Potential and challenges for sustainable progress in human longevity

Domain: neuroscience
Published: 2026-01-24
Editorial outcome: major_revision

Abstract
Decelerating gains in life expectancy (e0) in high-income countries have raised concerns about the future of human longevity. To enhance our understanding of these developments, we examine subnational (N=450) mortality trends in Western Europe in the period 1992-2019. Between 1992 and 2005, gains in life expectancy were both substantial and widespread. Laggard regions experienced the fastest improvements, yielding rapid regional convergence. Between 2005 and 2019, however, gains in these regions decelerated, while remaining remarkably stable in vanguard regions, suggesting that it remains possible to continue extending longevity. The observed slowing of e0 gains is strongly associated with mortality at ages 55-74, which increased in this period across large areas of Western Europe, particularly in Germany and France. In this work, we show that monitoring mortality trends at a fine geographical level is crucial for revealing both the potential for, and challenges to, sustainable progress in human longevity.

Introduction
Life expectancy is a key indicator of public health and societal well-being, providing insights into the overall quality of life. While countries with high levels of income per capita (thereafter high-income countries) have generally enjoyed rising life expectancy over recent decades, in recent years, gains have been modest: stagnating mortality trends were reported1–3 even before the adverse effects of the COVID-19 pandemic on mortality trends4–6. These developments have raised concerns about the sustainability of further progress in human longevity and the prospects for extending life expectancy in the future.

Potential limits on the extension of life expectancy have been a long-standing subject of intense scientific debate7. Two decades ago, Oeppen and Vaupel8 argued that these limits were far from being reached, since ‘female life expectancy in the record-holding country has risen for 160 years at a steady pace of 3 months per year’. Examining trends in life expectancy from 1990 to 2019 for the eight countries where it was highest in 2019, Olshansky and colleagues9 concluded that ‘radical human life extension is implausible in [the 21st] century’, as life expectancy gains have considerably declined over the last three decades. Using mortality data from 16 countries with high life expectancy, Dowd and colleagues1 pointed out the uncertainties surrounding the future of life expectancy, in a context of slowing gains. A more recent cohort analysis of 23 high-income countries supports these conclusions, finding again that life expectancy gains are decelerating10. In this general context, some individual countries have attracted growing attention in recent years. This is particularly true for the United States, where rising drug-related mortality and the failure to reduce cardiovascular mortality have been identified as the major drivers of the stagnation in life expectancy in the 2010s2. High cardiovascular mortality in the US has also been identified as the main reason for its unfavourable position compared to other high-income countries11,12. The same applies to England and Wales, where life expectancy has diverged from the rest of Europe, stagnating since the mid-2010s13. Another prominent case is Germany, where life expectancy gains have recently slowed14.

Inevitably, analyses of mortality conducted at the national level have largely overlooked significant regional variations, which present a more detailed and nuanced picture15. One of the notable exceptions is a study using county-level data by race for the US to define ‘ten Americas’ with different life expectancy trends since 200016. It showed that the life expectancy of whites in low-income Appalachia and the lower Mississippi Valley has stagnated since 2000, while the life expectancy of Asian Americans continued to rise even during the 2010s. Rashid and colleagues17, analysing data from 6791 communities in England, showed that female life expectancy fell between 2014 and 2019 in 1,270 communities, and male life expectancy in 784 communities. Mühlichen et al.18 reported substantial regional variations in Germany, with some regions making substantial progress since the 1990s and others struggling to keep pace. While they provide valuable insights, these studies only look at one or a limited number of countries.

In this work, we substantially extend existing knowledge through a comprehensive analysis of life expectancy trends in 450 subnational regions covering 13 Western European countries (representing a total population of approximately 400 million people in 2019) over the period 1992-2019. This period presents an ideal time window, covering a relatively stable time before the global COVID-19 pandemic, which heavily impacted life expectancy in European regions19,20. Western Europe, with its great diversity of demographic, economic, and institutional contexts, is very well suited for such an analysis. Notably, it includes several of the countries with the highest levels of life expectancy, such as Spain, Italy and Switzerland. Another advantage of Western Europe is that detailed and reliable long-term mortality data stratified by year, region, sex, and age are generally available. Nevertheless, the data routinely collected by national statistical offices are far from homogeneous and cannot be directly used for comparative analyses (Supplementary Table 3). We thus begin by harmonizing the data to ensure a uniform structure and the comparability of spatial units over the observation period. We then apply a state-of-the-art demographic methodology21,22 to smooth the data and calculate mortality indicators. We use life expectancy at birth, a widely used and understood, easily interpretable mortality measure, as the main mortality outcome. It also has the advantage of providing more stable statistical estimates compared to partial life expectancy or life expectancy at specific ages, which is a particularly valuable feature for small spatial units. Finally, its use facilitates comparisons with other national and regional studies.

The main added value of our analysis lies in its extensive geographical coverage and high spatial resolution. By analysing smoothed, long-term mortality trajectories across a contiguous set of regions in Western Europe, we can unravel emerging trends and patterns that are often obscured in national-level analyses. We particularly focus on disparities in pathways towards longevity limits observed between vanguard and laggard regions. These complementary concepts of vanguard and laggard populations have often been used in demographic research to describe diverse mortality trajectories at the subnational level. There has been a worldwide general trend towards widening health inequalities by socioeconomic status (SES), which is measured by different dimensions, including the level of educational attainment. In general, the population groups with higher SES enjoy faster declines in mortality and gains in life expectancy than the groups with the lowest SES23. Substantial and increasing inequalities in mortality by marital status have also been reported24. Our analysis contributes to debates on the possibility of increasing human longevity, as vanguard populations pave the way towards higher life expectancy for other groups7. Finally, identifying geographical longevity hot and cold spots can deepen our understanding of trends in life expectancy and inform targeted public health interventions, including better allocation of health care resources.

Results
Evolution of longevity gains
Figure 1 offers a dual perspective on annual gains in life expectancy at birth (hereafter life expectancy or e0) across 450 regions in Western Europe from 1992 (the earliest year for which data are available for all of the countries included) to 2019 (the last year before the COVID-19 pandemic). The x-axis represents the average level of these gains, while the y-axis represents their variability, quantified by their standard deviation. This visualization provides a general overview of the evolution of annual life expectancy improvements and how equitably they have been distributed across European regions.

Two distinct periods can be identified. From 1992 to the mid-2000s, average annual gains in both male and female life expectancy remained remarkably stable, as indicated by the nearly vertical trend lines. During this period, gains in male life expectancy were larger (gold circles), averaging approximately 100 days per year (more than three months), while average gains in female life expectancy (purple triangles) were significantly smaller, at about 72 days per year. At the same time, regional variability in life expectancy gains was highest in the early 1990s, with standard deviations of 35 days for males and 28 days for females. This variability declined over the subsequent years, reaching a nadir in 2004-2005. Over this time, the pace of longevity improvements had become more homogeneous across European regions, with standard deviations decreasing by nearly 50% for both sexes and converging to approximately 15 days.

After this decade of improvements, the mid-2000s marked an important turning point away from longevity trends in Western Europe. Since then, life expectancy gains have steadily declined, while regional heterogeneity has gradually increased, especially for males. By the early 2010s, annual gains had dropped to 80 days for males and 50 days for females. These declines continued during the 2010s, falling to just 55 and 35 days respectively in 2018-2019. This represents approximately a two-fold reduction in life expectancy gains compared to the 1990s.

In Fig. 2, we build upon the averages and standard deviations presented in Fig. 1 by displaying the life expectancy trends for vanguard and laggard regions. We define vanguard and laggard regions as the top and bottom deciles of the distribution, respectively. Additionally, we show trends in the maximum and minimum life expectancy observed annually across all spatial units. The solid black line between the vanguard (blue) and laggard (red) lines represents the average life expectancy for all regions combined. Table 1 gives the average life expectancy gains for the entire period as well as for each decade, specifically for these groups of regions. Supplementary Table 1 provides the same indicators but for the periods 1992–2004 and 2005–2019.

The solid black line, which flattens through the 2010s for both males and females, illustrates the general deceleration in longevity improvement during the last decade of the study period, as illustrated in Fig. 1. However, the patterns for the vanguard and laggard regions differ notably. The trends for the laggard group, as well as those for the region with the lowest female life expectancy in each year (the lowest line in the right-hand side panel of Fig. 2), show a more pronounced decline in life expectancy gains compared to the overall average. The values in Table 1 provide additional quantitative evidence for this visual representation. For laggard regions, male and female life expectancy gains decreased from 129 days (roughly four months) and 94 days (three months) per year, respectively in the 1990s to 59 days (two months) and 39 days (roughly one month) per year in the 2010s. For the minimum life expectancy region, male and female gains decreased from 129 days (roughly four months) and 94 days (three months), respectively in the 2000s to 105 days (three months and a half) and 45 days (one month and a half) in the 2010s.

By contrast, the upper two lines in Fig. 2 in both panels, representing the vanguard group, show no clear indication of a decline in life expectancy gains. This distinction from laggard groups is even more pronounced for the maximum life expectancy region, where the data reveal nearly linear upward trajectories, indicating relatively stable longevity gains over these three decades. The figures in Table 1 reflect a slight reduction in both male and female life expectancy gains in the vanguard regions, from 3 and 2 months per year, respectively in the 1990s to 2.5 and 1.5 months per year in the 2010s. Meanwhile, gains in the record-holding regions remained relatively constant, at approximately 2.5 (male) and 1.5 (female) months annually.

Across the three decades of the study period, the maximum and minimum levels of male and female life expectancy occurred in nine different European regions. Four Spanish regions (Araba/Alava, Guadalajara, Salamanca and Soria) largely dominated male or female longevity in Western Europe. The two exceptions to this Spanish dominance are the Swiss region of Zug, which held the record for male life expectancy between 2007 and 2018, and the western part of inner London, which held it in 2019. The regions with the lowest life expectancies in a given year include two Portuguese regions, the Azores (both male and female in different periods) and Madeira (male), as well as Scotland (female).

We continue by exploring the relationship between life expectancy ranking and gains in more detail. Each dot in Fig. 3 represents a specific region, positioned according to its rank in the life expectancy distribution (x-axis) and its life expectancy gain in a given period (y-axis). The three selected periods, 1992–1993, 2004–2005 and 2018–2019, are distinguished by colour. The solid lines depict smooth functions for each period. The first- and last-time intervals correspond to the beginning and endpoints of our study period, and 2004–2005 represents the pivotal point in the life expectancy gains pathway seen in Fig. 1. Note that as all regions are ranked according to the estimated value of life expectancy at the beginning of a period, the ordering of individual regions (their location on the x-axis) varies between the different periods.

In 1992–1993, it was the laggard regions that saw the largest gains in both male and female life expectancy, as indicated by the cloud of green points in the upper right corner of Fig. 3 (both panels). During the same period, the vanguard regions (the top decile for life expectancy) experienced substantially lower gains. In contrast, the orange line for the second period (2004–2005) is relatively flat across the ranking for both sexes. This suggests that there was no clear relationship between ranking and life expectancy gains in 2004–2005. Finally, in 2018–2019, the relationship reversed for males, as indicated by the downward-sloping violet line in the left panel of Fig. 3. This indicates that, in contrast to the earlier periods, vanguard regions experienced higher gains in male life expectancy than the laggard regions in the late 2010s. The same is true for female life expectancy, although the difference between vanguard and laggard regions is smaller. The shift to a positive relationship between life expectancy and longevity gains is largely due to a significant (approximately fourfold) decrease in life expectancy gains at the lower end of the longevity distribution between 1992 and 2019. Annual gains in life expectancy at the upper end of the distribution, in contrast, remained relatively stable across the three periods.

