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.

Variation in mortality burden of the COVID-19 pandemic across federal states in Germany.

BACKGROUND: Intra-annual excess mortality is the most reliable measure of losses of lives due to short-term risk factors. The objectives of our study are (i) to estimate excess mortality across German states in the course of the coronavirus disease 2019 (COVID-19) pandemic years 2020 and 2021 and (ii) to identify possible regional-level determinants of spatial inequality in pandemic-related excess mortality. METHODS: We use weekly mortality data series for the calculation of weekly death rates, standardized by age for each federal state of Germany. We estimate the expected level of mortality as state-specific mortality trends and excess mortality in 2020 and 2021. We explore ecological statistical relationships between excess mortality, COVID-19 morbidity, and selected regional socioeconomic indicators using fixed-effects regression models. RESULTS: Our study shows that during the first pandemic year, there was South-to-North gradient in excess mortality in Germany, with excess mortality being higher in the South. Over the course of the second pandemic year 2021, this gradient changed to become an East-to-West gradient, with excess mortality being higher in the East. The results of the study show stronger effects of COVID-19 morbidity on excess mortality in East Germany. State-level indicators reflecting economic activity, employment, and capacity of intensive care units show significant correlations with excess mortality across the states. CONCLUSIONS: The results show pronounced state-level differences in the magnitude of excess mortality during the COVID-19 pandemic in Germany. Economic activity, employment and capacity of intensive care units were the most important state-level characteristics associated with the observed spatial variations in excess mortality.

Sensitivity Analysis of Excess Mortality due to the COVID-19 Pandemic.

Estimating excess mortality is challenging. The metric depends on the expected mortality level, which can differ based on given choices, such as the method and the time series length used to estimate the baseline. However, these choices are often arbitrary, and are not subject to any sensitivity analysis. We bring to light the importance of carefully choosing the inputs and methods used to estimate excess mortality. Drawing on data from 26 countries, we investigate how sensitive excess mortality is to the choice of the mortality index, the number of years included in the reference period, the method, and the time unit of the death series. We employ two mortality indices, three reference periods, two data time units, and four methods for estimating the baseline. We show that excess mortality estimates can vary substantially when these factors are changed, and that the largest variations stem from the choice of the mortality index and the method. We also find that the magnitude of the variation in excess mortality is country-specific, resulting in cross-country rankings changes. Finally, based on our findings, we provide guidelines for estimating excess mortality.

What should be the baseline when calculating excess mortality? New approaches suggest that we have underestimated the impact of the COVID-19 pandemic and previous winter peaks.

Excess mortality has been used to measure the impact of COVID-19 over time and across countries. But what baseline should be chosen? We propose two novel approaches: an alternative retrospective baseline derived from the lowest weekly death rates achieved in previous years and a within-year baseline based on the average of the 13 lowest weekly death rates within the same year. These baselines express normative levels of the lowest feasible target death rates. The excess death rates calculated from these baselines are not distorted by past mortality peaks and do not treat non-pandemic winter mortality excesses as inevitable. We obtained weekly series for 35 industrialized countries from the Human Mortality Database for 2000-2020. Observed, baseline and excess mortalities were measured by age-standardized death rates. We assessed weekly and annual excess death rates driven by the COVID-19 pandemic in 2020 and those related to seasonal respiratory infections in earlier years. There was a distinct geographic pattern with high excess death rates in Eastern Europe followed by parts of the UK, and countries of Southern and Western Europe. Some Asia-Pacific and Scandinavian countries experienced lower excess mortality. In 2020 and earlier years, the alternative retrospective and the within-year excess mortality figures were higher than estimates based on conventional metrics. While the latter were typically negative or close to zero in years without extraordinary epidemics, the alternative estimates were substantial. Cumulation of this "usual" excess over 2-3 years results in human losses comparable to those caused by COVID-19. Challenging the view that non-pandemic seasonal winter mortality is inevitable would focus attention on reducing premature mortality in many countries. As SARS-CoV-2 is unlikely to be the last respiratory pathogen with the potential to cause a pandemic, such measures would also strengthen global resilience in the face of similar threats in the future.

Effects of covid-19 pandemic on life expectancy and premature mortality in 2020: time series analysis in 37 countries.

