The mortality burden related to COVID-19 in 2020 and 2021 - years of life lost and excess mortality in 13 countries and sub-national regions in Southern and Eastern Europe, and Central Asia.

Introduction: Between 2021 and 2023, a project was funded in order to explore the mortality burden (YLL-Years of Life Lost, excess mortality) of COVID-19 in Southern and Eastern Europe, and Central Asia. Methods: For each national or sub-national region, data on COVID-19 deaths and population data were collected for the period March 2020 to December 2021. Unstandardized and age-standardised YLL rates were calculated according to standard burden of disease methodology. In addition, all-cause mortality data for the period 2015-2019 were collected and used as a baseline to estimate excess mortality in each national or sub-national region in the years 2020 and 2021. Results: On average, 15-30 years of life were lost per death in the various countries and regions. Generally, YLL rates per 100,000 were higher in countries and regions in Southern and Eastern Europe compared to Central Asia. However, there were differences in how countries and regions defined and counted COVID-19 deaths. In most countries and sub-national regions, YLL rates per 100,000 (both age-standardised and unstandardized) were higher in 2021 compared to 2020, and higher amongst men compared to women. Some countries showed high excess mortality rates, suggesting under-diagnosis or under-reporting of COVID-19 deaths, and/or relatively large numbers of deaths due to indirect effects of the pandemic. Conclusion: Our results suggest that the COVID-19 mortality burden was greater in many countries and regions in Southern and Eastern Europe compared to Central Asia. However, heterogeneity in the data (differences in the definitions and counting of COVID-19 deaths) may have influenced our results. Understanding possible reasons for the differences was difficult, as many factors are likely to play a role (e.g., differences in the extent of public health and social measures to control the spread of COVID-19, differences in testing strategies and/or vaccination rates). Future cross-country analyses should try to develop structured approaches in an attempt to understand the relative importance of such factors. Furthermore, in order to improve the robustness and comparability of burden of disease indicators, efforts should be made to harmonise case definitions and reporting for COVID-19 deaths across countries.

Age-specific and cause-specific mortality contributions to the socioeconomic gap in life expectancy in Germany, 2003-21: an ecological study.

BACKGROUND: Earlier death among people in socioeconomically deprived circumstances has been found internationally and for various causes of death, resulting in a considerable life-expectancy gap between socioeconomic groups. We examined how age-specific and cause-specific mortality contributions to the socioeconomic gap in life expectancy have changed at the area level in Germany over time. METHODS: In this ecological study, official German population and cause-of-death statistics provided by the Federal Statistical Office of Germany for the period Jan 1, 2003, to Dec 31, 2021, were linked to district-level data of the German Index of Socioeconomic Deprivation. Life-table and decomposition methods were applied to calculate life expectancy by area-level deprivation quintile and decompose the life-expectancy gap between the most and least deprived quintiles into age-specific and cause-specific mortality contributions. FINDINGS: Over the study period, population numbers varied between 80 million and 83 million people per year, with the number of deaths ranging from 818 000 to 1 024 000, covering the entire German population. Between Jan 1, 2003, and Dec 31, 2019, the gap in life expectancy between the most and least deprived quintiles of districts increased by 0·7 years among females (from 1·1 to 1·8 years) and by 0·1 years among males (from 3·0 to 3·1 years). Thereafter, during the COVID-19 pandemic, the gap increased more rapidly to 2·2 years in females and 3·5 years in males in 2021. Between 2003 and 2021, the causes of death that contributed the most to the life-expectancy gap were cardiovascular diseases and cancer, with declining contributions of cardiovascular disease deaths among those aged 70 years and older and increasing contributions of cancer deaths among those aged 40-74 years over this period. COVID-19 mortality among individuals aged 45 years and older was the strongest contributor to the increase in life-expectancy gap after 2019. INTERPRETATION: To reduce the socioeconomic gap in life expectancy, effective efforts are needed to prevent early deaths from cardiovascular disease and cancer in socioeconomically deprived populations, with cancer prevention and control becoming an increasingly important field of action in this respect. FUNDING: German Cancer Aid and European Research Council.

The Burden of Disease due to COVID-19 (BoCO-19): A study protocol for a secondary analysis of surveillance data in Southern and Eastern Europe, and Central Asia.

