Including the smoking epidemic in internationally coherent mortality projections

Contributions of causes of death to differentials in life expectancy by internal migrant status in the Netherlands. A population register based study, 2015-2019

Important health differences exist in the context of international migration and residential mobility. Less is known about health differences regarding the medium-distance level of internal migration. This study examines life expectancy gaps between internal movers and stayers in the Netherlands and their underlying processes by assessing the contribution of different causes of death by age and sex. It uses individually-linked death counts and population exposures extracted from population registers, covering the native Dutch population aged 10+ from 2015 to 2019. The pooled data were disaggregated by causes-of-death group (neurodegenerative diseases, cardiovascular diseases, lifestyle-related mortality, external causes, and other causes), internal migrant status (movers and stayers, based on past 10-year residence in the 40 NUTS-3 [Nomenclature of Territorial Units for Statistics, level 3] regions), age, and sex. Comparing movers and stayers, we computed life expectancy at age 10 (e10), age- and cause-specific mortality risks, and applied decomposition methods to assess contributions of causes of death to e10 gaps. In the Netherlands in 2015-2019, e10 was lower for movers between NUTS-3 regions than stayers (males: 2.49 years; females: 3.51 years), due to excess mortality for movers at most ages. Movers only had a lower mortality than stayers at younger working ages (males: ages 20-44; females: ages 20-34). Mortality from neurodegenerative diseases and cardiovascular diseases were the largest contributors to the e10 gap, especially at ages 75+ and for females. Mortality from lifestyle-related and external causes of death contributed less, with the largest contributions for females aged 75-89 and males aged 45-69. The lower e10 of movers in the Netherlands is likely explained by health selection effects-in particular care-related moves as coping behaviour-rather than by causal effects through risk accumulation. Research focusing on regional or spatial heterogeneity of the mover-stayer health gap would be insightful to further understand these processes.

A modal age at death approach to forecasting adult mortality

Recent studies have shown that there are some advantages to forecasting mortality with indicators other than age-specific death rates. The mean, median, and modal ages at death can be directly estimated from the age-at-death distribution, as can information on lifespan variation. The modal age at death has been increasing linearly since the second half of the twentieth century, providing a strong basis from which to extrapolate past trends. The aim of this paper is to develop a forecasting model that is based on the regularity of the modal age at death and that can also account for changes in lifespan variation. We forecast mortality at ages 40 and above in 10 West European countries. The model we introduce increases forecast accuracy compared with other forecasting models and provides consistent trends in life expectancy and lifespan variation at age 40 over time.

Future trends of life expectancy by education in the Netherlands

Background

National projections of life expectancy are made periodically by statistical offices or actuarial societies in Europe and are widely used, amongst others for reforms of pension systems. However, these projections may not provide a good estimate of the future trends in life expectancy of different social-economic groups. The objective of this study is to provide insight in future trends in life expectancies for low, mid and high educated men and women living in the Netherlands.

Methods

We used a three-layer Li and Lee model with data from neighboring countries to complement Dutch time series.

Results

Our results point at further increases of life expectancy between age 35 and 85 and of remaining life expectancy at age 35 and age 65, for all education groups in the Netherlands. The projected increase in life expectancy is slightly larger among the high educated than among the low educated. Life expectancy of low educated women, particularly between age 35 and 85, shows the smallest projected increase. Our results also suggest that inequalities in life expectancies between high and low educated will be similar or slightly increasing between 2018 and 2048. We see no indication of a decline in inequality between the life expectancy of the low and high educated.

Conclusions

The educational inequalities in life expectancy are expected to persist or slightly increase for both men and women. The persistence and possible increase of inequalities in life expectancy between the educational groups may cause equity concerns of increases in pension age that are equal among all socio-economic groups.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13275-w.

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 combined impact of smoking, obesity and alcohol on life-expectancy trends in Europe

BACKGROUND

Smoking, obesity and alcohol abuse greatly affect mortality and exhibit a distinct time dynamic, with their prevalence and associated mortality rates increasing and (eventually) declining over time. Their combined impact on secular trends in life expectancy is unknown but is relevant for understanding these trends. We therefore estimate the combined impact of smoking, obesity and alcohol on life-expectancy trends in Europe.