OBJECTIVE: To estimate the changes in life expectancy and years of life lost in 2020 associated with the covid-19 pandemic. DESIGN: Time series analysis. SETTING: 37 upper-middle and high income countries or regions with reliable and complete mortality data. PARTICIPANTS: Annual all cause mortality data from the Human Mortality Database for 2005-20, harmonised and disaggregated by age and sex. MAIN OUTCOME MEASURES: Reduction in life expectancy was estimated as the difference between observed and expected life expectancy in 2020 using the Lee-Carter model. Excess years of life lost were estimated as the difference between the observed and expected years of life lost in 2020 using the World Health Organization standard life table. RESULTS: Reduction in life expectancy in men and women was observed in all the countries studied except New Zealand, Taiwan, and Norway, where there was a gain in life expectancy in 2020. No evidence was found of a change in life expectancy in Denmark, Iceland, and South Korea. The highest reduction in life expectancy was observed in Russia (men: -2.33, 95% confidence interval -2.50 to -2.17; women: -2.14, -2.25 to -2.03), the United States (men: -2.27, -2.39 to -2.15; women: -1.61, -1.70 to -1.51), Bulgaria (men: -1.96, -2.11 to -1.81; women: -1.37, -1.74 to -1.01), Lithuania (men: -1.83, -2.07 to -1.59; women: -1.21, -1.36 to -1.05), Chile (men: -1.64, -1.97 to -1.32; women: -0.88, -1.28 to -0.50), and Spain (men: -1.35, -1.53 to -1.18; women: -1.13, -1.37 to -0.90). Years of life lost in 2020 were higher than expected in all countries except Taiwan, New Zealand, Norway, Iceland, Denmark, and South Korea. In the remaining 31 countries, more than 222 million years of life were lost in 2020, which is 28.1 million (95% confidence interval 26.8m to 29.5m) years of life lost more than expected (17.3 million (16.8m to 17.8m) in men and 10.8 million (10.4m to 11.3m) in women). The highest excess years of life lost per 100 000 population were observed in Bulgaria (men: 7260, 95% confidence interval 6820 to 7710; women: 3730, 2740 to 4730), Russia (men: 7020, 6550 to 7480; women: 4760, 4530 to 4990), Lithuania (men: 5430, 4750 to 6070; women: 2640, 2310 to 2980), the US (men: 4350, 4170 to 4530; women: 2430, 2320 to 2550), Poland (men: 3830, 3540 to 4120; women: 1830, 1630 to 2040), and Hungary (men: 2770, 2490 to 3040; women: 1920, 1590 to 2240). The excess years of life lost were relatively low in people younger than 65 years, except in Russia, Bulgaria, Lithuania, and the US where the excess years of life lost was >2000 per 100 000. CONCLUSION: More than 28 million excess years of life were lost in 2020 in 31 countries, with a higher rate in men than women. Excess years of life lost associated with the covid-19 pandemic in 2020 were more than five times higher than those associated with the seasonal influenza epidemic in 2015.

The short-term mortality fluctuation data series, monitoring mortality shocks across time and space.

The COVID-19 pandemic has revealed substantial coverage and quality gaps in existing international and national statistical monitoring systems. It is striking that obtaining timely, accurate, and comparable across countries data in order to adequately respond to unexpected epidemiological threats is very challenging. The most robust and reliable approach to quantify the mortality burden due to short-term risk factors is based on estimating weekly excess deaths. This approach is more reliable than monitoring deaths with COVID-19 diagnosis or calculating incidence or fatality rates affected by numerous problems such as testing coverage and comparability of diagnostic approaches. In response to the emerging data challenges, a new data resource on weekly mortality has been established. The Short-term Mortality Fluctuations (STMF, available at www.mortality.org ) data series is the first international database providing open-access harmonized, uniform, and fully documented data on weekly all-cause mortality. The STMF online vizualisation tool provides an opportunity to perform a quick assessment of the excess weekly mortality in one or several countries by means of an interactive graphical interface.

Method for reconstructing mortality by educational groups.

