When do mothers bury a child? Heterogeneity in the maternal age at offspring loss

The experience of losing a child is increasingly uncommon worldwide but is no less devastating for parents who experience it. An overlooked aspect of this phenomenon is its timing: at which age do bereft parents lose a child and how are these ages at loss distributed? We use demographic methods to explore the mean and variability of maternal age at child loss in 18 countries for the 1850-2000 birth cohorts. We find that the distribution of age of child loss is bimodal, with one component representing young offspring deaths and another representing adult offspring deaths. Offspring loss is transitioning from being a relatively common life event, mostly experienced by young mothers, to a rare one spread throughout the maternal life course. Moreover, there is no evidence of convergence in the variability of age at offspring loss. These results advance the formal demography of kinship and underline the need to support bereaved parents across the life course.

Smooth Hazards With Multiple Time Scales

Hazard models are the most commonly used tool to analyze time-to-event data. If more than one time scale is relevant for the event under study, models are required that can incorporate the dependence of a hazard along two (or more) time scales. Such models should be flexible to capture the joint influence of several time scales, and nonparametric smoothing techniques are obvious candidates.P $$ P $$ -splines offer a flexible way to specify such hazard surfaces, and estimation is achieved by maximizing a penalized Poisson likelihood. Standard observation schemes, such as right-censoring and left-truncation, can be accommodated in a straightforward manner. Proportional hazards regression with a baseline hazard varying over two time scales is presented. Efficient computation is possible by generalized linear array model (GLAM) algorithms or by exploiting a sparse mixed model formulation. A companion R-package is provided.

Unpacking occupational and sex divides to understand the moderate progress in life expectancy in recent years (France, 2010's)

PURPOSE

The growth in life expectancy (LE) slows down recently in several high-income countries. Among the underlying dynamics, uneven progress in LE across social groups has been pointed out. However, these dynamics has not been extensively studied, partly due to data limitations. In this paper, we explore this area for the 2010 decade using recent French data.

METHODS

We utilize the recent change in French census mortality follow-up data (EDP) and apply P-spline models to estimate LEs across five occupational classes (OCs) and indicators of lifespan heterogeneity (edagger) within these OCs, for seven triennial periods (2011-2013 to 2017-2019).

RESULTS

First, we found a similar ranking of OCs along the LE gradient over time and across sexes, from manual workers to higher-level OCs. Noteworthy, the lowest LE in women overlaps with the highest one in men drawing a sex-OC gradient. Second, we observe varying progress of LEs. In women, LE increases in higher-level OCs meanwhile it levels off in manual workers, so that the OCs gap widens (up to 3.4 years in 2017-2019). Conversely, in men LE stalls in higher-level OCs and increases in manual workers so that the gap, which is much larger than in women (+5.7 years in 2017-2019), is tending to narrow. Finally, the lifespan homogenizes in OCs only when LE is low.

CONCLUSION

Overall, the limited LE progress in France results from LE stalling in the middle of the sex-OC gradient, though LE increases at both ends. At the lower end, LE progress and lifespan homogenization suggest that laggards benefit recently improvements achieved earlier in other OCs. At the upper end, LE progress may come from a vanguard group within higher-lever OC, benefiting new sources of improvements. These findings underscore the need for further research to explore the diverse mortality dynamics coexisting in the current health landscape.

Decomposition of life expectancy differentials with (and without) conditions by educational attainment for major groups of causes in contemporary Spain: where is the advantage?

Introduction

Healthy life expectancy is higher among individuals with higher socioeconomic standing. However, it is unclear whether such advantage is attributable to longer (i.e., mortality advantage) or to healthier (morbidity advantage) lifespans across different health conditions.

Objective

Estimate the contribution of mortality and morbidity components in differences in condition-free life expectancies (CFLE) and life expectancy with conditions (LEWC) for five major groups of conditions by sex and educational attainment, instead of using a global indicator of morbidity.

Methods

Using the Sullivan Method, we computed remaining life expectancies at age 40 and 65, CFLE, and LEWC and applied a stepwise decomposition technique, using national health surveys along with mortality data, in a cross-sectional analysis.

Results

An educational gradient was present in almost all conditions, with different intensities. For females, morbidity was the main contributor to educational differences in health expectancies, but mainly in the older age groups. For males, the drivers behind higher health expectancies for high-educated males were evenly distributed across mortality and morbidity between ages 40 and 65, but after that, the mortality gradient vanished between high-educated and middle-educated individuals.

Discussion

The changing contribution of the mortality and morbidity gradient for different conditions across age-groups brings evidence to adequately plan health policies to mitigate health gaps and improve quality of life of the populations in a lower social standing.