METHODS

We used estimated national age-specific smoking-, obesity- and alcohol-attributable mortality fractions for 30 European countries by sex, 1990-2014, which we aggregated multiplicatively to obtain lifestyle-attributable mortality. We estimated potential gains in life expectancy by eliminating lifestyle-attributable mortality and compared past trends in life expectancy at birth (e0) with and without lifestyle-attributable mortality. We examined all countries combined, by region and individually.

RESULTS

Among men, the combined impact of smoking, obesity and alcohol on e0 declined from 6.6 years in 1990 to 5.8 years in 2014, mainly due to declining smoking-attributable mortality. Among women, the combined impact increased from 1.9 to 2.3 years due to mortality increases in all three lifestyle-related factors. The observed increase in e0 over the 1990-2014 period was 5.0 years for men and 4.0 years for women. After excluding lifestyle-attributable mortality, this increase would have been 4.2-4.3 years for both men and women.

CONCLUSION

Without the combined impact of smoking, obesity and alcohol, the increase over time in life expectancy at birth would have been smaller among men but larger among women, resulting in a stable increase in e0, parallel for men and women.

Future life expectancy in Europe taking into account the impact of smoking, obesity, and alcohol

Introduction: In Europe, women can expect to live on average 82 years and men 75 years. Forecasting how life expectancy will develop in the future is essential for society. Most forecasts rely on a mechanical extrapolation of past mortality trends, which leads to unreliable outcomes because of temporal fluctuations in the past trends due to lifestyle 'epidemics'. Methods: We project life expectancy for 18 European countries by taking into account the impact of smoking, obesity, and alcohol on mortality, and the mortality experiences of forerunner populations. Results: We project that life expectancy in these 18 countries will increase from, on average, 83.4 years for women and 78.3 years for men in 2014 to 92.8 years for women and 90.5 years for men in 2065. Compared to others (Lee-Carter, Eurostat, United Nations), we project higher future life expectancy values and more realistic differences between countries and sexes. Conclusions: Our results imply longer individual lifespans, and more elderly in society. Funding: Netherlands Organisation for Scientific Research (NWO) (grant no. 452-13-001).

Accounting for Smoking in Forecasting Mortality and Life Expectancy

Smoking is one of the main risk factors that has affected human mortality and life expectancy over the past century. Smoking accounts for a large part of the nonlinearities in the growth of life expectancy and of the geographic and sex differences in mortality. As Bongaarts (2006) and Janssen (2018) suggested, accounting for smoking could improve the quality of mortality forecasts due to the predictable nature of the smoking epidemic. We propose a new Bayesian hierarchical model to forecast life expectancy at birth for both sexes and for 69 countries with good data on smoking-related mortality. The main idea is to convert the forecast of the non-smoking life expectancy at birth (i.e., life expectancy at birth removing the smoking effect) into life expectancy forecast through the use of the age-specific smoking attributable fraction (ASSAF). We introduce a new age-cohort model for the ASSAF and a Bayesian hierarchical model for non-smoking life expectancy at birth. The forecast performance of the proposed method is evaluated by out-of-sample validation compared with four other commonly used methods for life expectancy forecasting. Improvements in forecast accuracy and model calibration based on the new method are observed.

The Role of Smoking in Country Differences in Life Expectancy Across Europe, 1985-2014

INTRODUCTION

Smoking contributes substantially to mortality levels and trends. Its role in country differences in mortality has, however, hardly been quantified. The current study formally assesses the-so far unknown-changing contribution of smoking to country differences in life expectancy at birth (e0) across Europe.

METHODS

Using all-cause mortality data and indirectly estimated smoking-attributable mortality rates by age and sex for 30 European countries from 1985 to 2014, the differences in e0 between each individual European country and the weighted average were decomposed into a smoking- and a nonsmoking-related part.

RESULTS

In 2014, e0 ranged from 70.8 years in Russia to 83.1 years in Switzerland. Men exhibited larger country differences than women (variance of 21.9 and 7.0 years, respectively). Country differences in e0 increased up to 2005 and declined thereafter. Among men, the average contribution of smoking to the country differences in e0 was highest around 1990 (47%) and declined to 35% in 2014. Among women, the average relative contribution of smoking declined from 1991 to 2011, and smoking resulted in smaller differences with the average e0 level in the majority of European countries. For both sexes combined, the contribution of smoking to country differences in e0 was higher than 20% throughout the period.

