Every time a patient goes into surgery, gives birth, or battles cancer, there is a good chance they will need someone else’s blood. Blood donors in South Korea are overwhelmingly in their teens, twenties, and thirties; blood recipients, instead, are typically in their seventies and eighties – and increasingly so, given population aging. The arithmetic, replicated country by country across East Asia, is unforgiving. Admittedly, this is just another example of the possible imbalances caused by population aging; however, its nature and the emotional impact of the topic seem to make it worth special consideration.

The arithmetic of aging and blood

Yu et al. (2020) were among the first to model what may happen to blood supply and demand in China between 2017 and 2036, as the country ages. Supply is projected to fall by 16 per cent while demand will rise by 33 per cent, leaving a potential shortfall of 21.2 million units by 2036, equivalent to more than a third of projected annual demand (a unit is approximately 450 ml, or a pint). The driving mechanism is straightforward: fewer people in the peak donating ages of 18–34, and far more in the heavy-transfusion-using ages of 70 and above, where blood use per person runs more than 25 times higher than among teenagers.

Not surprisingly, the same trend can be observed also in other countries. In Taiwan, for instance, the pool of eligible blood donors, people aged 17 to 65, is expected to shrink from 16 million in 2025 to just 8.7 million by 2060. Blood demand is projected to exceed supply by around 2027, with the annual shortfall surpassing one million units by 2060 (Lin et al. 2026). 

The Korean case

A similar problem will emerge in South Korea, one of the world’s fastest-aging societies (Kim and Kim 2020). In 2024, Korea’s blood donation rate (number of donations per 100 people) stood at 5.6% (Korean Redcross Blood Services 2025): higher than Japan’s 4.0% or France’s 3.9%, and among the highest globally. But that headline figure conceals a structural fragility: more than half of all donations come from people aged 16 to 29, a cohort shrinking rapidly as the country’s birth rate, already the world’s lowest, continues to fall (Figure 1). 

At the same time, elderly patients, who consume the most blood per person, are growing in number at a pace few health systems have had to manage before (Kim et al. 2024). 

From research to legislation

On 13 May 2026, South Korea’s Ministry of Health and Welfare announced the country’s Second Blood Management Basic Plan, covering 2026 to 2030. It is the most significant revision of blood donation rules in a decade.

The centerpiece is an increase in the upper age limit for blood donation. Currently, whole-blood donation is permitted up to the age of 69, and, for people aged 65 to 69, only if they donated between the ages of 60 and 64. Under the new plan, that ceiling may rise by even more than five years, as officials have not ruled out scrapping the age limit altogether, replacing it with an individual health assessment.

A second change abolishes a long-running chemical test on donated blood: the alanine aminotransferase test (ALT), which measures a liver enzyme. Introduced in the 1990s to guard against hepatitis, it was overtaken by more precise direct virus-detection methods but survived as a bureaucratic habit. Between 2020 and 2025, roughly 33% of donated blood (170 thousand units) was discarded because of ALT results alone, enough to fill hundreds of thousands of standard transfusion bags. That waste will now stop.

The plan also proposes practical incentives: streaming-service subscriptions as gifts for young donors, and regular blood donation buses in areas with no permanent blood bank.

The safety case for expanding the donor age is well supported. Older donors in good health produce blood that is as safe as that of younger donors, and several European countries, such as the UK and Italy, already permit donations beyond 65 under specific conditions (National Health Service (NHS) Blood and Transplant 2026; Ministero della Salute e Centro Nazionale Sangue 2026). The practical question is whether enough healthy older adults will come forward to make a meaningful difference. Korea’s experiment is likely to be watched closely.

From science to policy

Blood supply is also a demographic problem. Typically, donor eligibility rules were written for a young population, who also kept transfusion demand relatively low, and the system was in balance. But the world is rapidly changing: the sooner countries acknowledge the mismatch, and change their rules accordingly, the better placed they will be to keep their hospitals running.

The change in Korean law is a rare, clear example of research shaping policy, and quickly. Other aging societies would do well to read the evidence before the blood runs short.

