Projected Life Expectancy for Adolescents With HIV in the US.

Importance: Life expectancy is a key measure of overall population health. Life expectancy estimates for youth with HIV in the US are needed in the current HIV care and treatment context to guide health policies and resource allocation. Objective: To compare life expectancy between 18-year-old youth with perinatally acquired HIV (PHIV), youth with nonperinatally acquired HIV (NPHIV), and youth without HIV. Design, Setting, and Participants: Using a US-focused adolescent-specific Monte Carlo state-transition HIV model, we simulated individuals from age 18 years until death. We estimated probabilities of HIV treatment and care engagement, HIV progression, clinical events, and mortality from observational cohorts and clinical trials for model input parameters. The simulated individuals were 18-year-old race and ethnicity-matched youth with PHIV, youth with NPHIV, and youth without HIV; 47%, 85%, and 50% were assigned male sex at birth, respectively. Individuals were categorized by US Centers for Disease Control and Prevention-defined HIV acquisition risk: men who have sex with men, people who ever injected drugs, heterosexually active individuals at increased risk for HIV infection, or average risk for HIV infection. Distributions were 3%, 2%, 12%, and 83% for youth with PHIV and youth without HIV, and 80%, 6%, 14%, and 0% for youth with NPHIV, respectively. Among the simulated youth in this analysis, individuals were 61% Black, 24% Hispanic, and 15% White, respectively. Exposures: HIV status by timing of acquisition. Main Outcomes: Life expectancy loss for youth with PHIV and youth with NPHIV: difference between mean projected life expectancy under current and ideal HIV care scenarios compared with youth without HIV. Uncertainty intervals reflect varying adolescent HIV-related mortality inputs (95% CIs). Results: Compared with youth without HIV (life expectancy: male, 76.3 years; female, 81.7 years), male youth with PHIV and youth with NPHIV had projected life expectancy losses of 10.4 years (95% CI, 5.5-18.1) and 15.0 years (95% CI, 9.3-26.8); female youth with PHIV and youth with NPHIV had projected life expectancy losses of 11.8 years (95% CI, 6.4-20.2) and 19.5 years (95% CI, 13.8-31.6), respectively. When receiving ideal HIV care, life expectancy losses were projected to improve for youth with PHIV (male: 0.5 years [95% CI, 0.3-1.8]: female: 0.6 years [95% CI, 0.4-2.1]) but were projected to persist for youth with NPHIV (male: 6.0 years [95% CI, 5.0-9.1]; female: 10.4 years [95% CI, 9.4-13.6]). Conclusions: This adolescent-focused microsimulation modeling analysis projected that youth with HIV would have shorter life expectancy than youth without HIV. Projected differences were larger for youth with NPHIV compared with youth with PHIV. Differences in mortality by sex at birth, sexual behavior, and injection drug use contributed to lower projected life expectancy among youth with NPHIV. Interventions focused on HIV care and social factors are needed to improve life expectancy for youth with HIV in the US.

Life tables of annual life expectancy and risk factors for mortality in cats in the UK.

OBJECTIVES: The aims of the present study were to generate the first life tables for the UK companion cat population overall as well as broken down by sex and breed status, and to quantify associations between mortality and traits such as sex, neuter status, breed status and body weight in relation to mortality. METHODS: Life table construction and modelling included data on 7936 confirmed deaths in cats under primary veterinary care at clinics participating in the VetCompass Programme in 2019. The life tables were built for cats overall, female and male cats, and crossbred and purebred cats. Multivariable generalised linear regression models were generated to explore the risk factors for a shortened lifespan. RESULTS: Life expectancy at age 0 for UK companion cats overall was 11.74 years (95% confidence interval [CI] 11.61-11.87). The probability of death at each year interval increased with age from year interval 3-4, with the probability value not exceeding 0.05 before year 9. Female cats (12.51 years; 95% CI 12.32-12.69) had a 1.33-year longer life expectancy than male cats (11.18 years; 95% CI 11.01-11.38) at age 0. Among the 12 breeds (including crossbred) analysed, Burmese and Birman had the longest life expectancy at year 0, showing 14.42 years (95% CI 12.91-15.93) and 14.39 years (95% CI 12.87-15.91), respectively. Sphynx had the shortest life expectancy at year 0 among the analysed breeds at 6.68 years (95% CI 4.53-8.83). Being entire, purebred and with a non-ideal body weight were significantly linked to a decreased lifespan. CONCLUSIONS AND RELEVANCE: The life tables presented here for companion cats in the UK overall, by sex, and by crossbred and purebred cats can contribute to a better understanding of the life trajectory of cats, helping with evidence-based decision-making for cat owners and the veterinary profession. We have also provided an updated life expectancy at age 0 for various cat breeds for 2019 and showed evidence of the association between non-ideal weight and a decreased lifespan.

