Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations

Unrecognised COVID-19 deaths in central Europe: The importance of cause-of-death certification for the COVID-19 burden assessment

OBJECTIVE

In Central Europe, the increase in mortality during the COVID-19 pandemic exceeded the number of deaths registered due to coronavirus disease. Excess deaths reported to causes other than COVID-19 may have been due to unrecognised coronavirus disease, the interruptions in care in the overwhelmed health care facilities, or socioeconomic effects of the pandemic and lockdowns. Death certificates provide exhaustive medical information, allowing us to assess the extent of unrecognised COVID-19 deaths.

MATERIALS AND METHODS

Data from 187,300 death certificates with a COVID-19 mention from Austria, Bavaria (Germany), Czechia, Lithuania and Poland, 2020-2021, was used. The two step analysis uses Cause of Death Association Indicators (CDAIs) and Contributing CDAIs to identify and measure the statistical strength of associations between COVID-19 and all other medical mentions.

RESULTS

15,700 deaths were reported with COVID-19 only as a contributing condition (comorbidity). In three cases out of four, a typical, statistically significant coronavirus complication or pre-existing condition was registered as the underlying causes of death. In Austria, Bavaria, Czechia and Lithuania the scale of COVID-19 mortality would have been up to 18-27% higher had COVID-19 been coded as the underlying cause of death. Unrecognised coronavirus deaths were equivalent to the entire surplus of excess mortality beyond registered COVID-19 deaths in Austria and the Czech Republic, and its large proportion (25-31%) in Lithuania and Bavaria.

CONCLUSIONS

Death certificates with typical coronavirus complications or comorbidities as the underlying causes of death and contributing COVID-19 mentions were plausibly unrecognized coronavirus deaths.

Regional inequalities in excess mortality and its community determinants during the early COVID-19 pandemic in South Korea

BACKGROUND

Excess mortality during the COVID-19 pandemic provides a comprehensive measure of disease burden, and its local variation highlights regional health inequalities. We investigated local excess mortality in 2020 and its determinants at the community level.

METHODS

We collected data from 250 districts in South Korea, including monthly all-cause mortality for 2015-2020 and community characteristics from 2019. Excess mortality rate was defined as the difference between observed and expected mortality rates. A Seasonal Autoregressive Integrated Moving Average model was applied to predict the expected rates for each district. Penalized regression methods were used to derive relevant community predictors of excess mortality based on the elastic net.

RESULTS

In 2020, South Korea exhibited significant variation in excess mortality rates across 250 districts, ranging from no excess deaths in 46 districts to more than 100 excess deaths per 100 000 residents in 30 districts. Economic status or the number of medical centres in the community did not correlate with excess mortality rates. The risk was higher in ageing, remote communities with limited cultural and sports infrastructure, a higher density of welfare facilities, and a higher prevalence of hypertension. Physical distancing policies and active social engagement in voluntary activities protected from excess mortality.

CONCLUSION

Substantial regional disparities in excess mortality existed within South Korea during the early stages of COVID-19 pandemic. Weaker segments of the community were more vulnerable. Local governments should refine their preparedness for future novel infectious disease outbreaks, considering community circumstances.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness

Introduction

Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness.

Methods

We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues.

Results

We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values.

Conclusion

Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Regional excess mortality in France during COVID-19 pandemic: the first three epidemic periods (March 2020-June 2021)

BACKGROUND

This study aimed to describe the mortality excess during the three first epidemic periods of COVID-19 in all regions of France.

METHODS

Two complementary approaches were implemented. First, we described the number of death of patients infected with or diagnosed with COVID-19 in health care (HC) and medico-social (MS) institutions. Then, we estimated general all-cause mortality excess (all ages) by comparing the mortality observed with the expected mortality. We used a daily number of death model according to a negative binomial distribution, as a function of the long-term trend in mortality (penalized spline function of time) and its seasonal variation (cyclic spline function). The model provided expected mortality during epidemic periods with a 95% credibility interval. Each region defined three epidemic periods, including the overseas territories.

RESULTS

The two approaches were consistent in the most affected regions but there are major regional disparities that vary according to the epidemic period. There is an east-west gradient in the relative excess of deaths from all-causes during each epidemic period. The deaths observed in HC and MS institutions alone do not explain the excess (or deficit) of mortality in each region and epidemic period.

CONCLUSION

An analysis by age group according to the two approaches and a comparison of death specific causes could provide a better understanding of these differences. Electronic death registration system (mortality by medical causes) would allow a rapid mortality related estimation to an emerging pathology like Coronavirus Disease-2019 (COVID-19) but is still insufficient for real-time medical causes of death monitoring.

