Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

In Austria, the first confirmed COVID-19 death occurred in early March 2020. Since then, the question as to whether and, if so, to what extent the COVID-19 pandemic has increased overall mortality has been raised in the public and academic discourse. In an effort to answer this question, Statistics Vienna (City of Vienna, Department for Economic Affairs, Labour and Statistics) has evaluated the weekly mortality trends in Vienna, and compared them to the trends in other Austrian provinces. For our analysis, we draw on data from Statistics Austria and the Austrian Agency for Health and Food Safety (AGES), which are published along with data on the actual and the expected weekly numbers of deaths via the Vienna Mortality Monitoring website. Based on the definition of excess mortality as the actual number of reported deaths from all causes minus the expected number of deaths, we calculate the weekly prediction intervals of the expected number of deaths for two age groups (0 to 64 years and 65 years and older). The temporal scope of the analysis covers not only the current COVID-19 pandemic, but also previous flu seasons and summer heat waves. The results show the actual weekly numbers of deaths and the corresponding prediction intervals for Vienna and the other Austrian provinces since 2007. Our analysis underlines the importance of comparing time series of COVID-19-related excess deaths at the sub-national level in order to highlight within-country heterogeneities.

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THE US MIDLIFE MORTALITY CRISIS CONTINUES: INCREASED DEATH RATES FROM CAUSES OTHER THAN COVID-19 DURING 2020

10.1101/2021.05.17.21257241 ◽

2021 ◽

Author(s):

Dana A Glei

Keyword(s):

Indirect Effect ◽

Causes Of Death ◽

Excess Mortality ◽

Negative Binomial ◽

National Level ◽

Age Groups ◽

Negative Binomial Regression ◽

Negative Binomial Regression Model ◽

Age Group ◽

External Causes

COVID-19 has prematurely ended many lives, particularly among the oldest Americans, but the pandemic has also had an indirect effect on health and non-COVID mortality among the working-age population, who have suffered the brunt of the economic consequences. This analysis quantifies the changes in mortality for selected causes of death during the COVID 19 pandemic up to December 31, 2020, and investigates whether the levels of excess mortality varied by age group. The data comprise national-level monthly death counts by age group and selected causes of death from January 1999 to December 2020 combined with annual mid-year population estimates over the same period. A negative binomial regression model was used to estimate monthly cause-specific excess mortality during 2020 controlling for the pre-pandemic mortality patterns by age, calendar year, and season. To determine whether excess mortality varied by age, we tested interactions between broad age groups and dichotomous indicators for the pre-pandemic (January-February) and the pandemic (March-December) portions of 2020. In relative terms, excess all cause mortality (including COVID-19) peaked in December at ages 25-44 (RR=1.58 relative to 2019, 95% CI=1.50-1.68). Excluding COVID-19, all of the excess mortality occurred between ages 15 and 64, peaking in July among those aged 25-44 (RR=1.45, 95% CI 1.37-1.53). We find notable excess mortality during March-December 2020 for many causes (i.e., influenza/pneumonia, other respiratory diseases, diabetes, heart disease, cerebrovascular disease, kidney disease, and external causes), but almost exclusively among young and midlife (aged 25-74) Americans. For those aged 75 and older, there was little excess mortality from causes other than COVID-19 except from Alzheimer's disease. Excess non-COVID mortality may have resulted, at least partly, from incorrectly classified COVID-19 deaths, but neither misclassification nor an atypical flu season that disproportionately affected younger people is likely to explain the increase in mortality from external causes, which was evident even during January-February 2020. Exploratory analyses suggest that drug-related mortality may be driving the early rise in external mortality. The growth in drug overdoses well before there was any hint of a pandemic suggests it is probably not solely an indirect effect of COVID-19, although the pandemic may have exacerbated the problem.