CONCLUSIONS

Smoking contributed substantially to the country differences in e0 in Europe, their increases up to 1991, and their decreases since 2005, especially among men. Policies that discourage smoking can help to reduce inequalities in mortality levels across Europe in the long run.

IMPLICATIONS

Smoking contributes substantially to country differences in life expectancy at birth (e0) in Europe, particularly among men, for whom the contribution was highest around 1990 (47%) and declined to 35% in 2014. In line with the anticipated progression of the smoking epidemic, the differences between European countries in e0 due to smoking are expected to further decline among men, but to increase among women. The role of smoking in mortality convergence since 2005 illustrates that smoking policies can help to reduce inequalities in life expectancy levels across Europe, particularly when they target smoking in countries with low e0.

Smoking epidemic in Europe in the 21st century

OBJECTIVE

To estimate smoking-attributable mortality in the long-term future in 29 European countries using a novel data-driven forecasting approach that integrates the wave pattern of the smoking epidemic and the cohort dimension.

METHODS

We estimated and forecasted age-specific and age-standardised smoking-attributable mortality fractions (SAMF) and 95% projection intervals for 29 European countries by sex, 1950-2100, using age-period-cohort modelling with a generalised logit link function. We projected the (decelerating) period increases (women) by a quadratic curve to obtain future declines, and extrapolated the past period decline (men). In addition, we extrapolated the recent cohort trend.

RESULTS

SAMF among men are projected to decline from, on average, 25% in 2014 (11% (Sweden)-41% (Hungary)) to 11% in 2040 (range: 6.3%-15.4%), 7% in 2065 (range: 5.9%-9.4%) and 6% in 2100. SAMF among women in 21 non-Eastern European countries, currently at an average of 16%, are projected to reach peak levels in 2013 (Northern Europe), 2019 (Western Europe), 2027 (Greece, Italy) and 2022 (Central Europe), with maximum levels of, on average, 17% (8% (Greece)-28% (Denmark)), and to decline to 10% in 2040 (range: 4%-20%), 5% in 2065 (range: 3.5%-7.6%) and 4% in 2100. For women, a short-term shift in the peak of the inverse U-shaped age pattern to higher ages is projected, and crossovers between the age-specific trends.

CONCLUSION

Our novel forecasting method enabled realistic estimates of the mortality imprint of the smoking epidemic in Europe up to 2100. The high peak values in smoking-attributable mortality projected for women warrant attention.

Similarities and Differences Between Sexes and Countries in the Mortality Imprint of the Smoking Epidemic in 34 Low-Mortality Countries, 1950-2014

INTRODUCTION

The smoking epidemic greatly affected mortality levels and trends, especially among men in low-mortality countries. The objective of this article was to examine similarities and differences between sexes and low-mortality countries in the mortality imprint of the smoking epidemic. This will provide important additions to the smoking epidemic model, but also improve our understanding of the differential impact of the smoking epidemic, and provide insights into its future impact.

METHODS

Using lung-cancer mortality data for 30 European and four North American or Australasian countries, smoking-attributable mortality fractions (SAMF) by sex, age (35-99), and year (1950-2014) were indirectly estimated. The timing and level of the peak in SAMF35-99, estimated using weighting and smoothing, were compared.

RESULTS

Among men in all countries except Bulgaria, a clear wave pattern was observed, with SAMF35-99 peaking, on average, at 33.4% in 1986. Eastern European men experienced the highest (40%) and Swedish men the lowest (16%) peak. Among women, SAMF35-99 peaked, on average, at 18.1% in 2007 in the North American/Australasian countries and five Northwestern European countries, and increased, on average, to 7.5% in 2014 in the remaining countries (4% in Southern and Eastern Europe). The average sex difference in the peak is at least 25.6 years in its timing and at most 22.9 percentage points in its level.

CONCLUSIONS

Although the progression of smoking-attributable mortality in low-mortality countries was similar, there are important unexpected sex and country differences in the maximum mortality impact of the smoking epidemic driven by cross-country differences in economic, political, and emancipatory progress.

IMPLICATIONS

The formal, systematic, and comprehensive analysis of similarities and differences between sexes and 34 low-mortality countries in long-term time trends (1950-2014) in smoking-attributable mortality provided important additions to the Global Burden of Disease study and the descriptive smoking epidemic model (Lopez et al.). Despite a general increase followed by a decline, the timing of the maximum mortality impact differs more between sexes than previously anticipated, but less between regions. The maximum mortality impact among men differs considerably between countries. The observed substantial diversity warrants country-specific tobacco control interventions and increased attention to the current or expected higher smoking-attributable mortality shares among women compared to men.