References

Yu X et al. Population-based projections of blood supply and demand, China, 2017–2036. Bulletin of the World Health Organization, 2020;98(1):10–18. doi:10.2471/BLT.19.233361

Kim KW and Kim OS. Super aging in South Korea unstoppable but mitigatable: A sub-national scale population projection for best policy planning. Spatial Demography, 2020; 8:155–173. doi: 10.1007/s40980-020-00061-8

Kim OS et al. Future blood debt: projecting blood supply and demand of Korea based on subnational population projections (2021–2050). Journal of Korean Medical Science, 2024;39:e168. doi:10.3346/jkms.2024.39.e168

Lin Y-M et al. Forecasting blood supply and demand under population aging: implications and challenges for healthcare resource allocation. BMC Health Services Research, 2026;26:369. doi:10.1186/s12913-026-14150-9

Ministero della Salute e Centro Nazionale Sangue.  Accessed 15 June 2026. (In Italian) https://www.donailsangue.salute.gov.it/it/sangue-pagina/chi-puo-donare/

National Health Service (NHS) Blood and Transplant. Accessed 15 June 2026. https://www.blood.co.uk/who-can-give-blood/

Korean Ministry of Health and Welfare. Second Blood Management Basic Plan 2026–2030. Announced 13 May 2026. (In Korean) https://www.mohw.go.kr/board.es?mid=a10503010100&bid=0027&act=view&list_no=1490506&tag=&nPage=1

Korean Redcross Blood Services. Blood Services Statistical Yearbook 2025. (In Korean) https://www.bloodinfo.net/knrcbs/na/ntt/selectNttList.do?mi=1158&bbsId=1061&nttSn=548750

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Studies of various wars repeatedly show that women who live in a war zone face not only immediate violence but also elevated risks long after conflicts end. In a recent study (Lin and Smith-Greenaway 2026), we revisited this finding from a new angle. We asked: “Does women’s war exposure increase their risk of experiencing domestic violence because of their own exposure to war, or because they marry men who were exposed to war?” Answering this question is critical for designing effective interventions. To date, most programs to reduce domestic violence in post-conflict settings have focused primarily on women (Spangaro et al. 2021).

The Nigerian Civil War (1967–1970)

The Nigerian Civil War, also known as the Biafran War, began in 1967 when the Igbo-dominated Eastern region declared itself independent, and the federal government responded with military force to suppress the secessionist movement, leading to war between the Nigerian government and the secessionist state of Biafra. Sparked by ethnic tensions, political instability, and the fight for control over oil resources (Uche 2008), the conflict led to devastating humanitarian crises, including widespread famine and mass casualties, as well as economic devastation. 

Given that the war was a relatively short–lived (July 1967–January 1970), geographically concentrated ethnic conflict, ethnicity (Igbo) and birth cohort (conceived or born during the war years) function as fixed markers of war exposure, allowing us to analyze wives’ and husbands’ war exposure separately in nationally representative couple data. Specifically, we analyzed the 2008 Demographic and Health Survey couple data, allowing us to account for both wives’ and husbands’ experiences and traits when studying intimate partner violence. As the survey focuses on reproductive age women (15–49 years old) and their spouses, and given the timing of the war, these data allow us to capture wives and husbands who were exposed to war at some point between being in utero and early adolescence (age 12 for wives) and late adolescence and early adulthood (age 22 for husbands), respectively. Figure 1 shows that, in the data, 11% of wives and 19% of husbands were exposed to war in early life.

Whose war exposure matters: hers or his, and why? 

Women are significantly more likely to experience intimate partner violence in their marriage if they or their husband were exposed to war in early life (Figure 2), with a larger difference in this probability by husbands’ war exposure than by wives’ own exposure. By age 49, the probability of experiencing domestic violence is about 15% higher for war-exposed wives than for those not exposed. However, by the same age, the probability is more than 20% higher for wives whose husbands were exposed to war in early life compared to those whose husbands were not.

This descriptive pattern, suggesting that husbands’ war exposure may matter more, persists in multivariate models. Indeed, wives’ war-related risk of experiencing domestic violence appears to be driven entirely by their husbands’ war exposure. That is, the apparent link between women’s exposure to the Nigerian Civil War and their risk of experiencing intimate partner violence disappears once we take into account their husbands’ exposure to war. For women with a war-exposed husband, the risk of experiencing intimate partner violence is increased by 45%.