Epidemic outcomes following government responses to COVID-19: Insights from nearly 100,000 models

Government responses to COVID-19 are among the most globally impactful events of the 21st century. The extent to which responses-such as school closures-were associated with changes in COVID-19 outcomes remains unsettled. Multiverse analyses offer a systematic approach to testing a large range of models. We used daily data on 16 government responses in 181 countries in 2020-2021, and 4 outcomes-cases, infections, COVID-19 deaths, and all-cause excess deaths-to construct 99,736 analytic models. Among those, 42% suggest outcomes improved following more stringent responses ("helpful"). No subanalysis (e.g. limited to cases as outcome) demonstrated a preponderance of helpful or unhelpful associations. Among the 14 associations with P values < 1 × 10-30, 5 were helpful and 9 unhelpful. In summary, we find no patterns in the overall set of models that suggests a clear relationship between COVID-19 government responses and outcomes. Strong claims about government responses' impacts on COVID-19 may lack empirical support.

Spatial disparities in the mortality burden of the covid-19 pandemic across 569 European regions (2020-2021)

Since its emergence in December 2019, the COVID-19 pandemic has resulted in a significant increase in deaths worldwide. This article presents a detailed analysis of the mortality burden of the COVID-19 pandemic across 569 regions in 25 European countries. We produce age and sex-specific excess mortality and present our results using Age-Standardised Years of Life Lost in 2020 and 2021, as well as the cumulative impact over the two pandemic years. Employing a forecasting approach based on CP-splines that considers regional diversity and provides confidence intervals, we find notable losses in 362 regions in 2020 (440 regions in 2021). Conversely, only seven regions experienced gains in 2020 (four regions in 2021). We also estimate that eight regions suffered losses exceeding 20 years of life per 1000 population in 2020, whereas this number increased to 75 regions in 2021. The contiguity of the regions investigated in our study also reveals the changing geographical patterns of the pandemic. While the highest excess mortality values were concentrated in the early COVID-19 outbreak areas during the initial pandemic year, a clear East-West gradient appeared in 2021, with regions of Slovakia, Hungary, and Latvia experiencing the highest losses. This research underscores the importance of regional analyses for a nuanced comprehension of the pandemic's impact.

Excess mortality in Denmark, Finland, Norway and Sweden during the COVID-19 pandemic 2020-2022

BACKGROUND

The Nordic countries represent a unique case study for the COVID-19 pandemic due to socioeconomic and cultural similarities, high-quality comparable administrative register data and notable differences in mitigation policies during the pandemic. We aimed to compare weekly excess mortality in the Nordic countries across the three full pandemic years 2020-2022.

METHODS

Using data on weekly all-cause mortality from official administrative registers in Denmark, Finland, Norway and Sweden, we employed time series regression models to assess mortality developments within each pandemic year, with the period 2010-2019 used as reference period. We then compared excess mortality across the countries in 2020-2022, taking differences in population size and age- and sex-distribution into account. Results were age- and sex-standardized to the Danish population of 2020. Robustness was examined with a variety of sensitivity analyses.

RESULTS

While Sweden experienced excess mortality in 2020 [75 excess deaths per 100 000 population (95% prediction interval 29-122)], Denmark, Finland and Norway experienced excess mortality in 2022 [52 (14-90), 130 (83-177) and 88 (48-128), respectively]. Weekly death data reveal how mortality started to increase in mid-2021 in Denmark, Finland and Norway, and continued above the expected level through 2022.

CONCLUSION

Although the Nordic countries experienced relatively low pandemic excess mortality, the impact and timing of excess mortality differed substantially. These estimates-arguably the most accurate available for any region in capturing pandemic-related excess deaths-may inform future research and policy regarding the complex mortality dynamics in times of a health crisis such as the COVID-19 pandemic.

Magnitude and determinants of excess total, age-specific and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project

INTRODUCTION

To examine the impact of the COVID-19 pandemic on mortality, we estimated excess all-cause mortality in 24 countries for 2020 and 2021, overall and stratified by sex and age.

METHODS

Total, age-specific and sex-specific weekly all-cause mortality was collected for 2015-2021 and excess mortality for 2020 and 2021 was calculated by comparing weekly 2020 and 2021 age-standardised mortality rates against expected mortality, estimated based on historical data (2015-2019), accounting for seasonality, and long-term and short-term trends. Age-specific weekly excess mortality was similarly calculated using crude mortality rates. The association of country and pandemic-related variables with excess mortality was investigated using simple and multilevel regression models.

