Excess Mortality in Austria during the COVID-19 Pandemic

Gradual changes in the age distribution of excess deaths in the years following the 1918 influenza pandemic in Copenhagen: Using epidemiological evidence to detect antigenic drift

Important questions remain regarding differences in geographical and age-specific mortality patterns as the COVID-19 pandemic evolved in consecutive waves, and how COVID-19 mortality compares to seasonal influenza. In a pandemic situation, excess all-cause mortality provides a more complete and robust measure than cause-specific mortality. Data submitted by 26 countries participating in the European Mortality Monitoring (EuroMOMO) network between 2020 and 2023 was analysed to quantify excess all-cause mortality during the COVID-19 pandemic. Excess mortality from this period was compared to previous influenza seasons from 2014 to 2019. Pooled estimates of excess mortality showed four main waves during the COVID-19 pandemic period, most markedly in people aged 65 years and above, with timing and magnitude that varied between countries. Here we show that prior to implementation of control measures and COVID-19 vaccination, excess mortality greatly exceeded typical seasonal influenza mortality, but later during the pandemic was at levels comparable to influenza.

BackgroundThere is a limited body of research specifically examining gender inequality in excess mortality and its variations across age groups and geographical locations during the COVID-19 pandemic. This study aims to fill this gap by analyzing the patterns of gender inequality in excess all-cause mortality in Thailand during the COVID-19 pandemic.MethodsData pertaining to all-cause deaths and population between January 1, 2010, and December 31, 2021, were obtained from Thailand’s Bureau of Registration Administration. A seasonal autoregressive integrated moving average (SARIMA) technique was used to estimate excess mortality during the pandemic between January 2020 to December 2021. Gender differential excess mortality was measured as the difference in age-standardized mortality rates between men and women.ResultsOur SARIMA-based estimate of all-cause mortality in Thailand during the COVID-19 pandemic amounted to 1,032,921 deaths, with COVID-19-related fatalities surpassing official figures by 1.64 times. The analysis revealed fluctuating patterns of excess and deficit in all-cause mortality rates across different phases of the pandemic, as well as among various age groups and regions. In 2020, the most pronounced gender disparity in excess all-cause mortality emerged in April, with 4.28 additional female deaths per 100,000, whereas in 2021, the peak gender gap transpired in August, with 7.52 more male deaths per 100,000. Individuals in the 80 + age group exhibited the largest gender gap for most of the observed period. Gender differences in excess mortality were uniform across regions and over the period observed. Bangkok showed the highest gender disparity during the peak of the fourth wave, with 24.18 more male deaths per 100,000.ConclusionThe findings indicate an overall presence of gender inequality in excess mortality during the COVID-19 pandemic in Thailand, observed across age groups and regions. These findings highlight the need for further attention to be paid to gender disparities in mortality and call for targeted interventions to address these disparities.

While basically all countries have been hit by the COVID-19 pandemic, the impact has varied in large degrees among countries. In the present study, national differences in six COVID-19 indicators (COVID-19 deaths per capita, excess mortality, change in GDP per capita, vaccination rate, stringency index, and overall impact of the pandemic) were studied in relation to socio-economic and Hofstede's cultural dimensions by using the latest data available. The results differed to some degree from the studies conducted in the earlier stage of the pandemic. COVID-19 deaths per capita were predicted by Uncertainty Avoidance (UA) and Indulgence (IVR); excess mortality by UA; the impact of pandemics by Power Distance (PDI), Long-term Orientation (LTOWS) and IVR; change in GDP per capita by PDI; vaccination rate by Individualism and UA; and Stringency Index by LTOWS. In addition to further clarifying the role of cultural dimensions in the pandemic, three conclusions can be drawn. First, the pandemic reached different countries at different times, which is reflected in the results. The conclusion about the role of socio-economic and cultural factors can be drawn only after the pandemic. Second, cultural dimensions were related to COVID-19 measures only when socio-economic indicators were not considered but lost their significance when socio-economic variables were entered into the models. Cultural dimensions influence the outcome variables via socio-economic factors. Third, earlier studies have focused mainly on COVID-19 deaths. The impact of the COVID-19 pandemic is a complex phenomenon and cannot be reduced to the death rate.

Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy. Mortality records (ISTAT 2015-2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering. (2) Distinct functional regression by cluster. The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began. The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services' responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.