BACKGROUND: The lack of classification by educational attainment in death and population exposure data at older ages is an important constraint for studying changes and patterns of mortality disparities by education in Denmark and Sweden. The missing educational distribution of population also restricts analyses aiming at estimating contributions of compositional change to the improvements in national longevity. This study proposes a transparent approach to solve the two methodological issues allowing to obtain robust education-specific mortality estimates and population weights. METHODS: Using nonparametric approach, we redistribute the unknown cases and extrapolate the mortality curves of these sub-populations with the help of population-level data on an aggregate level from the Human Mortality Database. RESULTS: We present reconstructed and harmonized education-specific abridged and complete life tables for Sweden and Denmark covering 5-year-long periods from 1991-1995 to 2011-2015. The newly estimated life tables are in good agreement with the national life tables and show plausible age- and education-specific patterns. The observed changes in life expectancy by education suggest about the widening longevity gap between the highest and lowest educated for males and females in both countries. CONCLUSIONS: The proposed simple and transparent method can be applied in similar country-specific cases showing large proportions of missing education or other socio-economic characteristics at older ages.

Excess deaths associated with covid-19 pandemic in 2020: age and sex disaggregated time series analysis in 29 high income countries.

OBJECTIVE: To estimate the direct and indirect effects of the covid-19 pandemic on mortality in 2020 in 29 high income countries with reliable and complete age and sex disaggregated mortality data. DESIGN: Time series study of high income countries. SETTING: Austria, Belgium, Czech Republic, Denmark, England and Wales, Estonia, Finland, France, Germany, Greece, Hungary, Israel, Italy, Latvia, Lithuania, the Netherlands, New Zealand, Northern Ireland, Norway, Poland, Portugal, Scotland, Slovakia, Slovenia, South Korea, Spain, Sweden, Switzerland, and United States. PARTICIPANTS: Mortality data from the Short-term Mortality Fluctuations data series of the Human Mortality Database for 2016-20, harmonised and disaggregated by age and sex. INTERVENTIONS: Covid-19 pandemic and associated policy measures. MAIN OUTCOME MEASURES: Weekly excess deaths (observed deaths versus expected deaths predicted by model) in 2020, by sex and age (0-14, 15-64, 65-74, 75-84, and ≥85 years), estimated using an over-dispersed Poisson regression model that accounts for temporal trends and seasonal variability in mortality. RESULTS: An estimated 979 000 (95% confidence interval 954 000 to 1 001 000) excess deaths occurred in 2020 in the 29 high income countries analysed. All countries had excess deaths in 2020, except New Zealand, Norway, and Denmark. The five countries with the highest absolute number of excess deaths were the US (458 000, 454 000 to 461 000), Italy (89 100, 87 500 to 90 700), England and Wales (85 400, 83 900 to 86 800), Spain (84 100, 82 800 to 85 300), and Poland (60 100, 58 800 to 61 300). New Zealand had lower overall mortality than expected (-2500, -2900 to -2100). In many countries, the estimated number of excess deaths substantially exceeded the number of reported deaths from covid-19. The highest excess death rates (per 100 000) in men were in Lithuania (285, 259 to 311), Poland (191, 184 to 197), Spain (179, 174 to 184), Hungary (174, 161 to 188), and Italy (168, 163 to 173); the highest rates in women were in Lithuania (210, 185 to 234), Spain (180, 175 to 185), Hungary (169, 156 to 182), Slovenia (158, 132 to 184), and Belgium (151, 141 to 162). Little evidence was found of subsequent compensatory reductions following excess mortality. CONCLUSION: Approximately one million excess deaths occurred in 2020 in these 29 high income countries. Age standardised excess death rates were higher in men than women in almost all countries. Excess deaths substantially exceeded reported deaths from covid-19 in many countries, indicating that determining the full impact of the pandemic on mortality requires assessment of excess deaths. Many countries had lower deaths than expected in children <15 years. Sex inequality in mortality widened further in most countries in 2020.

An open-sourced, web-based application to analyze weekly excess mortality based on the Short-term Mortality Fluctuations data series.