Forecasting Inequalities in Survival to Retirement Age by Socioeconomic Status in Denmark and Sweden

In Denmark and Sweden, statutory retirement age is indexed to life expectancy to account for mortality improvements in their populations. However, mortality improvements have not been uniform across different sub-populations. Notably, in both countries, individuals of lower socioeconomic status (SES) have experienced slower mortality improvements. As a result, a uniform rise in the statutory retirement age could disproportionally affect these low-SES groups and may unintentionally lead to a reverse redistribution effect, shifting benefits from short-lived low-SES individuals to long-lived high-SES individuals. The aim of this study is twofold: to quantify and contextualise mortality inequalities by SES in Denmark and Sweden, and to assess how indexing retirement age will affect future survival to retirement age by SES in these countries. We used Danish and Swedish registry data (1988-2019), to aggregate individuals aged 50 + based on their demographic characteristics and SES. We computed period life tables by year, sex, and SES to estimate the difference in survival across different SES groups. We then forecast mortality across SES groups to assess how indexing retirement age will affect survival inequalities to retirement age, using two forecasting models-the Mode model and the Li-Lee model. Mortality inequalities are comparable in Denmark and Sweden, even though the latter generally has higher survival. We also find that indexing retirement age to life expectancy will have two main consequences: it will reduce the probability of reaching retirement for all SES groups, particularly those of low SES, and time spent in retirement will be reduced, particularly for those of high SES.

[Family reconstitution and online genealogies to analyze the sex-specific differential mortality in the historical context]

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.

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background

Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches.

Methods

We assembled a "toolbox" of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the "cancer incidence panel").

Results

Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods.

Conclusions

Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

Area-based social inequalities in adult mortality: construction of French deprivation-specific life tables for the period 2016-2018

Background

In order to tackle social inequalities in mortality, it is crucial to quantify them. We produced French deprivation-specific life tables for the period 2016-2018 to measure the social gradient in adult all-cause mortality.

Methods

Data from the Permanent Demographic Sample (EDP) were used to provide population and death counts by age, sex and deprivation quintile. The European Deprivation Index (EDI), applied at a sub-municipal geographical level, was used as an ecological measure of deprivation. Smoothed mortality rates were calculated using a one-dimensional Poisson counts smoothing method with P-Splines. We calculated life expectancies by age, sex and deprivation quintile as well as interquartile mortality rate ratios (MRR).

Results

At the age of 30, the difference in life expectancy between the most and least deprived groups amounted to 3.9 years in males and 2.2 years in females. In terms of relative mortality inequalities, the largest gaps between extreme deprivation groups were around age 55 for males (MRR = 2.22 [2.0; 2.46] at age 55), around age 50 in females (MRR = 1.77 [1.48; 2.1] at age 47), and there was a decrease or disappearance of the gaps in the very older adults.

Conclusions

There is a strong social gradient in all-cause mortality in France for males and females. The methodology for building these deprivation-specific life tables is reproducible and could be used to monitor its development. The tables produced should contribute to improving studies on net survival inequalities for specific diseases by taking into account the pre-existing social gradient in all-cause mortality.

Sex differences in cause-specific mortality: regional trends in seven European countries, 1996-2019

BACKGROUND

Male excess mortality is mostly related to non-biological factors, and is thus of high social- and health-policy concern. Previous research has mainly focused on national patterns, while subnational disparities have been less in the focus. This study takes a spatial perspective on subnational patterns, covering seven European countries at the crossroad between Eastern and Western Europe.

METHODS

We analyze a newly gathered spatially detailed data resource comprising 228 regions with well-established demographic methods to assess the contribution of specific causes of death to the evolution of sex mortality differentials (SMDs) since the mid-1990s.

RESULTS

Our results show that declines in SMDs were mostly driven by a reduction of male excess mortality from cardiovascular diseases and neoplasms (about 50-60% and 20-30%, respectively). In Western Europe, trends in deaths from neoplasms contributed more to the reduction of SMDs, while among regions located in Eastern-Central Europe narrowing SMDs were mostly driven by changes in cardiovascular disease-related deaths. Moreover, men show up to three times higher mortality levels from external causes as compared to women in several analyzed regions. But in absolute terms, external deaths play only a minor role in explaining SMDs due to their small contribution to overall mortality.

CONCLUSIONS

We conclude that examining the regional development of SMDs is useful for introducing targeted social and health policies in order to reduce and prevent mortality inequalities between women and men.

Dementia incidence trend in England and Wales, 2002-19, and projection for dementia burden to 2040: analysis of data from the English Longitudinal Study of Ageing

BACKGROUND

Dementia incidence declined in many high-income countries in the 2000s, but evidence on the post-2010 trend is scarce. We aimed to analyse the temporal trend in England and Wales between 2002 and 2019, considering bias and non-linearity.

METHODS

Population-based panel data representing adults aged 50 years and older from the English Longitudinal Study of Ageing were linked to the mortality register across wave 1 (2002-03) to wave 9 (2018-19) (90 073 person observations). Standard criteria based on cognitive and functional impairment were used to ascertain incident dementia. Crude incidence rates were determined in seven overlapping initially dementia-free subcohorts each followed up for 4 years (ie, 2002-06, 2004-08, 2006-10, 2008-12, 2010-14, 2012-16, and 2014-18). We examined the temporal trend of dementia incidence according to age, sex, and educational attainment. We estimated the trend of dementia incidence adjusted by age and sex with Cox proportional hazards and multistate models. Restricted cubic splines allowed for potential non-linearity in the time trend. A Markov model was used to project future dementia burden considering the estimated incidence trend.