Mortality and years of life lost related to adverse drug events in Brazil.

OBJECTIVE: To assess regional and national mortality and years of life lost (YLL) related to adverse drug events in Brazil. METHODS: This is an ecological study in which death records from 2009 to 2018 from the Mortality Information System were analyzed. Codes from the International Classification of Diseases 10th revision (ICD-10) that indicated drugs as the cause of death were identified. The number of deaths and the YLL due to adverse drug events were obtained. Crude, age- and gender-specific, and age-adjusted mortality rates and YLL rates per 100,000 inhabitants were formed by year, age group, gender, and Brazilian Federative Unit. Rate ratios were calculated by comparing rates from 2009 to 2018. A joinpoint regression model was applied for temporal analysis. RESULTS: For the selected ICD-10 codes, a total of 95,231 deaths and 2,843,413 YLL were recorded. Mortality rates from adverse drug events increased by a mean of 2.5% per year, and YLL rates increased by 3.7%. Increases in rates were observed in almost all age groups for both genders. Variations in rates were found between Federative Units, with the highest age-adjusted mortality and YLL rates occurring in the Distrito Federal. CONCLUSIONS: The numbers and rates of deaths and YLL increased during the study period, and variations in rates of deaths and YLL were observed between Brazilian Federative Units. Information on multiple causes of death from death certificates can be useful for quantifying adverse drug events and analyzing them geographically, by age and by gender.

Trends of Gaps Between Health-Adjusted Life Expectancy and Life Expectancy at the Regional Level in Korea Using a Group-Based Multi-Trajectory Modeling Approach (2008-2019).

BACKGROUND: Health-adjusted life expectancy (HALE) is an indicator of the average lifespan in good health. Through this study, we aimed to identify regional disparities in the gap between HALE and life expectancy, considering the trends that have changed over time in Korea. METHODS: We employed a group-based multi-trajectory modeling approach to capture trends in the gap between HALE and life expectancy at the regional level from 2008 to 2019. HALE was calculated using incidence-based "years lived with disability." This methodology was also employed in the Korean National Burden of Disease Study. RESULTS: Based on five different information criteria, the most fitted number of trajectory groups was seven, with at least 11 regions in each group. Among the seven groups, one had an exceptionally large gap between HALE and life expectancy compared to that of the others. This group was assigned to 17 regions, of which six were metropolitan cities. CONCLUSION: Based on the results of this study, we identified regions in which health levels have deteriorated over time, particularly within specific areas of metropolitan cities. These findings can be used to design comprehensive policy interventions for community health promotion and urban regeneration projects in the future.

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 association between disability and mortality: a mixed-methods study.

