COVID-19: Heterogeneous Excess Mortality and "Burden of Disease" in Germany and Italy and Their States and Regions, January-June 2020

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.

Age and sex differences in cause-specific excess mortality and years of life lost associated with COVID-19 infection in the Swedish population

BACKGROUND

Estimating excess mortality and years of life lost (YLL) attributed to coronavirus disease 19 (COVID-19) infection provides a comprehensive picture of the mortality burden on society. We aimed to estimate the impact of the COVID-19 pandemic on age- and sex-specific excess mortality and YLL in Sweden during the first 17 months of the pandemic.

METHODS

In this population-based observational study, we calculated age- and sex-specific excess all-cause mortality and excess YLL during 2020 and the first 5 months of 2021 and cause-specific death [deaths from cardiovascular disease (CVD), cancer, other causes and deaths excluding COVID-19] in 2020 compared with an average baseline for 2017-19 in the whole Swedish population.

RESULTS

COVID-19 deaths contributed 9.9% of total deaths (98 441 deaths, 960 305 YLL) in 2020, accounting for 75 151 YLL (7.7 YLL/death). There were 2672 (5.7%) and 1408 (3.0%) excess deaths, and 19 141 (3.8%) and 3596 (0.8%) excess YLL in men and women, respectively. Men aged 65-110 years and women aged 75-110 years were the greatest contributors. Fewer deaths and YLL from CVD, cancer and other causes were observed in 2020 compared with the baseline adjusted to the population size in 2020.

CONCLUSIONS

Compared with the baseline, excess mortality and YLL from all causes were experienced in Sweden during 2020, with a higher excess observed in men than in women, indicating that more men died at a younger age while more women died at older ages than expected. A notable reduction in deaths and YLL due to CVD suggests a displacement effect from CVD to COVID-19.

Excess mortality during the SARS-CoV-2 pandemic in the City of Frankfurt/Main, Germany, in 2020 and 2021, adjusted for age trends and pandemic phases

Aims

Excess mortality during the SARS-CoV-2 pandemic has been studied in many countries. Accounting for population aging has important implications for excess mortality estimates. We show the importance of adjustment for age trends in a small-scale mortality analysis as well as the importance of analysing different pandemic phases for mortality in an urban population.

Methods

Population data for Frankfurt/Main for 2016-2021 were obtained from the Municipal Office of Statistics, City of Frankfurt/Main. Mortality data from 2016 to 2021 were provided by the Hessian State Authority. For standardized mortality ratios (SMR=observed number of deaths divided by the expected number of deaths), the expected number of deaths was calculated in two ways: For SMR_crude, the mean mortality rate from the years 2016-2019 was multiplied by the total number of residents in 2020 and 2021 separately. For SMR_adjusted, this procedure was performed separately for five age groups, and the numbers of expected deaths per age group were added.

Results

SMR_crude was 1.006 (95% CI: 0.980-1.031) in 2020, and 1.047 (95% CI: 1.021-1.073) in 2021. SMR_adjusted was 0.976 (95% CI: 0.951-1.001) in 2020 and 0.998 (95% CI: 0.973-1.023) in 2021. Excess mortality was observed during pandemic wave 2, but not during pandemic waves 1 and 3.

Conclusion

Taking the aging of the population into account, no excess mortality was observed in Frankfurt/Main in 2020 and 2021. Without adjusting for population aging trends in Frankfurt /Main, mortality would have been greatly overestimated.

COVID-19 as a game-changer? The impact of the pandemic on urban trajectories

Will the COVID-19 pandemic interrupt the recent European urbanization trends - and if so - what is the magnitude of this sudden shock, and how deaths, births, and net migration contribute to this disruption? Until now, most discussions on the topic have circled either around the anecdotal evidence of city center decline, or contrarian speculations about residential inertia and the forthcoming business-as-usual. Bringing clarity to the uncertainty and confusion surrounding COVID-19, this paper seeks to detect overarching patterns in and the magnitude of its sudden shock to long-term urban trajectories, understood as a reversal of the pre-pandemic population development trend, across European cities in the early 2020s. It reveals that during the first year of COVID-19, population growth in European cities significantly slowed down to -0.3 % per annum, with 28 % of all European cities having experienced a U-turn from population growth to loss. Out-migration was the main driver of such rapid urban shrinkage, while excess mortality associated with COVID-19 has also contributed to population loss in several European city-regions; some, especially, smaller cities suffered from a significant drop in birth rates. Based on the factorial, hierarchical, and temporal dimensions of the COVID-19 crisis, the paper provides a plausible forecast about the future of Europe's post-coronavirus city.

Deaths during the first year of the COVID-19 pandemic: insights from regional patterns in Germany and Poland

BACKGROUND

Given the nature of the spread of SARS-CoV-2, strong regional patterns in the fatal consequences of the COVID-19 pandemic related to local characteristics such as population and health care infrastructures were to be expected. In this paper we conduct a detailed examination of the spatial correlation of deaths in the first year of the pandemic in two neighbouring countries - Germany and Poland, which, among high income countries, seem particularly different in terms of the death toll associated with the COVID-19 pandemic. The analysis aims to yield evidence that spatial patterns of mortality can provide important clues as to the reasons behind significant differences in the consequences of the COVID-19 pandemic in these two countries.