FINDINGS

Incidence rate standardised by age and sex declined from 2002 to 2010 (from 10·7 to 8·6 per 1000 person-years), then increased from 2010 to 2019 (from 8·6 to 11·3 per 1000 person-years). Adjusting for age and sex, and accounting for missing dementia cases due to death, estimated dementia incidence declined by 28·8% from 2002 to 2008 (incidence rate ratio 0·71, 95% CI 0·58-0·88), and increased by 25·2% from 2008 to 2016 (1·25, 1·03-1·54). The group with lower educational attainment had a smaller decline in dementia incidence from 2002 to 2008 and a greater increase after 2008. If the upward incidence trend continued, there would be 1·7 million (1·62-1·75) dementia cases in England and Wales by 2040, 70% more than previously forecast.

INTERPRETATION

Dementia incidence might no longer be declining in England and Wales. If the upward trend since 2008 continues, along with population ageing, the burden on health and social care will be large.

FUNDING

UK Economic and Social Research Council.

Comparative age-period-cohort analysis

BACKGROUND

Cancer surveillance researchers analyze incidence or mortality rates jointly indexed by age group and calendar period using age-period-cohort models. Many studies consider age- and period-specific rates in two or more strata defined by sex, race/ethnicity, etc. A comprehensive characterization of trends and patterns within each stratum can be obtained using age-period-cohort (APC) estimable functions (EF). However, currently available approaches for joint analysis and synthesis of EF are limited.

METHODS

We develop a new method called Comparative Age-Period-Cohort Analysis to quantify similarities and differences of EF across strata. Comparative Analysis identifies whether the stratum-specific hazard rates are proportional by age, period, or cohort.

RESULTS

Proportionality imposes natural constraints on the EF that can be exploited to gain efficiency and simplify the interpretation of the data. Comparative Analysis can also identify differences or diversity in proportional relationships between subsets of strata ("pattern heterogeneity"). We present three examples using cancer incidence from the United States Surveillance, Epidemiology, and End Results Program: non-malignant meningioma by sex; multiple myeloma among men stratified by race/ethnicity; and in situ melanoma by anatomic site among white women.

CONCLUSIONS

For studies of cancer rates with from two through to around 10 strata, which covers many outstanding questions in cancer surveillance research, our new method provides a comprehensive, coherent, and reproducible approach for joint analysis and synthesis of age-period-cohort estimable functions.

Smoothing Lexis diagrams using kernel functions: A contemporary approach

Lexis diagrams are rectangular arrays of event rates indexed by age and period. Analysis of Lexis diagrams is a cornerstone of cancer surveillance research. Typically, population-based descriptive studies analyze multiple Lexis diagrams defined by sex, tumor characteristics, race/ethnicity, geographic region, etc. Inevitably the amount of information per Lexis diminishes with increasing stratification. Several methods have been proposed to smooth observed Lexis diagrams up front to clarify salient patterns and improve summary estimates of averages, gradients, and trends. In this article, we develop a novel bivariate kernel-based smoother that incorporates two key innovations. First, for any given kernel, we calculate its singular values decomposition, and select an optimal truncation point-the number of leading singular vectors to retain-based on the bias-corrected Akaike information criterion. Second, we model-average over a panel of candidate kernels with diverse shapes and bandwidths. The truncated model averaging approach is fast, automatic, has excellent performance, and provides a variance-covariance matrix that takes model selection into account. We present an in-depth case study (invasive estrogen receptor-negative breast cancer incidence among non-Hispanic white women in the United States) and simulate operating characteristics for 20 representative cancers. The truncated model averaging approach consistently outperforms any fixed kernel. Our results support the routine use of the truncated model averaging approach in descriptive studies of cancer.

Contribution of avoidable mortality to life expectancy inequalities in Wales: a decomposition by age and by cause between 2002 and 2020

OBJECTIVES

To explore the contribution of avoidable mortality to life expectancy inequalities in Wales during 2002-2020.

DESIGN

Observational study.

SETTING

Wales, 2002-20, including early data from the COVID-19 pandemic.

METHODS

We used routine statistics for 2002-2020 on population and deaths in Wales stratified by age, sex, deprivation quintile and cause of death. We estimated the contribution of avoidable causes of death and specific age-categories using the Arriaga decomposition method to highlight priorities for action.

RESULTS

Life expectancy inequalities rose 2002-20 amongst both sexes, driven by serial decreases in life expectancy amongst the most deprived quintiles. The contributions of amenable and preventable mortality to life expectancy inequalities changed relatively little between 2002 and 2020, with larger rises in non-avoidable causes. Key avoidable mortality conditions driving the life expectancy gap in the most recent period of 2018-2020 for females were circulatory disease, cancers, respiratory disease and alcohol- and drug-related deaths, and also injuries for males.

CONCLUSIONS

Life expectancy inequalities widened during 2002-20, driven by deteriorating life expectancy in the most deprived quintiles. Sustained investment in prevention post-COVID-19 is needed to address growing health inequity in Wales; there remains a role for the National Health Service in ensuring equitable healthcare access to alongside wider policies that promote equity.

Evaluation of Lee-Carter model to breast cancer mortality prediction in China and Pakistan

Background

Precise breast cancer-related mortality forecasts are required for public health program and healthcare service planning. A number of stochastic model-based approaches for predicting mortality have been developed. The trends shown by mortality data from various diseases and countries are critical to the effectiveness of these models. This study illustrates the unconventional statistical method for estimating and predicting the mortality risk between the early-onset and screen-age/late-onset breast cancer population in China and Pakistan using the Lee-Carter model.