Among a range of potential explanations – including husbands’ experience of war-related family loss, their human capital accumulation (such as education and employment), and their marital characteristics (including fertility, child mortality, and polygamy) – men’s risk behaviors emerge as particularly important. Alcohol use helps explain part of the association linking husbands’ war exposure and their perpetration of violence against their partners.

Where do we go from here?

These findings highlight the importance of targeting men in post-conflict interventions, contrary to current efforts that have focused primarily on war-exposed women, as men’s wartime experiences play a central role in shaping their risk of perpetrating violent behavior years later. The results also point to alcohol consumption as a key driver of intimate partner violence, suggesting that interventions addressing substance use may be especially effective.

At the same time, important questions remain. Alcohol use only partially explains the link between men’s war exposure and their violent behavior; further research is needed to identify additional mechanisms. Understanding these pathways will be critical for developing more effective policies and interventions. Taken together, the findings underscore the potential for the collateral damage of war to reverberate within families and intimate relationships long after a conflict has ended.

References

Ajefu, Joseph B., and Daniela Casale. 2025. “Long-Term Effects of Childhood Exposure to War on Domestic Violence.” The European Journal of Development Research 37: 124–51. https://doi.org/10.1057/s41287-024-00659-4. 

Torrisi, Orsola. 2023. “Young-Age Exposure to Armed Conflict and Women’s Experiences of Intimate Partner Violence.” Journal of Marriage and Family 85: 7–32. https://doi.org/10.1111/jomf.12876.

Lin, Yingyi, and Emily Smith-Greenaway. 2026. “War, Gender, and Family Dynamics: A Couple Analysis,” Population and Development Review https://doi.org/10.1111/padr.70062 

Spangaro, Jo, Chye Toole-Anstey, Catherine L. MacPhail, Delia C. Rambaldini-Gooding, Lynne Keevers, and Claudia Garcia-Moreno. 2021. “The Impact of Interventions to Reduce Risk and Incidence of Intimate Partner Violence and Sexual Violence in Conflict and Post-Conflict States and Other Humanitarian Crises in Low and Middle Income Countries: A Systematic Review.” Conflict and Health 15 (1): 86. https://doi.org/10.1186/s13031-021-00417-x. 

Uche, Chibuike. 2008. “Oil, British Interests and The Nigerian Civil War.” The Journal of African History 49 (1): 111–35. https://doi.org/10.1017/S0021853708003393.

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There are striking disparities in life expectancy across sociodemographic groups in the United States, shaped by structural forces such as racism, class inequality, and policy environments. To what extent do sociodemographic characteristics structure, or fail to structure, individual lifespans? 

In a recent paper (Breen and Seltzer 2026), we used U.S. Census data linked to Social Security mortality records to assess how well early-adulthood social, economic, and demographic characteristics predict individual lifespan in a cohort of men born in 1910 and observed through their deaths between 1975 and 2005 (N = 121,000). These data come from the UC-Berkeley CenSoc project. Despite large group-level disparities, we found that sociodemographic characteristics measured in early adulthood explain less than 2% of the overall variation in individual lifespan. These findings reaffirm a central demographic regularity: variance in life expectancy between groups is small compared to variation in lifespan within groups.

Mortality demography, at its core, is concerned with population rates and group-level disparities. What does a predictive perspective add? First, the use of predictive performance as a diagnostic tool quantifies how much of the variation in lifespan within a cohort is captured by sociodemographic indicators of structural advantage and disadvantage. This approach generalizes traditional decompositions of lifespan variation (which in most empirical applications have examined one categorical covariate at a time) by translating multiple covariates into measures of explanatory power at the individual level. In short, prediction reframes classical decomposition as an inquiry into individual-level explanatory power, leveraging both categorical and continuous characteristics.

Second, limited predictability reveals how inequality and uncertainty coexist: stochasticity shapes the overall distribution of lifespans, while structural disparities shift its mean and skewness but remain limited in their ability to determine individual outcomes.