BACKGROUND: Globally, 1·3 billion people have a disability and are more likely to experience poor health than the general population. However, little is known about the mortality or life expectancy gaps experienced by people with disabilities. We aimed to undertake a systematic review and meta-analysis of the association between disability and mortality, compare these findings to the evidence on the association of impairment types and mortality, and model the estimated life expectancy gap experienced by people with disabilities. METHODS: We did a mixed-methods study, which included a systematic review and meta-analysis, umbrella review, and life expectancy modelling. For the systematic review and meta-analysis, we searched MEDLINE, Global Health, PsycINFO, and Embase for studies published in English between Jan 1, 2007, and June 7, 2023, investigating the association of mortality and disability. We included prospective and retrospective cohort studies and randomised controlled trials with a baseline assessment of disability and a longitudinal assessment of all-cause mortality or cause-specific mortality. Two reviewers independently assessed study eligibility, extracted the data, and assessed risk of bias. We did a random-effects meta-analysis to calculate a pooled estimate of the mortality rate ratio for people with disabilities compared with those without disabilities. We did an umbrella review of meta-analyses examining the association between different impairment types and mortality. We used life table modelling to translate the mortality rate ratio into an estimate of the life expectancy gap between people with disabilities and the general population. The systematic review and meta-analysis is registered with PROSPERO, CRD42023433374. FINDINGS: Our search identified 3731 articles, of which 42 studies were included in the systematic review. The meta-analysis included 31 studies. Pooled estimates showed that all-cause mortality was 2·24 times (95% CI 1·84-2·72) higher in people with disabilities than among people without disabilities, although heterogeneity between the studies was high (τ2=0·28, I2=100%). Modelling indicated a median gap in life expectancy of 13·8 years (95% CI 13·1-14·5) by disability status. Cause-specific mortality was also higher for people with disabilities, including for cancer, COVID-19, cardiovascular disease, and suicide. The umbrella review identified nine meta-analyses, which showed consistently elevated mortality rates among people with different impairment types. INTERPRETATION: Mortality inequities experienced by people with disabilities necessitate health system changes and efforts to address inclusion and the social determinants of health. FUNDING: National Institute for Health and Care Research, Rhodes Scholarship, Indonesia Endowment Funds for Education, Foreign, Commonwealth and Development Office (Programme for Evidence to Inform Disability Action), and the Arts and Humanities Research Council.

[Is mortality an objective indicator for measuring the impact of an epidemic?: The example of Covid-19 in Switzerland]. / La mortalité est-elle un indicateur objectif pour mesurer l'impact d'une épidémie ?. L'exemple du Covid-19 en Suisse.

Measuring the health impact of an epidemic using appropriate indicators is necessarily complex. Mortality does not sum up all the issues, but at least it seems to be an objective indicator. There are, however, a number of different mortality indicators, which do not all convey the same message. During the Covid-19 epidemic in Switzerland, the mortality rate rose by 10.2% in 2020, while life expectancy fell by "only" 0.8%, or 8.3 months, a decline described as "modest" or "complete freefall" depending on when it was published. In reality, the population living in Switzerland in 2020 lost an average of "only" 2.4 days, as the epidemic did not last their entire lives. The use of such an indicator, in comparison with losses due to other factors, would enable us to better estimate the real impact of an epidemic.

Mesurer l'impact sanitaire d'une épidémie à l'aide d'indicateurs appropriés est forcément complexe. La mortalité ne résume pas tous les enjeux mais semble au moins être un indicateur objectif. Il existe cependant différents indicateurs de mortalité ne donnant pas tous le même message. Lors de l'épidémie de Covid-19 en Suisse, le taux de mortalité a augmenté de 10,2 % en 2020, alors que l'espérance de vie n'a diminué « que ¼ de 0,8 %, ou 8,3 mois, recul par ailleurs qualifié de « modeste ¼ ou de « chute libre ¼ selon quand il a été publié. En réalité, la population vivant en Suisse en 2020 n'a perdu en moyenne « que ¼ 2,4 jours car l'épidémie n'a pas duré toute sa vie. L'utilisation d'un tel indicateur, en comparaison avec les pertes dues à d'autres facteurs, permettrait une meilleure estimation de l'impact réel d'une épidémie.

[Impact of changes in malignant tumor death spectrum on life expectancy in Tianjin residents from 1999-2019].

Objective: To analyze the effects of changes in the spectrum of deaths from malignant tumors on the life expectancies of residents of different ages, sexes, and regions (urban or rural) in Tianjin from 1999 to 2019. Methods: The Abridged Life Table method and the Arriaga's decomposition method were used to calculate the effects of changes in spectrum of deaths from malignant tumors on the life expectancies of Tianjin residents of different ages, sexes, and regions. Results: During 1999-2019, the life expectancies increased by 4.96 years and 5.69 years for males and females, respectively, in Tianjin. The decreases in the mortalities from malignant neoplasms contributed 0.12 year (3.30%) and 0.03 year (0.77%) for males and females, respectively, to the increase during 1999-2007, and 0.05 year (3.13%) and 0.12 year (6.08%) for males and females, respectively, during 2007-2019. The decreases in the mortality rates of malignant tumors contributed the most to the increase among residents in the 60-69 years group, and the decreases in mortality rates of lung, gastric, esophageal, and liver cancers had relatively larger contribution. Lung cancer had a negative effect on the life expectancies of men and rural residents, but a positive effect on those of women and urban residents. The significant increases in the mortality rates of lung, colorectal, and pancreatic cancers in the ≥85 years group had a large negative effect on the overall life expectancy. Breast and ovarian cancers contributed negatively to the life expectancy of female residents. Conclusion: The overall increase in the life expectancy in Tianjin from 1999 to 2019 was mainly attributed to the elderly and the decreases in the mortality rates of gastric, esophageal, and liver cancers, among other malignancies, while the increases in the mortality rates of lung, colorectal, gallbladder, pancreatic, and breast cancers were the most significant factors hindering the increase of the life expectancy in Tianjin.