Methods

Longitudinal death data for female breast cancer from 1990 to 2019 obtained from the Global Burden of Disease study database were used to compare statistical approach between early-onset (age group, 25-49 years) and screen-age/late-onset (age group, 50-84 years) population. We evaluated the model performance both within (training period, 1990-2010) and outside (test period, 2011-2019) data forecast accuracy using the different error measures and graphical analysis. Finally, using the Lee-Carter model, we predicted the general index for the time period (2011 to 2030) and derived corresponding life expectancy at birth for the female breast cancer population using life tables.

Results

Study findings revealed that the Lee-Carter approach to predict breast cancer mortality rate outperformed in the screen-age/late-onset compared with that in the early-onset population in terms of goodness of fit and within and outside forecast accuracy check. Moreover, the trend in forecast error was decreasing gradually in the screen-age/late-onset compared with that in the early-onset breast cancer population in China and Pakistan. Furthermore, we observed that this approach had provided almost comparable results between the early-onset and screen-age/late-onset population in forecast accuracy for more varying mortality behavior over time like in Pakistan. Both the early-onset and screen-age/late-onset populations in Pakistan were expected to have an increase in breast cancer mortality by 2030. whereas, for China, it was expected to decrease in the early-onset population.

Conclusion

The Lee-Carter model can be used to estimate breast cancer mortality and so to project future life expectancy at birth, especially in the screen-age/late-onset population. As a result, it is suggested that this approach may be useful and convenient for predicting cancer-related mortality even when epidemiological and demographic disease data sets are limited. According to model predictions for breast cancer mortality, improved health facilities for disease diagnosis, control, and prevention are required to reduce the disease's future burden, particularly in less developed countries.

Mortality as a Function of Survival

Everyone has a chronological age. Because survivorship declines relentlessly in populations with age-specific death rates greater than zero, everyone also has a survivorship age ("s-age"), the age at which a proportion s of the population is still alive. S-ages can be estimated for both periods and cohorts. While trajectories of mortality over chronological ages differ (e.g., across populations, over time, by sex, or by any subpopulation), mortality trajectories over s-ages are similar, a sign that populations experience similar mortality dynamics at specific levels of survivorship. We show that this important demographic regularity holds for 23 sex-specific populations analyzed during a period comprising more than 100 years.

Mortality and life expectancy trends in Spain by pension income level for male pensioners in the general regime retiring at the statutory age, 2005-2018

Background

Research has generally found a significant inverse relationship in mortality risk across socioeconomic (SE) groups. This paper focuses on Spain, a country for which there continues to be very little evidence available concerning retirement pensioners. We draw on the Continuous Sample of Working Lives (CSWL) to investigate disparities in SE mortality among retired men aged 65 and above over the longest possible period covered by this data source: 2005–2018. We use the initial pension income (PI) level as our single indicator of the SE status of the retired population.

Methods

The mortality gradient by income is quantified in two ways: via an indicator referred to as “relative mortality”, and by estimating changes in total life expectancy (LE) by PI level at ages 65 and 75 over time. We show that, should the information provided by the relative mortality ratio not be completely clear, a second indicator needs to be introduced to give a broad picture of the true extent of inequality in mortality.

Results

The first indicator reveals that, for the period covered and for all age groups, the differences in death rates across PI levels widens over time. At older age groups, these differences across PI levels diminish. The second indicator shows that disparities in LE at ages 65 and 75 between pensioners in the lowest and highest income groups are relatively small, although slightly higher than previously reported for Spain. This gap in LE widens over time, from 1.49 to 2.54 years and from 0.71 to 1.40 years respectively for pensioners aged 65 and 75. These differences are statistically significant.

Conclusions

Along with other behavioral and structural aspects, a combination of factors such as the design of the pension system, the universality and quality of the health system, and high levels of family support could explain why LE inequalities for retired Spanish men are relatively small. To establish the reasons for this increased inequality in LE, more research needs to be carried out. An analysis of all Spanish social security records instead of just a sample would provide us with more information.

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

This paper proposes a spline mortality model for generating smooth projections of mortality improvement rates. In particular, we follow the two-dimensional cubic B-spline approach developed by Currie et al. (Stat Model 4(4):279-298, 2004), and adopt the Bayesian estimation and LASSO penalty to overcome the limitations of spline models in forecasting mortality rates. The resulting Bayesian spline model not only provides measures of stochastic and parameter uncertainties, but also allows external opinions on future mortality to be consistently incorporated. The mortality improvement rates projected by the proposed model are smoothly transitioned from the historical values with short-term trends shown in recent observations to the long-term terminal rates suggested by external opinions. Our technical work is complemented by numerical illustrations that use real mortality data and external rates to showcase the features of the proposed model.