RESULTS

Excess cumulative mortality for both 2020 and 2021 was found in Austria, Brazil, Belgium, Cyprus, England and Wales, Estonia, France, Georgia, Greece, Israel, Italy, Kazakhstan, Mauritius, Northern Ireland, Norway, Peru, Poland, Slovenia, Spain, Sweden, Ukraine, and the USA. Australia and Denmark experienced excess mortality only in 2021. Mauritius demonstrated a statistically significant decrease in all-cause mortality during both years. Weekly incidence of COVID-19 was significantly positively associated with excess mortality for both years, but the positive association was attenuated in 2021 as percentage of the population fully vaccinated increased. Stringency index of control measures was positively and negatively associated with excess mortality in 2020 and 2021, respectively.

CONCLUSION

This study provides evidence of substantial excess mortality in most countries investigated during the first 2 years of the pandemic and suggests that COVID-19 incidence, stringency of control measures and vaccination rates interacted in determining the magnitude of excess mortality.

Excess Mortality Stratified by Age and Sex for Croatia and Croatian Counties during the 2020-2021 COVID-19 Pandemic

Excess mortality is often used to estimate the effect of a certain crisis on the population. It is defined as the number of deaths during a crisis exceeding the expected number based on historical trends. Here, we calculated excess mortality due to the COVID-19 pandemic for Croatia in the 2020-2021 period. The excess was calculated on the national and county level for different age and sex categories. In addition to the absolute number, the excess mortality was also expressed as a ratio of excess deaths to the predicted baseline and excess mortality rate. We showed that using both measures is necessary to avoid incorrect conclusions. The estimated excess mortality on the national level was 14,963, corresponding to an excess percentage of 14.3%. With respect to sex, there was a higher excess mortality rate for men compared to women. An exponential relationship was observed between age and the excess mortality rate.These trends wee representative of most counties as well, with large variations in the magnitude of the effect. However, there were also exceptions to the general rule. The reasons for these deviations were discussed in terms of between-county differences in demographic structure, population density and special events that took place during the pandemic.

Variability in excess deaths across countries with different vulnerability during 2020-2023

Excess deaths provide total impact estimates of major crises, such as the COVID-19 pandemic. We evaluated excess death trajectories across countries with accurate death registration and population age structure data and assessed relationships with vulnerability indicators. Using the Human Mortality Database on 34 countries, excess deaths were calculated for 2020-2023 (to week 29, 2023) using 2017-2019 as reference, with adjustment for 5 age strata. Countries were divided into less and more vulnerable; the latter had per capita nominal GDP < $30,000, Gini > 0.35 for income inequality and/or at least ≥2.5% of their population living in poverty. Excess deaths (as proportion of expected deaths, p%) were inversely correlated with per capita GDP (r = -0.60), correlated with proportion living in poverty (r = 0.66), and modestly correlated with income inequality (r = 0.45). Incidence rate ratio for deaths was 1.062 (95% CI, 1.038-1.087) in more versus less vulnerable countries. Excess deaths started deviating in the two groups after the first wave. Between-country heterogeneity diminished gradually within each group. Less vulnerable countries had mean p% = -0.8% and 0.4% in 0-64 and >65-y-old strata. More vulnerable countries had mean p% = 7.0% and 7.2%, respectively. Lower death rates were seen in children of age 0-14 y during 2020-2023 versus prepandemic years. While the pandemic hit some countries earlier than others, country vulnerability dominated eventually the cumulative impact. Half the analyzed countries witnessed no substantial excess deaths versus prepandemic levels, while the others suffered major death tolls.

Excess death estimates from multiverse analysis in 2009-2021

Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. The relative ranking of different years for the specific country was also largely independent of baseline. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer an approach that better accounts for the uncertainty in estimating expected mortality patterns, comparative mortality trends across different countries, and the nature of observed mortality peaks.

Excess mortality during the Coronavirus disease pandemic in Korea

BACKGROUND

Although the ongoing epidemics of Coronavirus disease 2019 (COVID-19) may have affected the mortality trend of the nation, the national level assessment of excess mortality (changes in overall mortality in the entire population) is still scarce in Korea. Therefore, this study evaluated the excess mortality during the COVID-19 pandemic in Korea using the certified mortality data.

METHODS

Monthly mortality and population data from January 2013 to June 2022 was obtained from the National Health Insurance Service database and Statistics Korea. A quasi-Poisson interrupted time-series model adjusted for age structure, population, seasonality, and long-term trends was used to estimate the counterfactual projections (expected) of mortality during the COVID-19 pandemic (March 2020 to June 2022). The absolute difference (observed-expected) and ratio (observed / expected) of mortality were calculated. Stratified analysis based on pandemic years (years 2020, 2021, and 2022), sex, and age groups (aged 0-4, 5-19, 20-64, and ≥ 65 years) were conducted.