INTRODUCTION: The COVID-19 pandemic has had an extensive impact on public health worldwide. However, in many countries burden of disease indicators for COVID-19 have not yet been calculated or used for monitoring. The present study protocol describes an approach developed in the project "The Burden of Disease due to COVID-19. Towards a harmonization of population health metrics for the surveillance of dynamic outbreaks" (BoCO-19). The process of data collection and aggregation across 14 different countries and sub-national regions in Southern and Eastern Europe and Central Asia is described, as well as the methodological approaches used. MATERIALS AND METHODS: The study implemented in BoCO-19 is a secondary data analysis, using information from national surveillance systems as part of mandatory reporting on notifiable diseases. A customized data collection template is used to gather aggregated data on population size as well as COVID-19 cases and deaths. Years of life lost (YLL), as one component of the number of Disability Adjusted Life Years (DALY), are calculated as described in a recently proposed COVID-19 disease model (the 'Burden-EU' model) for the calculation of DALY. All-cause mortality data are collected for excess mortality sensitivity analyses. For the calculation of Years lived with disability (YLD), the Burden-EU model is adapted based on recent evidence. Because Covid-19 cases vary in terms of disease severity, the possibility and suitability of applying a uniform severity distribution of cases across all countries and sub-national regions will be explored. An approach recently developed for the Global Burden of Disease Study, that considers post-acute consequences of COVID-19, is likely to be adopted. Findings will be compared to explore the quality and usability of the existing data, to identify trends across age-groups and sexes and to formulate recommendations concerning potential improvements in data availability and quality. DISCUSSION: BoCO-19 serves as a collaborative platform in order to build international capacity for the calculation of burden of disease indicators, and to support national experts in the analysis and interpretation of country-specific data, including their strengths and weaknesses. Challenges include inherent differences in data collection and reporting systems between countries, as well as assumptions that have to be made during the calculation process.

Recommendations for collecting and analysing migration-related determinants in public health research.

Background: According to the definition of the German Federal Statistical Office, about every fourth person living in Germany has a so-called migration background (MB), i.e., the person or at least one of their parents was born without German citizenship. However, MB has been defined differently in many studies. Also, the MB summarises people in different living situations, making differentiated analysis in health science more difficult. This article formulates recommendations for the collection and analysis of migration-related, as well as social and structural, determinants of health. Indicators for capturing relevant determinants of health: As part of the Improving Health Monitoring in Migrant Populations project (IMIRA), the previous approaches to operationalise and measure migration-related determinants were revised based on literature research and exchange formats, such as workshops, meetings, congress contributions, etc. Instead of MB, the country of birth of the respondents and their parents, duration of residence, citizenship(s), residence status, and German language proficiency should be recorded as minimum indicators and analysed as individual variables. Further social and structural determinants, such as socioeconomic position, working and housing conditions, or self-reported discrimination, should be included. Conclusions: In order to describe health inequalities and to specifically identify the needs of people with a history of migration, a mutual and differentiated consideration of migration-related and social determinants of health is essential.

The Burden of Disease in Germany at the National and Regional Level.

BACKGROUND: .Summary measures such as disability-adjusted life years (DALY) are becoming increasingly important for the standardized assessment of the burden of disease due to death and disability. The BURDEN 2020 pilot project was designed as an independent burden-of-disease study for Germany, which was based on nationwide data, but which also yielded regional estimates. METHODS: DALY is defined as the sum of years of life lost due to death (YLL) and years lived with disability (YLD). YLL is the difference between the age at death due to disease and the remaining life expectancy at this age, while YLD quantifies the number of years individuals have spent with health impairments. Data are derived mainly from causes of death statistics, population health surveys, and claims data from health insurers. RESULTS: In 2017, there were approximately 12 million DALY in Germany, or 14 584 DALY per 100 000 inhabitants. Conditions which caused the greatest number of DALY were coronary heart disease (2321 DALY), low back pain (1735 DALY), and lung cancer (1197 DALY). Headache and dementia accounted for a greater disease burden in women than in men, while lung cancer and alcohol use disorders accounted for a greater disease burden in men than in women. Pain disorders and alcohol use disorders were the leading causes of DALY among young adults of both sexes. The disease burden rose with age for some diseases, including cardiovascular diseases, dementia, and diabetes mellitus. For some diseases and conditions, the disease burden varied by geographical region. CONCLUSION: The results indicate a need for age- and sex-specific prevention and for differing interventions according to geographic region. Burden of disease studies yield comprehensive population health surveillance data and are a useful aid to decision-making in health policy.