Geography of longevity gains
Figure 2 reveals the regions with the highest and lowest life expectancies in different years within the study period. Our next analyses explore geographical disparities in Western Europe in greater detail. Figures 4 and 5 map regional male and female life expectancy gains (panels a, c and e) and ranking (panels b, d and f), in 1992–1993, 2004–2005 and 2018–2019. The vanguard (dark blue) and laggard (dark red) groups, shown in the panels b, d and f and denoted as D1 and D10, refer to the top and bottom deciles, and correspond to the groups of regions represented in Figs. 2 and 3. We also highlight the second (D2) and ninth deciles (D9) of the life expectancy distribution, in order to better understand the geographic clustering of laggard and vanguard regions. In Supplementary Fig. 1, we also show the average annual gains in life expectancy for the periods 1992–2004 and 2005–2019.

Figure 4a, c and e shows substantial gains in male life expectancy in 1992–1993 and 2004–2005 across all regions in Western Europe. The largest gains in 1992–1993 were observed in eastern Germany and several northern Italian regions. By 2004–2005, the largest increases were still seen in northern Italy, as well as in several regions in eastern Germany, the UK and Portugal. By 2018–2019, however, life expectancy gains had slowed everywhere, particularly in Germany and the UK, with the exception of Northern Ireland and southern England.

The overall geographical ranking of male life expectancy appears relatively stable at first glance (see Fig. 4b, d and f). But important shifts occurred within national populations, particularly in regions that either led or lagged in life expectancy. In 1992-1993, laggard regions were mainly located in eastern Germany, Portugal, near the Belgian-French border, and Scotland. By 2018-2019, these clusters had expanded into western Germany and southern Denmark, persisted in the Belgian-French border region, and disappeared from northern Portugal. Apart from the cluster around the Belgian-French border, life expectancy gains in laggard regions became much smaller over time. Vanguard regions in 1992, on the other hand, were primarily found in Italy (excluding the north), central Spain, and southern England. Over time, this cluster expanded into northern Italy and Switzerland but retreated from southern Italy, parts of Spain, and southern France.

A similar pattern is observed for female longevity. Laggard zones expanded into western Germany, emerged in Wallonia (southern Belgium), and disappeared from Portugal, Sicily, and Denmark (see Fig. 5b, d and f). Life expectancy gains in today’s laggard regions are significantly lower than those recorded in the former laggard regions (see Fig. 5a, c and e). The geography of vanguard regions for female life expectancy also changed over the study period. Over time, the number of regions among those with the highest female life expectancies in France dwindled, and by 2019, these vanguard regions were primarily concentrated in eastern Spain and northern Italy. For females as for males, absolute life expectancy gains in the vanguard regions of 2018–2019 were comparable to those observed in 1992–1993 for the original vanguard regions. Specifically, western France, northern Italy, and north-eastern Spain reported life expectancy increases of two months per year.

Age-specific trends
Examining age-specific trends provides further insight into the observed patterns. Table 2 summarizes the average annual changes in the probability of dying in specific age ranges (35–54, 55–74, and 75–84) across all regions in our panel. The results are stratified by sex and decade, which enables us to identify the specific age groups and periods where mortality improvements were slowest. Supplementary Table 2 provides the same indicators but for the periods 1992–2004 and 2005–2019.

The results show that the pace of mortality improvements has particularly slowed between the ages of 55 and 74. While in the 1990s, female mortality in this age range was decreasing at 2.1% per year, in the 2010s, the rate was just 0.7%. And among males, the pace of mortality reduction at these ages declined from 2 to 1.2% per year over the same period. Notably, no such decline is observed for the other age groups. The pace of mortality reduction was steady at around 2% per year for ages 35 to 54 (slightly lower for female than male mortality), and about 1.5% per year for ages 75 to 84 (slightly lower for male than female mortality).

The spatial patterns of annual change in the probability of dying between the ages of 55 and 74 (20q55) can be seen in Fig. 6, for males (panels a, c and e) and females (panels b, d and f). Overall, a marked deceleration between 1992 and 2019 is evident. The decreasing pace of overall life expectancy gains is strongly associated to this deceleration (see Supplementary Fig. 2). Even more alarmingly, in several regions 20q55 actually increased at the end of the period (in light pink). This adverse trend is particularly pronounced among males in eastern Germany and females in western Germany. Conversely, the probability of dying between ages 55 and 74 continued to decline steadily in most of the vanguard regions, such as in northern Italy, Switzerland and eastern France.

The results of the analyses conducted for 20q35 and 10q75 appear in the Appendix (Supplementary Figs. 3 and 4). No negative mortality trends were observed in the 35-54 and 75–84 age groups. However, one important observation stands out: in the United Kingdom, the probability of dying between the ages of 35 and 54 increased during the final pre-pandemic years. This unfavourable trend is particularly pronounced for males, with the exclusion of southern England.

Methods
Datapreparation
We collected subnational death and population counts for 13 Western European countries by age group and sex from Eurostat, the Human Mortality Database52, and national statistical offices. To ensure the comparability of the selected spatial units in terms of size and structure, we relied mainly on the Nomenclature of Territorial Units for Statistics (NUTS), using NUTS 3 level units for Denmark, France, Italy, Luxembourg, Spain, and Switzerland, NUTS 2 units for Austria, Belgium, the Netherlands, Portugal, and England and Wales, and NUTS 0 units for Ireland, Northern Ireland and Scotland. For Germany, we used a national spatial classification (Raumordnungsregionen)18. Due to territorial changes over time and data availability issues, minor adjustments of the raw data had to be made (see Supplementary Table 3 for details).

In the cases of Italy and Austria, mortality data did not differentiate between deaths occurring under one year of age and those occurring between ages 1 and 4: only aggregated death counts for ages 0-4 were available. To refine our life table estimates, we disaggregated these deaths, assuming that the ratio of the mortality rate for children under one year old to that of children aged 1-4 was constant across all regions of a country and equal to the national-level ratio obtained from the Human Mortality Database52. This procedure was applied separately for each year and sex.

Methodology
In this study, we model mortality over age and time for each spatial unit independently to ensure that region-specific mortality developments are accurately captured. Our primary objective is to filter out short-term fluctuations and statistical noise while retaining significant trends that extend beyond simple linear patterns. Achieving this requires a flexible method that does not impose overly restrictive structures, coupled with a robust approach capable of handling small sample sizes. For these reasons, we opted for P-splines, which allow for distinct smoothing parameters over age and time, enabling different degrees of smoothness across these domains53,54. This flexibility is particularly valuable in mortality analysis, where patterns can vary sharply by age and evolve either gradually or abruptly over time. P-splines also provide a robust and computationally efficient framework for handling noisy or sparse data, which is common in small-area estimation. To further speed up computations, an important consideration when dealing with a large number of regions, we used generalized linear array models (GLAM) framework, which enables fast evaluation of design matrices and penalty structures55. Since our analysis includes age 0, where mortality behaves very differently, we use a modified basis that partially decouples infant mortality from the rest of the age pattern21.

A key challenge when working with very small sample areas is that objective criteria for selecting smoothing parameters often lead to excessive smoothing, potentially reducing complex trends to over-simplified linear approximations, particularly over time. To address this, we constrained the range of smoothing parameters and selected the optimal value within this range using the Bayesian information criterion (BIC). This approach serves as an implicit prior, effectively avoiding both over-smoothing (e.g., imposing linearity over time) and overfitting (e.g., directly interpolating observed data).

An additional advantage of the P-spline framework is its integration within the composite link model (CLM), which accommodates age-group structures frequently found in small-area mortality data. This approach allows us to simultaneously smooth age-time patterns and disaggregate grouped age data, yielding estimates for every single year of age. Given the large number of spatial units with consistent age-group structures over time, we adopted the computational method proposed by Camarda and Durbán22, which significantly enhances computational efficiency for smoothing and ungrouping tasks.

To illustrate our methods, Supplementary Fig. 5 presents the trend in life expectancy at birth for 10 French and Dutch regions with varying profiles (geographical location, population size, life expectancy levels) for females from 1992 to 2019. The dots represent life expectancy levels calculated using raw data, while the lines represent life expectancy levels estimated using our methods, along with 95% confidence intervals.