RESULTS

An 8.7% increase in mortality was observed during the COVID-19 pandemic [absolute difference: 61,277 persons; ratio (95% confidence interval (CI)): 1.087 (1.066, 1.107)]. The gap between observed and estimated mortality became wider with continuation of the pandemic [ratio (95% CI), year 2020: 1.021 (1.003, 1.040); year 2021: 1.060 (1.039, 1.080), year 2022: 1.244 (1.219, 1.270)]. Although excess mortality across sex was similar, the adult [aged 20-64, ratio (95% CI): 1.059 (1.043, 1.076)] and elderly [aged 65-, ratio (95% CI): 1.098 (1.062, 1.135)] population showed increased excess mortality during the pandemic.

CONCLUSIONS

Despite Korea's successful quarantine policy response, the continued epidemic has led to an excess mortality. The estimated mortality exceeded the number of deaths from COVID-19 infection. Excess mortality should be monitored to estimate the overall impact of the pandemic on a nation.

Evaluation of concordance in estimation of excess mortality due to COVID-19 pandemic

BACKGROUND

The World Health Organization (WHO) kept track of COVID-19 data at country level daily during the pandemic that included the number of tests, infected cases and fatalities. This daily record was susceptible to change depending on the time and place and impacted by underreporting. In addition to reporting cases of excess COVID-19-related deaths, the WHO also provided estimates of excess mortality based on mathematical models.

OBJECTIVE

To evaluate the WHO reported and model-based estimate of excess deaths to determine the degree of agreement and universality.

METHODOLOGY

Epidemiological data gathered from nine different countries between April 2020 and December 2021 are used in this study. These countries are India, Indonesia, Italy, Russia, United Kingdom, Mexico, the United States, Brazil and Peru and each of them recorded more than 1.5 million deaths from COVID-19 during these months. Statistical tools including correlation, linear regression, intraclass correlation and Bland-Altman plots are used to assess the degree of agreement between reported and model-based estimates of excess deaths.

RESULTS

The WHO-derived mathematical model for estimating excess deaths due to COVID-19 was found to be appropriate for only four of the nine chosen countries, namely Italy, United Kingdom, the United States and Brazil. The other countries showed proportional biases and significantly high regression coefficients.

CONCLUSION

The study revealed that, for some of the chosen nations, the mathematical model proposed by the WHO is practical and capable of estimating the number of excess deaths brought on by COVID-19. However, the derived approach cannot be applied globally.

Variability in excess deaths across countries with different vulnerability during 2020-2023

Excess deaths provide total impact estimates of major crises, such as the COVID-19 pandemic. We evaluated excess death's trajectories during 2020-2023 across countries with accurate death registration and population age structure data; and assessed relationships with economic indicators of vulnerability. Using the Human Mortality Database on 34 countries, excess deaths were calculated for 2020-2023 (to week 29, 2023) using 2017-2019 as reference, with weekly expected death calculations and adjustment for 5 age strata. Countries were divided into less and more vulnerable; the latter had per capita nominal GDP<$30,000, Gini>0.35 for income inequality and/or at least 2.5% of their population living in poverty. Excess deaths (as proportion of expected deaths, p%) were inversely correlated with per capita GDP (r=-0.60), correlated with proportion living in poverty (r=0.66) and modestly correlated with income inequality (r=0.45). Incidence rate ratio for deaths was 1.06 (95% confidence interval, 1.04-1.08) in the more versus less vulnerable countries. Excess deaths started deviating in the two groups after the first wave. Between-country heterogeneity diminished over time within each of the two groups. Less vulnerable countries had mean p%=-0.8% and 0.4% in 0-64 and >65 year-old strata while more vulnerable countries had mean p%=7.0% and 7.2%, respectively. Usually lower death rates were seen in children 0-14 years old during 2020-2023 versus pre-pandemic years. While the pandemic hit some countries earlier than others, country vulnerability dominated eventually the cumulative impact. Half of the analyzed countries witnessed no substantial excess deaths versus pre-pandemic levels, while the other half suffered major death tolls.

The contribution of population age-sex structure to the excess mortality estimates of 2020-2021 in Denmark, Finland, Iceland, Norway, and Sweden

The Nordic countries offer an ideal case study of the COVID-19 pandemic due to their comparability, high data quality, and variable mitigations. We investigated the age- and sex-specific mortality patterns during 2020-2021 for the five Nordic countries and analysed the total age- and sex-adjusted excess deaths, ratios of actual to expected death rates, and age-standardized excess death estimates. We assessed excess deaths using several time periods and sensitivity tests, and 42 sex and age groups. Declining pre-pandemic age-specific death rates reflected improving health demographics. These affect the expected death estimates and should be accounted for in excess mortality models. Denmark had the highest death rates both before and during the pandemic, whereas in 2020 Sweden had the largest mortality increase. The age-standardized mortality of Denmark, Iceland and Norway was lowest in 2020. 2021 was one of the lowest mortality years for all Nordic countries. The total excess deaths in 2020-2021 were dominated by 70-89-year-olds, were not identified in children, and were more pronounced among men than women. Sweden had more excess deaths in 2020 than in 2021, whereas Finland, Norway and Denmark had the opposite. Our study provides new details on Nordic sex- and age-specific mortality during the first two years of the pandemic and shows that several metrics are important to enable a full understanding and comparison of the pandemic mortality.

Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared

During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option.

Estimates of excess mortality during the COVID-19 pandemic strongly depend on subjective methodological choices

Excess mortality is often used to assess the health impact of the COVID-19 pandemic. It involves comparing the number of deaths observed during the pandemic with the number of deaths that would counterfactually have been expected in the absence of the pandemic. However, published data on excess mortality often vary even for the same country. The reason for these discrepancies is that the estimation of excess mortality involves a number of subjective methodological choices. The aim of this paper was to summarize these subjective choices. In several publications, excess mortality was overestimated because population aging was not adjusted for. Another important reason for different estimates of excess mortality is the choice of different pre-pandemic reference periods that are used to estimate the expected number of deaths (e.g., only 2019 or 2015-2019). Other reasons for divergent results include different choices of index periods (e.g., 2020 or 2020-2021), different modeling to determine expected mortality rates (e.g., averaging mortality rates from previous years or using linear trends), the issue of accounting for irregular risk factors such as heat waves and seasonal influenza, and differences in the quality of the data used. We suggest that future studies present the results not only for a single set of analytic choices, but also for sets with different analytic choices, so that the dependence of the results on these choices becomes explicit.

Estimation of Excess Mortality in Germany During 2020-2022

Background This study estimates the burden of COVID-19 on mortality in Germany. It is expected that many people have died because of the new COVID-19 virus who otherwise would not have died. Estimating the burden of the COVID-19 pandemic on mortality by the number of officially reported COVID-19-related deaths has been proven to be difficult due to several reasons. Because of this, a better approach, which has been used in many studies, is to estimate the burden of the COVID-19 pandemic by calculating the excess mortality for the pandemic years. An advantage of such an approach is that additional negative impacts of a pandemic on mortality are covered as well, such as a possible pandemic-induced strain on the healthcare system. Methods To calculate the excess mortality in Germany for the pandemic years 2020 to 2022, we compare the reported number of all-cause deaths (i.e., the number of deaths independently of underlying causes) with the number of statistically expected all-cause deaths. For this, the state-of-the-art method of actuarial science, based on population tables, life tables, and longevity trends, is used to estimate the expected number of all-cause deaths from 2020 to 2022 if there had been no pandemic. Results The results show that the observed number of deaths in 2020 was close to the expected number with respect to the empirical standard deviation; approximately 4,000 excess deaths occurred. By contrast, in 2021, the observed number of deaths was two empirical standard deviations above the expected number and even more than four times the empirical standard deviation in 2022. In total, the number of excess deaths in the year 2021 is about 34,000 and in 2022 about 66,000 deaths, yielding a cumulated 100,000 excess deaths in both years. The high excess mortality in 2021 and 2022 was mainly due to an increase in deaths in the age groups between 15 and 79 years and started to accumulate only from April 2021 onward. A similar mortality pattern was observed for stillbirths with an increase of about 9.4% in the second quarter and 19.4% in the fourth quarter of the year 2021 compared to previous years. Conclusions These findings indicate that something must have happened in spring 2021 that led to a sudden and sustained increase in mortality, although no such effects on mortality had been observed during the early COVID-19 pandemic so far. Possible influencing factors are explored in the discussion.

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.

Estimates of COVID-19 deaths in Mainland China after abandoning zero COVID policy

BACKGROUND

China witnessed a surge of Omicron infections after abandoning 'zero COVID' strategies on 7 December 2022. The authorities report very sparse deaths based on very restricted criteria, but massive deaths are speculated.

METHODS

We aimed to estimate the COVID-19 fatalities in Mainland China until summer 2023 using the experiences of Hong Kong and of South Korea in 2022 as prototypes. Both these locations experienced massive Omicron waves after having had very few SARS-CoV-2 infections during 2020-2021. We estimated age-stratified infection fatality rates (IFRs) in Hong Kong and South Korea during 2022 and extrapolated to the population age structure of Mainland China. We also accounted separately for deaths of residents in long-term care facilities in both Hong Kong and South Korea.

RESULTS

Infection fatality rate estimates in non-elderly strata were modestly higher in Hong Kong than South Korea and projected 987,455 and 619,549 maximal COVID-19 deaths respectively, if the entire China population were infected. Expected COVID-19 deaths in Mainland China until summer 2023 ranged from 49,962 to 691,219 assuming 25-70% of the non-elderly population being infected and variable protection of elderly (from none to three-quarter reduction in fatalities). The main analysis (45% of non-elderly population infected and fatality impact among elderly reduced by half) estimated 152,886-249,094 COVID-19 deaths until summer 2023. Large uncertainties exist regarding potential changes in dominant variant, health system strain and impact on non-COVID-19 deaths.

