[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.

The Dangers of Drawing Cohort Profiles From Period Data: A Research Note.

Drawing cohort profiles and cohort forecasts from grids of age-period data is common practice in demography. In this research note, we (1) show how demographic measures artificially fluctuate when calculated from the diagonals of age-period rates because of timing and cohort-size bias, (2) estimate the magnitude of these biases, and (3) illustrate how prediction intervals for cohort indicators of mortality may become implausible when drawn from Lee-Carter methods and age-period grids. These biases are surprisingly large, even when the cohort profiles are created from single-age, single-year period data. The danger is that we overinterpret deviations from expected trends that were induced by our own data manipulation.

The underwhelming German life expectancy.

This article contributes to the discussion on the determinants of diverging life expectancy in high-income countries, with a focus on Germany. To date, much of this discourse has centered around the social determinants of health, issues of healthcare equity, poverty and income inequality, and new epidemics of opioids and violence. Yet despite doing well on all of these metrics and having numerous advantages such as comparatively strong economic performance, generous social security, and an equitable and well-resourced health care system, Germany has been a long-time life expectancy laggard among the high-income countries. Using aggregated population-level mortality data for Germany and selected six high-income countries (Switzerland, France, Japan, Spain, the United Kingdom, and the United States) from the Human Mortality Database and WHO Mortality Database, we find that the German longevity shortfall is mainly explained by a longstanding disadvantage in survival among older adults and adults nearing statutory retirement age, which mainly stems from sustained excess cardiovascular disease mortality, even when compared to other laggard countries such as the US and the UK. Patchy contextual data suggests that the unfavorable pattern of cardiovascular mortality may be driven by underperforming primary care and disease prevention. More systematic and representative data on risk factors are needed to strengthen the evidence base on the determinants of the controversial and long-standing health gap between more successful countries and Germany. The German example calls for broader narratives of population health that embed the variety of epidemiological challenges populations face around the globe.

Flexible transition timing in discrete-time multistate life tables using Markov chains with rewards.

Discrete-time multistate life tables are attractive because they are easier to understand and apply in comparison with their continuous-time counterparts. While such models are based on a discrete time grid, it is often useful to calculate derived magnitudes (e.g. state occupation times), under assumptions which posit that transitions take place at other times, such as mid-period. Unfortunately, currently available models allow very few choices about transition timing. We propose the use of Markov chains with rewards as a general way of incorporating information on the timing of transitions into the model. We illustrate the usefulness of rewards-based multistate life tables by estimating working life expectancies using different retirement transition timings. We also demonstrate that for the single-state case, the rewards approach matches traditional life-table methods exactly. Finally, we provide code to replicate all results from the paper plus R and Stata packages for general use of the method proposed.

A global assessment of the impact of violence on lifetime uncertainty.

Uncertainty around age at death, or lifetime uncertainty, is a key public health indicator and a marker of inequality in survival. How does the extent of violence affect lifetime uncertainty? We address this question by quantifying the impact of violence on dispersion in the ages at death, the metric most used to measure lifetime uncertainty. Using mortality data from the Global Burden of Disease Study and the Internal Peace Index between 2008 and 2017, we find that the most violent countries are also those with the highest lifetime uncertainty. In the Middle East, conflict-related deaths are the largest contributor to lifetime uncertainty. In Latin America, a similar pattern is attributable to homicides. The effects are larger in magnitude for men, but the consequences remain considerable for women. Our study points to a double burden of violence on longevity: Not only does violence shorten individual lives, but it also makes the length of life less predictable.

A distributional approach to measuring lifespan stratification.

The study of the mortality differences between groups has traditionally focused on metrics that describe average levels of mortality, for example life expectancy and standardized mortality rates. Additional insights can be gained by using statistical distance metrics to examine differences in lifespan distributions between groups. Here, we use a distance metric, the non-overlap index, to capture the sociological concept of stratification, which emphasizes the emergence of unique, hierarchically layered social strata. We show an application using Finnish registration data that cover the entire population over the period from 1996 to 2017. The results indicate that lifespan stratification and life-expectancy differences between income groups both increased substantially from 1996 to 2008; subsequently, life-expectancy differences declined, whereas stratification stagnated for men and increased for women. We conclude that the non-overlap index uncovers a unique domain of inequalities in mortality and helps to capture important between-group differences that conventional approaches miss.

Inequalities in Retirement Life Span in the United States.

