COVID-19 fatality in Germany: Demographic determinants of variation in case-fatality rates across and within German federal states during the first and second waves

Comparison of fatalities due to COVID-19 and other nonexternal causes during the first five pandemic waves : Results from multiple cause of death statistics in Bavaria

BACKGROUND

Older age is a risk factor for a fatal course of SARS-CoV‑2 infection, possibly due to comorbidities whose exact role in this context, however, is not yet well understood. In this paper, the characteristics and comorbidities of persons who had died of COVID-19 in Bavaria by July 2022 are shown and compared with the characteristics of other fatalities during the pandemic.

METHODS

Based on data from multiple cause of death statistics, odds ratios for dying from COVID-19 (compared to dying from other nonexternal causes of death) were calculated by using logistic regression models, stratified by age, sex, and pandemic waves.

RESULTS

In Bavaria, a total of 24,479 persons (6.5% of all deaths) officially died from COVID-19 between March 2020 and July 2022. In addition to increasing age and male sex, preexisting diseases and comorbidities such as obesity, degenerative diseases of the nervous system, dementia, renal insufficiency, chronic lower respiratory diseases, and diabetes mellitus were significantly associated with COVID-19-related deaths. Dementia was mainly associated with increased COVID-19 mortality during the first and second waves, while obesity was strongly associated during the fourth wave.

DISCUSSION

The frequency of specific comorbidities in COVID-19 deaths varied over the course of the pandemic. This suggests that wave-specific results also need to be interpreted against the background of circulating virus variants, changing immunisation levels, and nonpharmaceutical interventions in place at the time.

Analysis of problems and potentials for increasing pandemic resilience in public health administrations in Saxony-Anhalt, Germany-a mixed-methods approach

INTRODUCTION

The COVID-19 pandemic has shown the importance of resilient, modern, and well-equipped public health administrations from national to communal levels. In Germany, the surveillance, contact tracing, and local adaptions went through local health offices, revealing both their important role and also their lack of equipment and general preparation for health crises. Research on the mode of operation of the public health service (PHS), especially in a time of crisis, is rare. The present study aims to qualitatively and quantitatively assess problem areas, conflict potentials, and challenges that have become apparent for the PHS of Saxony-Anhalt during the pandemic. It focuses on the individual insight of employees of the PHS of Saxony-Anhalt and its 14 health offices to derive concrete needs and fields of action for increasing pandemic preparedness. Furthermore, the prospective personnel and resource-based requirements as well as the necessary structural and organisational changes of the public health departments are to be considered.

METHODS AND ANALYSIS

The study will follow a sequential mixed-methods approach. Introductory expert interviews (n=12) with leading staff of Saxony-Anhalt's PHS will be conducted, followed by focus group interviews (n=4) with personnel from all departments involved in the pandemic response. Thereafter, a quantitative survey will be carried out to validate and complement the results of the qualitative phase.

ETHICS AND DISSEMINATION

Ethical approval was obtained by the Martin-Luther-Universität Halle-Wittenberg ethics commission (Ref number 2023-102). The authors will submit the results of the study to relevant peer-reviewed journals and give national and international oral presentations to researchers, members of the PHS, and policymakers.

Effects of the COVID-19 pandemic on life expectancy and premature mortality in the German federal states in 2020 and 2021

The mortality impact of COVID-19 has mainly been studied at the national level. However, looking at the aggregate impact of the pandemic at the country level masks heterogeneity at the subnational level. Subnational assessments are essential for the formulation of public health policies. This is especially important for federal countries with decentralised healthcare systems, such as Germany. Therefore, we assess geographical variation in the mortality impact of COVID-19 for the 16 German federal states in 2020 and 2021 and the sex differences therein. For this purpose, we adopted an ecological study design, using population-level mortality data by federal state, age, and sex, for 2005-2021 obtained from the German Federal Statistical Office. We quantified the impact of the pandemic using the excess mortality approach. We estimated period life expectancy losses (LE losses), excess premature mortality, and excess deaths by comparing their observed with their expected values. The expected mortality was based on projected age-specific mortality rates using the Lee-Carter methodology. Saxony was the most affected region in 2020 (LE loss 0.77 years, 95% CI 0.74;0.79) while Saarland was the least affected (-0.04, -0.09;0.003). In 2021, the regions with the highest losses were Thuringia (1.58, 1.54;1.62) and Saxony (1.57, 1.53;1.6) and the lowest in Schleswig-Holstein (0.13, 0.07;0.18). Furthermore, in 2021, eastern regions experienced higher LE losses (mean: 1.13, range: 0.85 years) than western territories (mean: 0.5, range: 0.72 years). The regional variation increased between 2020 and 2021, and was higher among males than among females, particularly in 2021. We observed an unequal distribution of the mortality impact of COVID-19 at the subnational level in Germany, particularly in 2021 among the male population. The observed differences between federal states might be partially explained by the heterogeneous spread of the virus in 2020 and by differences in the population's propensity to follow preventive guidelines.