During the COVID-19 pandemic, excess mortality has generally been estimated comparing overall mortality in a given year with either past mortality levels or past mortality trends, with different results. Our objective was to illustrate and compare the two approaches using mortality data for Switzerland in 2022, the third year of the COVID-19 pandemic. Using data from the Swiss Federal Statistical Office, standardized mortality rates and life expectancies in 2022 were compared with those of the last pre-pandemic year 2019 (first approach), as well as with those that would be expected if the pre-pandemic downward trend in mortality had continued during the pandemic (second approach). The pre-pandemic trend was estimated via a Poisson log-linear model on age-specific mortality over the period 2010-19. Using the first approach, we estimated in Switzerland in 2022 an excess mortality of 2.6% (95%CI: 1.0%-4.1%) for men and 2.5% (95%CI: 1.0%-4.0%) for women, while the excess mortality rose to 8.4% (95%CI: 6.9%-9.9%) for men and 6.0% (95%CI: 4.6%-7.5%) for women using the second approach. Age classes over 80 were the main responsible for the excess mortality in 2022 for both sexes using the first approach, although a significant excess mortality was also found in most age classes above 30 using the second approach. Life expectancy in 2022 has been reduced by 2.7 months for men and 2.4 months for women according to the first approach, whereas it was reduced by respectively 8.8 and 6.0 months according to the second approach. The excess mortality and loss of life expectancy in Switzerland in 2022 are around three times greater if the pre-pandemic trend is taken into account than if we simply compare 2022 with 2019. These two different approaches, one being more speculative and the other more factual, can also be applied simultaneously and provide complementary results. In Switzerland, such a dual-approach strategy has shown that the pre-pandemic downward trend in mortality is currently halted, while pre-pandemic mortality levels have largely been recovered by 2022.

County-level impact of the COVID-19 Pandemic on excess mortality among U.S. veterans: A population-based study

Counting the global COVID-19 dead

Excess deaths, an indicator that compares total mortality rates before and during a pandemic, offer a comprehensive view of the pandemic's impact. However, discrepancies may arise from variations in estimating expected deaths. This study aims to compare excess deaths in Korea during the coronavirus disease 2019 pandemic using 3 methods and to analyze patterns using the most appropriate method. Expected deaths from 2020 to 2022 were estimated using mortality data from 2015-2019 as reference years. This estimation employed 3 approaches: (1) simple average, (2) age-adjusted average, and (3) age-adjusted linear regression. Excess deaths by age, gender, and cause of death were also presented. The number of excess deaths varied depending on the estimation method used, reaching its highest point with the simple average and its lowest with the age-adjusted average. Age-adjusted linear regression, which accounts for both the aging population and declining mortality rates, was considered most appropriate. Using this model, excess deaths were estimated at 0.3% for 2020, 4.0% for 2021, and 20.7% for 2022. Excess deaths surged among individuals in their 20s throughout the pandemic, largely attributed to a rise in self-harm and suicide. Additionally, the results indicated sharp increases in deaths associated with "endocrine, nutritional, and metabolic diseases" and "symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified." Substantial variations in excess deaths were evident based on estimation method, with a notable increase in 2022. The heightened excess deaths among young adults and specific causes underscore key considerations for future pandemic responses.

ZusammenfassungZiel der StudieZiel dieser Analyse war es, Zusammenhänge zuuntersuchen zwischen Personen, die die ausdauerorientiertenBewegungsempfehlungen erfüllen und dem Wissen um dieBewegungsempfehlungen, die wahrgenommene bewegungsförderliche Kultur undUmgebung sowie den Gesundheitszustand getrennt nach Bundesländern.MethodikFür die Analyse wurden Daten aus demösterreichischen Bewegungsmonitoring 2017, der österreichischenGesundheitsbefragung 2014, der Mortalitätsstatistik 2015, derFit-Sport-Austria Datenbank, der Wirtschaftskammer, dem Verkehrsministerium undeiner Online-Lauf-Plattform herangezogen. Korrelationskoeffizienten (Pearson)verschiedener Parameter mit den Bevölkerungsanteilen, die dieausdauerorientierten Bewegungsempfehlungen erfüllten, wurdenerrechnet.ErgebnisseAusdauerorientierte Bewegung korrelierte signifikant mit demWissen zum empfohlenen Bewegungsumfang (0,91), Angeboten in (0,87) undsubjektiver Erreichbarkeit von Sportvereinen (0,85), Erreichbarkeit vonGrünräumen (0,84), Anzahl an Sportvereinen mit zertifiziertengesundheitsförderlichen Angeboten (0,76), und den MöglichkeitenLaufen zu gehen (0,72). Außerdem korrelierte ausdauerorientierteBewegung negativ mit der altersstandardisierten kardiovaskulärenMortalität (−0,96), der Gesamtmortalität(−0,95), der Prävalenz von Adipositas (−0,83), derMortalität an ischämischen Herzerkrankungen (−0,76), derPrävalenz chronischer Krankheiten (−0,68), sowie positiv mit dersubjektiv wahrgenommenen Gesundheit (0,79).SchlussfolgerungZum einen zeigten sich positive signifikanteZusammenhänge zwischen Wissen um die Bewegungsempfehlungen, derInfrastruktur und der bewegungsförderlichen Kultur mit demBewegungsverhalten. Zum anderen korrelierte das Bewegungsverhalten mit demGesundheitszustand in den jeweiligen Bundesländern. Zusätzlichlassen sich spezifische Empfehlungen, zugeschnitten auf die einzelnenBundesländer, ableiten.