Burden of Disease of COVID-19: Strengthening the Collaboration for National Studies.

Objectives: Quantifying the combined impact of morbidity and mortality is a key enabler to assessing the impact of COVID-19 across countries and within countries relative to other diseases, regions, or demographics. Differences in methods, data sources, and definitions of mortality due to COVID-19 may hamper comparisons. We describe efforts to support countries in estimating the national-level burden of COVID-19 using disability-adjusted life years. Methods: The European Burden of Disease Network developed a consensus methodology, as well as a range of capacity-building activities to support burden of COVID-19 studies. These activities have supported 11 national studies so far, with study periods between January 2020 and December 2021. Results: National studies dealt with various data gaps and different assumptions were made to face knowledge gaps. Still, they delivered broadly comparable results that allow for interpretation of consistencies, as well as differences in the quantified direct health impact of the pandemic. Discussion: Harmonized efforts and methodologies have allowed for comparable estimates and communication of results. Future studies should evaluate the impact of interventions, and unravel the indirect health impact of the COVID-19 crisis.

The COVID-19 Disease Burden in Germany in 2020­Years of Life Lost to Death and Disease Over the Course of the Pandemic.

BACKGROUND: The SARS-CoV-2 pandemic presented major challenges to the health sector in 2020. The burden of disease arising from COVID-19 can be expressed as the number of years of life lost to disease or death. For example, death at age 40 involves a loss of far more years of life than death at age 80. METHODS: The disability-adjusted life years (DALY) lost to COVID-19 were calculated as the sum of the years of life lost through death (YLL) and the number of years lived with disability (YLD), on the basis of laboratory-confirmed notifiable cases of SARS-CoV-2 infection in Germany in 2020 (documented as of 18 January 2021). The methodology was based on that used in the Global Burden of Disease Study. Pre-existing diseases do not enter into the determination of YLL; rather, the residual life expectancy that is applied in this calculation corresponds to a mean age-specific level of morbidity. RESULTS: 305 641 years of life were lost to COVID-19 in Germany in 2020. The percentage of DALY lost by persons under 70 was 34.8% in men and 21.0% in women. 99.3% of the COVID-19 disease burden was accounted for by death (YLL). The daily average years of life lost due to death was lower for COVID-19 than for the major non-communicable diseases. Persons who died of COVID-19 lost a mean of 9.6 years of life; those who were under 70 when they died lost a mean of 25.2 years of life. Men lost more years of life than women (11.0 vs. 8.1 years). CONCLUSION: The effects of COVID-19 on public health can be expressed through the burden of disease indicators. This method yields additional information that should be put to use early in the course of future outbreaks.

[Migration and health in Germany-available data sources]. / Migration und Gesundheit in Deutschland ­ eine Bestandsaufnahme der Datenquellen.

BACKGROUND: Migration background plays an important role in analyses of health inequalities in Germany. The heterogeneity of people with and without migration background requires a differentiated recording of migration-related characteristics. The latest overview of representative data sources from the Health Reporting (GBE) that included information on migration background was compiled in 2008. AIM: The aim of this article is to describe existing data sources reporting the health situation of people with and without a migration background. MATERIALS AND METHODS: Starting from the websites and publications of owners of GBE data, representative studies and routine data sources were identified. All sources that consider at least one migration-related characteristic were included. For all included studies, migration-related characteristics, information on the social situation, and health-related indicators were collected. RESULTS: A total of 46 data sources (including 19 routine data sources and 27 studies) were included. The most common indicators of the migration background are nationality (n = 36) and the country of birth (n = 29). Health-related indicators cover a wide range of issues. DISCUSSION: Routine data sources continue to collect little information on the migration background (usually only nationality) and thus constrain migration-differentiated analyses of the health situation. Survey data allow for more nuanced analysis. However, the actual analysis possibilities and content knowledge of the respective data sources were not the subject of this article.

BURDEN 2020-Burden of disease in Germany at the national and regional level.