The COVID-19 pandemic stimulated the interest of scientists, decision makers and the general public in short-term mortality fluctuations caused by epidemics and other natural or man-made disasters. To address this interest and provide a basis for further research, in May 2020, the Short-term Mortality Fluctuations data series was launched as a new section of the Human Mortality Database. At present, this unique data resource provides weekly mortality death counts and rates by age and sex for 38 countries and regions. The main objective of this paper is to detail the web-based application for visualizing and analyzing the excess mortality based on the Short-term Mortality Fluctuation data series. The application yields a visual representation of the database that enhances the understanding of the underlying data. Besides, it enables the users to explore data on weekly mortality and excess mortality across years and countries. The contribution of this paper is twofold. First, to describe a visualization tool that aims to facilitate research on short-term mortality fluctuations. Second, to provide a comprehensive open-source software solution for demographic data to encourage data holders to promote their datasets in a visual framework.

Prevalence, correlates, and mortality impacts of ventricular arrhythmia among older men and women: a population-based cohort study in Moscow.

BACKGROUND: In Russia, cardiovascular disease (CVD) mortality is high and the mortality gap between men and women is large. Conventional risk factors cannot explain these phenomena. Ventricular arrhythmia (VA) is an important contributor to the death toll in community-based populations. The study examines the prevalence and the mortality impacts of VA in men and women and the role of VA in the male mortality excess at older ages. METHODS: This is a secondary analysis of data from the Stress, Aging, and Health in Russia (SAHR) study that was fielded in 2007-9 in Moscow (1800 individuals, mean age 68.8 years), with mean mortality follow-up of 7.4 years (416 deaths, 248 CVD deaths). Indicators reflecting the frequency and the complexity of VA were derived from 24-h ambulatory ECG recordings. Other covariates were: socio-demographic characteristics, conventional risk factors, markers of inflammation, reported myocardial infarction, and stroke. The impacts of VA and other variables on CVD and all-cause mortality among men and women were estimated with the proportional hazard models. We assessed the contributions of VAs to the male-female mortality gap using hazard models that do and do not include groups of the predictors. Logistic models were used to assess the associations between VA and other biomarkers. RESULTS: VAs were about twice as prevalent among men as among women. In both sexes, they were significantly associated with CVD and all-cause mortality independently of conventional risk factors. The highest hazard ratios (HRs) for CVD death were found for the runs of ventricular premature complexes (VPCs) HR = 2.45, 95% CI 1.63-3.68 for men and 2.75, 95% CI 1.18-6.40 for women. The mortality impacts of the polymorphic VPCs were significant among men only (HR = 1.50, 95% CI 1.08-2.07). VA indicators can potentially explain 12.3% and 9.1% of the male-female gaps in mortality from CVD and all causes, respectively. VAs were associated with ECG-registered ischemic problems and reported MI, particularly among men. CONCLUSIONS: VA indicators predicted mortality in older Muscovites independently of other risk factors, and have the potential to explain a non-trivial share of the excess male mortality. The latter may be related to more severe coronary problems in men compared to women.

Atrial fibrillation among Russian men and women aged 55 years and older: prevalence, mortality, and associations with biomarkers in a population-based study.

OBJECTIVE: To examine the prevalence of atrial fibrillation (AF), its impacts on cardiovascular disease (CVD) and all-cause mortality, and the associations between AF and inflammatory and serum biomarkers in a population-based sample of Muscovites. METHODS: The study is a secondary analysis of data from the Stress, Aging and Health in Russia (SAHR) survey that includes information on 1800 individuals with an average age of 68.5 years at baseline, and on their subsequent mortality during 7.4 years on average. AF is detected by 12-lead electrocardiogram (ECG) and 24-hour Holter monitoring. The statistical analysis includes proportional hazard and logistic regression models. RESULTS: Of the 1732 participants with relevant Holter data, AF was detected in 100 (74 by ECG and Holter, 26 by Holter only). The prevalence of AF was 5.8% for men and 7.4% for women. The fully adjusted model showed strongly elevated hazard of CVD and all-cause mortality in men and women with long non-self-limiting AF (LAF). LAF was found to be negatively associated with elevated total and low-density lipoprotein cholesterol and to be positively associated with elevated markers of inflammation in women. CONCLUSIONS: The study assessed for the first time the prevalence and the risks of death related to AF among older Russians. LAF was shown to be a strong and independent predictor of CVD and all-cause mortality. AF is unlikely to contribute to the large excess male mortality in Russia. The finding that one-quarter of AF cases were detected only by Holter monitoring demonstrates the usefulness of diagnostics with prolonged ECG registration.