CONCLUSIONS

The most critical factor that can affect total COVID-19 fatalities in China is the extent to which the elderly can be protected.

Excess death estimates from multiverse analysis in 2009-2021

Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. The relative ranking of different years for the specific country was also largely independent of baseline. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer a more unbiased approach to understand comparative mortality trends across different countries, the range of uncertainty around estimates, and the nature of observed mortality peaks.

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.

Differential COVID-19 infection rates in children, adults, and elderly: Systematic review and meta-analysis of 38 pre-vaccination national seroprevalence studies

Background

Debate exists about whether extra protection of elderly and other vulnerable individuals is feasible in COVID-19. We aimed to assess the relative infection rates in the elderly vs the non-elderly and, secondarily, in children vs adults.

Methods

We performed a systematic review and meta-analysis of seroprevalence studies conducted in the pre-vaccination era. We identified representative national studies without high risk of bias through SeroTracker and PubMed searches (last updated May 17, 2022). We noted seroprevalence estimates for children, non-elderly adults, and elderly adults, using cut-offs of 20 and 60 years (or as close to these ages, if they were unavailable) and compared them between different age groups.

Results

We included 38 national seroprevalence studies from 36 different countries comprising 826 963 participants. Twenty-six of these studies also included pediatric populations and twenty-five were from high-income countries. The median ratio of seroprevalence in elderly vs non-elderly adults (or non-elderly in general, if pediatric and adult population data were not offered separately) was 0.90-0.95 in different analyses, with large variability across studies. In five studies (all in high-income countries), we observed significant protection of the elderly with a ratio of <0.40, with a median of 0.83 in high-income countries and 1.02 elsewhere. The median ratio of seroprevalence in children vs adults was 0.89 and only one study showed a significant ratio of <0.40. The main limitation of our study is the inaccuracies and biases in seroprevalence studies.

Conclusions

Precision shielding of elderly community-dwelling populations before the availability of vaccines was indicated in some high-income countries, but most countries failed to achieve any substantial focused protection.

Registration

Open Science Framework (available at: https://osf.io/xvupr).

Estimates of COVID-19 deaths in Mainland China after abandoning zero COVID policy

Background

China witnessed a surge of Omicron infections after abandoning zero COVID strategies on December 7, 2022. The authorities report very sparse deaths based on very restricted criteria, but massive deaths are speculated.

Methods

We aimed to estimate the COVID-19 fatalities in Mainland China until summer 2023 using the experiences of Hong Kong and of South Korea in 2022 as prototypes. Both these locations experienced massive Omicron waves after having had very few SARS-CoV-2 infections during 2020-2021. We estimated age-stratified infection fatality rates (IFRs) in Hong Kong and South Korea during 2022 and extrapolated to the population age structure of Mainland China. We also accounted separately for deaths of residents in long-term care facilities in both Hong Kong and South Korea.

Results

IFR estimates in non-elderly strata were modestly higher in Hong Kong than South Korea and projected 987,455 and 619,549 maximal COVID-19 deaths, respectively, if the entire China population was infected. Expected COVID-19 deaths in Mainland China until summer 2023 ranged from 49,962 to 691,219 assuming 25-70% of the non-elderly population being infected and variable protection of elderly (from none to three-quarter reduction in fatalities). The main analysis (45% of non-elderly population infected and fatality impact among elderly reduced by half) estimated 152,886-249,094 COVID-19 deaths until summer 2023. Large uncertainties exist regarding potential changes in dominant variant, health system strain, and impact on non-COVID-19 deaths.

Conclusions

The most critical factor that can affect total COVID-19 fatalities in China is the extent to which the elderly can be protected.

Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m²/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.

