Causes of death in Germany: A time series analysis of official statistics from 1990 to 2020

Abstract Background The analysis of the temporal patterns of causes of death is one of the most important tasks in population health monitoring and forecasts. In the present study, a detailed time series analysis of official statistics is performed in order to identify major temporal trends in the distribution of health risks in the German population. Methods Official statistics on causes of death from 1990 to 2020 are utilised. The causes of death are classified according to the International Classification of Diseases (10th Revision). Temporal trends of death cases per 100,000 population and ten-year forecasts are estimated with integrated autoregressive moving average models (ARIMA). Results Cardiovascular diseases, neoplasms and cerebrovascular diseases have accounted for more than 70% of all deaths between 1990-2020. In contrast, urogenital, infectious and muscular-related diseases have been reported for less than 2% of deaths during the same period. Annual deaths per 100,000 population due to cardiovascular and cerebrovascular diseases largely decreased between 1990-2020 (-11.07 95% CI [-15.17; -6.97] and -4.02 95% CI [-6.85; -1.20], respectively). Concerning other causes of deaths, no temporal trends were observed, with the exception of diseases of the nervous and digestive system (0.83 95% CI [0.08; 1.58]). The forecasts for the decade 2020-2030 suggest that cardiovascular diseases and neoplasms are expected to remain the most frequent causes of death in Germany and could account for about 67% of all deaths. Conclusions The results indicate that non-communicable diseases, in particular the group of cardiovascular diseases and neoplasms, will remain the major driver of mortality in Germany over the next decade. Key messages Cardiovascular health has greatly improved over the last decades in Germany. Non-communicable diseases are likely to remain the main drivers of morbidity and mortality over the next decade.

To achieve the goal of sustainable employment, considering the profile of the Portuguese working population (PWP), is needed a range of strategies to ensure long, productive, and sustainable careers allied with a better quality of working life, health, and wellbeing, but also with public health policies grounded on scientifically validated and reliable data. This is possible through a comprehensive working system approach that ensures workers will be mentally and physically able to remain at work by the balance between work demands and individual resources allied with public health policies transfer into the workplaces by organizations' leadership and policy makers. The Portuguese Observatory on Occupational Factors (Popsy@Work) aims at addressing this global challenge by: i) digitally collecting psychosocial data on the PWP; ii) implementing and strengthening of a psychosocial occupational health surveillance digital system; iii) providing reference values for the PWP concerning Psychosocial Health; iv) Transferring to society knowledge and best practices; v) Raising awareness on the importance of psychosocial management in occupational settings based on science. Popsy@work is a digital platform that collects and aggregates psychosocial data analytically and creates a visualization hub adding value to data on the PWP and giving science back to society in a usable way, empowering workers, strengthening organizations and grounding public policies. Pospy@Work considers the development of strategic intelligence on levels and inequalities of psychosocial health and well-being in occupational settings by robust metrics and reference data. Creating opportunities for national policy dialogue on inequalities, including the psychosocial health of Improvements in life expectancy have slowed in high income countries, with uncertain causes. We assessed the contribution of different causes of death to changes in life expectancy, and changes in population exposure to major risk factors in 16 European Economic Area countries plus the 4 nations of the United Kingdom from 1990-2013 and 2013-2019, using the Global Burden of Disease Study. After decades of steady improvements in life expectancy, all countries experienced smaller annual improvements after 2013. Norway experienced the smallest mean annual rate of change in improvement from pre 2013 to post 2013 of 0.03 years, and Northern Ireland (followed closely by Scotland and England) experienced the largest annual reduction from pre to post 2013 of 0.25 years. The cause of death responsible for the largest reductions in life expectancy improvements after 2013 was cardiovascular disease, followed by neoplasms. The largest reductions in deaths from cardiovascular disease were attributable to seven major risk factors: high LDL cholesterol, tobacco, dietary risks, high fasting plasma glucose, high systolic blood pressure, high body mass index, and low physical activity. The risk factors for deaths from neoplasm were similar. Exposure to tobacco remains a high risk but exposure declined steadily. Exposure to the other risks generally worsened after 2013, particularly for BMI and high fasting plasma glucose. The European countries that had better maintained reductions in deaths from cardiovascular disease and neoplasms also experienced larger improvements in life expectancy. These changes were underpinned by changing exposure to major risks. Policy responses to the slowdown in life expectancy improvements should include reducing population exposure to major risks, including the broader risks from diet and low physical activity, through prevention and addressing the broad social and commercial determinants of health as well as adequate funding for health services.

Background:
The analysis of the temporal patterns of causes of death is one of the most important tasks in population health monitoring and forecasts. In the present study, a detailed time series analysis of official statistics is performed in order to identify major temporal trends in the distribution of health risks in the German population.

Methods:
Official statistics on causes of death from 1990 to 2020 are utilised. The causes of death are classified according to the International Classification of Diseases (10th Revision). Temporal trends of death cases per 100,000 population and ten-year forecasts are estimated with integrated autoregressive moving average models (ARIMA). Results: Cardiovascular diseases, neoplasms and cerebrovascular diseases have accounted for more than 70% of all deaths between 1990-2020. In contrast, urogenital, infectious and muscularrelated diseases have been reported for less than 2% of deaths during the same period. Annual deaths per 100,000 population due to cardiovascular and cerebrovascular diseases largely decreased between 1990-2020 (-11.07 95% CI [-15.17; -6.97] and -4.02 95% CI [-6.85; -1.20], respectively). Concerning other causes of deaths, no temporal trends were observed, with the exception of diseases of the nervous and digestive system (0.83 95% CI [0.08; 1.58]). The forecasts for the decade 2020-2030 suggest that cardiovascular diseases and neoplasms are expected to remain the most frequent causes of death in Germany and could account for about 67% of all deaths.

Background:
Multimorbidity (MM) is associated with lower quality of life, greater disability, and higher use of health services. It is a complex problem that is difficult to capture due to the broad spectrum of concurrent chronic diseases involved. There is a need to identify and characterize patterns of chronic conditions in the local context of specific population groups. The DEMMOCAD project aims to respond to this knowledge gap by detecting patterns of MM and their inequalities in the province of Cadiz (Spain).

Methods:
A cross-sectional study was carried out by means of telephone interviews with people over 50 years of age. The final sample was 1592 individuals with MM. A latent class analysis was carried out to identify patterns of MM from 31 chronic conditions. First, the appropriate number of classes was established, considering model fit indices, class size, and clinical interpretability. Subsequently, covariates were introduced into the model using the three-step approach, a technique that minimizes biases in the multinomial regression model.

Results:
Preliminary analyses of the goodness-of-fit indices of the model derived five MM patterns (entropy = 0.727): (C1) mild MM; (C2) cardiovascular; (C3) musculoskeletal; (C4) musculoskeletal plus mental; and (C5) complex MM. Compared with class C1, persons in class C5 were significantly older and less educated, class C4 had a lower income, class C3 was smokers and disabled, and class C2 was characteristic among older males. All four classes also showed lower scores on mental and physical dimensions of the SF12 scale compared to class C1.

Conclusions:
In addition to providing an adjusted characterization of the population of the area analyzed, these initial findings highlight the existence of social inequalities in multimorbidity at the local level that should be addressed by implementing policies targeting the most vulnerable groups in Cadiz (low socioeconomic status groups, people with disabilities, and the elderly).

Key messages:
Five patterns of multimorbidity were identified in the province of Cadiz (Spain). Tailored policies are needed to reduce social inequalities in multimorbidity among vulnerable groups in this local area.