All calculations were carried out using R version 4.4.256. Routines used to compute the regional life tables, as well as those used to generate all our figures, are openly accessible in an Open Source Framework repository, which provides detailed documentation to facilitate their use: https://osf.io/3dhmz. This repository also includes a full regional dataset containing annual population counts, life expectancy at birth, and year-to-year changes in life expectancy at birth by sex for the 450 regions in our panel.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "1. Key Results\nThis article presents a comprehensive analysis of subnational trends in life expectancy across 450 regions in Western Europe between 1992 and 2019. The authors identify a period of convergence in mortality trends up to 2005, followed by a marked deceleration and divergence. This divergence is primarily due to a sharper slowdown in life expectancy improvements among laggard regions compared to vanguard regions, which maintained a more stable pace of progress.\nThe paper shows that the stagnation is largely attributable to increasing mortality or slowed progress in the 55–74 age group. This work contributes valuable insights into the geographic dynamics of longevity, highlighting both potential limits and windows for further gains.\n2. Validity\nThe empirical analysis is generally sound and based on well-established demographic techniques. However, several methodological and interpretative issues warrant clarification:\n- The use of averages across regions may mask substantial intra-regional social inequalities in mortality. These internal disparities may affect both the observed means and variances and deserve at least a brief acknowledgment in the limitations. Notably, recent work has documented the persistence and transformation of social mortality gradients across Europe, both in the long run (Luque de Haro, 2024) and in recent decades (Mackenbach et al., 2018), which makes it crucial to consider socioeconomic variation within regions as a potentially confounding or explanatory factor.\n- The paper would benefit from a more explicit justification of the three biennia selected (1992–93, 2004–05, and 2018–19). While it appears that the choice of 2004–05 is intended to reflect a turning point in the trend of life expectancy gains, this rationale should be stated more explicitly. Additionally, this biennium does not correspond to the exact midpoint of the observation period (which would fall closer to 2005–06). Clarifying this choice is especially relevant given that the right-hand panels of Figures 4 and 5 — which summarize decile-level transformations in life expectancy rankings — reflect cumulative changes over the entire period, not just within these biennia.\n- In Figures 4 and 5, the left-hand panels illustrate the pace of life expectancy improvements during the selected biennia. While this snapshot is informative for assessing whether top-ranked regions are maintaining or accelerating their lead, it would be valuable to complement it with an additional visualization showing cumulative gains in life expectancy over broader subperiods (e.g., 1992–2005 and 2005–2019) for each territorial unit. Such a depiction would enhance the reader’s ability to interpret long-term regional dynamics and evaluate convergence/divergence trajectories over time.\n- The assertion \"as expected\" (p. 9, l. 188) is not sufficiently supported by a prior argument or theoretical expectation, and should either be justified or rephrased.\n3. Significance\nThe manuscript addresses an important issue of broad interest in demography and public health: the evolving potential for further gains in life expectancy in high-income settings. While the empirical contribution is strong, the paper would benefit from a richer theoretical contextualization. In particular, the discussion could be strengthened by connecting the findings to one of the classical debates in historical demography (notably the McKeown thesis, Szreter’s critiques, and the role of behavioral and institutional factors in the mortality decline).\nFurthermore, the discussion could benefit from a consideration of countervailing mechanisms, that is, forces or processes that may undermine or reverse the expected trajectory of health improvements, even in regions with relatively favorable socioeconomic and institutional conditions. This notion has been acknowledged in the literature on socioeconomic status and health disparities, for instance by Lutfey and Freese (2005), who note the possibility that certain mechanisms may disadvantage higher-status groups under specific circumstances.\nIn the context of this study, such countervailing mechanisms may include rising exposure to environmental risks (e.g., pollution, heat stress), the diffusion of unhealthy consumption patterns, or emerging inequalities in health behaviors (tobacco alcohol and other drugs consumption) and chronic disease management. A recent study by Masselot et al. (2023) has shown that climate-related factors, particularly heatwaves, are already contributing significantly to excess mortality across European cities. Acknowledging these emerging threats would provide a more nuanced interpretation of why some regions experience stagnation or reversal in longevity trends despite apparent structural progress.\n4. Data and Methodology\n- The spatial disaggregation used is a major strength comparing with other national studies. Nevertheless, the variation in population sizes across regions may influence both mean life expectancy and its variance (e.g., smaller units tend to exhibit more variability). A supplementary table indicating the population size of each unit at different time points would be a valuable addition.\n- The use of NUTS-based spatial units, while standard in European demographic research, results in considerable variation in population size across countries and regions. For instance, NUTS-3 regions in Spain or Italy differ markedly from NUTS-2 regions used in Portugal or Belgium, and from NUTS-0 units in Ireland or Luxembourg. These discrepancies may affect both the internal heterogeneity of units and the statistical stability of mortality estimates. A brief discussion of how these differences were addressed or accounted for would improve the transparency of the methodology. For example, even within the NUTS-3 level, there are stark contrasts: the Madrid region is a large, socioeconomically diverse metropolitan area with over six million inhabitants, while Teruel is sparsely populated, more homogeneous, and has fewer than 150,000 inhabitants. Such differences are likely to affect both variability and internal inequality, and merit consideration.\n- While the structure of the journal often places methods at the end, a brief summary of the main data sources and modeling strategy in the Introduction would aid clarity for readers.\n- It may be useful to include in the appendix the figure currently only available online showing all regional deciles, which would complement the information in Figures 4 and 5.\n5. Analytical Approach\n- The use of P-splines and CLM for small-area mortality estimation appears methodologically appropriate, but the details could be better summarized in the main text.\n- It would be helpful if the authors clarified how they validated the smoothing parameters and whether they tested alternative specifications or sensitivity to different degrees of smoothing.\n- The indicators chosen (e₀, 20q35, 20q55, 10q75) are relevant and well-established, although the discussion could explore whether the trends observed in 20q55 may also be influenced by alcohol- and diet-related mortality, not just smoking.\n- To improve transparency, the authors might consider including a supplementary figure comparing raw and smoothed life expectancy trajectories for a small number of representative regions. This would help readers better understand the impact of the smoothing procedure and assess whether key patterns are robust to different levels of smoothing. Such a visualization would complement the existing methodological description and strengthen the overall clarity of the analytical approach\n6. Suggested Improvements\n- Consider incorporating a broader discussion of behavioral risk factors beyond smoking (e.g., alcohol consumption, diet), especially considering the age group most affected by the stagnation (55–74).\n- Explore, if possible, common structural features of laggard and vanguard regions (e.g., education levels, income inequality, per capita income, health service access, productive structure) , which could enrich the interpretation of long-term regional dynamics.\n- If feasible, include a brief note acknowledging that even subnational units such as provinces (e.g., in Spain) may still mask significant internal disparities, and that this limitation could affect the interpretation of regional averages.\n- The authors state that “on average, these ‘new’ vanguard regions experienced absolute life expectancy gains comparable to those of the original vanguard regions.” While this is technically accurate, it may obscure an important dynamic. For a region to move into the top decile of life expectancy, it must have experienced faster gains than the original vanguards over a substantial part of the observation period. Therefore, the fact that these new vanguard regions are now improving at the same pace as the original ones suggests a relative deceleration in their trajectory. A brief clarification of this point would enhance the interpretation of regional dynamics and the discussion of convergence/divergence processes.\n7. Clarity and Context\n- The manuscript is generally well-written and accessible, but could benefit from clearer theoretical grounding, as noted above.\n- Adding more context about long-term historical trends in mortality and prior health transitions would help frame the results more robustly.\n8. References\n- The manuscript cites relevant and up-to-date sources. However, additional references to key debates in demographic history and epidemiology (e.g., McKeown, Szreter, Omran’s transition theory) would enhance the contextual depth of the discussion.\n9. Expertise and Scope\n- I am confident assessing the demographic interpretation of mortality trends, regional analysis, and socioeconomic contextualization.\n- I do not have deep expertise in the specific statistical implementation of P-splines or CLM, and thus cannot fully evaluate the appropriateness of the smoothing parameter selection beyond what is reported.", "reviewer_2": "Summary of Key Results\nThis manuscript examines changes in life expectancy from 1992 to 2019 across Western European regions. The principal strength of the study lies in the diversity of regions analyzed simultaneously, which may hold important implications for public policy if interpreted appropriately. The authors use these regions to rank gains in life expectancy and classify them as either laggards or vanguards in life expectancy improvements. The results indicate a slowdown in the pace of mortality reductions during the second half of the study period. Furthermore, the authors investigate the drivers of this deceleration in gains in e0, identifying the 55–74 age group as having the greatest impact on the reduction in gains.\nSignificance and Impact\nThe findings may hold considerable relevance for policymakers and researchers examining the deceleration in life expectancy improvements. The analysis is insightful, illustrating the value of considering both local and broader contextual factors when evaluating the limits of longevity.\nData and Methodology\nThe study is fully reproducible (with the exception of the dataset, which is not fully publicly available; however, this is not the responsibility of the authors). The documentation for code usage is generally thorough and accessible, except the file “0-Functions-R,” which would benefit from more detailed documentation regarding the purpose and function of each component. The code is well-structured and clear. A minor technical note is that the analysis relies on the MortalitySmooth library, which has been deprecated on CRAN. Since this requires additional steps for installation in R, it would be helpful to provide users with brief guidance on how to complete this process.\nTwo methodological concerns are not addressed in the manuscript and warrant discussion. First, the issue of mixing NUTS classification levels. The NUTS classification distinguishes between NUTS-1 (major socio-economic regions), NUTS-2 (basic regions for regional policies), and NUTS-3 (small regions for specific diagnoses). The authors do not clarify why it is methodologically appropriate to compare, for example, Spain at the NUTS-3 level with Portugal at the NUTS-2 level. This inconsistency may obscure heterogeneities that the paper aims to uncover. What changes, if any, arise when the analysis is restricted to the NUTS-2 level?\nSecond, the use of P-splines raises concerns. Although commonly used and well-documented in demographic research, P-splines are known to perform poorly at the extremes of the age distribution, particularly at ages 0 and w. Given the aims of the paper, overfitting at age 0 may be especially problematic, as errors at this age can propagate through the life table and significantly affect estimates of e0. The code suggests that some correction was applied for age 0, but this is not explained in the methods or the appendix. The authors are encouraged to elaborate on how or if this issue was addressed.\nAnalytical Approach\nThe methods employed are appropriate for addressing the research question. They are sufficiently comprehensive, and the discussion of results is both adequate and original.\nSuggested Improvements\nIn the abstract, the authors state: “… is strongly associated with mortality of the population aged 55–74, which increased by 2019…”. It appears the intention is to refer to the population rather than mortality increasing; the sentence is currently ambiguous.\nThe rationale for using regional estimates of e0 and comparing them should be emphasized more clearly early in the manuscript. Although the importance of this approach becomes clearer in the results and discussion sections, it would benefit from stronger justification earlier on.\nThe authors explain why the analysis concludes in 2019, but do not justify why it begins in 1992. This should be addressed.\nAlthough two distinct periods of e0 gains are identified in Figure 1, these classifications are not applied in the subsequent analysis. Incorporating them may enhance the strength of the findings.