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Excess Mortality Associated With COVID-19 by Demographic Group: Evidence From Florida and Ohio

Public Health Reports ◽

10.1177/00333549211041550 ◽

2021 ◽

pp. 003335492110415

Author(s):

Troy Quast ◽

Ross Andel

Keyword(s):

Excess Mortality ◽

Prediction Interval ◽

National Level ◽

Age Groups ◽

Black Population ◽

Infection Rates ◽

Black Adults ◽

Demographic Groups ◽

Younger Age ◽

Excess Deaths

Objective COVID-19 mortality varies across demographic groups at the national level, but little is known about potential differences in COVID-19 mortality across states. The objective of this study was to estimate the number of all-cause excess deaths associated with COVID-19 in Florida and Ohio overall and by sex, age, and race. Methods We calculated the number of weekly and cumulative excess deaths among adults aged ≥20 from March 15 through December 5, 2020, in Florida and Ohio as the observed number of deaths less the expected number of deaths, adjusted for population, secular trends, and seasonality. We based our estimates on death certificate data from the previous 10 years. Results The results were based on ratios of observed-to-expected deaths. The ratios were 1.17 (95% prediction interval, 1.14-1.21) in Florida and 1.15 (95% prediction interval, 1.11-1.19) in Ohio. Although the largest number of excess deaths occurred in the oldest age groups, in both states the ratios of observed-to-expected deaths were highest among adults aged 20-49 (1.21; 95% prediction interval, 1.11-1.32). The ratio of observed-to-expected deaths for the Black population was especially elevated in Florida. Conclusions Although excess deaths were largely concentrated among older cohorts, the high ratios of observed-to-expected deaths among younger age groups indicate widespread effects of COVID-19. The high levels of observed-to-expected deaths among Black adults may reflect in part disparities in infection rates, preexisting conditions, and access to care. The finding of high excess deaths among Black adults deserves further attention.

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Abstract 6082: Sex Differences in Mortality after Acute Myocardial Infarction: Changes from 1994 to 2006

Circulation ◽

10.1161/circ.118.suppl_18.s_1057-b ◽

2008 ◽

Vol 118 (suppl_18) ◽

Author(s):

Viola Vaccarino ◽

Lori Parsons ◽

Eric D Peterson ◽

William J Rogers ◽

Catarina Kiefe ◽

...

Keyword(s):

Myocardial Infarction ◽

Hospital Mortality ◽

Excess Mortality ◽

Age Groups ◽

Case Fatality ◽

Patient Characteristics ◽

National Registry ◽

Mortality Trends ◽

Younger Women ◽

Study Population

Introduction. In the past 10 years, several studies have shown that younger, but not older, women have a higher hospital mortality than age-matched men. We examined whether such mortality differences have declined in recent years. Methods. We investigated temporal tends in the case fatality of MI according to sex and age (in 5 age groups) during a12-year period, 1994 to 2006. The study population included 916,380 MI patients from the National Registry of Myocardial Infarction (NRMI) who had a confirmed diagnosis of MI. Results. Hospital mortality declined markedly between 1994 and 2006 in all patients, but more so in women than in men in virtually every age group. The mortality reduction in 2006 relative to 1994 was largest in women <55 years old (53%) and lowest in men <55 years old (33%). In patients <55 years, the absolute decline in mortality was 3 times larger in women than in men (2.7% vs 0.9%). The sex difference in mortality decline became progressively lower in older patients (p=0.002 for the interaction between sex, age and year). As a result, the excess mortality in younger women compared with men was less pronounced in 2004 – 06 than in 1994 –95 (Figure ). Changes in patient characteristics and treatments over time accounted in part for these mortality trends. Conclusion. In recent years, women, particularly younger women, experienced larger improvements in hospital mortality after MI than men. As a result, the higher mortality of younger MI women compared with men has narrowed.

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Population Involvement in Dealing with Local Community Problems in the Rural Areas of Latvia

10.22616/esrd.2021.55.060 ◽

2021 ◽

Author(s):

Modrite Pelse ◽

◽

Liga Svanberga ◽

Arianna Todorova ◽

Sabine Berzina ◽

...