Representativeness is crucial for inferring demographic processes from online genealogies: Evidence from lifespan dynamics

Crowdsourced online genealogies have an unprecedented potential to shed light on long-run population dynamics, if analyzed properly. We investigate whether the historical mortality dynamics of males in familinx, a popular genealogical dataset, are representative of the general population, or whether they are closer to those of an elite subpopulation in two territories. The first territory is the German Empire, with a low level of genealogical coverage relative to the total population size, while the second territory is The Netherlands, with a higher level of genealogical coverage relative to the population. We find that, for the period around the turn of the 20th century (for which benchmark national life tables are available), mortality is consistently lower and more homogeneous in familinx than in the general population. For that time period, the mortality levels in familinx resemble those of elites in the German Empire, while they are closer to those in national life tables in The Netherlands. For the period before the 19th century, the mortality levels in familinx mirror those of the elites in both territories. We identify the low coverage of the total population and the oversampling of elites in online genealogies as potential explanations for these findings. Emerging digital data may revolutionize our knowledge of historical demographic dynamics, but only if we understand their potential uses and limitations.

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.

Stillbirths in Germany: On the rise, but no additional increases during the first COVID-19 lockdown

OBJECTIVE

To examine possible changes in the rate of stillbirths in Germany during the first COVID-19 lockdown.

METHODS

Population-level data of live births and stillbirths occurring between January 1995 and July 2020 were used and negative binomial regression was applied to estimate the rate of stillbirths in this period. The actual rate was compared to the expected figure for 2020.

RESULTS

A steady increase in stillbirths was detected in Germany since 2013. The stillbirth rate for January to July 2020 (4.148) was slightly lower than that of the same period in 2019 (4.242). Furthermore, all monthly rates of stillbirths during the first half of 2020 lie inside the 95% prediction interval of expected stillbirth rates for this period.

CONCLUSION

A growing body of studies on the indirect effect of the COVID-19 pandemic on stillbirths shows mixed and context-dependent evidence. In contrast to other European countries, stillbirth rates have been on the rise in Germany in the last decade. However, stillbirth rates during the first half of 2020 were not higher than expected. The results suggest that stillbirth rates have not changed during the first-wave COVID-19 lockdown in this high-income setting. However, further studies on the causes of the increasing trend in stillbirths in Germany are needed.

What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database

This study analyzed whether there are different patterns of mortality decline among low-mortality countries by identifying the role played by all the mortality components. We implemented a cluster analysis using a functional data analysis (FDA) approach, which allowed us to consider age-specific mortality rather than summary measures, as it analyses curves rather than scalar data. Combined with a functional principal component analysis, it can identify what part of the curves is responsible for assigning one country to a specific cluster. FDA clustering was applied to the data from 32 countries in the Human Mortality Database from 1960 to 2018 to provide a comprehensive understanding of their patterns of mortality. The results show that the evolution of developed countries followed the same pattern of stages (with different timings): (1) a reduction of infant mortality, (2) an increase of premature mortality and (3) a shift and compression of deaths. Some countries were following this scheme and recovering the gap with precursors; others did not show signs of recovery. Eastern European countries were still at Stage (2), and it was not clear if and when they will enter Stage 3. All the country differences related to the different timings with which countries underwent the stages, as identified by the clusters.

The rate by which mortality increase with age is the same for those who experienced chronic disease as for the general population

Background

Mortality doubles approximately every 6–7 years during adulthood. This exponential increase in death risk with chronological age is the population-level manifestation of ageing, and often referred to as the rate-of-ageing.

Objective

We explore whether the onset of severe chronic disease alters the rate-of-ageing.

Methods

Using Swedish register data covering the entire population of the birth cohorts 1927–30, we analyse whether being diagnosed with myocardial infarction, diabetes or cancer results in a deviation of the rate-of-ageing from those of the total population. We also quantify the long-term mortality effects of these diseases, using ages with equivalent mortality levels for those with disease and the total population.

Results

None of the diseases revealed a sustained effect on the rate-of-ageing. After an initial switch upwards in the level of mortality, the rate-of-ageing returned to the same pace as for the total population. The time it takes for the rate to return depends on the disease. The long-term effects of diabetes and myocardial infarction amount to mortality levels that are equivalent to those aged 5–7 years older in the total population. For cancer, the level of mortality returns to that of the total population.

Conclusion

Our results suggest an underlying process of ageing that causes mortality to increase at a set pace, with every year older we become. This process is not affected by disease history. The persistence of the rate-of-ageing motivates a critical discussion of what role disease prevention can play in altering the progression of ageing.

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.

Differences in Life Expectancy Between Self-Employed Workers and Paid Employees when Retirement Pensioners: Evidence from Spanish Social Security Records

The aim of this paper is to examine differences in life expectancy (LE) between self-employed (SE) and paid employee (PE) workers when they become retirement pensioners, looking at levels of pension income using administrative data from Spanish social security records. We draw on the Continuous Sample of Working Lives (CSWL) to quantify changes in total life expectancy at age 65 (LE₆₅) among retired men over the longest possible period covered by this data source: 2005-2018. These changes are broken down by pension regime and initial pension income level for three periods. The literature presents mixed evidence, even for the same country-for Japan and Italy, for example-with some studies pointing to higher life expectancy for SE than for PE retirement pensioners while others argue the opposite. In Spain, LE₆₅ is slightly higher for the SE than for PE workers when retirement pensioners. For 2005-2010, a gap in life expectancy of 0.23 years between SE and PE retirement pensioners is observed. This widens to 0.55 years for 2014-2018. A similar trend can be seen if pension income groups are considered. For 2005-2010, the gap in LE₆₅ between pensioners in the lowest and highest income groups is 1.20 years. This widens over time and reaches 1.51 years for 2014-2018. Although these differences are relatively small, they are statistically significant. According to our research, the implications for policy on social security are evident: differences in life expectancy by socioeconomic status and pension regime should be taken into account for a variety of issues involving social security schemes. These include establishing the age of eligibility for retirement pensions and early access to benefits, computing the annuity factors used to determine initial retirement benefits and valuing the liabilities taken on for retirement pensioners.