[The gender aspects of changes in mortality during COVID-19 pandemic as exemplified by Moscow].

The mortality is a major component of damage caused by COVID-19. The comparative analysis of changes in mortality was carried out on the basis of the ROSSTAT data over 2012-2020 to determine differences in losses of male and female population caused by pandemic in Moscow. It is demonstrated that at close trends in mortality of males and females before pandemic, in 2020 their mortality changed differently. At equal increase of male and female mortality, main contribution into excess mortality (excluding contribution of COVID-19) was made approximately equally by diseases of nervous system and circulatory system in males and diseases of nervous system in females. The male mortality from COVID-19 is 1.9 times higher than female mortality. As a result of younger average age of death the amount of economic losses in terms of years of potential life lost (PYLL) due to premature death of males because of COVID-19 exceeds economic losses due to premature death of females up to 2 times. Although the average age of death of females from all causes decreased by smaller amount, their values of PYLL increased more, mainly due to higher rate of female mortality from disease of nervous system and from mortality related to drug addiction. In Moscow, the highest increase of PYLL is conditioned by dearth related to drug addiction and alcohol consumption. In the structure of this indicator in males they are ranked fourth and fifth. In females, alcohol-related deaths are ranked as sixth and drug-related deaths as eighth. The pandemic, contributing into increase in economic losses, didn't change their leading causes: diseases of circulatory system, external causes and neoplasms in males; neoplasms, diseases of circulatory system and external causes in females. The value of PYLL due to death from COVID-19 takes sixth place in males and fourth place in females.

[Ethics of a long life]. / Ethik des langen Lebens.

The increase in average life expectancy that has taken place since 1850 and is continuing globally to the present day can be seen as a major achievement of civilization. However, many are skeptical about demographic change and the continuing trend of increasing life expectancy beyond current limits. The reasons for this lie in deeply rooted cultural attitudes towards old age and the elderly.This article counters these attitudes with principles for a long life that emphasize the benefits of the first revolution of life extension. Research should be promoted that can ensure this gain and holds out the prospect of a further extension of the human lifespan as a result of a second revolution.

[Regional differences and trends in healthy life expectancy in Germany]. / Regionale Unterschiede und Trends in gesunder Lebenserwartung in Deutschland.

BACKGROUND: Against the background of increasing life expectancy, the question arises in which state of health the additional years of life are spent. The aim of this study is to assess for the first time regional differences in healthy life expectancy for Germany. METHODS: The concept of healthy life expectancy allows for the combination of regional differences in health status and mortality in a single measure. This article uses the concept of partial healthy life expectancy. We use official data on deaths and population numbers to calculate abridged life tables. Data from the Socio-Economic Panel (SOEP) are used to determine the age- and sex-specific prevalences of health status. Regional differences are analyzed from 2002 to 2019 by dividing Germany into four regions (North, South, East, West). RESULTS: The regional differences in healthy life expectancy in Germany are greater than differences in life expectancy, and trends in healthy life expectancy partly differ from the corresponding trends in mortality. These differences over time also vary according to age: while healthy life expectancy has tended to stagnate and, in some cases, decline among the population aged between 20 and 64, the number and proportion of years in good health has increased among older adults up to the age of 79. CONCLUSION: There are striking regional differences and trends in the distribution of expected years in good health in Germany. The timely identification of regionally divergent developments could facilitate the implementation of targeted health-promoting measures.

[Healthier at work for longer? Trends in life years free of cardiovascular and musculoskeletal diseases in the employed and general population based on health insurance claims data]. / Länger gesund im Beruf? Trends in den Lebensjahren frei von Herz-Kreislauf- und Muskel-Skelett-Erkrankungen in GKV-Daten für die Erwerbstätigen- und Allgemeinbevölkerung.