\nFor Figure 3, the color scale should be modified to ensure readability when printed in black and white.\nOn page 6, line 162, the authors state: “…today’s laggard regions are significantly lower than…”. It should be clarified whether this refers to statistical significance.\nIn the Shiny app, the figures lack a color legend, which should be included.\nOn page 12, line 273, the sentence “… phenomenon has only been observed in the United Kingdom…” should be nuanced to reflect that this applies only among the countries included in the study.\nQuestions for the authors:\n• Given that mortality at younger ages is relatively low in most of the regions analyzed, some may argue that other indicators, such as LE25 or LE65, may be more suitable for assessing changes in old-age mortality. Why were these not considered?\n• Since the analysis aims to identify the age group contributing most to the stagnation in life expectancy gains, why was an age decomposition not performed?\n• P-splines allow for the estimation of confidence intervals through bootstrapping. Would incorporating confidence intervals into the e0 estimates add value to the analysis, particularly in assessing whether regional and national differences are statistically significant?\n• In the discussion of future research, the authors suggest incorporating COVID-19 years. Why not include them in the current analysis?", "reviewer_3": "The paper presents a careful analysis of mortality and life expectancy trends across 450 subnational regions in Western Europe over a nearly 30-year period. It revealed sustained improvements in life expectancy in vanguard regions, while demonstrating different dynamics in laggard areas. This is an original, methodologically sound research, supported by beautiful visualization.\nA few comments: `\n• I wonder whether the Introduction could be slightly extended or adjusted to better reflect the broader context of diverse trajectories in life expectancy across vanguard and laggard subpopulations.\n• In the Discussion, you refer to convergence-divergence theory and suggest that divergence may result from faster growth in vanguard regions due to earlier adoption of innovations. However, based on your results, divergence since the mid-2000s appears to be more strongly driven by a slowdown in laggard regions. It would be useful to clarify this point and consider whether the convergence-divergence framework fully captures the observed pattern.\n• While the manuscript is descriptively rich, a more detailed discussion of the potential causal mechanisms behind the recent divergences would be beneficial. Other factors, such as differential access to healthcare, austerity measures, or economic restructuring, may be relevant beyond smoking and cardiovascular disease. These factors could be acknowledged, even if they are speculative.\n• Although the focus on the pre-pandemic period is justified, I suggest briefly commenting on how COVID-19 might have affected the trajectories described. Even a short reflection on the potential for COVID-19 to exacerbate or alter divergence patterns would be a useful addition.\nMinor comments:\n• I recommend replacing “during the pre-pandemic period” with the actual years (“1992–2019”). Consider using “slowdown” or similar alternatives instead of “deceleration” (in some cases). I would also advise avoiding strong phrases like “we strongly advocate.”\n• The paper opens with references to COVID-19, which is not the focus of the study. It might make more sense to start with literature on pre-pandemic stagnation in mortality improvements and only later mention the pandemic if needed.\n• The following might be a useful addition to the discussion of stagnating mortality improvements in other high-income countries:\nTimonin, S., Leon, D. A., Banks, E., Adair, T., & Canudas-Romo, V. (2024). Faltering mortality improvements at young-middle ages in high-income English-speaking countries. International Journal of Epidemiology, 53(3). https://doi.org/10.1093/ije/dyae128\n• Should confidence intervals be shown, at least for regions with maximum and minimum life expectancy? Also, I couldn’t find a legend explaining the meaning of the colored lines. Using different Y-axes for males and females makes comparisons difficult and may be misleading.\n• Typically, D1 refers to the most deprived group and D10 to the least deprived (e.g., in the UK and Australia)."}, "author_response": "We are very grateful to the reviewers for their constructive comments and suggestions, which\nhelped us to improve the manuscript. Below we provide our point-by-point responses in blue.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\n1. Key Results\nThis article presents a comprehensive analysis of subnational trends in life expectancy across\n450 regions in Western Europe between 1992 and 2019. The authors identify a period of\nconvergence in mortality trends up to 2005, followed by a marked deceleration and\ndivergence. This divergence is primarily due to a sharper slowdown in life expectancy\nimprovements among laggard regions compared to vanguard regions, which maintained a\nmore stable pace of progress. The paper shows that the stagnation is largely attributable to\nincreasing mortality or slowed progress in the 55–74 age group. This work contributes valuable\ninsights into the geographic dynamics of longevity, highlighting both potential limits and\nwindows for further gains.\n2. Validity\nThe empirical analysis is generally sound and based on well-established demographic\ntechniques. However, several methodological and interpretative issues warrant clarification:\nThe use of averages across regions may mask substantial intra-regional social inequalities in\nmortality. These internal disparities may affect both the observed means and variances and\ndeserve at least a brief acknowledgment in the limitations. Notably, recent work has\ndocumented the persistence and transformation of social mortality gradients across Europe,\nboth in the long run (Luque de Haro, 2024) and in recent decades (Mackenbach et al., 2018),\nwhich makes it crucial to consider socioeconomic variation within regions as a potentially\nconfounding or explanatory factor.\nFollowing the suggestion of the Reviewer, we now briefly acknowledge this limitation (Line\n350):\n“Moreover, while we aimed for the most detailed geographical units and the longest possible\ntime series, differences in data availability obliged us to use less detailed information in some\ncountries. This averaging of outcomes across larger areas may conceal inequalities in longevity,\nwhich recent studies have shown to be important for interpreting spatial disparities [47, 48].”\nThe paper would benefit from a more explicit justification of the three biennia selected (1992–\n93, 2004–05, and 2018–19). While it appears that the choice of 2004–05 is intended to reflect\na turning point in the trend of life expectancy gains, this rationale should be stated more\nexplicitly. Additionally, this biennium does not correspond to the exact midpoint of the\nobservation period (which would fall closer to 2005–06). Clarifying this choice is especially\n1\nrelevant given that the right-hand panels of Figures 4 and 5 — which summarize decile-level\ntransformations in life expectancy rankings — reflect cumulative changes over the entire\nperiod, not just within these biennia.\nOur paper focuses on the analysis of long-term mortality trends for the period 1992-2019. The\ninitial point of the analysis (1992) corresponds to the earliest year for which the data were\navailable for all countries included in our sample. The year 2019 corresponds to the last year\nbefore the COVID-19 pandemic. Indeed, 2004-2005 reflects a turning point in the trend of life\nexpectancy gains. This point also falls very close to the mid-point of the entire period. As a\nresult, we ended up with three snapshots: 1992-1993, 2004-2005, and 2018-2019. Although\nthe respective clarification was provided in our initial submission (p.4, lines 112-116), we agree\nthat more explanations are needed.\nThe first sentence in the ‘Results’ section (Line 72) reads now as follows:\n“Figure 1 offers a dual perspective on annual gains in life expectancy at birth (hereafter life\nexpectancy or e0) across 450 regions in Western Europe from 1992 (the earliest year for which\ndata are available for all of the countries included) to 2019 (the last year before the COVID-19\npandemic).”\nWe would like to clarify that the purpose of Figure 1 was not only to provide the global\noverview of the changes in life expectancy gains but also to justify the usage of 2004-2005 as\none of the three snapshots over the observation period. These snapshots were then used\nconsistently for Figures 3–6.\nIn Figures 4 and 5, the left-hand panels illustrate the pace of life expectancy improvements\nduring the selected biennia. While this snapshot is informative for assessing whether top-\nranked regions are maintaining or accelerating their lead, it would be valuable to complement\nit with an additional visualization showing cumulative gains in life expectancy over broader\nsubperiods (e.g., 1992–2005 and 2005–2019) for each territorial unit. Such a depiction would\nenhance the reader’s ability to interpret long-term regional dynamics and evaluate\nconvergence/divergence trajectories over time.\nWe agree with this suggestion. We added Supplementary Figure 1 to Supplementary\nInformation.\nThe assertion 'as expected' (p. 9, l. 188) is not sufficiently supported by a prior argument or\ntheoretical expectation, and should either be justified or rephrased.\n‘as expected’ has been removed from this sentence.\n3. Significance\nThe manuscript addresses an important issue of broad interest in demography and public\nhealth: the evolving potential for further gains in life expectancy in high-income settings. While\nthe empirical contribution is strong, the paper would benefit from a richer theoretical\ncontextualization. In particular, the discussion could be strengthened by connecting the\n2\nfindings to one of the classical debates in historical demography (notably the McKeown thesis,\nSzreter’s critiques, and the role of behavioral and institutional factors in the mortality decline).\nThank you for these very useful suggestions. We have added the following paragraphs to the\n‘Discussion’ section (Line 306):\n“Although our analyses are mainly aimed at providing novel empirical insights, and a detailed\nanalysis of mortality determinants is beyond the scope of this study, it is important to highlight\nfactors and potential causal mechanisms behind the observed subnational trends and\ndifferentials. Broadly speaking, it makes sense to situate the discussion of these factors within\nlong-standing debates on the role of broad socioeconomic change versus targeted\ninterventions in shaping mortality decline [35, 36]. The classical McKeown thesis attributes\nrapid mortality decline in industrialized countries to broad economic and social changes, rather\nthan to targeted public health or medical interventions. While this controversial thesis has since\nbeen overturned on empirical grounds, it posed the fundamental question of whether\npopulation health is determined primarily by broad-based social, political and economic\nconditions or by more targeted policies and interventions [37].\nAt the subnational level, this broader debate finds a close parallel in discussions of the relative\nrole of individual versus contextual determinants of health [38]. Accordingly, regional\ndisparities in mortality can be interpreted in terms of compositional effects (reflecting the\nunequal distribution of individual risk factors across areas) or contextual effects (the influence\nof place-based conditions and policies). From a compositional perspective, our finding that\nstagnation in life expectancy has been most pronounced at ages 55–74 highlights the\ncontribution of lifestyle-related factors such as alcohol consumption, drug use, poor diet and\nphysical inactivity [1, 39]. At the same time, contextual factors provide important\ncomplementary explanations. Recent research has emphasized the impact of austerity\nmeasures and so-called deaths of despair (suicide, drug misuse and alcohol-specific causes) on\nstagnating mortality in the UK [40-42]. In our study, the regional divergence in life expectancy\nobserved since the mid-2000s broadly coincides with the period of the 2008 financial crisis and\nits aftermath, when economic growth slowed across Western Europe while becoming\nincreasingly concentrated in major metropolitan regions [43, 44]. These urban areas, which\nhost high-skill service industries, are now among the best-performing regions in terms of\nlongevity. For instance, between 2005 and 2019, the western part of central London rose from\n132nd to 1st place for males and from 247th to 15th for females, while Paris advanced from\n40th to 32nd for males and from 45th to 4th for females.”\nFurthermore, the discussion could benefit from a consideration of countervailing mechanisms,\nthat is, forces or processes that may undermine or reverse the expected trajectory of health\nimprovements, even in regions with relatively favorable socioeconomic and institutional\nconditions. This notion has been acknowledged in the literature on socioeconomic status and\nhealth disparities, for instance by Lutfey and Freese (2005), who note the possibility that\ncertain mechanisms may disadvantage higher-status groups under specific circumstances. In\nthe context of this study, such countervailing mechanisms may include rising exposure to\nenvironmental risks (e.g., pollution, heat stress), the diffusion of unhealthy consumption\npatterns, or emerging inequalities in health behaviors (tobacco alcohol and other drugs\nconsumption) and chronic disease management. A recent study by Masselot et al. (2023) has\nshown that climate-related factors, particularly heatwaves, are already contributing\nsignificantly to excess mortality across European cities. Acknowledging these emerging threats\nwould provide a more nuanced interpretation of why some regions experience stagnation or\nreversal in longevity trends despite apparent structural progress.\nThis is a very good suggestion, thank you. The issues mentioned are indeed among the\nchallenges implied by the title of the paper and should therefore be emphasized. We added\nthe following paragraph in the discussion (Line 333):\n“Looking ahead, emerging public health threats could slow or even reverse longevity\nimprovements, even in regions with favourable socioeconomic conditions. Previous research\nhas suggested that countervailing mechanisms such as increased exposure to environmental\nrisks and the diffusion of unhealthy behaviours may disproportionately affect higher-status\ngroups [45]. Climate-related risks, notably heatwaves, are already having a measurable impact\non mortality in European cities, with particularly strong effects observed in eastern Europe [46].