Evaluating Spatial, Cause-Specific and Seasonal Effects of Excess Mortality Associated with the COVID-19 Pandemic: The Case of Germany, 2020

BACKGROUND

Evaluating mortality effects of the COVID-19 pandemic using all-cause mortality data for national populations is inevitably associated with the risk of masking important subnational differentials and hampering targeted health policies. This study aims at assessing simultaneously cause-specific, spatial and seasonal mortality effects attributable to the pandemic in Germany in 2020.

METHODS

Our analyses rely on official cause-of-death statistics consisting of 5.65 million individual death records reported for the German population during 2015-2020. We conduct differential mortality analyses by age, sex, cause, month and district (N = 400), using decomposition and standardisation methods, comparing each strata of the mortality level observed in 2020 with its expected value, as well as spatial regression to explore the association of excess mortality with pre-pandemic indicators.

RESULTS

The spatial analyses of excess mortality reveal a very heterogenous pattern, even within federal states. The coastal areas in the north were least affected, while the south of eastern Germany experienced the highest levels. Excess mortality in the most affected districts, with standardised mortality ratios reaching up to 20%, is driven widely by older ages and deaths reported in December, particularly from COVID-19 but also from cardiovascular and mental/nervous diseases.

CONCLUSIONS

Our results suggest that increased psychosocial stress influenced the outcome of excess mortality in the most affected areas during the second lockdown, thus hinting at possible adverse effects of strict policy measures. It is essential to accelerate the collection of detailed mortality data to provide policymakers earlier with relevant information in times of crisis.

COVID-19-related deaths: a 2-year inter-wave comparison of mortality data from Germany

PURPOSE

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has caused substantial mortality worldwide. We investigated clinical and demographic features of COVID-19-related deaths that occurred between March 2020 and January 2022 in Regensburg, Germany.

METHODS

We compared data across four consecutive time periods: March 2020 to September 2020 (period 1), October 2020 to February 2021 (period 2), March 2021 to August 2021 (period 3), and September 2021 to January 2022 (period 4).

RESULTS

Overall, 405 deaths in relation to COVID-19 were reported. The raw case fatality ratio (CFR) was 0.92. In periods 1 to 4, the CFRs were 1.70%, 2.67%, 1.06%, and 0.36%. The age-specific CFR and mortality were highest in persons aged ≥ 80 years in period 2 while mortality in younger cases increased with time. The median age at death was 84 years and it varied slightly across periods. Around 50% of cases of death were previously hospitalized. In all time periods, the cause of death was mostly attributed to COVID-19. Over the four periods, we did not find significant changes in the distribution of sex and risk factors for severe disease. The most frequent risk factor was cardio-circulatory disease.

CONCLUSION

In conclusion, the CFR decreased over time, most prominently for period 4. Mortality was considerable and younger cases were increasingly at risk.