Age-stratified infection fatality rate of COVID-19 in the non-elderly population

The largest burden of COVID-19 is carried by the elderly, and persons living in nursing homes are particularly vulnerable. However, 94% of the global population is younger than 70 years and 86% is younger than 60 years. The objective of this study was to accurately estimate the infection fatality rate (IFR) of COVID-19 among non-elderly people in the absence of vaccination or prior infection. In systematic searches in SeroTracker and PubMed (protocol: https://osf.io/xvupr), we identified 40 eligible national seroprevalence studies covering 38 countries with pre-vaccination seroprevalence data. For 29 countries (24 high-income, 5 others), publicly available age-stratified COVID-19 death data and age-stratified seroprevalence information were available and were included in the primary analysis. The IFRs had a median of 0.034% (interquartile range (IQR) 0.013-0.056%) for the 0-59 years old population, and 0.095% (IQR 0.036-0.119%) for the 0-69 years old. The median IFR was 0.0003% at 0-19 years, 0.002% at 20-29 years, 0.011% at 30-39 years, 0.035% at 40-49 years, 0.123% at 50-59 years, and 0.506% at 60-69 years. IFR increases approximately 4 times every 10 years. Including data from another 9 countries with imputed age distribution of COVID-19 deaths yielded median IFR of 0.025-0.032% for 0-59 years and 0.063-0.082% for 0-69 years. Meta-regression analyses also suggested global IFR of 0.03% and 0.07%, respectively in these age groups. The current analysis suggests a much lower pre-vaccination IFR in non-elderly populations than previously suggested. Large differences did exist between countries and may reflect differences in comorbidities and other factors. These estimates provide a baseline from which to fathom further IFR declines with the widespread use of vaccination, prior infections, and evolution of new variants.

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches-Italy, 2020-2021

INTRODUCTION

Excess mortality (EM) is a valid indicator of COVID-19's impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations.

METHODS

We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons.

RESULTS

We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE.

DISCUSSION

Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

Time-varying risk of death after SARS-CoV-2 infection in Swedish long-term care facility residents: a matched cohort study

OBJECTIVES

To evaluate whether SARS-CoV-2 infection in residents of long-term care (LTC) facilities is associated with higher mortality after the acute phase of infection, and to estimate survival in uninfected residents.

DESIGN

Extended follow-up of a previous, propensity score-matched, retrospective cohort study based on the Swedish Senior Alert register.

SETTING

LTC facilities in Sweden.

PARTICIPANTS

n=3604 LTC residents with documented SARS-CoV-2 until 15 September 2020 matched to 3604 uninfected controls using time-dependent propensity scores on age, sex, health status, comorbidities, prescription medications, geographical region and Senior Alert registration time. In a secondary analysis (n=3731 in each group), geographical region and Senior Alert registration time were not matched for in order to increase the follow-up time in controls and allow for an estimation of median survival.

PRIMARY OUTCOME MEASURES

All-cause mortality until 24 October 2020, tracked using the National Cause of Death Register.

RESULTS

Median age was 87 years and 65% were women. Excess mortality peaked at 5 days after documented SARS-CoV-2-infection (HR 21.5, 95% CI 15.9 to 29.2), after which excess mortality decreased. From the second month onwards, mortality rate became lower in infected residents than controls. The HR for death during days 61-210 of follow-up was 0.76 (95% CI 0.62 to 0.93). The median survival of uninfected controls was 1.6 years, which was much lower than the national life expectancy in Sweden at age 87 (5.05 years in men, 6.07 years in women).

CONCLUSIONS

The risk of death after SARS-CoV-2 infection in LTC residents peaked after 5 days and decreased after 2 months, probably because the frailest residents died during the acute phase, leaving healthier residents remaining. The limited life expectancy in this population suggests that LTC resident status should be accounted for when estimating years of life lost due to COVID-19.

Time-varying risk of death after SARS-CoV-2 infection in Swedish long-term care facility residents: a matched cohort study

OBJECTIVES

To evaluate whether SARS-CoV-2 infection in residents of long-term care (LTC) facilities is associated with higher mortality after the acute phase of infection, and to estimate survival in uninfected residents.

DESIGN

Extended follow-up of a previous, propensity score-matched, retrospective cohort study based on the Swedish Senior Alert register.

SETTING

LTC facilities in Sweden.

PARTICIPANTS

n=3604 LTC residents with documented SARS-CoV-2 until 15 September 2020 matched to 3604 uninfected controls using time-dependent propensity scores on age, sex, health status, comorbidities, prescription medications, geographical region and Senior Alert registration time. In a secondary analysis (n=3731 in each group), geographical region and Senior Alert registration time were not matched for in order to increase the follow-up time in controls and allow for an estimation of median survival.

PRIMARY OUTCOME MEASURES

All-cause mortality until 24 October 2020, tracked using the National Cause of Death Register.

RESULTS

Median age was 87 years and 65% were women. Excess mortality peaked at 5 days after documented SARS-CoV-2-infection (HR 21.5, 95% CI 15.9 to 29.2), after which excess mortality decreased. From the second month onwards, mortality rate became lower in infected residents than controls. The HR for death during days 61-210 of follow-up was 0.76 (95% CI 0.62 to 0.93). The median survival of uninfected controls was 1.6 years, which was much lower than the national life expectancy in Sweden at age 87 (5.05 years in men, 6.07 years in women).

CONCLUSIONS

The risk of death after SARS-CoV-2 infection in LTC residents peaked after 5 days and decreased after 2 months, probably because the frailest residents died during the acute phase, leaving healthier residents remaining. The limited life expectancy in this population suggests that LTC resident status should be accounted for when estimating years of life lost due to COVID-19.

