The age structure of cases as the key of COVID-19 severity: Longitudinal population-based analysis of European countries during 150 days

Scand J Public Health. 2022 Aug;50(6):738-747. doi: 10.1177/14034948211042486. Epub 2021 Dec 20.

Abstract

Aims: Over a million confirmed cases of the coronavirus disease (COVID-19) across 16 European countries were observed during the first wave of the pandemic. Epidemiological measures like the case fatality rate (CFR) are generally used to determine the severity of the illness. The aim is to investigate the impact of the age structure of reported cases on the reported CFR and possibilities of its demographic adjustment for a better cross-country comparison (age-standardized CFRs, time delay between cases detection and death).

Methods: This longitudinal study uses prospective, population-based data covering 150 days, starting on the day of confirmation of the 100th case in each country. COVerAGE-DB and the Human Mortality Database were used in this regard. The age-standardized CFRs were calculated with and without the time delay of the number of deaths after the confirmation of the cases.

Results: The observed decline in the CFRs at the end of the first wave is partly given by the changes in the age structure of confirmed cases. Using the adjusted (age-standardized) CFRs with time delay, the risk of death among confirmed cases is much more stable in comparison to crude (observed) CFRs.

Conclusions: Preventing the spread of COVID-19 among the elderly is an important way to positively influence the overall fatality rate, decrease the number of deaths, and not overload the health systems. The crude CFRs (still often presented) are not sufficient for a proper evaluation of the development across populations nor as a means of identifying the influencing factors.

Keywords: COVID-19; Europe; GIS; age structure; confirmed cases; cross-country comparison; deaths; delay; fatality; incidence; longitudinal population-based analysis; standardization.

MeSH terms

  • Aged
  • COVID-19* / epidemiology
  • Humans
  • Longitudinal Studies
  • Pandemics
  • Prospective Studies
  • SARS-CoV-2