Finally, the low predictability of longevity based on sociodemographic characteristics is itself demographically meaningful. It provides empirical insight into a universal form of uncertainty, one that individuals consider when making life decisions (e.g., “Will I outlive my retirement savings?”). This perspective aligns with the emerging field of uncertainty demography (Trinitapoli 2023), which calls for making uncertainty more central to demographic inquiry and highlights the distinctive capacity of demographic approaches in illuminating the role of uncertainty in social life.

Why a cohort approach matters

Most studies of individual mortality prediction use a period design: researchers pool individuals of varying ages and predict if they will die in a fixed window, such as the next five years. In those models, age does most of the predictive work: older people die sooner. Even omitting age, covariates like self-rated health or homeownership status partly function as proxies for age. This makes period predictions useful for many practical applied forecasting purposes, but less well suited for understanding the extent to which observable sociodemographic characteristics explain variation in lifespan.

A cohort design solves this by holding age constant. We follow a single birth cohort: men born in 1910, observed in the 1940 Census at ages 29 or 30, with deaths captured in Social Security records between ages 65 and 95. This allows us to directly assess the age at which a given member of this cohort dies, given the observed sociodemographic characteristics in early adulthood. To our knowledge, this is the first U.S. cohort-based test of lifespan predictability.

Within-group vs. between-group variation

In our data, we observe a clear educational gradient. College-educated men in this cohort lived nearly three years longer at age 65 than men with only elementary schooling. We observe a similar gradient for wage and salary income. These between-group differences align with past findings and are consistent with well-documented patterns of mortality inequality.

However, our best predictive model, an ensemble model combining seven different machine learning algorithms, explained only 1.3% of the variation in age at death in our holdout set. Predictions clustered tightly between ages 76 and 82, indicating that the model could not reliably distinguish those who were dying earlier or later. Robustness checks confirm this is not an artifact of focusing on one male birth cohort: replicating the analysis for each birth cohort from 1900-1920 produced similar results, as did a separate sample including women.

Why are predictions so weak even in the presence of strong between-group disparities? Among men with college degrees, some died in their late 60s and others lived past 95. The same is true for every educational and income group. The roughly three-year between-group gap is substantial, but it is small relative to the 25+ year spread of ages at death within any group. This is a long-standing demographic regularity, which has been nicely documented using decomposition approaches considering one covariate at a time (Caswell 2023; van Raalte et al. 2012); our prediction exercise gives it a new empirical lens. 

This highlights a core tension: there are substantial and important between-group disparities, but individual-level variation is great enough that we cannot use these sociodemographic characteristics to accurately predict lifespan for a given person. In other words, predictability is low not because between-group inequality is absent, but because within-group variation dominates overall variation in lifespan.

This echoes a classic result of Vaupel (1988): even in a hypothetical setting where all frailty (disciplinary jargon for mortality risk) is directly inherited from parents, frailty explains only 2–5 percent of lifespan variance. Lifespan is shaped, in large part, by stochasticity. More broadly, this finding also aligns with past research on the limited predictability of major life outcomes (Salganik et al. 2020). 

Predictability also varies across subgroups. Our models explained a smaller share of lifespan variance among Black Americans, low-income men, and men with low education. The most disadvantaged face not only shorter average lifespans but also more unpredictable ones. This inequality matters because lifespan uncertainty plays into decisions about saving, retirement, family formation, and health investments. 

Interpretation and takeaways 

Low predictability should not be read as evidence that structural inequality does not matter. Between-group disparities remain substantial, policy-relevant, and central to mortality demography. Mortality is simply a highly stochastic process, not predetermined by individual-level sociodemographic characteristics. 

These findings align with the emerging field of uncertainty demography (Trinitapoli 2023), which treats uncertainty as a constitutive feature of population processes rather than residual noise. In this sense, lifespan uncertainty is not merely noise to be explained away by better covariates and models, but a defining demographic feature that shapes how individuals and populations experience mortality.

References

Breen, C. F., and Seltzer, N. (2026). Structured Inequality, Uncertain Lifespans: Demographic Perspectives on Predicting Individual-Level Longevity. Population and Development Review. https://onlinelibrary.wiley.com/doi/10.1111/padr.70065

Caswell, H. (2023). The contributions of stochastic demography and social inequality to lifespan variability. Demographic Research 49: 309–354.

Salganik, M. J., et al. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. PNAS 117(15): 8398–8403. 