Keyword(s):

Young People ◽

Rural Areas ◽

Public Involvement ◽

Local Community ◽

National Level ◽

Age Groups ◽

Surrounding Area ◽

The Public ◽

Active Population ◽

Municipal Employee

The public prefers to express their opinions on the development of the surrounding area, make assessments and comments, as well as participate in surveys. However, the involvement of the public itself in improving the immediate surroundings and in solving the problems of its fellows is not always sufficient. The research aims to determine whether there are differences in public involvement in addressing municipal problems across various population groups within a municipality. The paper presents the results of an extensive survey. The research considered problems within one municipality in Latvia – Jelgava municipality – and analysed the rural territories located in the immediate vicinity of the centre of the municipality as well as those being the furthest from the centre. The results of the research revealed that young people were most satisfied with their lives in their municipality if their places of residence were closer to the centre of the municipality. The ability to influence the decisions of one’s own local government was highly valued by residents in the age group from 26 to 44 years in the rural territories that were in the immediate vicinity of the centre the municipality, yet this possibility was most often rated as weak among the youth living in the most remote rural territories from the centre of the municipality. Population involvement in solving a problem relevant to the society was the most frequently used way when the population requested a municipal employee to solve this problem. A large segment of the society in rural areas admitted that they did nothing, and this passivity was also evident in the group of young people who lived further away from the centre of the municipality. The involvement of the population in national-level public activities across all age groups and territories was quite equal, as the active population were involved in Saeima elections, campaigns for collecting signatures and donating various thing

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Subnational mortality estimates for India in 2019: a baseline for evaluating excess deaths due to the COVID-19 pandemic

BMJ Global Health ◽

10.1136/bmjgh-2021-007399 ◽

2021 ◽

Vol 6 (11) ◽

pp. e007399

Author(s):

Chalapati Rao ◽

Amrit Jose John ◽

Ajit Kumar Yadav ◽

Mansha Siraj

Keyword(s):

Excess Mortality ◽

National Level ◽

Age Groups ◽

Uttar Pradesh ◽

The Elderly ◽

Registration System ◽

Infant Deaths ◽

Civil Registration ◽

The Impact ◽

Mortality Estimates

BackgroundEstimates of excess mortality are required to assess and compare the impact of the COVID-19 pandemic across populations. For India, reliable baseline prepandemic mortality patterns at national and subnational level are necessary for such assessments. However, available data from the Civil Registration System (CRS) is affected by incompleteness of death recording that varies by sex, age and location.MethodsUnder-reporting of CRS 2019 deaths was assessed for three age groups (< 5 years, 15–59 years and ≥60 years) at subnational level, through comparison with age-specific death rates from alternate sources. Age-specific corrections for under-reporting were applied to derive adjusted death counts by sex for each location. These were used to compute life expectancy (LE) at birth by sex in 2019, which were compared with subnational LEs from the Global Burden of Disease (GBD) 2019 Study.ResultsA total of 9.92 million deaths (95% UI 9.70 to 10.02) were estimated across India in 2019, about 2.28 million more than CRS reports. Adjustments to under-five and elderly mortality accounted for 30% and 56% of additional deaths, respectively. Adjustments in Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh accounted for 75% of all additional deaths. Adjusted LEs were below corresponding GBD estimates by ≥2 years for males at national level and in 20 states, and by ≥1 year for females in 12 states.ConclusionsThese results represent the first-ever subnational mortality estimates for India derived from CRS reported deaths, and serve as a baseline for assessing excess mortality from the COVID-19 pandemic. Adjusted life expectancies indicate higher mortality patterns in India than previously perceived. Under-reporting of infant deaths and those among women and the elderly is evident in many locations. Further CRS strengthening is required to improve the empirical basis for local mortality measurement across the country.

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Impact of COVID-19 on mortality in Croatia

Stanovnistvo ◽

10.2298/stnv2101001c ◽

2021 ◽

Vol 59 (1) ◽

pp. 1-16

Author(s):

Ivan Cipin ◽

Dario Mustac ◽

Petra Medjimurec

Keyword(s):