Mortality Forecasting with the Lee-Carter Method: Adjusting for Smoothing and Lifespan Disparity

Reliable mortality forecasts are an essential component of healthcare policies in ageing societies. The Lee-Carter method and its later variants are widely accepted probabilistic approaches to mortality forecasting, due to their simplicity and the straightforward interpretation of the model parameters. This model assumes an invariant age component and linear time component for forecasting. We apply the Lee-Carter method on smoothed mortality rates obtained by LASSO-type regularization and hence adjust the time component with the observed lifespan disparity. Smoothing with LASSO produces less error during the fitting period than do spline-based smoothing techniques. As a more informative indicator of longevity, matching with lifespan disparity makes the time component more reflective of mortality improvements. The forecasts produced by the new method were more accurate during out-of-sample evaluation and provided optimistic forecasts for many low-mortality countries.

Leaders and Laggards in Life Expectancy Among European Scholars From the Sixteenth to the Early Twentieth Century

When did mortality first start to decline, and among whom? We build a large, new data set with more than 30,000 scholars covering the sixteenth to the early twentieth century to analyze the timing of the mortality decline and the heterogeneity in life expectancy gains among scholars in the Holy Roman Empire. The large sample size, well-defined entry into the risk group, and heterogeneity in social status are among the key advantages of the new database. After recovering from a severe mortality crisis in the seventeenth century, life expectancy among scholars started to increase as early as in the eighteenth century, well before the Industrial Revolution. Our finding that members of scientific academies-an elite group among scholars-were the first to experience mortality improvements suggests that 300 years ago, individuals with higher social status already enjoyed lower mortality. We also show, however, that the onset of mortality improvements among scholars in medicine was delayed, possibly because these scholars were exposed to pathogens and did not have germ theory knowledge that might have protected them. The disadvantage among medical professionals decreased toward the end of the nineteenth century. Our results provide a new perspective on the historical timing of mortality improvements, and the database accompanying our study facilitates replication and extensions.

A multi-country comparison of stochastic models of breast cancer mortality with P-splines smoothing approach

BACKGROUND

Precise predictions of incidence and mortality rates due to breast cancer (BC) are required for planning of public health programs as well as for clinical services. A number of approaches has been established for prediction of mortality using stochastic models. The performance of these models intensely depends on different patterns shown by mortality data in different countries.

METHODS

The BC mortality data is retrieved from the Global burden of disease (GBD) study 2017 database. This study include BC mortality rates from 1990 to 2017, with ages 20 to 80+ years old women, for different Asian countries. Our study extend the current literature on Asian BC mortality data, on both the number of considered stochastic mortality models and their rigorous evaluation using multivariate Diebold-Marino test and by range of graphical analysis for multiple countries.

RESULTS

Study findings reveal that stochastic smoothed mortality models based on functional data analysis generally outperform on quadratic structure of BC mortality rates than the other lee-carter models, both in term of goodness of fit and on forecast accuracy. Besides, smoothed lee carter (SLC) model outperform the functional demographic model (FDM) in case of symmetric structure of BC mortality rates, and provides almost comparable results to FDM in within and outside data forecast accuracy for heterogeneous set of BC mortality rates.

CONCLUSION

Considering the SLC model in comparison to the other can be obliging to forecast BC mortality and life expectancy at birth, since it provides even better results in some cases. In the current situation, we can assume that there is no single model, which can truly outperform all the others on every population. Therefore, we also suggest generating BC mortality forecasts using multiple models rather than relying upon any single model.

Insight into stagnating adult life expectancy: Analyzing cause of death patterns across socioeconomic groups

This study analyzes the complexity of female longevity improvements. As socioeconomic status is found to influence health and mortality, we partition all individuals, at each age in every year, into five socioeconomic groups based on an affluence measure that combine an individual's income and wealth. We identify the particular socioeconomic groups that have been driving the standstill for Danish females at older ages. Within each socioeconomic group, we further analyze the cause of death patterns. The decline in life expectancy for Danish females is present for four out of five subgroups, however, with particular large decreases for the low-middle and middle-affluence groups. Cancers, smoking-related lung and bronchus causes, and other diseases particularly contribute to the stagnation. For four of the five socioeconomic groups only small cardiovascular improvement are experienced during the period of stagnating life expectancy compared to an equally long and subsequent period.

Modal lifespan and disparity at older ages by leading causes of death: a Canada-U.S. comparison

The U.S. elderly experience shorter lifespans and greater variability in age at death than their Canadian peers. In order to gain insight on the underlying factors responsible for the Canada-U.S. old-age mortality disparities, we propose a cause-of-death analysis. Accordingly, the objective of this paper is to compare levels and trends in cause-specific modal age at death (M) and standard deviation above the mode (SD(M +)) between Canada and the U.S. since the 1970s. We focus on six broad leading causes of death, namely cerebrovascular diseases, heart diseases, and four types of cancers. Country-specific M and SD(M +) estimates for each leading cause of death are calculated from P-spline smooth age-at-death distributions obtained from detailed population and cause-specific mortality data. Our results reveal similar levels and trends in M and SD(M +) for most causes in the two countries, except for breast cancer (females) and lung cancer (males), where differences are the most noticeable. In both of these instances, modal lifespans are shorter in the U.S. than in Canada and U.S. old-age mortality inequalities are greater. These differences are explained in part by the higher stratification along socioeconomic lines in the U.S. than in Canada regarding the adoption of health risk behaviours and access to medical services.

