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

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.

SUBMITTER: Fountoulakis KN

PROVIDER: S-EPMC7454744 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Fountoulakis Konstantinos N KN Fountoulakis Nikolaos K NK Koupidis Sotirios A SA Prezerakos Panagiotis E PE

Journal of public health (Oxford, England) 20201101 4

<h4>Background</h4>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.<h4>Methods</h4>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 in ...[more]

PMID: 32728758

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Project description:BackgroundAfter claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures.MethodsWe process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making.FindingsPopulation density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance.InterpretationStates forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.

Project description:BackgroundDisease control involves multiple actions overtime to halt the spread of COVID-19. The role of a country's governance in slowing the spread of COVID-19 has not yet been well investigated.ObjectiveThis study aims to investigate the association between governance and the trend of COVID-19 incidence in countries with the highest prevalence. We hypothesized that countries with better governance are more likely to mitigate the spread of COVID-19 than countries with worse governance.MethodsWe analyzed 62 most prevalent countries with at least 10,000 accumulative confirmed cases from January 22 to June 15, 2020. Countries were further grouped into three different levels of governance (25 better governance, 24 fair governance, and 13 worse governance), identified outbreak and mitigation periods using the joinpoint regression model, and compared the number of days and average daily percent change in incidence in two periods by governance level using the one-way analysis of variance.FindingsThe average outbreak period in the 62 countries lasted 84.0 days. Sixty percent of countries (N = 37) had experienced outbreak periods, followed by a mitigation period. In contrast, the rest forty percent of countries (N = 25) still had a rising trend. In the outbreak period, better governance countries had a more rapid increase but a shorter outbreak period (71.2 days) than countries with fair (93.5 days) and worse (90.8 days) governance. Most countries with better governance (84.0%) revealed a declining trend in COVID-19 incidence, while such a trend was less than half of fair and worse governance countries (38.5%-41.7%).ConclusionsCountries with better governance are more resilient during the COVID-19 crisis. While the mitigation of COVID-19 is observed in most better governance countries, the incidence of COVID-19 is still surging in most fair and worse governance countries, and the possibility of a recurring epidemic of COVID-19 in countries cannot be ignored.

Project description:ImportanceDescriptive data have revealed significant racial/ethnic disparities in coronavirus disease 2019 (COVID-19) cases in the US, but underlying mechanisms of disparities remain unknown.ObjectiveTo examine the association between county-level sociodemographic risk factors and US COVID-19 incidence and mortality.Design, setting, and participantsThis cross-sectional study analyzed the association between US county-level sociodemographic risk factors and COVID-19 incidence using mixed-effects negative binomial regression, and COVID-19 mortality using zero-inflated negative binomial regression. Data on COVID-19 incidence and mortality were collected from January 20 to July 29, 2020. The association of social risk factors with weekly cumulative incidence and mortality was also examined by interacting time with the index measures, using a random intercept to account for repeated measures.Main outcomes and measuresSociodemographic data from publicly available data sets, including the US Centers for Disease Control and Prevention's Social Vulnerability Index (SVI), which includes subindices of socioeconomic status, household composition and disability, racial/ethnic minority and English language proficiency status, and housing and transportation.ResultsAs of July 29, 2020, there were a total of 4 289 283 COVID-19 cases and 147 074 COVID-19 deaths in the US. An increase of 0.1 point in SVI score was associated with a 14.3% increase in incidence rate (incidence rate ratio [IRR], 1.14; 95% CI, 1.13-1.16; P < .001) and 13.7% increase in mortality rate (IRR, 1.14; 95% CI, 1.12-1.16; P < .001), or an excess of 87 COVID-19 cases and 3 COVID-19 deaths per 100 000 population for a SVI score change from 0.5 to 0.6 in a midsize metropolitan county; subindices were also associated with both outcomes. A 0.1-point increase in the overall SVI was associated with a 0.9% increase in weekly cumulative increase in incidence rate (IRR, 1.01; 95% CI, 1.01-1.01; P < .001) and 0.5% increase in mortality rate (IRR, 1.01; 95% CI, 1.01-1.01; P < .001).Conclusions and relevanceIn this cross-sectional study, a wide range of sociodemographic risk factors, including socioeconomic status, racial/ethnic minority status, household composition, and environmental factors, were significantly associated with COVID-19 incidence and mortality. To address inequities in the burden of the COVID-19 pandemic, these social vulnerabilities and their root causes must be addressed.

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

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

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