BACKGROUND: Evidence-based policy measures need non-interest-guided information about the health status of a population and the diseases that affect the population the most. In such cases, a national burden of disease study can provide reliable insights at the regional level. AIM: This article presents the potential of the BURDEN 2020 project and its expected outcome for Germany at the national and regional level. METHODS: The BURDEN 2020 project uses several indicators including years of life lost (YLL) to cover the impact of mortality and years lived with disability (YLD) to cover morbidity. The sum of both is the measure of population health called disability adjusted life years (DALY). RESULTS: The study ranks individual diseases and risk factors based on their impact on population health. The burden of disease approach is assumed to be sensitive to subnational differences and may generate immediate benefits for regional planning. The BURDEN 2020 study will pilot a national burden of disease study for Germany that will later be transformed into a continuous data processing and visualization tool. This is done by using, modifying and supplementing the methodology employed by the Global Burden of Disease (GBD) study to better fit the needs of health policy in Germany. This study is aimed at calculating the disease burden for up to 17 preselected diseases. Furthermore, the estimates of burden of disease are attributed to a selected set of risk factors. CONCLUSION: The Burden 2020 study will provide the results of a new, health-related data processing system to the public. This includes a noninterest-guided presentation of the burden of disease (DALY) in Germany at the national and regional level.

[Continuity of hospital identifiers in hospital discharge data - Analysis of the nationwide German DRG Statistics from 2005 to 2013]. / Kontinuität der Institutionskennzeichen in Krankenhausabrechnungsdaten ­ Analyse der bundesweiten DRG-Statistik von 2005 bis 2013.

BACKGROUND: In Germany, nationwide hospital discharge data (DRG statistics provided by the research data centers of the Federal Statistical Office and the Statistical Offices of the 'Länder') are increasingly used as data source for health services research. Within this data hospitals can be separated via their hospital identifier ([Institutionskennzeichen] IK). However, this hospital identifier primarily designates the invoicing unit and is not necessarily equivalent to one hospital location. Aiming to investigate direction and extent of possible bias in hospital-level analyses this study examines the continuity of the hospital identifier within a cross-sectional and longitudinal approach and compares the results to official hospital census statistics. METHODS: Within the DRG statistics from 2005 to 2013 the annual number of hospitals as classified by hospital identifiers was counted for each year of observation. The annual number of hospitals derived from DRG statistics was compared to the number of hospitals in the official census statistics 'Grunddaten der Krankenhäuser'. Subsequently, the temporal continuity of hospital identifiers in the DRG statistics was analyzed within cohorts of hospitals. RESULTS: Until 2013, the annual number of hospital identifiers in the DRG statistics fell by 175 (from 1,725 to 1,550). This decline affected only providers with small or medium case volume. The number of hospitals identified in the DRG statistics was lower than the number given in the census statistics (e.g., in 2013 1,550 IK vs. 1,668 hospitals in the census statistics). The longitudinal analyses revealed that the majority of hospital identifiers persisted in the years of observation, while one fifth of hospital identifiers changed. CONCLUSION: In cross-sectional studies of German hospital discharge data the separation of hospitals via the hospital identifier might lead to underestimating the number of hospitals and consequential overestimation of caseload per hospital. Discontinuities of hospital identifiers over time might impair the follow-up of hospital cohorts. These limitations must be taken into account in analyses of German hospital discharge data focusing on the hospital level.

The health status of first- and second-generation Turkish immigrants in Germany.

OBJECTIVES: Since previous research on immigrants' health status is rare, this study focuses on Turkish immigrants currently living in Germany and evaluates their subjective health status using relatively new data from the Generations and Gender Survey (2005/2006). METHODS: The cross-sectional study includes around 10,000 German natives and 4,000 Turkish immigrants. Logistic regression models are estimated to compare the health of first- and second-generation Turkish immigrants with that of German natives. Additionally, separate models for all three groups are estimated to detect variations in the factors influencing the health status. RESULTS: Whereas the descriptive results indicate a worse health status, Turkish immigrants are as healthy as the native German population when different variables related to socio-economic status and coping resources are taken into account. Furthermore, Turkish immigrants in East Germany are healthier than their East German counterparts. The factors influencing the health status do not vary for the observed groups. CONCLUSIONS: Socio-economic status and coping resources are key determinants of the (subjective) health status of Turkish immigrants. They must be strengthened to improve immigrants' level of health.