ESTIMATING AND FORECASTING THE SMOKING-ATTRIBUTABLE MORTALITY FRACTION FOR BOTH GENDERS JOINTLY IN OVER 60 COUNTRIES

Smoking is one of the leading preventable threats to human health and a major risk factor for lung cancer, upper aero-digestive cancer, and chronic obstructive pulmonary disease. Estimating and forecasting the smoking attributable fraction (SAF) of mortality can yield insights into smoking epidemics and also provide a basis for more accurate mortality and life expectancy projection. Peto et al. (1992) proposed a method to estimate the SAF using the lung cancer mortality rate as an indicator of exposure to smoking in the population of interest. Here we use the same method to estimate the all-age SAF (ASAF) for both genders for over 60 countries. We document a strong and cross-nationally consistent pattern of the evolution of the SAF over time. We use this as the basis for a new Bayesian hierarchical model to project future male and female ASAF from over 60 countries simultaneously. This gives forecasts as well as predictive distributions that can be used to find uncertainty intervals for any quantity of interest. We assess the model using out-of-sample predictive validation, and find that it provides good forecasts and well calibrated forecast intervals, comparing favorably with other methods.

Progression of the smoking epidemic in high-income regions and its effects on male-female survival differences: a cohort-by-age analysis of 17 countries

BACKGROUND

Of all lifestyle behaviours, smoking caused the most deaths in the last century. Because of the time lag between the act of smoking and dying from smoking, and because males generally take up smoking before females do, male and female smoking epidemiology often follows a typical double wave pattern dubbed the 'smoking epidemic'. How are male and female deaths from this epidemic differentially progressing in high-income regions on a cohort-by-age basis? How have they affected male-female survival differences?

METHODS

We used data for the period 1950-2015 from the WHO Mortality Database and the Human Mortality Database on three geographic regions that have progressed most into the smoking epidemic: high-income North America, high-income Europe and high-income Oceania. We examined changes in smoking-attributable mortality fractions as estimated by the Preston-Glei-Wilmoth method by age (ages 50-85) across birth cohorts 1870-1965. We used these to trace sex differences with and without smoking-attributable mortality in period life expectancy between ages 50 and 85.

RESULTS

In all three high-income regions, smoking explained up to 50% of sex differences in period life expectancy between ages 50 and 85 over the study period. These sex differences have declined since at least 1980, driven by smoking-attributable mortality, which tended to decline in males and increase in females overall. Thus, there was a convergence between sexes across recent cohorts. While smoking-attributable mortality was still increasing for older female cohorts, it was declining for females in the more recent cohorts in the US and Europe, as well as for males in all three regions.

CONCLUSIONS

The smoking epidemic contributed substantially to the male-female survival gap and to the recent narrowing of that gap in high-income North America, high-income Europe and high-income Oceania. The precipitous decline in smoking-attributable mortality in recent cohorts bodes somewhat hopeful. Yet, smoking-attributable mortality remains high, and therefore cause for concern.

Modeling and forecasting sex differences in mortality: a sex-ratio approach

Female and male life expectancies have converged in most industrialized societies in recent decades. To achieve coherent forecasts between females and males, this convergence needs to be considered when forecasting sex-specific mortality. We introduce a model forecasting a matrix of the age-specific death rates of sex ratio, decomposed into two age profiles and time indices-before and after age 45-using principal component analysis. Our model allows visualization of both age structure and general level over time of sex differences in mortality for these two age groups. Based on a prior forecast for females, we successfully forecast male mortality convergence with female mortality. The usefulness of the developed model is illustrated by its comparison with other coherent and independent models in an out-of-sample forecast evaluation for 18 countries. The results show that the new proposal outperformed the other models for most countries.

Mortality and life expectancy forecast for (comparatively) high mortality countries

Background

The Lee–Carter method and its later variants are widely accepted extrapolative methods for forecasting mortality and life expectancy in industrial countries due to their simplicity and availability of high quality long time series data.

Objective

We compared and contrasted mortality forecasting models for higher mortality regimes that lack long time series data of good quality, which is common in several Central and Eastern European (CEE) countries.