\n2023). These factors are likely to become increasingly influential in the future, although well-\ndesigned policy interventions could mitigate their adverse effects on population health.”\n4. Data and Methodology\nThe spatial disaggregation used is a major strength comparing with other national studies.\nNevertheless, the variation in population sizes across regions may influence both mean life\nexpectancy and its variance (e.g., smaller units tend to exhibit more variability). A\nsupplementary table indicating the population size of each unit at different time points would\nbe a valuable addition.\nFollowing your suggestion, we have added in our Open Science Framework repository a full\nregional dataset that contains annual population counts, life expectancy at birth, and year-to-\nyear changes in life expectancy at birth by sex for the 450 regions in our panel indicating the\npopulation size of each unit at different time points. We have mentioned this dataset at the\nend of the Methodology section (Line 455).\n“This repository also includes a full regional dataset containing annual population counts, life\nexpectancy at birth, and year-to-year changes in life expectancy at birth by sex for the 450\nregions in our panel.”\nThe use of NUTS-based spatial units, while standard in European demographic research,\nresults in considerable variation in population size across countries and regions. For instance,\nNUTS-3 regions in Spain or Italy differ markedly from NUTS-2 regions used in Portugal or\nBelgium, and from NUTS-0 units in Ireland or Luxembourg. These discrepancies may affect\nboth the internal heterogeneity of units and the statistical stability of mortality estimates. A\n4\nbrief discussion of how these differences were addressed or accounted for would improve the\ntransparency of the methodology. For example, even within the NUTS-3 level, there are stark\ncontrasts: the Madrid region is a large, socioeconomically diverse metropolitan area with over\nsix million inhabitants, while Teruel is sparsely populated, more homogeneous, and has fewer\nthan 150,000 inhabitants. Such differences are likely to affect both variability and internal\ninequality, and merit consideration.\nYou raised legitimate concerns about the comparability of mortality estimates between\ndifferent spatial units. We must emphasize that the selection of spatial units for our analysis\nwas driven by data availability. Demographic statistics are typically tabulated according to\nestablished administrative units, which align with the NUTS classification. Although the NUTS\nclassification was created to maintain comparable regional units across the EU, substantial\ncomparability issues could arise due to variations in population size, population density, and\nadministrative structures of the regions at the same NUTS level. Therefore, the methodological\nissues you highlighted are mainly related to the limitations of the NUTS classification per se\nrather than the design of our study. As mentioned above, there was no other alternative for\nus as to use the regional mortality data available at different NUTS levels.\nThere was considerable variation among the European countries in terms of data availability.\nFor some countries, the detailed regional mortality data are readily available online, while for\nothers such data can only be obtained upon special request. In certain cases, the desired level\nof geographical details is not possible to obtain because of data protection rules. While\nbuilding our dataset we aimed at the highest level of geographical detail, the longest possible\ntime series, and the largest possible number of countries. We also kept in mind data\ncomparability issues and the suitability of the data for visualizations, especially mortality maps.\nAlthough we believe that we ended with a decent data sample, we agree that the\ncomparability between spatial units warrant thorough discussion. Thus, we added the\nfollowing paragraph under the ‘Strengths and limitations’ section (Line 342).\n“A major strength of our work lies in the unique combination of wide geographical coverage\nwith high spatial resolution mortality series. We invested substantial effort to harmonize\nmortality data across regions and over time, ensuring comparability despite differences in\nnational statistical practices (see Supplementary Table 3 for details on the implemented\nadjustments). This harmonization was essential to meeting our study’s central objective:\nassessing long-term longevity trends both by individual region and by contrasting vanguard\nand laggard groups. Nonetheless, perfect comparability could not be achieved. The use of the\nNUTS classification, though standard in European demographic research, entails considerable\nvariation in the population size of spatial units within the same NUTS level between countries.\nMoreover, while we aimed for the most detailed geographical units and the longest possible\ntime series, differences in data availability obliged us to use less detailed information in some\ncountries. This averaging of outcomes across larger areas may conceal inequalities in longevity,\nwhich recent studies have shown to be important for interpreting spatial disparities [47, 48].”\nmain data sources and modeling strategy in the Introduction would aid clarity for readers.\nAs suggested, we added a brief summary of the main data sources and modelling strategy to\nthe Introduction (Line 45). The revised text reads as follows:\n“Another advantage of Western Europe is that detailed and reliable long-term mortality data\nstratified by year, region, sex, and age are generally available. Nevertheless, the data routinely\ncollected by national statistical offices are far from homogeneous and cannot be directly used\nfor comparative analyses (Supplementary Table 3). We thus begin by harmonizing the data to\nensure a uniform structure and the comparability of spatial units over the observation period.\nWe then apply a state-of-the-art demographic methodology [21, 22] to smooth the data and\ncalculate mortality indicators.”\nIt may be useful to include in the appendix the figure currently only available online showing\nall regional deciles, which would complement the information in Figures 4 and 5.\nWe assume that the reviewer refers to the maps available in our Shiny App, which display the\nrank of each region in the life expectancy distribution by each year and sex. In the article, we\nchose to present such maps only for three benchmark periods, and only for the vanguard and\nlaggard regions. We would prefer not to overwhelm the reader with excessive additional\ninformation. Instead, we provide the opportunity to explore our results in full using the Shiny\nApp, which we have further improved.\nhttps://histdemo.shinyapps.io/ReLoG_Europe/\n5. Analytical Approach\nThe use of P-splines and CLM for small-area mortality estimation appears methodologically\nappropriate, but the details could be better summarized in the main text.\nWe have expanded the Methodology section to better summarize the rationale behind the use\nof P-splines in our context. Specifically, we now emphasize their flexibility in allowing different\nsmoothing parameters over age and time, which is particularly valuable given the distinct\nmortality patterns across ages (e.g., infant vs. adult mortality) and their evolution over time.\nWe also highlight that P-splines provide a robust and efficient approach for dealing with sparse\nor noisy data, which is common in small-area mortality estimation. Finally, we clarify that the\nuse of the Generalized Linear Array Models (GLAM) framework allows for faster computation,\nwhich is especially important when analyzing a large number of regions. We hope this revision\n(and the associated added references) improves the clarity and accessibility of our\nmethodological choices.\nIt would be helpful if the authors clarified how they validated the smoothing parameters and\nwhether they tested alternative specifications or sensitivity to different degrees of smoothing.\nThank you for the insightful comment regarding the validation of the smoothing parameters\nand the potential sensitivity of our results to different degrees of smoothing. In our analysis,\nsmoothing parameters - controlling the amount of smoothness in the P-spline framework - are\nselected using the Bayesian Information Criterion (BIC). This criterion balances model fit and\ncomplexity, where fit is measured by the Deviance in a Poisson setting (McCullagh & Nelder,\n1989, p. 34), and complexity is quantified through the effective dimension, computed as the\ntrace of the hat matrix (Hastie & Tibshirani, 1990, p. 52). A key advantage of P-splines is that\nboth these quantities are computed analytically from the estimation algorithm. BIC, in\nparticular, has been shown to perform well in the context of mortality modelling, often\nfavouring more parsimonious models than criteria such as AIC (Currie et al., 2004; Camarda,\n2012).\nIn dealing with regional data, we encountered significant heterogeneity: small regions often\nexhibit high variability due to sparse populations, while larger regions provide more stable\ninformation. Rather than imposing a fixed degree of smoothing, we allowed the smoothing\nparameters to be selected by BIC, enabling the level of smoothness to adapt to the data\ncharacteristics of each region.\nA further strength of our approach lies in the anisotropic nature of P-splines, which allows\nseparate tuning of smoothness over age and time. We permitted a wide range of values for\nthe smoothing parameter over age, while imposing a mild prior on the parameter over time\nto avoid extremes, namely, overly small values that could lead to overfitting, and overly large\nvalues that would flatten trends excessively. Even so, the candidate values for the time-\ndimension smoothing spanned a broad interval (from 10⁻² to 10²), ensuring substantial\nflexibility: values smaller than 10⁻² would lead to practically interpolating mortality over time,\nwhereas values larger than 10² would practically reduce the fit to a linear time trend at each\nage. BIC then selects the optimal smoothing level within this range independently for each\nregion.\nWe hope this additional clarification and evidence help convey the rationale and robustness\nof our methodology.\nThe indicators chosen (e₀, 20q35, 20q55, 10q75) are relevant and well-established, although\nthe discussion could explore whether the trends observed in 20q55 may also be influenced by\nalcohol- and diet-related mortality, not just smoking.\nPlease see our response to the similar point below (6. Suggested Improvements).\nExplore, if possible, common structural features of laggard and vanguard regions (e.g.,\neducation levels, income inequality, per capita income, health service access, productive\nstructure), which could enrich the interpretation of long-term regional dynamics.\nWe are afraid that this would too ambitious to accomplish within one study. However, we do\nagree that this would enrich the interpretation of long-term regional dynamics, and thus, we\nmention in the discussion (Line 379) that “identifying common characteristics (e.g.\nsocioeconomic or cultural factors) that differentiate vanguard from laggard regions could shed\nmore light on the reasons why some areas perform better than others. Addressing this\n7\nquestion would eventually require systematic analyses that integrate mortality outcomes with\narea-level contextual variables. While this lies beyond the scope of our study, it represents a\npromising and necessary direction for future research”.\nTo improve transparency, the authors might consider including a supplementary figure\ncomparing raw and smoothed life expectancy trajectories for a small number of representative\nregions. This would help readers better understand the impact of the smoothing procedure\nand assess whether key patterns are robust to different levels of smoothing. Such a\nvisualization would complement the existing methodological description and strengthen the\noverall clarity of the analytical approach.\nWe appreciate the reviewer’s suggestion to improve transparency by visually comparing raw\nand smoothed life expectancy trajectories for a small number of regions.\nIn line with these recommendations, we include a new Supplementary Figure 5. This figure\nshows the observed and estimated life expectancy at birth (with 95% confidence intervals) for\n10 different French and Dutch regions, separately for males and females (based on publicly\navailable data). These regions present large differences in terms of population size, location\nand level of life expectancy. The figure demonstrates that our BIC-driven procedure captures\na wide array of regional time trends without imposing a common structure. Using alternative\ninformation criteria would likely have resulted in different smoothing selections that may not\nhave captured the observed patterns as accurately, potentially leading to less reliable\nconclusions in subsequent analyses. We have added a paragraph at the end of the\nMethodology section (Line 447) to describe this new Figure:\n“To illustrate our methods, Supplementary Figure 5 presents the trend in life expectancy at\nbirth for 10 French and Dutch regions with varying profiles (geographical location, population\nsize, life expectancy levels) for female from 1992 to 2019. The dots represent life expectancy\nlevels calculated using raw data, while the lines represent life expectancy levels estimated\nusing our methods, along with 95% confidence intervals.”\nTo facilitate transparency and reproducibility, we have also updated our OSF repository to\ninclude fully reproducible code for this additional analysis.\n6. Suggested Improvements\nConsider incorporating a broader discussion of behavioral risk factors beyond smoking (e.g.,\nalcohol consumption, diet), especially considering the age group most affected by the\nstagnation (55–74).