the first wave of the COVID-19 pandemic, school closing was the most effective NPI, followed by workplace closing, business and venue closing and public event bans. Public information campaigns and mask wearing requirements were also effective in controlling the pandemic while being less disruptive for the population than other NPIs. There was no evidence on the effectiveness of public transport closure, testing and contact tracing strategies and quarantining or isolation of individuals. Early implementation was associated with a higher effectiveness in reducing COVID-19 cases and deaths, while general stringency of the NPIs was not. Conclusions In this systematic review, we found that school closing, followed by workplace closing, business and venue closing and public event bans were the most effective NPIs in controlling the spread of COVID-19. An early response and a combination of specific social distancing measures are effective at reducing COVID-19 cases and deaths. Continuous monitoring of NPIs effectiveness is needed in order to adapt decision making.

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“…Gini indices of social inequality and poverty rates) and differentials in mortality rates by COVID-19; the adjusted linear regression model showed that the speed for the implementation of response strategies to the outbreak was the main predictor of mortality. 32 …”

Section: Discussionmentioning

confidence: 99%

Poverty and survival from COVID-19 in Mexico

Millán‐Guerrero

Caballero-Hoyos

Monárrez‐Espino

2020

Journal of Public Health

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Background Recent evidence points to the relevance of poverty and inequality as factors affecting the spread and mortality of the COVID-19 pandemic in Latin America. This study aimed to determine whether COVID-19 patients living in Mexican municipalities with high levels of poverty have a lower survival compared with those living in municipalities with low levels. Methods Retrospective cohort study. Secondary data was used to define the exposure (multidimensional poverty level) and outcome (survival time) among patients diagnosed with COVID-19 between 27 February and 1 July 2020. Crude and adjusted hazard ratios (HR) from Cox regression were computed. Results Nearly 250 000 COVID-19 patients were included. Mortality was 12.3% reaching 59.3% in patients with ≥1 comorbidities. Multivariate survival analyses revealed that individuals living in municipalities with extreme poverty had 9% higher risk of dying at any given time proportionally to those living in municipalities classified as not poor (HR 1.09; 95% CI 1.06–1.12). The survival gap widened with the follow-up time up to the third to fourth weeks after diagnosis. Conclusion Evidence suggests that the poorest population groups have a lower survival from COVID-19. Thus, combating extreme poverty should be a central preventive strategy.

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

Cited by 26 publications

References 8 publications

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19

Poverty and survival from COVID-19 in Mexico

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