Background: Beyond the direct COVID-19 effects, the pandemic's broader impact on vulnerable groups, such as patients with systemic sclerosis (SSc), is particularly concerning, especially regarding any resulting increase in overall mortality due to healthcare access disruptions. We aimed to determine excess all-cause mortality in SSc patients before and during the pandemic. Methods: We examined mortality data from Thailand's Ministry of Public Health database for adults with SSc (ICD-10: M34). According to the WHO methodology, a negative binomial distribution model was used to estimate the expected number of deaths using pre-pandemic data (1 January 2015-31 December 2019). We evaluated actual versus expected deaths during the pandemic (1 January 2020 to 31 December 2022), defining excess mortality as the difference between observed and projected deaths under normal conditions. Results: The total number of all-cause deaths in Thailand was 2,325,384 in the pre-pandemic period and 1,634,121 during the pandemic period. The mortality rate among patients with SSc was 3693 before and 3107 during the pandemic. Of those with SSc, 1785 of the deceased were female, and the observed mortality was significantly lower than expected, with an excess death count of -368 (95% CI: -459 to -277), as well as in males with an excess death count of -123 (95% CI: -198 to -48). However, younger SSc patients (aged 18-29 years) experienced significantly higher excess mortality, with an excess death count of 11 (95% CI: 4-18). Conclusions: During the COVID-19 pandemic, neither sex had significantly higher SSc mortality; however, mortality in younger SSc patients increased significantly compared to pre-pandemic levels, underscoring the need for tailored therapies.

This study aims to assess whether there was any excess mortality among the Aboriginal and non-Aboriginal populations in the Northern Territory (NT), Australia, during the COVID-19 pandemic. A time-series analysis using death data (1997-2023) was applied separately to the monthly and yearly death counts to develop an excess mortality surveillance model (using Auto-Regressive Integrated Moving Average (ARIMA)) for the NT population. Excess mortality was calculated by comparing expected deaths with actual deaths. In 2022, there was a statistically significant excess mortality of 193 (p <0.01), compared with 82 recorded COVID-19 deaths. Excess mortality was significant for both the Aboriginal (N=91) and non-Aboriginal (N=102) populations in 2022. Even though some months had significant excess mortality among both Aboriginal and non-Aboriginal populations, the recorded COVID-19 deaths were not high in these months. This was associated with the peak of COVID-19 fatalities. The ARIMA model demonstrates deviations from expected deaths and helps understand the pandemic's impact on the NT. Excess deaths occurred in 2022; however, no large spikes in most of the months suggest public health success in the NT.

Excess pneumonia and influenza death as herald wave of COVID-19 in England and Wales, United Kingdom

The mortality impact of the COVID-19 pandemic in Ukraine has remained incomplete. We estimated excess deaths associated with the pandemic in Ukraine during 2020 and 2021. Excess deaths may be attributed directly to SARS-CoV-2 infection or indirectly to deaths associated with social and economic upheavals resulting from the pandemic. Data on all deaths registered in government-controlled Ukraine from 2016–2021 (N = 3,657,475) were utilized. Using a model-based approach, we predicted monthly excess deaths in 2020 and 2021. We estimated 47,578 excess deaths in 2020 as a whole (7.71% of all recorded deaths). This figure reflects both positive (higher than expected) excess deaths from June-December and negative (lower than expected) deaths in January and March-May. From June-December 2020, we estimated 59,363 excess deaths (15.75% of all recorded deaths in those months). In 2021, we estimated 150,049 excess deaths (21.01% of all recorded deaths). Positive excess deaths were detected across age groups even groups younger than 40 years. The number of excess deaths exceeded that of deaths with COVID-19 coded on the death certificate by more than two-fold in 2020, but that difference narrowed in 2021. We furthermore provide provisional estimates of the effect of low vaccine coverage on excess deaths in 2021 drawing from European cross-national evidence and provisional estimates of the hypothetical evolution of the pandemic in 2022 to serve as a rough basis for future studies analyzing the joint impacts of the COVID-19 pandemic and the Russian invasion on Ukrainian demography.

Determining 'excess mortality' makes it possible to compare the burden of disasters between countries and over time, and thus also to evaluate the success of mitigation measures. However, the debate on coronavirus disease 2019 (Covid-19) has exposed that calculations of excess mortalities vary considerably depending on the method and its specification. Moreover, it is often unclear what exactly is meant by 'excess mortality'. We define excess mortality as the excess over the number of deaths that would have been expected counter-factually, that is without the catastrophic event in question. Based on this definition, we use a very parsimonious calculation method, namely the linear extrapolation of death figures from previous years to determine the excess mortality during the Covid-19 pandemic. But unlike most other literature on this topic, we first evaluated and optimized the specification of our method using a larger historical data set in order to identify and minimize estimation errors and biases. The result shows that excess mortality rates in the literature are often inflated. Moreover, they would have exhibited considerable excess mortalities in the period before Covid-19, if this value had already been of public interest at that time. Three conclusions can be drawn from this study and its findings: (i) All calculation methods for current figures should first be evaluated against past figures. (ii) To avoid alarm fatigue, thresholds should be introduced which would differentiate between 'usual fluctuations' and 'remarkable excess'. (iii) Statistical offices could provide more realistic estimates.