\nSuch discussion is now incorporated in the following paragraph (Line 316):\n“At the subnational level, this broader debate finds a close parallel in discussions of the relative\nrole of individual versus contextual determinants of health [38]. Accordingly, regional\ndisparities in mortality can be interpreted in terms of compositional effects (reflecting the\nunequal distribution of individual risk factors across areas) or contextual effects (the influence\nof place-based conditions and policies). From a compositional perspective, our finding that\nstagnation in life expectancy has been most pronounced at ages 55–74 highlights the\ncontribution of lifestyle-related factors such as alcohol consumption, drug use, poor diet and\nphysical inactivity [1, 39]. At the same time, contextual factors provide important\ncomplementary explanations. Recent research has emphasized the impact of austerity\nmeasures and so-called deaths of despair (suicide, drug misuse and alcohol-specific causes) on\nstagnating mortality in the UK [40-42]. In our study, the regional divergence in life expectancy\nobserved since the mid-2000s broadly coincides with the period of the 2008 financial crisis and\nits aftermath, when economic growth slowed across Western Europe while becoming\nincreasingly concentrated in major metropolitan regions [43, 44]. These urban areas, which\nhost high-skill service industries, are now among the best-performing regions in terms of\nlongevity. For instance, between 2005 and 2019, the western part of central London rose from\n132nd to 1st place for males and from 247th to 15th for females, while Paris advanced from\n40th to 32nd for males and from 45th to 4th for females.”\nIf feasible, include a brief note acknowledging that even subnational units such as provinces\n(e.g., in Spain) may still mask significant internal disparities, and that this limitation could\naffect the interpretation of regional averages.\nWe added the following statements to the ‘Strength and limitation’ section (Line 350):\n“Moreover, while we aimed for the most detailed geographical units and the longest possible\ntime series, differences in data availability obliged us to use less detailed information in some\ncountries. This averaging of outcomes across larger areas may conceal inequalities in longevity,\nwhich recent studies have shown to be important for interpreting spatial disparities [47, 48].”\nThe authors state that 'on average, these ‘new’ vanguard regions experienced absolute life\nexpectancy gains comparable to those of the original vanguard regions.' While this is\ntechnically accurate, it may obscure an important dynamic. For a region to move into the top\ndecile of life expectancy, it must have experienced faster gains than the original vanguards\nover a substantial part of the observation period. Therefore, the fact that these new vanguard\nregions are now improving at the same pace as the original ones suggests a relative\ndeceleration in their trajectory. A brief clarification of this point would enhance the\ninterpretation of regional dynamics and the discussion of convergence/divergence processes.\nThis sentence is more linked to our perception of progress in human longevity. In a sense, our\napproach is in line with Oeppen and Vaupel (2002), as well as Vallin & Meslé (2008), who\nrevealed a linear evolution of the life expectancy frontier using national data. Their work\nsimilarly highlights episodes of reranking, where new vanguard countries can experience faster\ngains before stabilizing when sticking to the frontier, with Japan being a particularly illustrative\ncase. In our paper, we conduct the same type of analysis but at the regional level, thereby\npositioning our work in this lineage. We agree that the respective sentence in the ‘Results’\nsection requires more elaboration. However, we also feel that it is appropriately done in the\ndiscussion through the following paragraph (Line 275):\n“This changing geography is crucial for understanding the potential for a longer life in Europe.\nTracking life expectancy gains among the regions that constituted the vanguard in the 1990s\nreveals a slowing pace, and suggests possible limits to expanding human longevity. But this\nperspective overlooks the dynamic reranking of regions over time. When this reranking is taken\ninto account, we observe that the pace of longevity gains has remained remarkably stable,\nparticularly for the top-performing regions. This reinforces the thesis that human life\nexpectancy has not yet reached its limits [8].”\n7. Clarity and Context\nThe manuscript is generally well-written and accessible, but could benefit from clearer\ntheoretical grounding, as noted above.\nAdding more context about long-term historical trends in mortality and prior health\ntransitions would help frame the results more robustly.\nIn the light of the previous comments and suggestions, we have substantially expanded the\ndiscussion section. The revised version provides now more theoretical background and a\ndeeper discussion.\n8. References\nThe manuscript cites relevant and up-to-date sources. However, additional references to key\ndebates in demographic history and epidemiology (e.g., McKeown, Szreter, Omran’s transition\ntheory) would enhance the contextual depth of the discussion.\nThe suggested literature has been incorporated into the discussion.\n9. Expertise and Scope\nI am confident assessing the demographic interpretation of mortality trends, regional\nanalysis, and socioeconomic contextualization.\nI do not have deep expertise in the specific statistical implementation of P-splines or CLM,\nand thus cannot fully evaluate the appropriateness of the smoothing parameter selection\nbeyond what is reported.\nWe hope that our additional clarifications and the respective changes in the manuscript have\nimproved transparency and helped to convey the rationale and robustness of our\nmethodology.\nReferences\nLuque de Haro, V. A. (2024). Social inequalities in adult mortality across Europe (18th–21st\ncenturies): A critical analysis of theories and evidence. Economics & Human Biology, 55.\nhttps://doi.org/10.1016/j.ehb.2024.101438\nLutfey, K., & Freese, J. (2005). Toward some fundamentals of fundamental causality:\nSocioeconomic status and health in the routine clinic visit for diabetes. American Journal of\nSociology, 110(5), 1326–1372. https://doi.org/10.1086/428914\nMackenbach, J. P., Valverde, J. R., Artnik, B., et al. (2018). Trends in health inequalities in 27\nEuropean countries. Proceedings of the National Academy of Sciences, 115(25), 6440–6445.\nhttps://doi.org/10.1073/pnas.1800028115\nMasselot, P., Mistry, M., Vanoli, J., et al. (2023). Excess mortality attributed to heat and cold:\na health impact assessment study in 854 cities in Europe. The Lancet Planetary Health, 7(4),\ne271–e281. https://doi.org/10.1016/S2542-5196(23)00023-2\nReviewer #2 (Remarks to the Author):\nSummary of Key Results\nThis manuscript examines changes in life expectancy from 1992 to 2019 across Western\nEuropean regions. The principal strength of the study lies in the diversity of regions analyzed\nsimultaneously, which may hold important implications for public policy if interpreted\nappropriately. The authors use these regions to rank gains in life expectancy and classify them\nas either laggards or vanguards in life expectancy improvements. The results indicate a\nslowdown in the pace of mortality reductions during the second half of the study period.\nFurthermore, the authors investigate the drivers of this deceleration in gains in e0, identifying\nthe 55–74 age group as having the greatest impact on the reduction in gains.\nSignificance and Impact\nThe findings may hold considerable relevance for policymakers and researchers examining the\ndeceleration in life expectancy improvements. The analysis is insightful, illustrating the value\nof considering both local and broader contextual factors when evaluating the limits of\nlongevity.\nThank you for your very positive feedback.\nThe study is fully reproducible (with the exception of the dataset, which is not fully publicly\navailable; however, this is not the responsibility of the authors). The documentation for code\nusage is generally thorough and accessible, except the file “0-Functions-R,” which would\nbenefit from more detailed documentation regarding the purpose and function of each\ncomponent. The code is well-structured and clear. A minor technical note is that the analysis\nrelies on the MortalitySmooth library, which has been deprecated on CRAN. Since this requires\nadditional steps for installation in R, it would be helpful to provide users with brief guidance\non how to complete this process.\nWe sincerely appreciate this constructive comment and your careful attention to our code. As\nwe are strongly committed to promoting open and reproducible science, we are especially\ngrateful for this level of engagement.\nWe apologize for the lack of detailed documentation in the original version of the 0-\nFunctions.R file and for the reliance on the now-archived MortalitySmooth package, which\nindeed introduces unnecessary barriers for users attempting to reproduce our analysis.\nIn the updated OSF repository, we have addressed both concerns. Rather than providing\ninstructions for installing archived packages, we have removed the dependency on\nMortalitySmooth altogether. The functions we previously relied on—mainly for GLAM\narithmetic and the greedy search algorithm used for smoothing parameter selection—are now\ndirectly included in the codebase. Specifically, these are implemented in 0-Functions.R and all\nroutines have been extensively commented to clarify their purpose and logic.\nonce again thank you for contributing to these enhancements through this detailed and\nthoughtful review.\nTwo methodological concerns are not addressed in the manuscript and warrant discussion.\nFirst, the issue of mixing NUTS classification levels. The NUTS classification distinguishes\nbetween NUTS-1 (major socio-economic regions), NUTS-2 (basic regions for regional policies),\nand NUTS-3 (small regions for specific diagnoses). The authors do not clarify why it is\nmethodologically appropriate to compare, for example, Spain at the NUTS-3 level with\nPortugal at the NUTS-2 level. This inconsistency may obscure heterogeneities that the paper\naims to uncover. What changes, if any, arise when the analysis is restricted to the NUTS-2 level?\nAlong with Reviewer 1, you raised legitimate concerns about the comparability of mortality\nestimates between different spatial units. We must emphasize that the selection of spatial\nunits for our analysis was driven by data availability. Demographic statistics are typically\ntabulated by established administrative units, which correspond to the NUTS classification.\nAlthough the NUTS classification was created to maintain comparable regional units across the\nEU, substantial comparability issues could arise due to variations in population size, population\ndensity, and administrative structures of the regions at the same NUTS level. Therefore, the\nmethodological issues you highlighted are mainly related to the limitations of the NUTS\nclassification per se rather than the design of our study. As mentioned above, there was no\nother alternative for us as to use the regional mortality data available at different NUTS levels.\nThere was considerable variation among the European countries in terms of data availability.\nFor some countries, the detailed regional mortality data can be easily accessed online, for the\nothers such data might be obtained only per special request. In certain cases, the desired level\nof geographical details is not possible to obtain because of data protection rules. While\nbuilding our dataset we aimed at the highest level of geographical detail, the longest possible\ntime series, and the largest possible number of countries. We also kept in mind data\ncomparability issues and the suitability of the data for visualizations, especially mortality maps.\nAlthough we believe that we ended with a decent data sample, we agree that the\ncomparability between spatial units warrant thorough discussion. Thus, we added the\nfollowing paragraph under the ‘Strengths and limitations’ section (Line 342).\n“A major strength of our work lies in the unique combination of wide geographical coverage\nwith high spatial resolution mortality series. We invested substantial effort to harmonize\nmortality data across regions and over time, ensuring comparability despite differences in\nnational statistical practices (see Supplementary Table 3 for details on the implemented\nadjustments). This harmonization was essential to meeting our study’s central objective:\nassessing long-term longevity trends both by individual region and by contrasting vanguard\nand laggard groups. Nonetheless, perfect comparability could not be achieved. The use of the\nNUTS classification, though standard in European demographic research, entails considerable\nvariation in the population size of spatial units within the same NUTS level between countries.\nMoreover, while we aimed for the most detailed geographical units and the longest possible\ntime series, differences in data availability obliged us to use less detailed information in some\ncountries. This averaging of outcomes across larger areas may conceal inequalities in longevity,\nwhich recent studies have shown to be important for interpreting spatial disparities [47, 48].”\nSecond, the use of P-splines raises concerns. Although commonly used and well-documented\nin demographic research, P-splines are known to perform poorly at the extremes of the age\ndistribution, particularly at ages 0 and w. Given the aims of the paper, overfitting at age 0 may\nbe especially problematic, as errors at this age can propagate through the life table and\nsignificantly affect estimates of e0. The code suggests that some correction was applied for\nage 0, but this is not explained in the methods or the appendix. The authors are encouraged\nto elaborate on how or if this issue was addressed.\nThank you for this insightful comment, which reflects a strong understanding of the\nmethodological foundations of our work, particularly regarding the limitations of P-splines at\nthe extremes of the age distribution. We greatly appreciate this level of engagement.\nIt is indeed correct that standard P-splines can perform poorly at age 0, where mortality\npatterns differ markedly from the rest of the age distribution. As the reviewer observed, we\naddressed this issue by applying a correction for infant mortality, although we regret that this\nwas not described in the original manuscript.