BACKGROUND: Our study focuses on the development of disease-free life years of two disease groups with high public health relevance: musculoskeletal diseases (MSD) and cardiovascular diseases (CVD). Against the backdrop of prolonged working lives, the development of disease-free life years in the working-age population is compared with the trend in the employed population. Differences between occupational groups are also examined. METHODS: The study is based on data from the statutory health insurance provider AOK Lower Saxony (N = 2,001,225). Incident cases were identified based on the diagnosis data. The expected years free of MSD and CVD were calculated using multistate life table analysis for three periods between 2006 and 2018. The occupational group is identified via the occupational key. Three groups are distinguished: unskilled and semi-skilled workers, skilled workers and specialists, and highly skilled workers. RESULTS: Life years free of MSD clearly decreased in the general population and among the employed population. The decrease was strongest in the higher-skilled occupational groups. Life years free of CVD increased in the general population. The increase was weaker among the employed population. The only occupational group showing increases were men in unskilled and semi-skilled occupations. DISCUSSION: The study shows that disease-free life years among employed persons developed in some cases worse than in the general population. For the analysed disease groups MSD and CVD, there are clear inequalities between occupational groups, which decreased somewhat over time. The inequalities and the decrease in years free of MSD highlight the high public health relevance and the need for effective prevention strategies to prevent CVD and MSD in working age.

[Family reconstitution and online genealogies to analyze the sex-specific differential mortality in the historical context]. / Familienrekonstitution und Online-Genealogien zur Analyse der geschlechtsspezifischen differenziellen Sterblichkeit im historischen Kontext.

BACKGROUND: Family reconstitution and data from online genealogies, such as FamiLinx, are two potential sources for investigating mortality dynamics for the period before official lifetables became available. In this paper, we use two of them, the family reconstitution of Imhof and the FamiLinx dataset based on geni.com, to estimate dynamics in life expectancy and discuss the sex-specific differential mortality in the German Empire. METHOD: Sex-specific lifetables are estimated for the territory of the German Empire from the individual data of the family reconstitution and the online genealogies. On the basis of these lifetables, we estimate the conditional life expectancy and derive the corresponding sex-specific differential mortality. Findings are compared with the official lifetable of the German Empire in 1871-1910. The contribution of each age group to the differential mortality is determined using the stepwise-replacement algorithm. RESULTS: The family reconstitution overestimates conditional life expectancy less than FamiLinx after 1871, when official lifetables are available in the German Empire. However, both sources fail to capture the sex-specific mortality differentials of the official lifetables at the end of the nineteenth century and show a higher life expectancy for males instead of females. The bias in sex-specific mortality rates is particularly pronounced in the age groups 15 to 45. DISCUSSION: Finally, we discuss possible explanations for the biased findings. Notability bias, the patriarchal approach to family trees, and maternal mortality are important mechanisms in the FamiLinx dataset. Censoring due to mobility serves as a potential reason for the bias in the family reconstitution.

[Genetics, epigenetics, and environmental factors in life expectancy-What role does nature-versus-nurture play in aging?] / Genetik, Epigenetik und Umweltfaktoren der Lebenserwartung ­ Welche Rolle spielt Nature-versus-Nurture beim Altern?

In Germany and worldwide, the average age of the population is continuously rising. With this general increase in chronological age, the focus on biological age, meaning the actual health and fitness status, is becoming more and more important. The key question is to what extent the age-related decline in fitness is genetically predetermined or malleable by environmental factors and lifestyle.Many epigenetic studies in aging research have provided interesting insights in this nature-versus-nurture debate. In most model organisms, aging is associated with specific epigenetic changes, which can be countered by certain interventions like moderate caloric restriction or increased physical activity. Since these interventions also have positive effects on lifespan and health, epigenetics appears to be the interface between environmental factors and the aging process. This notion is supported by the fact that an epigenetic drift occurs through the life course of identical twins, which is related to the different manifestations of aging symptoms. Furthermore, biological age can be determined with high precision based on DNA methylation patterns, further emphasizing the importance of epigenetics in aging.This article provides an overview of the importance of genetic and epigenetic parameters for life expectancy. A major focus will be on the possibilities of maintaining a young epigenome through lifestyle and environmental factors, thereby slowing down biological aging.

