Introduction Excess mortality is a suitable indicator of health consequences of COVID-19 because death from any cause is clearly defined contrary to death from Covid-19. We compared the overall mortality in 2020 with the overall mortality in 2016 to 2019 in Germany, Sweden and Spain. Contrary to other studies, we also took the demographic development between 2016 and 2020 and increasing life expectancy into account. Methods Using death and population figures from the EUROSTAT database, we estimated weekly and cumulative Standardized Mortality Ratios (SMR) with 95% confidence intervals (CI) for the year 2020. We applied two approaches to calculate weekly numbers of death expected in 2020: first, we used mean weekly mortality rates from 2016 to 2019 as expected mortality rates for 2020, and, second, to consider increasing life expectancy, we calculated expected mortality rates for 2020 by extrapolation from mortality rates from 2016 to 2019. Results In the first approach, the cumulative SMRs show that in Germany and Sweden there was no or little excess mortality in 2020 (SMR = 0.976 (95% CI: 0.974–0.978), and 1.030 (1.023–1.036), respectively), while in Spain the excess mortality was 14.8% (1.148 (1.144–1.151)). In the second approach, the corresponding SMRs for Germany and Sweden increased to 1.009 (1.007–1.011) and 1.083 (1.076–1.090), respectively, whereas results for Spain were virtually unchanged. Conclusion In 2020, there was barely any excess mortality in Germany for both approaches. In Sweden, excess mortality was 3% without, and 8% with consideration of increasing life expectancy.

Mucoepidermoid carcinoma (MEC) is the most common carcinoma of the salivary glands. Here, we have used two large patient cohorts with MECs comprising 551 tumors to study clinical, histological, and molecular predictors of survival. One cohort (n = 167), with known CRCT1/3-MAML2 fusion status, was derived from the Hamburg Reference Centre (HRC; graded with the AFIP and Brandwein systems) and the other (n = 384) was derived from the population-based Cancer Registry of North Rhine-Westphalia (LKR-NRW; graded with the AFIP system). The reliability of both the AFIP and Brandwein grading systems was excellent (n = 155). The weighted kappa for inter-rater agreement was 0.81 (95% CI 0.65–0.97) and 0.83 (95% CI 0.71–0.96) for the AFIP and Brandwein systems, respectively. The 5-year relative survival was 79.7% (95% CI 73.2–86.2%). Although the Brandwein system resulted in a higher rate of G3-MECs, survival in G3-tumors (AFIP or Brandwein grading) was markedly worse than in G1/G2-tumors. Survival in > T2 tumors was markedly worse than in those with lower T-stage. Also, fusion-negative MECs had a worse 5-year progression-free survival. The frequency of fusion-positive MECs in the HRC cohort was 78.4%, of which the majority (86.7%) was G1/G2-tumors. In conclusion, the AFIP and Brandwein systems are useful in estimating prognosis and to guide therapy for G3-MECs. However, their significance regarding young age (≤ 30 years) and location-dependent heterogeneity of in particular G2-tumors is more questionable. We conclude that CRTC1/3-MAML2 testing is a useful adjunct to histologic scoring of MECs and for pinpointing tumors with poor prognosis with higher precision, thus avoiding overtreatment.

Purpose The aim of this project was to provide an overview of the epidemiology of primary salivary gland carcinomas (SGC) in terms of incidence, distribution of clinicopathological features and survival in one of the largest cancer registries in Europe. Methods Data were collected from patients with SGC of the major salivary glands registered in the population-based state cancer registry (Landeskrebsregister LKR) in North Rhine-Westphalia (NRW), Germany from 01/01/2009 to 12/31/2018. Age standardization of incidence was performed and relative survival estimates were computed by sex, histological group, age group and T-, N-, and M-stage. Results A total of 1680 patients were included in this analysis. The most frequent tumor localization was the parotid gland (78%). Adenocarcinoma (not otherwise specified) was the most common tumor entity (18.5%). Most tumors were found in stages T1–T3 (29% T1; 29% T2; 28% T3). The age-standardized incidence rate (ASR) for SGC was 0.65/100,000 and remained stable during the observation period. There was an age-dependent incidence increasing especially from the age 70 years and onwards. The overall 5-year relative survival (RS) for all patients with SGC was 69.2%. RS was 80–95.6% for T1–2 stage tumors, 60.3% for T3, 47.3% for T4 stage, 87.4% for N0 and 51.2% for N1–2, 74.4% for M0 and 44.9% for M1. Conclusion Age-standardized incidence for SGC has been stable for the observed 10-year period. Smaller tumors and those without lymph node or distant metastases had a better RS than more advanced tumors.

Zusammenfassung Einleitung (Über)sterblichkeit und verlorene Lebensjahre sind wichtige Maße für gesundheitliche Risiken durch die Corona-Pandemie. Das Ziel dieses Beitrags ist es, methodische Faktoren zu benennen, die die Berechnung der Sterblichkeit beeinflussen, und auf mögliche Fehlinterpretationen von verlorenen Lebensjahren hinzuweisen. Methodik Standardisierte Mortalitätsratios (SMRs) können für den Vergleich von Sterblichkeiten verwendet werden (z. B. bedeutet ein SMR von 1,015 eine Übersterblichkeit von 1,5%, ein SMR von 0,990 eine Untersterblichkeit von 1,0%). In dieser Studie werden SMRs als Assoziationsmaße für die Sterblichkeit in Deutschland mit unterschiedlicher Methodik für das Jahr 2020 berechnet. Insbesondere wird der Einfluss unterschiedlicher Datenquellen und Referenzperioden untersucht. Ferner wird geprüft, welchen Einfluss es auf die berechnete Sterblichkeit hat, die steigende Lebenserwartung zu berücksichtigen. Darüber hinaus werden publizierte Ergebnisse zu verlorenen Lebensjahren kritisch diskutiert. Ergebnisse Die Nutzung aktueller Daten des Statistischen Bundesamts vom Januar 2022, in denen die Sterblichkeit für 5-Jahres-Altersgruppen berichtet wird, führt zu höheren SMR-Werten als die Nutzung vorläufiger Daten vom Februar 2021 mit 20-Jahres-Altersklassen (SMR=0,997, 95% Konfidenzintervall (KI): 0,995–0,999 versus SMR=0,976 (95% KI: 0,974–0,978)). Die Wahl des Referenzzeitraums hat großen Einfluss auf die berechnete Sterblichkeit (für Männer: SMR=1,024 (95% KI: 1,022–1,027) mit 2019 als Referenzjahr versus SMR=0,998 (95% KI: 0,996–1,001) mit 2016 bis 2019 als Referenzzeitraum). Analysen, in denen bei der Berechnung erwarteter Sterbefälle die sinkende Mortalität in den Jahren 2016 bis 2019 in das Jahr 2020 fortgeschrieben wird, führen zu deutlich höheren SMR-Werten (für Männer SMR=1,024 (95% KI: 1,021–1,026) mit, und SMR=0,998 (95% KI: 0,996–1,001) ohne Fortschreibung der sinkenden Mortalität). Zahlen zu pandemiebedingten verlorenen Lebensjahren pro an COVID-19 Verstorbenem sind mit Vorsicht zu interpretieren: Eine Berechnung aus der in Sterbetafeln angegebenen verbleibenden Lebenszeit führt zu irreführenden Ergebnissen. Schlussfolgerung Bei Berechnung zur Sterblichkeit und zu verlorenen Lebensjahren während der Pandemie sind eine Reihe methodischer Annahmen zu treffen, die erheblichen Einfluss auf die Ergebnisse haben und bei der Interpretation der Ergebnisse beachtet werden müssen.

No abstract