Decomposing Current Mortality Differences Into Initial Differences and Differences in Trends: The Contour Decomposition Method.

This study proposes a new decomposition method that permits a difference in an aggregate measure at a final time point to be split into additive components corresponding to the initial differences in the event rates of the measure and differences in trends in these underlying event rates. For instance, when studying divergence in life expectancy, this method allows researchers to more easily contrast age-specific mortality trends between populations by controlling for initial age-specific mortality differences. Two approaches are assessed: (1) an additive change method that uses logic similar to cause-of-death decomposition, and (2) a contour decomposition method that extends the stepwise replacement algorithm along an age-period demographic contour. The two approaches produce similar results, but the contour method is more widely applicable. We provide a full description of the contour replacement method and examples of its application to life expectancy and lifetime disparity differences between the United States and England and Wales in the period 1980-2010.

Identifying potential differences in cause-of-death coding practices across Russian regions.

BACKGROUND: Reliable and comparable data on causes of death are crucial for public health analysis, but the usefulness of these data can be markedly diminished when the approach to coding is not standardized across territories and/or over time. Because the Russian system of producing information on causes of death is highly decentralized, there may be discrepancies in the coding practices employed across the country. In this study, we evaluate the uniformity of cause-of-death coding practices across Russian regions using an indirect method. METHODS: Based on 2002-2012 mortality data, we estimate the prevalence of the major causes of death (70 causes) in the mortality structures of 52 Russian regions. For each region-cause combination we measured the degree to which the share of a certain cause in the mortality structure of a certain region deviates from the respective inter-regional average share. We use heat map visualization and a regression model to determine whether there is regularity in the causes and the regions that is more likely to deviate from the average level across all regions. In addition to analyzing the comparability of cause-specific mortality structures in a spatial dimension, we examine the regional cause-of-death time series to identify the causes with temporal trends that vary greatly across regions. RESULTS: A high level of consistency was found both across regions and over time for transport accidents, most of the neoplasms, congenital malformations, and perinatal conditions. However, a high degree of inconsistency was found for mental and behavioral disorders, diseases of the nervous system, endocrine disorders, ill-defined causes of death, and certain cardiovascular diseases. This finding suggests that the coding practices for these causes of death are not uniform across regions. The level of consistency improves when causes of death can be grouped into broader diagnostic categories. CONCLUSION: This systematic analysis allows us to present a broader picture of the quality of cause-of-death coding at the regional level. For some causes of death, there is a high degree of variance across regions in the likelihood that these causes will be chosen as the underlying causes. In addition, for some causes of death the mortality statistics reflect the coding practices, rather than the real epidemiological situation.

Recalibration of the SCORE risk chart for the Russian population.

Persisting high levels of cardiovascular mortality in Russia present a specific case among developed countries. Application of cardiovascular risk prediction models holds great potential for primary prevention in this country. Using a unique set of cohort follow-up data from Moscow and Saint Petersburg, this study aims to test and recalibrate the Systematic Coronary Risk Evaluation (SCORE) methods for predicting CVD mortality risks in the general population. The study is based on pooled epidemiological cohort data covering the period 1975-2001. The algorithms from the SCORE project were used for the calibration of the SCORE equation for the Moscow and St. Petersburg populations (SCORE-MoSP). Age-specific 10-year cumulative cardiovascular mortality rates were estimated according to the original SCORE-High and SCORE-Low equations and compared to the estimates based on the recalibrated SCORE-MoSP model and observed CVD mortality rates. Ten-year risk prediction charts for CVD mortality were derived and compared using conventional SCORE-High and recalibrated SCORE-MoSP methods. The original SCORE-High model tends to substantially under-estimate 10-year cardiovascular mortality risk for females. The SCORE-MoSP model provided better results which were closer to the observed rates. For males, both the SCORE-High and SCORE-MoSP provided similar estimates which tend to under-estimate CVD mortality risk at younger ages. These differences are also reflected in the risk prediction charts. Using non-calibrated scoring models for Russia may lead to substantial under-estimation of cardiovascular mortality risk in some groups of individuals. Although the SCORE-MoSP provide better results for females, more complex scoring methods involving a wider range of risk factors are needed.