Local socio-structural predictors of COVID-19 incidence in Germany

Socio-economic conditions and social attitudes are known to represent epidemiological determinants. Credible knowledge on socio-economic driving factors of the COVID-19 epidemic is still incomplete. Based on linear random effects regression, an ecological model is derived to estimate COVID-19 incidence in German rural/urban districts from local socio-economic factors and popularity of political parties in terms of their share of vote. Thereby, records provided by Germany's public health institute (Robert Koch Institute) of weekly notified 7-day incidences per 100,000 inhabitants per district from the outset of the epidemic in 2020 up to December 1, 2021, are used to construct the dependent variable. Local socio-economic conditions including share of votes, retrieved from the Federal Statistical Office of Germany, have been used as potential risk factors. Socio-economic parameters like per capita income, proportions of protection seekers and social benefit claimants, and educational level have negligible impact on incidence. To the contrary, incidence significantly increases with population density and we observe a strong association with vote shares. Popularity of the right-wing party Alternative for Germany (AfD) bears a considerable risk of increasing COVID-19 incidence both in terms of predicting the maximum incidences during three epidemic periods (alternatively, cumulative incidences over the periods are used to quantify the dependent variable) and in a time-continuous sense. Thus, districts with high AfD popularity rank on top in the time-average regarding COVID-19 incidence. The impact of the popularity of the Free Democrats (FDP) is markedly intermittent in the course of time showing two pronounced peaks in incidence but also occasional drops. A moderate risk emanates from popularities of the Green Party (GRÜNE) and the Christian Democratic Union (CDU/CSU) compared to the other parties with lowest risk level. In order to effectively combat the COVID-19 epidemic, public health policymakers are well-advised to account for social attitudes and behavioral patterns reflected in local popularities of political parties, which are conceived as proper surrogates for these attitudes. Whilst causal relations between social attitudes and the presence of parties remain obscure, the political landscape in terms of share of votes constitutes at least viable predictive "markers" relevant for public health policy making.

The impact of travelling on the COVID-19 infection cases in Germany

BACKGROUND

COVID-19 continues to disrupt social lives and the economy of many countries and challenges their healthcare capacities. Looking back at the situation in Germany in 2020, the number of cases increased exponentially in early March. Social restrictions were imposed by closing e.g. schools, shops, cafés and restaurants, as well as borders for travellers. This reaped success as the infection rate descended significantly in early April. In mid July, however, the numbers started to rise again. Of particular reasons was that from mid June onwards, the travel ban has widely been cancelled or at least loosened. We aim to measure the impact of travellers on the overall infection dynamics for the case of (relatively) few infectives and no vaccinations available. We also want to analyse under which conditions political travelling measures are relevant, in particular in comparison to local measures. By travel restrictions in our model we mean all possible measures that equally reduce the possibility of infected returnees to further spread the disease in Germany, e.g. travel bans, lockdown, post-arrival tests and quarantines.

METHODS

To analyse the impact of travellers, we present three variants of an susceptible-exposed-infected-recovered-deceased model to describe disease dynamics in Germany. Epidemiological parameters such as transmission rate, lethality, and detection rate of infected individuals are incorporated. We compare a model without inclusion of travellers and two models with a rate measuring the impact of travellers incorporating incidence data from the Johns Hopkins University. Parameter estimation was performed with the aid of the Monte-Carlo-based Metropolis algorithm. All models are compared in terms of validity and simplicity. Further, we perform sensitivity analyses of the model to observe on which of the model parameters show the largest influence the results. In particular, we compare local and international travelling measures and identify regions in which one of these shows larger relevance than the other.

RESULTS

In the comparison of the three models, both models with the traveller impact rate yield significantly better results than the model without this rate. The model including a piecewise constant travel impact rate yields the best results in the sense of maximal likelihood and minimal Bayesian Information Criterion. We synthesize from model simulations and analyses that travellers had a strong impact on the overall infection cases in the considered time interval. By a comparison of the reproductive ratios of the models under traveller/no-traveller scenarios, we found that higher traveller numbers likely induce higher transmission rates and infection cases even in the further course, which is one possible explanation to the start of the second wave in Germany as of autumn 2020. The sensitivity analyses show that the travelling parameter, among others, shows a larger impact on the results. We also found that the relevance of travel measures depends on the value of the transmission parameter: In domains with a lower transmission parameter, caused either by the current variant or local measures, it is found that handling the travel parameters is more relevant than those with lower value of the transmission.

CONCLUSIONS

We conclude that travellers is an important factor in controlling infection cases during pandemics. Depending on the current situation, travel restrictions can be part of a policy to reduce infection numbers, especially when case numbers and transmission rate are low. The results of the sensitivity analyses also show that travel measures are more effective when the local transmission is already reduced, so a combination of those two appears to be optimal. In any case, supervision of the influence of travellers should always be undertaken, as another pandemic or wave can happen in the upcoming years and vaccinations and basic hygiene rules alone might not be able to prevent further infection waves.