The rise in the number of long-term survivors from different diseases can slow the increase in life expectancy of the total population

Background

Recent improvements in life expectancy in many countries stem from reduced mortality from cardiovascular disease and cancer above the age of 60. This is the combined result of decreased incidence and improved survival among those with disease. The latter has led to a higher proportion in the population of people with a past history of disease. This is a group with higher mortality than the general population. How growing shares of persons with past history of disease and improved survival with disease have affected changes in life expectancy of the total population is the objective of this paper.

Methods

Using register data for the total Swedish population, we stratified the population based on whether individuals have been diagnosed with myocardial infarction, stroke, hip fracture, colon cancer, or breast cancer. Using a novel decomposition approach, we decomposed the changes in life expectancy at age 60 between 1994 and 2016 into contributions from improved survival with disease and from changes in proportion of people with past history of disease.

Results

Improvements in survival from disease resulted in gains of life expectancy for the total population. However, while the contributions to life expectancy improvements from myocardial infarction, stroke and breast cancer were substantial, the contributions from the other diseases were minor. These gains were counteracted, to various degrees, by the increasing proportion of people with raised mortality due to a past history of disease. For instance, the impact on life expectancy by improved survival from breast cancer was almost halved by the increasing share of females with a past history of breast cancer.

Conclusion

Rising numbers of survivors of different diseases can slow the increase in life expectancy. This dynamic may represent the costs associated with successful treatment of diseases, and thus, a potential “failure of success.” This dynamic should be considered when assessing mortality and life expectancy trends. As populations are aging and disease survival continues to improve, this issue is likely to become even more important in the future.

The cross-sectional average length of healthy life (HCAL): a measure that summarizes the history of cohort health and mortality

Background

Healthy life years have superseded life expectancy (LE) as the most important indicator for population health. The most common approach to separate the total number of life years into those spent in good and poor health is the Sullivan method which incorporates the health dimension to the classic period life table, thus transforming the LE indicator into the health expectancy (HE) indicator. However, life years derived from a period life table and health prevalence derived from survey data are based on different conceptual frameworks.

Method

We modify the Sullivan method by combining the health prevalence data with the conceptually better fitting cross-sectional average length of life (CAL). We refer to this alternative HE indicator as the “cross-sectional average length of healthy life” (HCAL). We compare results from this alternative indicator with the conventional Sullivan approach for nine European countries. The analyses are based on EU-SILC data in three empirical applications, including the absolute and relative level of healthy life years, changes between 2008 and 2014, and the extent of the gender gap.

Results

HCAL and conventional HE differ in each of these empirical applications. In general, HCAL provides larger gains in healthy life years in recent years, but at the same time greater declines in the proportion of healthy life years. Regarding the gender gap, HCAL provides a more favourable picture for women compared to conventional HE. Nonetheless, the extent of these differences between the indicators is only of minor extent.

Conclusions

Albeit the differences between HE and HCAL are small, we found some empirical examples in which the two indicators led to different conclusions. It is important to note, however, that the measurement of health and the data quality are much more important for the healthy life years indicator than the choice of the variant of the Sullivan method. Nonetheless, we suggest to use HCAL in addition to HE whenever possible because it widens the spectrum of empirical analyses and serves for verification of results based on the highly sensitive HE indicator.

The increasing lifespan variation gradient by area-level deprivation: A decomposition analysis of Scotland 1981-2011

Life expectancy inequalities are an established indicator of health inequalities. More recent attention has been given to lifespan variation, which measures the amount of heterogeneity in age at death across all individuals in a population. International studies have documented diverging socioeconomic trends in lifespan variation using individual level measures of income, education and occupation. Despite using different socioeconomic indicators and different indices of lifespan variation, studies reached the same conclusion: the most deprived experience the lowest life expectancy and highest lifespan variation, a double burden of mortality inequality. A finding of even greater concern is that relative differences in lifespan variation between socioeconomic group were growing at a faster rate than life expectancy differences. The magnitude of lifespan variation inequalities by area-level deprivation has received limited attention. Area-level measures of deprivation are actively used by governments for allocating resources to tackle health inequalities. Establishing if the same lifespan variation inequalities emerge for area-level deprivation will help to better inform governments about which dimension of mortality inequality should be targeted. We measure lifespan variation trends (1981-2011) stratified by an area-level measure of socioeconomic deprivation that is applicable to the entire population of Scotland, the country with the highest level of variation and one of the longest, sustained stagnating trends in Western Europe. We measure the gradient in variation using the slope and relative indices of inequality. The deprivation, age and cause specific components driving the increasing gradient are identified by decomposing the change in the slope index between 1981 and 2011. Our results support the finding that the most advantaged are dying within an ever narrower age range while the most deprived are facing greater and increasing uncertainty. The least deprived group show an increasing advantage, over the national average, in terms of deaths from circulatory disease and external causes.