METHODS

Based on official health and population statistics on the level of counties, we explore the spatial nature of mortality in 2020 in the two countries - which, as we show, reflects important contextual differences. We investigate three different measures of deaths: the officially recorded COVID-19 deaths, the total values of excessive deaths and the difference between the two. We link them to important pre-pandemic regional characteristics such as population, health care and economic conditions in multivariate spatial autoregressive models. From the point of view of pandemic related fatalities we stress the distinction between direct and indirect consequences of COVID-19, separating the latter further into two types, the spatial nature of which is likely to differ.

RESULTS

The COVID-19 pandemic led to much more excess deaths in Poland than in Germany. Detailed spatial analysis of deaths at the regional level shows a consistent pattern of deaths officially registered as related to COVID-19. For excess deaths, however, we find strong spatial correlation in Germany but little such evidence in Poland.

CONCLUSIONS

In contrast to Germany, for Poland we do not observe the expected spatial pattern of total excess deaths and the excess deaths over and above the official COVID-19 deaths. This difference cannot be explained by pre-pandemic regional factors such as economic and population structures or by healthcare infrastructure. The findings point to the need for alternative explanations related to the Polish policy reaction to the pandemic and failures in the areas of healthcare and public health, which resulted in a massive loss of life.

[Factors Influencing Results of Mortality Measurement in the Corona Pandemic: Analyses of Mortality in Germany in 2020]

INTRODUCTION

(Excess) mortality and years of life lost are important measures of health risks from the Corona pandemic. The aim of this paper was to identify methodological factors that affect the calculation of mortality and further to point out possible misinterpretations of years of life lost.

METHODOLOGY

Standardized mortality ratios (SMRs) can be used to compare mortalities (e. g., an SMR of 1.015 means excess mortality of 1.5%, an SMR of 0.990 means that mortality is reduced by 1.0%). In this study, SMRs as a measure of association for mortality in Germany were calculated for 2020 using different methods. In particular, the influence of different data sources and reference periods was examined. Furthermore, its influence on the calculated mortality was also examined to take into account increasing life expectancy. In addition, published results on years of life lost were critically analyzed.

RESULTS

Using January 2022 data from the Federal Statistical Office on mortality for 5-year age groups resulted in higher SMR values than using preliminary data from February 2021 with 20-year age groups (SMR=0.997, 95% confidence interval (CI): 0.995-0.999 versus SMR=0.976 (95% CI: 0.974-0.978)). The choice of the reference period had a large impact on calculated mortality (for men, SMR=1.024 (95% CI: 1.022-1.027) with 2019 as the reference year versus SMR=0.998 (95% CI: 0.996-1.001) with 2016 to 2019 as the reference period). Analyses in which declining mortality in 2016 to 2019 was carried forward into 2020 when calculating expected deaths resulted in significantly higher SMR values (for men SMR=1.024 (95% CI: 1.021-1.026) with, and SMR=0.998 (95% CI: 0.996-1.001) without carrying forward declining mortality). Figures for pandemic-related years of life lost per person who died from COVID-19 should be interpreted with caution: Calculation from remaining life reported in mortality tables can lead to misleading results.

CONCLUSION

When calculating mortality and years of life lost during the pandemic, a number of methodological assumptions must be made that have a significant impact on the results and must be considered when interpreting the results.

Mortality in psoriatic arthritis patients, changes over time, and the impact of COVID-19: results from a multicenter Psoriatic Arthritis Registry (PsART-ID)

BACKGROUND

This study aimed to assess the mortality of PsA before and during the COVID-19 pandemic.

METHODS

From the prospective, multicenter PsART-ID (Psoriatic Arthritis Registry-International Database), patients from Turkey were analyzed by linking the registry to the Turkish Cause of Death Registry. The outcome of interest was death from any cause, pre-pandemic (since the onset of registry-March 2014-March 2020), and during the pandemic (March 2020-May 2021). The crude mortality rate and standardized mortality ratio (SMR) were determined.

RESULTS

There were 1216 PsA patients with a follow-up of 7500 patient-years. Overall, 46 deaths (26 males) were observed. In the pre-pandemic period, SMR for PsA vs the general population was 0.95 (0.61-1.49), being higher in males [1.56 (0.92-2.63)] than females [0.62 (0.33-1.17)]. The crude mortality rate in PsA doubled during the pandemic (pre-pandemic crude mortality rate: 5.07 vs 10.76 during the pandemic) with a higher increase in females (2.9 vs 8.72) than males (9.07 vs 14.73).