Data and methods

We utilized seven different variants of the Lee–Carter method and coherent mortality forecasts of various CEE countries, and the Bayesian Hierarchical Model used by the United Nations to produce probabilistic forecasts. The data of nine CEE countries with comparatively higher mortality have been considered.

Results

The performance of the forecasting models for the nine CEE countries was found to be lower than that observed for low-mortality countries. No model gives uniquely best performance for all the nine CEE countries. Most of the LC variants produced lower forecasts of life expectancies than current life expectancy values for Belarus, Russia, and Ukraine. A coherent mortality forecast could not overcome the limitations of single population forecasting techniques due to increasing mortality differences between these countries over the fitting period (mortality divergence). In the same context, the use of the probabilistic forecasting technique from the Bayesian framework resulted in a better forecast than some of the extrapolative methods but also produced a wider prediction interval for several countries. The more detailed analysis for Hungary indicates that a better fit of certain forecasting methods may occur in the later part of the life span rather than the whole life span.

Conclusion

These findings imply the necessity of inventing a new forecasting technique for high-mortality countries.

Comparing strategies for matching mortality forecasts to the most recently observed data: exploring the trade-off between accuracy and robustness

Background

Given the increased link between retirement age and payments to the development in life expectancy, a precise and regular forecast of life expectancy is of utmost importance. The choice of the jump-off rates, i.e. the rates in the last year of the fitting period, is essential for matching mortality forecasts to the most recently observed data. A general approach to the choice of the jump-off rates is currently lacking.

Objective

We evaluate six different options for the jump-off rates and examine their effects on the robustness and accuracy of the mortality forecast.

Data and methods

Death and exposure numbers by age for eight European countries over the years 1960–2014 were obtained from the Human Mortality Database. We examined the use of model values as jump-off rates versus observed values in the last year or averaged over the last couple of years. The future life expectancy at age 65 is calculated for different fitting periods and jump-off rates using the Lee-Carter model and examined on accuracy (mean absolute forecast error) and robustness (standard deviation of the change in projected e65).

Results

The choice for the jump-off rates clearly influences the accuracy and robustness of the mortality forecast, albeit in different ways. For most countries using the last observed values as jump-off rates resulted in the most accurate method, which relates to the relatively high estimation error of the model in recent years. The most robust method is obtained by using an average of observed years as jump-off rates. The more years that are averaged, the better the robustness, but accuracy decreases with more years averaged.

Conclusion

Carefully considering the best choice for the jump-off rates is essential when forecasting mortality. The best strategy for matching mortality forecasts to the most recently observed data depends on the goal of the forecast, the country-specific past mortality trends observed, and the model fit.

Past trends in obesity-attributable mortality in eight European countries: an application of age-period-cohort analysis

Objectives

To assess age, period, and birth cohort effects and patterns of obesity-attributable mortality in Czech Republic, Finland, France, Germany, Hungary, Italy, Poland, and the UK (UK).

Methods

We obtained obesity prevalence and all-cause mortality data by age (20–79), sex and country for 1990–2012. We applied Clayton and Schifflers’ age–period–cohort approach to obesity-attributable mortality rates (OAMRs).

Results

Between 1990 and 2012, obesity prevalence increased and age-standardised OAMRs declined, although not uniformly. The nonlinear birth cohort effects contributed significantly (p < 0.01) to obesity-attributable mortality trends in all populations, except in Czech Republic, Finland, and among German women, and Polish men. Their contribution was greater than 25% in UK and among French women, and larger than that of the nonlinear period effects. In the UK, mortality rate ratios (MRRs) increased among the cohorts born after 1950. In other populations with significant birth cohort effects, MRRs increased among the 1935–1960 cohorts and decreased thereafter.

Conclusions

Given its potential effects on obesity-attributable mortality, the cohort dimension should not be ignored and calls for interventions early in life next to actions targeting broader societal changes.

Electronic supplementary material

The online version of this article (10.1007/s00038-018-1126-2) contains supplementary material, which is available to authorized users.

Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts

Evaluating the predictive ability of mortality forecasts is important yet difficult. Death rates and mean lifespan are basic life table functions typically used to analyze to what extent the forecasts deviate from their realized values. Although these parameters are useful for specifying precisely how mortality has been forecasted, they cannot be used to assess whether the underlying mortality developments are plausible. We therefore propose that in addition to looking at average lifespan, we should examine whether the forecasted variability of the age at death is a plausible continuation of past trends. The validation of mortality forecasts for Italy, Japan, and Denmark demonstrates that their predictive performance can be evaluated more comprehensively by analyzing both the average lifespan and lifespan disparity—that is, by jointly analyzing the mean and the dispersion of mortality. Approaches that account for dynamic age shifts in survival improvements appear to perform better than others that enforce relatively invariant patterns. However, because forecasting approaches are designed to capture trends in average mortality, we argue that studying lifespan disparity may also help to improve the methodology and thus the predictive ability of mortality forecasts.

The German East-West Mortality Difference: Two Crossovers Driven by Smoking

Before the fall of the Berlin Wall, mortality was considerably higher in the former East Germany than in West Germany. The gap narrowed rapidly after German reunification. The convergence was particularly strong for women, to the point that Eastern women aged 50-69 now have lower mortality despite lower incomes and worse overall living conditions. Prior research has shown that lower smoking rates among East German female cohorts born in the 1940s and 1950s were a major contributor to this crossover. However, after 1990, smoking behavior changed dramatically, with higher smoking intensity observed among women in the eastern part of Germany. We forecast the impact of this changing smoking behavior on East-West mortality differences and find that the higher smoking rates among younger East German cohorts will reverse their contemporary mortality advantage. Mortality forecasting methods that do not account for smoking would, perhaps misleadingly, forecast a growing mortality advantage for East German women. Experience from other countries shows that smoking can be effectively reduced by strict anti-smoking policies. Instead, East Germany is becoming an example warning of the consequences of weakening anti-smoking policies and changing behavioral norms.

Probabilistic mortality forecasting with varying age-specific survival improvements

Many mortality forecasting approaches extrapolate past trends. Their predictions of the future development can be quite precise as long as turning points and/or age-shifts of mortality decline are not present. To account even for such mortality dynamics, we propose a model that combines recently developed ideas in a single framework. It (1) uses rates of mortality improvement to model the aging of mortality decline, and it (2) optionally combines the mortality trends of multiple countries to catch anticipated turning points. We use simulation-based Bayesian inference to estimate and run this model that also provides prediction intervals to quantify forecast uncertainty. Validating mortality forecasts for British and Danish women from 1991 to 2011 suggest that our model can forecast regular and irregular mortality developments and that it can perform at least as well as other widely accepted approaches like, for instance, the Lee-Carter model or the UN Bayesian approach. Moreover, prospective mortality forecasts from 2012 to 2050 suggest gradual increases for British and Danish life expectancy at birth.

Does the Impact of the Tobacco Epidemic Explain Structural Changes in the Decline of Mortality?

Since 1950, most developed countries have exhibited structural changes in mortality decline. This complicates extrapolative forecasts, such as the commonly used Lee–Carter model, that require the presence of a steady long-term trend. This study tests whether the impact of the tobacco epidemic explains the structural changes in mortality decline, as it is presumed in earlier studies. For this purpose, the time index of the Lee-Carter model in males was investigated in 20 developed countries between 1950 and 2011 for possible structural changes. It was found that removing the impact of smoking from mortality trends took away more than half of the 12 detected trend breaks. For the remaining trend breaks, adjusting for smoking attenuated the degree of change in mortality decline. Taking the tobacco epidemic into account should become standard procedure in mortality forecasts to avoid a misleading extrapolation of trends. Nevertheless, more research is needed to identify additional factors, such as health-care policies and innovations in medical treatment, to explain the remaining structural changes.

Forecasting differences in life expectancy by education

Forecasts of life expectancy (LE) have fuelled debates about the sustainability and dependability of pension and healthcare systems. Of relevance to these debates are inequalities in LE by education. In this paper, we present a method of forecasting LE for different educational groups within a population. As a basic framework we use the Li-Lee model that was developed to forecast mortality coherently for different groups. We adapted this model to distinguish between overall, sex-specific, and education-specific trends in mortality, and extrapolated these time trends in a flexible manner. We illustrate our method for the population aged 65 and over in the Netherlands, using several data sources and spanning different periods. The results suggest that LE is likely to increase for all educational groups, but that differences in LE between educational groups will widen. Sensitivity analyses illustrate the advantages of our proposed method.