A Comparison of Germany and the United Kingdom Indicates That More SARS-CoV-2 Circulation and Less Restrictions in the Warm Season Might Reduce Overall COVID-19 Burden

(1) Background: Between March 2020 and January 2022 severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) caused five infection waves in Europe. The first and the second wave was caused by wildtype SARS-CoV-2, while the following waves were caused by the variants of concern Alpha, Delta, and Omicron respectively. (2) Methods: In the present analysis, the first four waves were compared in Germany and the UK, in order to examine the COVID-19 epidemiology and its modulation by non-pharmaceutical interventions (NPI). (3) Results: The number of COVID-19 patients on intensive care units and the case fatality rate were used to estimate disease burden, the excess mortality to assess the net effect of NPI and other measures on the population. The UK was more severely affected by the first and the third wave while Germany was more affected by the second wave. The UK had a higher excess mortality during the first wave, afterwards the excess mortality in both countries was nearly identical. While most NPI were lifted in the UK in July 2021, the measures were kept and even aggravated in Germany. Nevertheless, in autumn 2021 Germany was much more affected, nearly resulting in a balanced sum of infections and deaths compared to the UK. Within the whole observation period, in Germany the number of COVID-19 patients on ICUs was up to four times higher than in the UK. Our results show that NPI have a limited effect on COVID-19 burden, seasonality plays a crucial role, and a higher virus circulation in a pre-wave situation could be beneficial. (4) Conclusions: Although Germany put much more effort and resources to fight the pandemic, the net balance of both countries was nearly identical, questioning the benefit of excessive ICU treatments and of the implementation of NPI, especially during the warm season.

Excess mortality according to group of causes in the first year of the COVID-19 pandemic in Brazil

ABSTRACT Objective: To estimate excess mortality by cause of death in Brazil and states in 2020. Methods: We estimated the expected number of deaths considering a linear trend analysis with the number of deaths between 2015 and 2019 for each group of causes and each federative unit. We calculated standardized mortality ratios (SMR) and 95% confidence intervals for each SMR assuming a Poisson distribution. We performed the analyses in the R program, version 4.1.3. Results: We observed a 19% excess in deaths in 2020 (SMR=1.19; 95%CI=1.18–1.20). The Infectious and Parasitic Diseases group stood out among the defined causes (SMR=4.80; 95%CI 4.78–4.82). The ill-defined causes showed great magnitude in this period (SMR=6.08; 95%CI 6.06–6.10). Some groups had lower-than-expected deaths: respiratory diseases (10% lower than expected) and external causes (4% lower than expected). In addition to the global analysis of the country, we identified significant heterogeneity among the federative units. States with the highest SMR are concentrated in the northern region, and those with the lowest SMR are concentrated in the southern and southeastern regions. Conclusion: Excess mortality occurs during the COVID-19 pandemic. This excess results not only from COVID-19 itself, but also from the social response and the management of the health system in responding to a myriad of causes that already had a trend pattern before it.

Excesso de mortalidade segundo grupo de causas no primeiro ano de pandemia de COVID-19 no Brasil

RESUMO Objetivo: Estimar o excesso de mortalidade segundo causa de óbito no Brasil e estados em 2020. Métodos: O número de óbitos esperado foi estimado considerando análise de tendência linear com o número de mortes entre os anos de 2015 e 2019, para cada grupo de causas e cada unidade da federação. Calculamos as razões de mortalidade padronizadas, e os intervalos com 95% de confiança para cada SMR foram calculados assumindo uma distribuição Poisson. As análises foram realizadas no programa R, versão 4.1.3. Resultados: Observamos um excesso de 19% nos óbitos em 2020 (SMR=1,19; IC=1,18–1,20). O grupo de Doenças Infecciosas e Parasitárias obteve maior destaque entre as causas definidas (SMR=4,80; IC95% 4,78–4,82). As causas mal definidas apresentaram grande magnitude neste período (SMR=6,08; IC95% 6,06–6,10). Há, ainda, grupos que apresentaram número de óbitos abaixo do esperado: doenças do aparelho respiratório (10% abaixo do esperado) e causas externas (4% abaixo do esperado). Além da análise global para o país, identificamos grande heterogeneidade entre as unidades da federação. Os estados com maiores SMR estão concentrados na região norte, e os que possuem menores SMR estão concentrados nas regiões sul e sudeste. Conclusões: Há um excesso de mortalidade ocorrendo durante a pandemia de COVID-19. Este excesso é resultado não apenas da COVID-19 em si, mas da resposta social e da gestão do sistema de saúde em responder a uma miríade de causas que já possuíam um ritmo de tendência anterior a ela.