CONCLUSION

The mortality in PsA was found similar to the general population in the pre-pandemic era. The mortality rates in PsA doubled during the pandemic. Whether PsA patients have more risk of mortality than the general population due to COVID-19 needs further studies. Key Points • Decrease in mortality in PsA might be expected with the more effective treatment options and better disease control. • A crude mortality rate is comparable to the general population and not increased until the pandemic. • Currently, there is a 2-fold increase in crude mortality rate possibly due to the COVID-19.

Real-time mortality statistics during the COVID-19 pandemic: A proposal based on Spanish data, January-March, 2021

Objectives

During the COVID-19 pandemic, surveillance systems worldwide underestimated mortality in real time due to longer death reporting lags. In Spain, the mortality monitor "MoMo" published downward biased excess mortality estimates daily. I study the correction of such bias using polynomial regressions in data from January to March 2021 for Spain and the Comunitat Valenciana, the region with the highest excess mortality.

Methods

This adjustment for real-time statistics consisted of (1) estimating forthcoming revisions with polynomial regressions of past revisions, and (2) multiplying the daily-published excess mortality by these estimated revisions. The accuracy of the corrected estimates compared to the original was measured by contrasting their mean absolute errors (MAE) and root mean square errors (RMSE).

Results

Applying quadratic and cubic regressions improved the first communication of cumulative mortality in Spain by 2-3%, on average, and the flow in registered deaths by 20%. However, for the Comunitat Valenciana, those corrections improved the first publications of the cumulative mortality by 36-45%, on average; their second publication, by 23-30%; and the third, by 15-21%. The flow of deaths registered each day improved by 62-63% on their first publication, by 19-36% on the second, and by 12-17% on the third.

Conclusion

It is recommended that MoMo's estimates for excess mortality be corrected from the effect of death reporting lags by using polynomial regressions. This holds for the flows in each date and their cumulative sum, as well as national and regional data. These adjustments can be applied by surveillance systems in other countries.

Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data

Estimating the coronavirus disease-2019 (COVID-19) infection fatality rate (IFR) has proven to be particularly challenging –and rather controversial– due to the fact that both the data on deaths and the data on the number of individuals infected are subject to many different biases. We consider a Bayesian evidence synthesis approach which, while simple enough for researchers to understand and use, accounts for many important sources of uncertainty inherent in both the seroprevalence and mortality data. With the understanding that the results of one's evidence synthesis analysis may be largely driven by which studies are included and which are excluded, we conduct two separate parallel analyses based on two lists of eligible studies obtained from two different research teams. The results from both analyses are rather similar. With the first analysis, we estimate the COVID-19 IFR to be 0.31% [95% credible interval (CrI) of (0.16%, 0.53%)] for a typical community-dwelling population where 9% of the population is aged over 65 years and where the gross-domestic-product at purchasing-power-parity (GDP at PPP) per capita is $17.8k (the approximate worldwide average). With the second analysis, we obtain 0.32% [95% CrI of (0.19%, 0.47%)]. Our results suggest that, as one might expect, lower IFRs are associated with younger populations (and may also be associated with wealthier populations). For a typical community-dwelling population with the age and wealth of the United States we obtain IFR estimates of 0.43% and 0.41%; and with the age and wealth of the European Union, we obtain IFR estimates of 0.67% and 0.51%.

Excess Mortality During the COVID-19 Pandemic in Jordan: Secondary Data Analysis

Background

All-cause mortality and estimates of excess deaths are commonly used in different countries to estimate the burden of COVID-19 and assess its direct and indirect effects.

Objective

This study aimed to analyze the excess mortality during the COVID-19 pandemic in Jordan in April-December 2020.

Methods

Official data on deaths in Jordan for 2020 and previous years (2016-2019) were obtained from the Department of Civil Status. We contrasted mortality rates in 2020 with those in each year and the pooled period 2016-2020 using a standardized mortality ratio (SMR) measure. Expected deaths for 2020 were estimated by fitting the overdispersed Poisson generalized linear models to the monthly death counts for the period of 2016-2019.

Results

Overall, a 21% increase in standardized mortality (SMR 1.21, 95% CI 1.19-1.22) occurred in April-December 2020 compared with the April-December months in the pooled period 2016-2019. The SMR was more pronounced for men than for women (SMR 1.26, 95% CI 1.24-1.29 vs SMR 1.12, 95% CI 1.10-1.14), and it was statistically significant for both genders (P<.05). Using overdispersed Poisson generalized linear models, the number of expected deaths in April-December 2020 was 12,845 (7957 for women and 4888 for men). The total number of excess deaths during this period was estimated at 4583 (95% CI 4451-4716), with higher excess deaths in men (3112, 95% CI 3003-3221) than in women (1503, 95% CI 1427-1579). Almost 83.66% of excess deaths were attributed to COVID-19 in the Ministry of Health database. The vast majority of excess deaths occurred in people aged 60 years or older.

Conclusions

The reported COVID-19 death counts underestimated mortality attributable to COVID-19. Excess deaths could reflect the increased deaths secondary to the pandemic and its containment measures. The majority of excess deaths occurred among old age groups. It is, therefore, important to maintain essential services for the elderly during pandemics.