Life Expectancy ◽

Poisson Regression ◽

Excess Mortality ◽

Age Groups ◽

Poisson Regression Model ◽

Decomposition Techniques ◽

Expected Number ◽

The Difference ◽

Age And Sex ◽

Related Mortality

The main goal of this paper is to assess the effects of the COVID-19 pandemic on mortality in Croatia. We estimate two effects of the pandemic on mortality: (1) excess mortality during 2020 and (2) the age- and cause-specific components of life expectancy decline in 2020. We calculate excess mortality in 2020 as the difference between the registered number of deaths in 2020 and the expected number of deaths from a Poisson regression model based on weekly death counts and population exposures by age and sex from 2016 to 2019. Using decomposition techniques, we estimate age- and cause-specific components (distinguishing COVID-19-related deaths from deaths from other causes) of life expectancy decline in 2020. Our results show that excess mortality in 2020 almost entirely results from the second, autumn-winter wave of the epidemic in Croatia. Expectedly, we find the highest excess in deaths in older age groups. In Croatia, life expectancy in 2020 fell by almost eight months for men and about seven months for women. This decline is mostly attributable to COVID-19-related mortality in older ages, especially among men.

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Spatial determinants of excess all-cause mortality during the first wave of the COVID-19 epidemic in France

BMC Public Health ◽

10.1186/s12889-021-12203-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Hugo Pilkington ◽

Thierry Feuillet ◽

Stéphane Rican ◽

Jeanne Goupil de Bouillé ◽

Olivier Bouchaud ◽

...

Keyword(s):

Primary Healthcare ◽

Excess Mortality ◽

Social Deprivation ◽

National Level ◽

Age Groups ◽

Geographical Location ◽

Age Group ◽

Contextual Variables ◽

Complex Situation ◽

All Cause Mortality

Abstract Background The first wave of the COVID-19 pandemic in France was associated with high excess mortality, and anecdotal evidence pointed to differing excess mortality patterns depending on social and environmental determinants. In this study we aimed to investigate the spatial distribution of excess mortality during the first wave of the COVID-19 pandemic in France and relate it at the subnational level to contextual determinants from various dimensions (socioeconomic, population density, overall health status, healthcare access etc.). We also explored whether the determinants identified at the national level varied depending on geographical location. Methods We used available national data on deaths in France to calculate excess mortality by department for three age groups: 0–49, 50–74 and > 74 yrs. between March 1st and April 27th, 2020. We selected 15 variables at the department level that represent four dimensions that may be related to overall mortality at the ecological level, two representing population-level vulnerabilities (morbidity, social deprivation) and two representing environmental-level vulnerabilities (primary healthcare supply, urbanization). We modelled excess mortality by age group for our contextual variables at the department level. We conducted both a global (i.e., country-wide) analysis and a multiscale geographically weighted regression (MGWR) model to account for the spatial variations in excess mortality. Results In both age groups, excess all-cause mortality was significantly higher in departments where urbanization was higher (50–74 yrs.: β = 15.33, p < 0.001; > 74 yrs.: β = 18.24, p < 0.001) and the supply of primary healthcare providers lower (50–74 yrs.: β = − 8.10, p < 0.001; > 74 yrs.: β = − 8.27, p < 0.001). In the 50–74 yrs. age group, excess mortality was negatively associated with the supply of pharmacists (β = − 3.70, p < 0.02) and positively associated with work-related mobility (β = 4.62, p < 0.003); in the > 74 yrs. age group our measures of deprivation (β = 15.46, p < 0.05) and morbidity (β = 0.79, p < 0.008) were associated with excess mortality. Associations between excess mortality and contextual variables varied significantly across departments for both age groups. Conclusions Public health strategies aiming at mitigating the effects of future epidemics should consider all dimensions involved to develop efficient and locally tailored policies within the context of an evolving, socially and spatially complex situation.

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Narrowing gap in regional and age-specific excess mortality in the first year and a half of COVID-19 in Hungary

10.1101/2022.01.05.22268786 ◽

2022 ◽

Author(s):

Csaba G. Toth

Keyword(s):

Mortality Rate ◽

Excess Mortality ◽

Age Groups ◽

Vaccination Schedule ◽

First Year ◽

Third Wave ◽

The Third ◽

Seasonal Flu ◽

Almost All ◽

Second Wave

In the first year and a half of the pandemic, the excess mortality in Hungary was 28,400, which was 1,700 lower than the official statistics on COVID-19 deaths. This discrepancy can be partly explained by protective measures instated during the COVID-19 pandemic that decreased the intensity of the seasonal flu outbreak, which caused on average 3,000 deaths per year. Compared to the second wave of the COVID-19 pandemic, the third wave showed a reduction in the differences in excess mortality between age groups and regions. The excess mortality rate for people aged 75+ fell significantly in the third wave, partly due to the vaccination schedule and the absence of a normal flu season. For people aged 40-77, the excess mortality rate rose slightly in the third wave. Between regions, excess mortality was highest in Northern Hungary and Western Transdanubia, and much lower in Central Hungary, where the capital is located. The excess mortality rate for men was almost twice as high as that for women in almost all age groups.