OBJECTIVES: The length of retirement life may be highly unequal due to persistent and significant discrepancies in old-age mortality. This study assesses gender and educational differences in the average retirement life span and the variation in retirement life span, taking into account individual labor force exit and reentry dynamics. METHODS: We used longitudinal data from the Health and Retirement Study from 1996 to 2016, focusing on respondents aged 50 and older (N = 32,228). Multistate life tables were estimated using discrete-time event history models. The average retirement life span, as well as absolute and relative variation in retirement life span, were calculated analytically. RESULTS: Among women, we found a persistent educational gradient in average retirement life span over the whole period studied; among men, the relationship between education and retirement expectancy differed across periods. Women and the lower-educated had higher absolute variation in retirement life span than men and the higher-educated-yet these relationships were reversed when examined by relative variation. DISCUSSION: Our multistate approach provides an accurate and comprehensive picture of the retirement life span of older Americans over the past two decades. Such findings should be considered in high-level discussions on Social Security. Potential reforms such as raising the eligibility age or cutting benefits may have unexpected implications for different social groups due to their differential effects on retirement initiation and reentry dynamics.

Quantifying the contribution of smoking to regional mortality disparities in Germany: a cross-sectional study.

OBJECTIVES: Substantial regional variation in smoking behaviour in Germany has been well documented. However, little is known about how these regional differences in smoking affect regional mortality disparities. We aim to assess the contribution of smoking to regional mortality differentials in Germany over the last four decades. DESIGN: A cross-sectional study using official cause-specific mortality data by German Federal State aggregated into five macro-regions: East, North, South, West-I and West-II. PARTICIPANTS: The entire population of Germany stratified by sex, age and region during 1980-2019. MAIN OUTCOME MEASURES: Smoking-attributable fraction estimated using the Preston-Glei-Wilmoth method; life expectancy at birth before and after the elimination of smoking-attributable deaths. RESULTS: In all macro-regions, the burden of past smoking has been declining among men but growing rapidly among women. The hypothetical removal of smoking-attributable deaths would eliminate roughly half of the contemporary advantage in life expectancy of the vanguard region South over the other macro-regions, apart from the East. In the latter, smoking only explains around a quarter (0.5 years) of the 2-year difference in male life expectancy compared with the South observed in 2019. Among women, eliminating smoking-attributable deaths would put the East in a more disadvantageous position compared with the South as well as the other macro-regions. CONCLUSION: While regional differences in smoking histories explain large parts of the regional disparities in male mortality, they are playing an increasingly important role for female mortality trends and differentials. Health policies aiming at reducing regional inequalities should account for regional differences in past smoking behaviour.

Why we should monitor disparities in old-age mortality with the modal age at death.

Indicators based a fixed "old" age threshold have been widely used for assessing socioeconomic disparities in mortality at older ages. Interpretation of long-term trends and determinants of these indicators is challenging because mortality above a fixed age that in the past would have reflected old age deaths is today mixing premature and old-age mortality. We propose the modal (i.e., most frequent) age at death, M, an indicator increasingly recognized in aging research, but which has been infrequently used for monitoring mortality disparities at older ages. We use mortality and population exposure data by occupational class over the 1971-2017 period from Finnish register data. The modal age and life expectancy indicators are estimated from mortality rates smoothed with penalized B-splines. Over the 1971-2017 period, occupational class disparities in life expectancy at 65 and 75 widened while disparities in M remained relatively stable. The proportion of the group surviving to the modal age was constant across time and occupational class. In contrast, the proportion surviving to age 65 and 75 has roughly doubled since 1971 and showed strong occupational class differences. Increasing socioeconomic disparities in mortality based on fixed old age thresholds may be a feature of changing selection dynamics in a context of overall declining mortality. Unlike life expectancy at a selected fixed old age, M compares individuals with similar survival chances over time and across occupational classes. This property makes trends and differentials in M easier to interpret in countries where old-age survival has improved significantly.

The Cross-sectional Average Inequality in Lifespan (CAL†): A Lifespan Variation Measure That Reflects the Mortality Histories of Cohorts.

Lifespan variation is a key metric of mortality that describes both individual uncertainty about the length of life and heterogeneity in population health. We propose a novel and timely lifespan variation measure, which we call the cross-sectional average inequality in lifespan, or CAL†. This new index provides an alternative perspective on the analysis of lifespan inequality by combining the mortality histories of all cohorts present in a cross-sectional approach. We demonstrate how differences in the CAL† measure can be decomposed between populations by age and cohort to explore the compression or expansion of mortality in a cohort perspective. We apply these new methods using data from 10 low-mortality countries or regions from 1879 to 2013. CAL† reveals greater uncertainty in the timing of death than the period life table-based indices of variation indicate. Also, country rankings of lifespan inequality vary considerably between period and cross-sectional measures. These differences raise intriguing questions as to which temporal dimension is the most relevant to individuals when considering the uncertainty in the timing of death in planning their life courses.

What have we learned about mortality patterns over the past 25 years?