\nSpecifically, we followed the approach proposed in Camarda (2019, Section 3.1), which\nmodifies the basis structure over age by introducing a specialized coefficient for age 0. This\ncoefficient is not connected to the variation of adjacent coefficients, effectively decoupling the\nestimation of infant mortality from that of older ages. In a one-dimensional setting, this\ncorresponds to directly estimating the log death rate at age 0; in our two-dimensional\nframework, we allow this coefficient to vary smoothly over time. This strategy prevents the\noften-abrupt mortality level at age 0 from distorting the smooth surface over age and time,\nespecially from age 1 onward, thereby improving both fit and interpretability.\nWe have now explicitly described this modelling choice in the Methodology section of the\nrevised manuscript and added the appropriate reference to Camarda (2019), where the\nrationale and benefits of this approach are clearly documented.\nRegarding ω, we fixed it to 95+ across all regions. This decision was made for consistency, as\ndata for deaths or exposures were often unavailable beyond this open age group in many\nregions. While we acknowledge that P-spline smoothing may also be less accurate at the oldest\nages, we believe that any misfit in this range has only a minimal impact on the estimates of life\nexpectancy at birth and virtually no effect on the probability of dying between ages 55 and 74,\nboth of which are central outcomes in our analysis.\nWe hope this clarification resolves the reviewer’s concern, and we thank them again for\nhighlighting this important modelling detail.\nThe methods employed are appropriate for addressing the research question. They are\nsufficiently comprehensive, and the discussion of results is both adequate and original.\nWe appreciate very much this positive assessment of our work.\nSuggested Improvements\nIn the abstract, the authors state: “… is strongly associated with mortality of the population\naged 55–74, which increased by 2019…”. It appears the intention is to refer to the population\nrather than mortality increasing; the sentence is currently ambiguous.\nThank you for noting this. We replaced the phrase ‘with mortality of the population aged 55–\n74’ with ‘mortality at ages 55–74’.\nThe rationale for using regional estimates of e0 and comparing them should be emphasized\nmore clearly early in the manuscript. Although the importance of this approach becomes\nclearer in the results and discussion sections, it would benefit from stronger justification earlier\non.\nAs suggested, we provided the rationale for using e0, also compared to alternative mortality\nindicators, to the Introduction (Line 51). The revised text reads as follows:\n“We use life expectancy at birth, a widely used and understood, easily interpretable mortality\nmeasure, as the main mortality outcome. It also has the advantage of providing more stable\nstatistical estimates compared to partial life expectancy or life expectancy at specific ages,\nwhich is a particularly valuable feature for small spatial units. Finally, its use facilitates\ncomparisons with other national and regional studies.”\nThe authors explain why the analysis concludes in 2019, but do not justify why it begins in\n1992. This should be addressed.\nAlthough two distinct periods of e0 gains are identified in Figure 1, these classifications are not\napplied in the subsequent analysis. Incorporating them may enhance the strength of the\nfindings.\n(Response to both comments) Our paper focuses on the analysis of long-term mortality trends\nfor the period 1992-2019. The initial point of the analysis (1992) corresponds to the earliest\nyear for which the data were available for all countries included in our sample. The year 2019\ncorresponds to the last year before the COVID-19 pandemic. Indeed, 2004-2005 reflects\nturning point in the trend of life expectancy gains. This point also falls very close to the mid-\npoint of the entire period. The pandemic years were deliberately excluded from the analysis\nfor the reasons explained in detail in our response to your last comment. As a result, we ended\nup with three snapshots: 1992-1993, 2004-2005, and 2018-2019. Although the respective\nclarification was provided in our initial submission (p.4, lines 112-116), we agree that more\nexplanations are needed.\noverview of the changes in life expectancy gains but also to justify the usage of 2004-2005 as\none of the three snapshots over the observation period. These snapshots were then used\nconsistently for Figures 3-6, as it would cumbersome to present these figures for each\nbiennium. By contrast, it was feasible to show the annual trends by vanguard and laggard\nregions in Figure 2. In Tables 1 and 2 we prefer to split the observation period 1992-2019 into\nthree decades. This allows direct comparisons of the estimated gains with widely cited Oeppen\n& Vaupel (2002) paper and their ‘0.3 years per decade’ argument, and recently, with the\nOlshansky et al (2024) paper defining ‘radical life extension’ as the average increase of 3 years\nper decade. Nevertheless, we agree that for sake of consistency it would be appropriate to add\nthe results stratified by two sub-periods: before 2004-2005 and after. The respective tables\nreplicating the results presented in Tables 1 and 2 appear now in the online supplementary\nmaterial (Supplementary Tables 1 and 2).\nThe first sentence in the ‘Results’ section (Line 72) reads now as follows:\n“Figure 1 offers a dual perspective on annual gains in life expectancy at birth (hereafter life\nexpectancy or e0) across 450 regions in Western Europe from 1992 (the earliest year for which\ndata are available for all of the countries included) to 2019 (the last year before the COVID-19\npandemic).”\nFor Figure 3, the color scale should be modified to ensure readability when printed in black\nand white.\nThis could be easily implemented. However, as the journal guidelines do not state that the\nreadability in black and white should be ensured, we used colours consistently for all figures.\nShould the article move to production, we are committed to adjust the figures, if it is necessary.\nOn page 6, line 162, the authors state: “…today’s laggard regions are significantly lower\nthan…”. It should be clarified whether this refers to statistical significance.\nWe didn’t imply statistical significance. To avoid confusion, we replaced the word\n‘significantly’ with ‘notably’.\nIn the Shiny app, the figures lack a color legend, which should be included\nThe colour legend does appear in the Shiny App. As this interactive application was primarily\ndesigned for desktop computers, the legend may not be fully visible on other devices. In some\ncases, the scale of the monitor has to be reduced (by pressing Ctrl-).\nOn page 12, line 273, the sentence “… phenomenon has only been observed in the United\nKingdom…” should be nuanced to reflect that this applies only among the countries included\nin the study.\nThis sentence has been rephrased as follows (Line 299):\n15\n“Within the set of countries analysed here, this pattern has so far been documented only in the\nUnited Kingdom, with a particular concentration in northern regions.”\nQuestions for the authors:\nGiven that mortality at younger ages is relatively low in most of the regions analyzed,\nsome may argue that other indicators, such as LE25 or LE65, may be more suitable for assessing\nchanges in old-age mortality. Why were these not considered?\nIn the light of this and the previous comments we added the following paragraph to the\nintroduction (Line 51):\n“We use life expectancy at birth, a widely used and understood, easily interpretable mortality\nmeasure, as the main mortality outcome. It also has the advantage of providing more stable\nstatistical estimates compared to partial life expectancy or life expectancy at specific ages,\nwhich is a particularly valuable feature for small spatial units. Finally, its use facilitates\ncomparisons with other national and regional studies.”\nSince the analysis aims to identify the age group contributing most to the stagnation in life\nexpectancy gains, why was an age decomposition not performed?\nWe agree that an age decomposition is a valuable demographic tool. However, given the broad\nreadership of Nature Communications, we aim to present a simpler and more accessible\nanalysis. We believe that the approach presented in the paper is sufficient to conclude that\nthe recent stagnation in life expectancy gains is closely linked to mortality dynamics around\nretirement age, which was one of our main conclusions. Therefore, while an age\ndecomposition could provide additional detail, we consider it not essential for supporting the\nkey messages of the study.\nP-splines allow for the estimation of confidence intervals through bootstrapping. Would\nincorporating confidence intervals into the e0 estimates add value to the analysis, particularly\nin assessing whether regional and national differences are statistically significant?\nWe appreciate this comment and agree that bootstrapping can be used to compute confidence\nintervals for e₀ and other metrics derived from P-spline models.\nHowever, we respectfully believe that including confidence intervals for each of the 450\nindividual e₀ estimates would not necessarily enhance the overall value of our analysis. Our\nprimary object of interest is the distribution of the estimated life expectancies, not the\nprecision of individual point estimates. What matters more in our context is the aggregate\npattern, not the variability around each estimate. For instance, only in Figure 2 do we present\nindividual e₀ values for the minimum and maximum of the distribution; elsewhere, we focus\ndirectly on distributional features or on specific segments such as the upper decile. Moreover,\npresenting confidence intervals for all 450 regions in the main text would be cumbersome and\nextremely difficult, both in terms of space and interpretability. For this reason, we chose not\nto include confidence intervals throughout the main text.\nparticularly when evaluating whether differences between specific regions are statistically\nmeaningful. To address this, we have added Supplementary Figure 5 presenting both observed\nand estimated life expectancy at birth for 10 French and Dutch regions, along with 95%\nconfidence intervals computed via bootstrapping from the P-spline model. These regions\npresent large differences in terms of population size, location and level of life expectancy.\nWe have added a paragraph at the end of the Methodology section (Line 447) to describe this new\nevidence:\n“To illustrate our methods, Supplementary Figure 5 presents the trend in life expectancy at\nbirth for 10 French and Dutch regions with varying profiles (geographical location, population\nsize, life expectancy levels) for female from 1992 to 2019. The dots represent life expectancy\nlevels calculated using raw data, while the lines represent life expectancy levels estimated\nusing our methods, along with 95% confidence intervals.”\nIn the discussion of future research, the authors suggest incorporating COVID-19 years. Why\nnot include them in the current analysis?\nAs we mentioned in the original submission (p.13, line 287), focusing on the pre-pandemic\nperiod is in line with previous research addressing similar research questions and dealing with\nlong-term mortality trends. More importantly, incorporating the pandemic years and applying\nour smoothing approach for the entire dataset would inevitably obscure the correct\nassessment of the structural trends, which was the main focus of this study. In addition,\nincluding these years would require methodological changes, since we cannot assume a\nsmooth time trend in the presence of a sudden longevity shock.\nWe believe that the COVID-19 pandemic should be viewed as a short-term crisis with\npotentially long-term implications for mortality trends and spatial patterns. Whether this\ntemporal shock will have a profound impact and alter regional mortality trends or these trends\nwill return on their long-term trajectories is a subject for further investigations. For now, we\nwould prefer to refrain from any speculations concerning this matter. To get first insights, it is\nessential to obtain mortality data beyond the pandemic years (at least for two-three calendar\nyears). Evaluating regional cause-specific mortality trends and patterns should further\nimprove our understanding of mortality trends.\nIn the light of this comment, we implemented several changes in the ‘Strengths and\nlimitations’ section (Line 355):\n“The extensive temporal coverage of this study is another major strength. Spanning nearly\nthree decades prior to the COVID-19 pandemic, it offers a unique opportunity to capture the\nevolution of life expectancy under relatively stable conditions. By leveraging and modelling\nlong-term mortality data, we can disentangle short-term fluctuations from structural trends,\nand thus provide a solid foundation for understanding the slowdown in longevity gains. Our\nanalysis deliberately focuses on the pre-pandemic period, consistent with earlier work on long-\n17\nterm mortality dynamics [1, 9]. Including the pandemic years would have inevitably interfered\nwith our assessment of structural trends, which was the main focus of this study. Consequently,\nour study does not take into account the mortality burden of COVID-19, which\ndisproportionately affected vanguard regions in 2020 (e.g., central Spain, northern Italy, and\nthe Paris region) and laggard regions in 2021 [20]. Future research should extend the scope to\nincorporate both the pandemic and post-pandemic years, in order to assess whether and how\nthis longevity crisis has reshaped trajectories of, and regional disparities in, life expectancy.”\nReviewer #3 (Remarks to the Author):\nThe paper presents a careful analysis of mortality and life expectancy trends across 450\nsubnational regions in Western Europe over a nearly 30-year period. It revealed sustained\nimprovements in life expectancy in vanguard regions, while demonstrating different dynamics\nin laggard areas. This is an original, methodologically sound research, supported by\nbeautiful visualization.\nWe appreciate very much your favourable assessment of our work.\nI wonder whether the Introduction could be slightly extended or adjusted to better reflect the\nbroader context of diverse trajectories in life expectancy across vanguard and laggard\nsubpopulations.\nWe agree with this suggestion. The following statements have been added in the introduction\n(Line 60):\n“These complementary concepts of vanguard and laggard populations have often been used\nin demographic research to describe diverse mortality trajectories at the subnational level.