Influence of Population Size, the Human Development Index and the Gross Domestic Product on Mortality by COVID-19 in the Southeast Region of Brazil

Excess Mortality During 2020 in Spain: The Most Affected Population, Age, and Educational Group by the COVID-19 Pandemic

OBJECTIVE

The objective of this work was to study mortality increase in Spain during the first and second academic semesters of 2020, coinciding with the first 2 waves of the Covid-19 pandemic; by sex, age, and education.

METHODS

An observational study was carried out, using linked populations and deaths' data from 2017 to 2020. The mortality rates from all causes and leading causes other than Covid-19 during each semester of 2020, compared to the 2017-2019 averages for the same semester, was also estimated. Mortality rate ratios (MRR) and differences were used for comparison.

RESULTS

All-cause mortality rates increased in 2020 compared to pre-covid, except among working-age, (25-64 years) highly-educated women. Such increases were larger in lower-educated people between the working age range, in both 2020 semesters, but not at other ages. In the elderly, the MMR in the first semester in women and men were respectively, 1.14, and 1.25 among lower-educated people, and 1.28 and 1.23 among highly-educated people. In the second semester, the MMR were 1.12 in both sexes among lower-educated people and 1.13 in women and 1.16 in men among highly-educated people.

CONCLUSION

Lower-educated people within working age and highly-educated people at older ages showed the greatest increase in all-cause mortality in 2020, compared to the pre-pandemic period.

Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium

Introduction

COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension.

Methods

We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis.

Results

Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition.

Conclusion

It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.