Socioeconomic disparities in life expectancy gains among retired German men, 1997-2016

Background

Although estimates of socioeconomic mortality disparities in Germany exist, the trends in these disparities since the 1990s are still unknown. This study examines mortality trends across socioeconomic groups since the late 1990s among retired German men aged 65 and above.

Methods

Large administrative data sets were used to estimate mortality among retired German men, grouped according to their working-life biographies. The data covered the years 1997–2016 and included more than 84.1 million person-years and 4.3 million deaths. Individual pension entitlements served as a measure of lifetime income. Changes in total life expectancy at age 65 over time were decomposed into effects of group-specific mortality improvements and effects of compositional change.

Results

Over the two decades studied, male mortality declined in all income groups in both German regions. As mortality improved more rapidly among higher status groups, the social gradient in mortality widened. Since 1997, the distribution of pension entitlements of retired East German men has shifted substantially downwards. As a result, the impact of the most disadvantaged group on total mortality has increased and has partly attenuated the overall improvement.

Conclusion

Our results demonstrate that socioeconomic deprivation has substantial effects on levels of mortality in postreunification Germany. While East German retirees initially profited from the transition to the West German pension system, subsequent cohorts had to face challenges associated with the transition to the market economy. The results suggest that postreunification unemployment and status decline had delayed effects on old-age mortality in East Germany.

How to estimate mortality trends from grouped vital statistics

Background

Mortality data at the population level are often aggregated in age classes, for example 5-year age groups with an open-ended interval for the elderly aged 85+. Capturing detailed age-specific mortality patterns and mortality time trends from such coarsely grouped data can be problematic at older ages, especially where open-ended intervals are used.

Methods

We illustrate the penalized composite link model (PCLM) for ungrouping to model cancer mortality surfaces. Smooth age-specific distributions from data grouped in age classes of adjacent calendar years were estimated by constructing a two-dimensional regression, based on B-splines, and maximizing a penalized likelihood. We show the applicability of the proposed model, analysing age-at-death distributions from cancers of all sites in Denmark from 1980 to 2014. Data were retrieved from the Danish Cancer Society and the Human Mortality Database.

Results

The main trends captured by PCLM are: (i) a decrease in cancer mortality rates after the 1990s for ages 50–75; (ii) a decrease in cancer mortality in later cohorts for young ages, especially, and very advanced ages. Comparing the raw data by single year of age, with the PCLM-ungrouped distributions, we clearly illustrate that the model fits the data with a high level of accuracy.

Conclusions

The PCLM produces detailed smooth mortality surfaces from death counts observed in coarse age groups with modest assumptions, that is Poisson distributed counts and smoothness of the estimated distribution. Hence, the method has great potential for use within epidemiological research when information is to be gained from aggregated data, because it avoids strict assumptions about the actual distributional shape.

Age distribution, trends, and forecasts of under-5 mortality in 31 sub-Saharan African countries: A modeling study

BACKGROUND

Despite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.

METHODS AND FINDINGS

Data came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee-Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.

CONCLUSIONS

To our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.

Modelling and forecasting adult age-at-death distributions

Age-at-death distributions provide an informative description of the mortality pattern of a population but have generally been neglected for modelling and forecasting mortality. In this paper, we use the distribution of deaths to model and forecast adult mortality. Specifically, we introduce a relational model that relates a fixed 'standard' to a series of observed distributions by a transformation of the age axis. The proposed Segmented Transformation Age-at-death Distributions (STAD) model is parsimonious and efficient: using only three parameters, it captures and disentangles mortality developments in terms of shifting and compression dynamics. Additionally, mortality forecasts can be derived from parameter extrapolation using time-series models. We illustrate our method and compare it with the Lee-Carter model and variants for females in four high-longevity countries. We show that the STAD fits the observed mortality pattern very well, and that its forecasts are more accurate and optimistic than the Lee-Carter variants.

Upsurge of Homicides and Its Impact on Life Expectancy and Life Span Inequality in Mexico, 2005-2015

OBJECTIVES

To quantify the effect of the upsurge of violence on life expectancy and life span inequality in Mexico after 2005.

METHODS

We calculated age- and cause-specific contributions to changes in life expectancy and life span inequality conditional on surviving to age 15 years between 1995 and 2015. We analyzed homicides, medically amenable conditions, diabetes, ischemic heart diseases, and traffic accidents by state and sex.

RESULTS

Male life expectancy at age 15 years increased by more than twice in 1995 to 2005 (1.17 years) than in 2005 to 2015 (0.55 years). Life span inequality decreased by more than half a year for males in 1995 to 2005, whereas in 2005 to 2015 the reduction was about 4 times smaller. Homicides for those aged between 15 and 49 years had the largest effect in slowing down male life expectancy and life span inequality. Between 2005 and 2015, three states in the north experienced life expectancy losses while 5 states experienced increased life span inequality.

CONCLUSIONS

Ten years into the upsurge of violence, Mexico has not been able to reduce the homicide levels to those before 2005. Life expectancy and life span inequality stagnated since 2005 for young men at the national level. In some states, males live shorter lives than in 2005, on average, and experience higher uncertainty in their eventual death.