[Mortality trends in Germany in an international context]. / Sterblichkeitsentwicklung in Deutschland im internationalen Kontext.

BACKGROUND AND AIM: Due to its strong economy and a well-developed healthcare system, Germany is well positioned to achieve above-average reductions in mortality. Nevertheless, in terms of life expectancy, Germany is increasingly falling behind Western Europe. We compare mortality trends in Germany with other Western European countries, covering the period from 1960 to 2019. The focus is on long-term trends in Germany's ranking in international mortality trends. In addition, we conduct a detailed mortality analysis by age. METHODS: Our analysis is mostly based on mortality data from the Human Mortality Database (HMD). Cause-specific mortality data originate from the database of the World Health Organization (WHO). For the international comparison of mortality trends, we use conventional mortality indicators (age-standardized mortality rate, period life expectancy). RESULTS: Compared to other Western European countries, Germany has higher mortality in the middle and older age groups. Germany's life expectancy gap compared to Western Europe has grown during the past 20 years. In 2000, Germany was 0.73 years behind for men and 0.74 years behind for women. By 2019, these figures had risen to 1.43 and 1.34 years, respectively. This is mainly due to mortality from non-communicable diseases. CONCLUSION: For Germany to catch up with other Western European countries, a stronger focus on further reducing mortality at ages 50+ is crucial. This also requires further research to understand the factors behind Germany's disadvantageous position.

[How has life expectancy without functional limitations developed in Germany? An analysis with data from the German Ageing Survey (DEAS)]. / Wie hat sich die Lebenserwartung ohne funktionelle Einschränkungen in Deutschland entwickelt? Eine Analyse mit Daten des Deutschen Alterssurveys (DEAS).

INTRODUCTION: The long-term increase in life expectancy raises the question of whether the increased life expectancy is accompanied by an extension of years without health limitations. The study analyzes how life expectancy without functional and mobility limitations from the ages of 46 and 65 and their proportions of remaining life expectancy have changed since 2008. METHODS: We analyze data from the German Ageing Survey of the 2008, 2014, and 2020/21 waves. Life expectancy without functional limitations (disability-free life expectancy-DFLE) was calculated using the Sullivan method. Severe functional limitations (using the Global Activity Limitation Indicator-GALI) and mobility limitations (climbing stairs, walking more than 1 km) were examined. RESULTS: Compression of morbidity in the GALI has been observed in 46- and 65-year-old men since 2014, but not in women of the same age. In terms of mobility, 46- and 65-year-old men show trends towards compression when climbing stairs and 46-year-old men when walking more than 1 km since 2014. The values for women have stagnated for the first two indicators mentioned, but not for 46-year-old women since 2014 when walking more than 1 km. DISCUSSION: Our analyses show different trends in DFLE depending on the indicator, age, and gender and do not allow a clear answer to the question of morbidity compression or expansion. We tend to see morbidity compression in men, whereas trends of stagnation or expansion tend to be seen in women. These results signal challenges in maintaining functional health, especially in women, and point to the need for targeted interventions to improve quality of life and healthy life expectancy.

[Healthy life expectancy: a critical look at the benefits and potential of the demographic health indicator]. / Gesunde Lebenserwartung: Ein kritischer Blick auf Nutzen und Potenziale des demographischen Gesundheitsindikators.

Demographic ageing yields many societal consequences that depend strongly on the health status of the population. Special indicators have been developed for tracking and assessing population health, which are referred to with the overarching term healthy life expectancy (HLE). The derivation of HLE is intuitive and easily comprehensible. However, an overly simplistic interpretation hides the extreme complexity inherent in adding the health dimension to the life table. This makes HLE compared to classic life expectancy (LE) extremely sensitive to certain conceptual and methodological features. In the article, this is presented in more detail for three aspects: the underlying definition of health, the choice of survey data as a basis for estimating health status, and the reporting behavior of survey participants. It is shown that the impact on HLE can be enormous, leading to considerable bias in the interpretation of levels and trends, but also in the analysis of differences between populations. Nevertheless, the extension of classical LE to HLE is an important achievement that must not be abandoned. Therefore, the article also discusses ways in which the HLE indicator could be made more robust and reliable. Until this is achieved, however, the high methodological sensitivity of HLE must not be ignored if it is used to assess the health status of populations and as a basis for health policy measures.