Quantifying the contribution of changes in healthcare expenditures and smoking to the reversal of the trend in life expectancy in the Netherlands

Background

Since 2001 the Netherlands has shown a sharp upturn in life expectancy (LE) after a longer period of slower improvement. This study assessed whether changes in healthcare expenditure (HCE) explain this reversal in trends in LE. As an alternative explanation, the impact of changes in smoking behavior was also evaluated.

Methods

To quantify the contribution of changes in HCE to changes in LE, we estimated a health-production function using a dynamic panel regression approach with data on 19 OECD countries (1980–2009), accounting for temporal and spatial correlation. Smoking-attributable mortality was estimated using the indirect Peto-Lopez method.

Results

As compared to 1990–1999, during 2000–2009 LE in the Netherlands increased by 1.8 years in females and by 1.5 years in males. Whereas changes in the impact of smoking between the two periods made almost no contribution to the acceleration of the increase in LE, changes in the trend of HCE added 0.9 years to the LE increase between 2000 and 2009. The exceptional reversal in the trend of LE and HCE was not found among the other OECD countries.

Conclusion

This study suggests that changes in Dutch HCE, and not in smoking, made an important contribution to the reversal of the trend in LE; these findings support the view that investments in healthcare are increasingly important for further progress in life expectancy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-2357-2) contains supplementary material, which is available to authorized users.

The future of smoking-attributable mortality: the case of England & Wales, Denmark and the Netherlands

AIMS

We formally estimate future smoking-attributable mortality up to 2050 for the total national populations of England & Wales, Denmark and the Netherlands, providing an update and extension of the descriptive smoking-epidemic model.

METHODS

We used smoking prevalence and population-level lung cancer mortality data for England & Wales, Denmark and the Netherlands, covering the period 1950-2009. To estimate the future smoking-attributable mortality fraction (SAF) we: (i) project lung cancer mortality by extrapolating age-period-cohort trends, using the observed convergence of smoking prevalence and similarities in past lung cancer mortality between men and women as input; and (ii) add other causes of death attributable to smoking by applying a simplified version of the indirect Peto-Lopez method to the projected lung cancer mortality.

FINDINGS

The SAF for men in 2009 was 19% (44 872 deaths) in England & Wales, 22% (5861 deaths) in Denmark and 25% (16 385 deaths) in the Netherlands. In our projections, these fractions decline to 6, 12 and 14%, respectively, in 2050. The SAF for women peaked at 14% (38 883 deaths) in 2008 in England & Wales, and is expected to peak in 2028 in Denmark (22%) and in 2033 in the Netherlands (23%). By 2050, a decline to 9, 17 and 19%, respectively, is foreseen. Different indirect estimation methods of the SAF in 2050 yield a range of 1-8% (England & Wales), 8-13% (Denmark) and 11-16% (the Netherlands) for men, and 7-16, 12-26 and 13-31% for women.

CONCLUSIONS

From northern European data we project that smoking-attributable mortality will remain important for the future, especially for women. Whereas substantial differences between countries remain, the age-specific evolution of smoking-attributable mortality remains similar across countries and between sexes.

The role of smoking in changes in the survival curve: an empirical study in 10 European countries

PURPOSE

We examined the role of smoking in the two dimensions behind the time trends in adult mortality in European countries, that is, rectangularization of the survival curve (mortality compression) and longevity extension (increase in the age-at-death).

METHODS

Using data on national sex-specific populations aged 50 years and older from Denmark, Finland, France, West Germany, Italy, the Netherlands, Norway, Sweden, Switzerland, and the United Kingdom, we studied trends in life expectancy, rectangularity, and longevity from 1950 to 2009 for both all-cause and nonsmoking-related mortality and correlated them with trends in lifetime smoking prevalence.

RESULTS

For all-cause mortality, rectangularization accelerated around 1980 among men in all the countries studied, and more recently among women in Denmark and the United Kingdom. Trends in lifetime smoking prevalence correlated negatively with both rectangularization and longevity extension, but more negatively with rectangularization. For nonsmoking-related mortality, rectangularization among men did not accelerate around 1980. Among women, the differences between all-cause mortality and nonsmoking-related mortality were small, but larger for rectangularization than for longevity extension. Rectangularization contributed less to the increase in life expectancy than longevity extension, especially for nonsmoking-related mortality among men.

CONCLUSIONS

Smoking affects rectangularization more than longevity extension, both among men and women.