In this paper, I examine progress in the field of mortality over the past 25 years. I argue that we have been most successful in taking advantage of an increasingly data-rich environment to improve aggregate mortality models and test pre-existing theories. Less progress has been made in relating our estimates of mortality risk at the individual level to broader mortality patterns at the population level while appropriately accounting for contextual differences and compositional change. Overall, I find that the field of mortality continues to be highly visible in demographic journals, including Population Studies. However much of what is published today in field journals could just as easily appear in neighbouring disciplinary journals, as disciplinary boundaries are shrinking.

The impact of income definitions on mortality inequalities.

Income is a strong predictor of adult mortality. Measuring income is not as simple as it may sound. It can be conceptualized at the individual or the household level, with the former better reflecting an individual's earning ability, and the latter better capturing living standards. Furthermore, respondents are often grouped into income categories based on their positions in the income distribution, and this operationalization can be done on the basis of age-specific or total population income distributions. In this study, we look at how four combinations of different conceptualizations (individual vs. household) and operationalizations (age-specific vs. total population) of income can affect mortality inequality estimates. Using Finnish registry data, we constructed period life tables for ages 25+ from 1996 to 2017 by gender and for four income definitions. The results indicated that the slope index of inequality for life expectancy varied by 1.1-5.7 years between income definitions, with larger differences observed for women than for men. The overall age patterns of relative index of inequality for mortality rates yielded by the four definitions were similar, but the levels differed. The period trends across income definitions were consistent for men, but not for women. We conclude that researchers should pay particular attention to the choice of the income definitions when analyzing the association between income and mortality, and when comparing the magnitude of inequality across studies and over time.

Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality.

The population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries at any given time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy, Germany, Spain, and New York City over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late June 2020, CFRs varied from 2.2% in South Korea to 14.0% in Italy. The age-structure of detected cases often explains more than two-thirds of cross-country variation in the CFR. In Italy, the CFR increased from 4.2% to 14.0% between March 9 and June 30, 2020, and more than 90% of the change was due to increasing age-specific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well-designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases.

Diversification in causes of death in low-mortality countries: emerging patterns and implications.

INTRODUCTION: An important role of public health organisations is to monitor indicators of variation, so as to disclose underlying inequality in health improvement. In industrialised societies, more individuals than ever are reaching older ages and have become more homogeneous in their age at death. This has led to a decrease in lifespan variation, with substantial implications for the reduction of health inequalities. We focus on a new form of variation to shed further light on our understanding of population health and ageing: variation in causes of death. METHODS: Data from the WHO Mortality Database and the Human Mortality Database are used to estimate cause-of-death distributions and life tables in 15 low-mortality countries. Cause-of-death variation, using 19 groups of causes, is quantified using entropy measures and analysed from 1994 to 2017. RESULTS: The last two decades have seen increasing diversity in causes of death in low-mortality countries. There have been important reductions in the share of deaths from diseases of the circulatory system, while the share of a range of other causes, such as diseases of the genitourinary system, mental and behavioural disorders, and diseases of the nervous system, has been increasing, leading to a more complex cause-of-death distribution. CONCLUSIONS: The diversification in causes of death witnessed in recent decades is most likely a result of the increase in life expectancy, together with better diagnoses and awareness of certain diseases. Such emerging patterns bring additional challenges to healthcare systems, such as the need to research, monitor and treat a wider range of diseases. It also raises new questions concerning the distribution of health resources.

Rethinking morbidity compression.

Studies of morbidity compression routinely report the average number of years spent in an unhealthy state but do not report variation in age at morbidity onset. Variation was highlighted by Fries (1980) as crucial for identifying disease postponement. Using incidence of first hospitalization after age 60, as one working example, we estimate variation in morbidity onset over a 27-year period in Denmark. Annual estimates of first hospitalization and the population at risk for 1987 to 2014 were identified using population-based registers. Sex-specific life tables were constructed, and the average age, the threshold age, and the coefficient of variation in age at first hospitalization were calculated. On average, first admissions lasting two or more days shifted towards older ages between 1987 and 2014. The average age at hospitalization increased from 67.8 years (95% CI 67.7-67.9) to 69.5 years (95% CI 69.4-69.6) in men, and 69.1 (95% CI 69.1-69.2) to 70.5 years (95% CI 70.4-70.6) in women. Variation in age at first admission increased slightly as the coefficient of variation increased from 9.1 (95% CI 9.0-9.1) to 9.9% (95% CI 9.8-10.0) among men, and from 10.3% (95% CI 10.2-10.4) to 10.6% (95% CI 10.5-10.6) among women. Our results suggest populations are ageing with better health today than in the past, but experience increasing diversity in healthy ageing. Pensions, social care, and health services will have to adapt to increasingly heterogeneous ageing populations, a phenomenon that average measures of morbidity do not capture.