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Analysis on the damage, vulnerability and correlation with temperature caused by heat waves in Jeju province(Korea), and Heatwave Impact Based Forecast and Warning Service(HIBFWS)

10.5194/egusphere-egu2020-6292 ◽

2020 ◽

Author(s):

Sanghyun Kim

Keyword(s):

Excess Mortality ◽

Heat Waves ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Heat Illness ◽

Environmental Vulnerability ◽

Risk Levels ◽

The Public ◽

Number Of Patients

In Korea, severe heat waves are frequent in summer, and the number of people who affected by them increases year by year. This study analyzes the correlation between excess mortality and the daily maximum temperature(Tmax) in August for the last decade(2009-2018). In addition, it analyzes Tmax when the patients by heat illness occur. The analysis shows a positive correlation(R=0.524, P=0.02) between the number of excess mortality and Tmax. In terms of patients by heat waves, the patients occur variously from 26&#8451; to 39&#8451;, and the maximum number of patients appears in 34~35&#8451;. In case of the duration of Tmax &#8805; 33&#8451;, the number of patients shows a peak at entrance of the period, and it drops after the 4th day and no patients showing after the 9th day. But, in case of Tmax < 33&#8451;, the heat illness in the 4th day occurs more than any other days, and it decreases slowly. In addition, it seems that it is not enough for the public to recognize accurately and respond risks appropriately with current temperature forecasts, so the Korea Meteorological Administration provides HIBFWS which includes countermeasures along with regional risk levels for the heatwave. Also, it analyzes socio-economic-environmental vulnerability for production of the information in Jeju province.

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Pooling European all-cause mortality: methodology and findings for the seasons 2008/2009 to 2010/2011

Epidemiology and Infection ◽

10.1017/s0950268812002580 ◽

2012 ◽

Vol 141 (9) ◽

pp. 1996-2010 ◽

Cited By ~ 11

Author(s):

J. NIELSEN ◽

A. MAZICK ◽

N. ANDREWS ◽

M. DETSIS ◽

T. M. FENECH ◽

...

Keyword(s):

Influenza A ◽

Excess Mortality ◽

National Level ◽

Age Groups ◽

Pooled Analysis ◽

European Countries ◽

Younger Age ◽

Influenza A H1n1 ◽

All Cause Mortality ◽

Fertile Women

SUMMARYSeveral European countries have timely all-cause mortality monitoring. However, small changes in mortality may not give rise to signals at the national level. Pooling data across countries may overcome this, particularly if changes in mortality occur simultaneously. Additionally, pooling may increase the power of monitoring populations with small numbers of expected deaths, e.g. younger age groups or fertile women. Finally, pooled analyses may reveal patterns of diseases across Europe. We describe a pooled analysis of all-cause mortality across 16 European countries. Two approaches were explored. In the ‘summarized’ approach, data across countries were summarized and analysed as one overall country. In the ‘stratified’ approach, heterogeneities between countries were taken into account. Pooling using the ‘stratified’ approach was the most appropriate as it reflects variations in mortality. Excess mortality was observed in all winter seasons albeit slightly higher in 2008/09 than 2009/10 and 2010/11. In the 2008/09 season, excess mortality was mainly in elderly adults. In 2009/10, when pandemic influenza A(H1N1) dominated, excess mortality was mainly in children. The 2010/11 season reflected a similar pattern, although increased mortality in children came later. These patterns were less clear in analyses based on data from individual countries. We have demonstrated that with stratified pooling we can combine local mortality monitoring systems and enhance monitoring of mortality across Europe.

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