Back

Post-pandemic mortality patterns and COVID-19 burden considering multiple death causes

Riedmann, U.; Levitt, M.; Pilz, S.; Ioannidis, J.

2025-09-06 infectious diseases
10.1101/2025.09.01.25334892 medRxiv
Show abstract

BackgroundPost-pandemic mortality rates can explore the residual COVID-19 burden and changes in other causes of death. Considering weighted multiple causes of death from death certificates (underlying and others) may help compare post-versus pre-pandemic mortality patterns, while potentially reducing the impact of cause misattribution. Estimates of post-pandemic impact are critical also for proper continuing public health policies (e.g. vaccinations). MethodsWe retrospectively analyse national all-cause mortality rate ratios between 2024 and pre-pandemic years (2017-2019) for sex-stratified 10-year age groups in Austria. In weighted analyses, the underlying death cause was weighted 50% and other causes shared the remaining 50%. Sensitivity analyses explored different weightings. Cause-specific weightings were also compared between 2024 and 2019. ResultsDespite 1,212 reported COVID-19 deaths in 2024, all-cause mortality rates were equal or lower in 2024 compared to 2019 in all strata at risk from COVID-19 (i.e., aged 60 years and over). All-cause mortality rates in 2024 were higher than in 2019 in adolescent and young adult strata. The ratio of weighted over unweighted COVID-19 death rates was 0.51-0.58 for age strata 60 years and older and even lower in sensitivity analyses, indicating that COVID-19 deaths were possibly overestimated. ConclusionsPost-pandemic COVID-19 deaths had no visible impact on mortality patterns in Austria and were possibly overcounted. Increased post-pandemic mortality patterns in the young are particularly worrisome.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.