\nThere has been a worldwide general trend towards widening socioeconomic health\ninequalities, with highly educated groups enjoying faster declines in mortality and gains in life\nexpectancy than the groups with the lowest socioeconomic status [23]. Substantial and\nincreasing inequalities in mortality by marital status have also been reported [24].”\nIn the Discussion, you refer to convergence-divergence theory and suggest that divergence\nmay result from faster growth in vanguard regions due to earlier adoption of innovations.\nHowever, based on your results, divergence since the mid-2000s appears to be more strongly\ndriven by a slowdown in laggard regions. It would be useful to clarify this point and consider\nwhether the convergence-divergence framework fully captures the observed pattern.\nGood point, thank you. We revised the respective paragraph that reads now as follows (Line\n252):\n“The driving forces behind this impressive reversal of fortunes can be better understood\nthrough the convergence–divergence framework, which explains the mechanisms leading\nmortality levels across populations to either converge or diverge. According to this theory,\nmajor innovations (e.g., drugs that reduce blood pressure) may initially trigger divergence, as\n18\nsome countries or groups are better positioned to benefit from them. Once access broadens,\nconvergence tends to follow [27, 28]. Importantly, convergence and divergence cycles do not\nnecessarily alternate in a linear way; a new divergence process may unfold alongside an\nongoing convergence process, complicating the interpretation of mortality trends. In our case,\nthe convergence observed before 2005 is fully consistent with this framework. By contrast, the\nsubsequent divergence was driven less by accelerated progress in vanguard regions than by a\nslowdown in laggard regions, a pattern also observed in another recent study [29]. This\nobservation does not call into question the validity or relevance of the convergence–divergence\nframework, but it highlights the difficulties facing laggard regions in maintaining momentum.”\nWhile the manuscript is descriptively rich, a more detailed discussion of the potential causal\nmechanisms behind the recent divergences would be beneficial. Other factors, such as\ndifferential access to healthcare, austerity measures, or economic restructuring, may be\nrelevant beyond smoking and cardiovascular disease. These factors could be acknowledged,\neven if they are speculative.\nThank you for these very useful suggestions. We have added the following paragraphs to the\n‘Discussion’ section (Line 306):\n“Although our analyses are mainly aimed at providing novel empirical insights, and a detailed\nanalysis of mortality determinants is beyond the scope of this study, it is important to highlight\nfactors and potential causal mechanisms behind the observed subnational trends and\ndifferentials. Broadly speaking, it makes sense to situate the discussion of these factors within\nlong-standing debates on the role of broad socioeconomic change versus targeted\ninterventions in shaping mortality decline [35, 36]. The classical McKeown thesis attributes\nrapid mortality decline in industrialized countries to broad economic and social changes, rather\nthan to targeted public health or medical interventions. While this controversial thesis has since\nbeen overturned on empirical grounds, it posed the fundamental question of whether\npopulation health is determined primarily by broad-based social, political and economic\nconditions or by more targeted policies and interventions [37].\nAt the subnational level, this broader debate finds a close parallel in discussions of the relative\nrole of individual versus contextual determinants of health [38]. Accordingly, regional\ndisparities in mortality can be interpreted in terms of compositional effects (reflecting the\nunequal distribution of individual risk factors across areas) or contextual effects (the influence\nof place-based conditions and policies). From a compositional perspective, our finding that\nstagnation in life expectancy has been most pronounced at ages 55–74 highlights the\ncontribution of lifestyle-related factors such as alcohol consumption, drug use, poor diet and\nphysical inactivity [1, 39]. At the same time, contextual factors provide important\ncomplementary explanations. Recent research has emphasized the impact of austerity\nmeasures and so-called deaths of despair (suicide, drug misuse and alcohol-specific causes) on\nstagnating mortality in the UK [40-42]. In our study, the regional divergence in life expectancy\nobserved since the mid-2000s broadly coincides with the period of the 2008 financial crisis and\n19\nits aftermath, when economic growth slowed across Western Europe while becoming\nincreasingly concentrated in major metropolitan regions [43, 44]. These urban areas, which\nhost high-skill service industries, are now among the best-performing regions in terms of\nlongevity. For instance, between 2005 and 2019, the western part of central London rose from\n132nd to 1st place for males and from 247th to 15th for females, while Paris advanced from\n40th to 32nd for males and from 45th to 4th for females.”\nAlthough the focus on the pre-pandemic period is justified, I suggest briefly commenting on\nhow COVID-19 might have affected the trajectories described. Even a short reflection on the\npotential for COVID-19 to exacerbate or alter divergence patterns would be a useful addition.\nAs we mentioned in the original submission (p.13, line 287), focusing on the pre-pandemic\nperiod is in line with previous research addressing similar research questions and dealing with\nlong-term mortality trends. More importantly, incorporating the pandemic years and applying\nour smoothing approach for the entire dataset would inevitably obscure the correct\nassessment of the structural trends, which was the main focus of this study. In addition,\nincluding these years would require methodological changes, since we cannot assume a\nsmooth time trend in the presence of a sudden longevity shock.\nWe believe that the COVID-19 pandemic should be viewed as a short-term crisis with\npotentially long-term implications for mortality trends and spatial patterns. Whether this\ntemporal shock will have a profound impact and alter regional mortality trends or these trends\nwill return on their long-term trajectories is a subject for further investigations. For now, we\nwould prefer to refrain from any speculations concerning this matter. To get first insights, it is\nessential to obtain mortality data beyond the pandemic years (at least for two-three calendar\nyears). Evaluating regional cause-specific mortality trends and patterns should further\nimprove our understanding of mortality trends.\nIn the light of this comment, we implemented several changes in the ‘Strengths and\nlimitations’ section (Line 355):\n“The extensive temporal coverage of this study is another major strength. Spanning nearly\nthree decades prior to the COVID-19 pandemic, it offers a unique opportunity to capture the\nevolution of life expectancy under relatively stable conditions. By leveraging and modelling\nlong-term mortality data, we can disentangle short-term fluctuations from structural trends,\nand thus provide a solid foundation for understanding the slowdown in longevity gains. Our\nanalysis deliberately focuses on the pre-pandemic period, consistent with earlier work on long-\nterm mortality dynamics [1, 9]. Including the pandemic years would have inevitably interfered\nwith our assessment of structural trends, which was the main focus of this study.\nConsequently, our study does not take into account the mortality burden of COVID-19, which\ndisproportionately affected vanguard regions in 2020 (e.g., central Spain, northern Italy, and\nthe Paris region) and laggard regions in 2021 [20]. Future research should extend the scope to\nincorporate both the pandemic and post-pandemic years, in order to assess whether and how\nthis longevity crisis has reshaped trajectories of, and regional disparities in, life expectancy.”\nI recommend replacing “during the pre-pandemic period” with the actual years (“1992–\n2019”). Consider using “slowdown” or similar alternatives instead of “deceleration” (in some\ncases). I would also advise avoiding strong phrases like “we strongly advocate.\nRevised accordingly.\nThe paper opens with references to COVID-19, which is not the focus of the study. It might\nmake more sense to start with literature on pre-pandemic stagnation in mortality\nimprovements and only later mention the pandemic if needed.\nAs suggested, the literature on pre-pandemic stagnation is cited at the very beginning of the\nintroduction.\nThe following might be a useful addition to the discussion of stagnating mortality\nimprovements in other high-income countries:\nTimonin, S., Leon, D. A., Banks, E., Adair, T., Canudas-Romo, V. (2024). Faltering mortality\nimprovements at young-middle ages in high-income English-speaking countries. International\nJournal of Epidemiology, 53(3). https://doi.org/10.1093/ije/dyae128\nThank you for this suggestion, which supports our argument of the importance of monitoring\nmortality at young and adult ages. We added the following sentences to the discussion (Line\n297):\n“Such adverse trends at younger ages are already evident in other English-speaking countries.\nIndeed, it has been argued that the slowdown in life expectancy gains before the pandemic\nobserved across these countries – with the exception of Ireland – largely stemmed from\nstagnating or rising mortality at young and middle adult ages [3].”\nShould confidence intervals be shown, at least for regions with maximum and minimum life\nexpectancy? Also, I couldn’t find a legend explaining the meaning of the colored lines. Using\ndifferent Y-axes for males and females makes comparisons difficult and may be misleading.\nWe appreciate this comment and fully agree that presenting uncertainty around selected\nestimates can be useful, particularly when evaluating whether differences between specific\nregions are statistically meaningful. To address this, we have added Supplementary Figure 5\npresenting both observed and estimated life expectancy at birth for 10 French and Dutch\nregions, along with 95% confidence intervals computed via bootstrapping from the P-spline\nmodel. These regions present large differences in terms of population size, location and level\nof life expectancy. We have added a paragraph at the end of the Methodology section (Line\n447) to describe this new evidence:\n“To illustrate our methods, Supplementary Figure 5 presents the trend in life expectancy at\nbirth for 10 French and Dutch regions with varying profiles (geographical location, population\nsize, life expectancy levels) for female from 1992 to 2019. The dots represent life expectancy\n21\nlevels calculated using raw data, while the lines represent life expectancy levels estimated\nusing our methods, along with 95% confidence intervals.”\nRegarding the use of different Y-axes for males and females, we agree that this choice may\nhinder direct comparison of longevity levels between the sexes. However, the intent of the\nfigure is not to compare males and females per se, but to highlight the differing time-trends\nacross various parts of the life expectancy distribution. We believe that using a common Y-axis\nwould improve comparability of levels but could obscure the heterogeneity in temporal trends,\nwhich is the key focus of this analysis.\nTypically, D1 refers to the most deprived group and D10 to the least deprived (e.g., in the UK\nand Austria).\nWe understand the point made by the Reviewer, and it would be fairly easy to switch the\nlabels for the vanguard and the laggard groups. We deliberately don’t use ‘least deprived’ or\n‘most deprived’ groups throughout the manuscript to avoid any parallels with the concept of\nsocioeconomic deprivation, which was out of the scope of this study. Assigning labels to our\ngroups was based on LE ranking. In our view, it is more intuitive to assign the first (top decile)\nthe label D1. This is also consistent with Figure 3, where we plot the regions from 1 to 450,\nand from the left to the right. Finally, although many studies refer to D1 and D10 as to the\nmost advantageous and disadvantages population groups, this is not a uniform practice (see,\nfor example, Tetzlaff et al. 2024).\nTetzlaff F et. al. (2024). Age-specific and cause-specific mortality contributions to the\nsocioeconomic gap in life expectancy in Germany, 2003–21: an ecological study. The Lancet\nPublic Health 9 (5): e295-e305 https://doi.org/10.1016/S2468-2667(24)00049-5 .\n22"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have carefully and thoroughly addressed all my previous comments. The revised manuscript shows substantial improvement in theoretical contextualization, methodological transparency, and clarity of presentation. The additional supplementary materials (Figures 1 and 5, Tables 1–2) meaningfully enhance interpretability and reproducibility. I find the paper suitable for publication in its current form.\nMinor issue\nPlease correct the bibliographic entry for Luque de Haro (2024) in the reference list.\nThe compound surname is incorrectly formatted as “De Haro,” and the journal title should include the ampersand symbol.\nCorrect form:\nLuque de Haro, V. A. (2024). Social inequalities in adult mortality across Europe (18th–21st centuries): A critical analysis of theories and evidence. Economics & Human Biology, 55, 101438. https://doi.org/10.1016/j.ehb.2024.101438", "reviewer_2": "The authors have done an excellent job addressing all previous comments and substantially improving the manuscript. The revised version is clear, comprehensive, and much stronger. The discussion has been expanded in meaningful ways, providing a richer theoretical context and situating the findings within broader demographic debates. All my earlier concerns have been carefully considered and satisfactorily resolved. In my opinion, the manuscript is now ready for publication. Congratulations to the authors!\n\nI have reviewed the revised version of the code. The authors have addressed my previous concerns thoroughly, providing clearer documentation and improving accessibility. The updated code is now well-structured and fully reproducible. I am satisfied with these revisions and appreciate the authors’ careful attention to detail.", "reviewer_3": "Many thanks for carefully addressing the comments of all the reviewers, including mine. I have no further suggestions."}, "author_response": ""}]

Decision letter