Trinitapoli, J. (2023). An Epidemic of Uncertainty: Navigating HIV and Young Adulthood in Malawi. University of Chicago Press. 

van Raalte, Alyson A., Anton E. Kunst, Olle Lundberg, Mall Leinsalu, Pekka Martikainen, Barbara Artnik, Patrick Deboosere, Irina Stirbu, Bogdan Wojtyniak, and Johan P. Mackenbach. 2012. “The Contribution of Educational Inequalities to Lifespan Variation.” Population Health Metrics 10(1):3. doi:10.1186/1478-7954-10-3.

Vaupel, J. W. (1988). Inherited frailty and longevity. Demography 25(2): 277–287.

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Observed gains in life expectancy have sparked debates about the life quality of these additional years. Healthy life expectancy (HLE) measures have contributed to this debate by focusing on the average years that individuals are expected to live in good health, without chronic conditions or limitations. However, to obtain HLE, researchers usually focus on a unique measure of health, leaving aside the complex relationship between unhealthy states – such as living with chronic conditions – and disability or limitations. By observing more than one measure of health, research can be more informative on healthcare and social care needs. 

In a recent paper (Calderón-Jaramillo et al. 2026), we introduced a more comprehensive indicator for estimating the years of life that individuals are expected to live with care needs by combining healthcare needs, measured through multimorbidity, with social care needs, operationalised as limitations in performing basic and instrumental activities of daily living (ADL and IADL, respectively). The comparative use of this measure in different Ibero-American countries provides insights into how specific ageing processes shape care needs after age 60 at country-level. 

A comprehensive perspective of healthcare and social care needs

Research on healthy life expectancy has creatively expanded the notions of good and bad health by incorporating different measures such as multimorbidity, self-rated health, disability and limitations (Saito et al. 2014). These indicators are usually based on certain physical or mental health functions (Beltrán-Sánchez et al., 2015), and are used to understand future healthcare demand by measuring the consequences of longevity on the time spent in healthy and unhealthy states at population level. However, they rarely consider interactions between different ways of measuring health problems and the implications for individual autonomy. Some studies have therefore proposed broader approaches to estimate healthy life expectancy, accounting simultaneously for chronic conditions and limitations in performing ADL (Lam et al., 2023; Shen & Payne, 2023). Along these lines, we propose an additional layer by using the experience of multiple chronic conditions, as well as limitations in ADL and IADL, as proxies of healthcare and social care needs. The synthetic indicator that we derive measures years of life expectancy with care needs, or YLCN.

There are two main benefits of combining measures of healthcare and social care needs when estimating YLCN. First, we can determine how increasing life expectancy is accompanied, or not, by the presence of chronic conditions and their potential repercussions on individual needs. Second, we can understand how social care needs may vary due to the prevalence of specific chronic conditions and their relationship with diverse epidemiological trends, including the different ways in which healthcare systems approach symptoms and outcomes. 

Differences in care needs across Ibero-American countries

Cross-country differences in HLE have usually been explained in terms of the demographic and epidemiological transition, with declining mortality across specific age groups attributed to a shift in causes of death from infectious to non-communicable and chronic diseases. This is a good theoretical model, but in practice, not all populations experienced these transitions in the same way or at the same pace. While European countries seem to generally align with this process, Latin American countries have faced a heterogeneous scenario (Alvarez et al., 2020; Calazans & Queiroz, 2020), with overlapping phases in many cases due to the persistence of infectious disease risks. This has implications for healthcare needs, as chronic conditions might be experienced alongside other acute conditions, and it also has consequences for individual dependency and disability associated with social care needs. 

The analysis of Brazil, Colombia, Mexico, Portugal and Spain provided some insights into these differences. Using 2015 data from surveys on ageing populations (respectively ELSI, SABE, MHAS and SHARE) and mortality registers from the Human Mortality Database, we estimated the years of life expectancy with care needs for men and women after age 60. 

Life expectancy at age 60 is higher in Europe than in Latin America, with women outliving men, as expected (Figure 1). However, women’s health is worse: they frequently have more than two chronic conditions (with healthcare needs) and spend more years with social care needs.

In general, longer life expectancy also means that individuals are expected to live, on average, more years with care needs. However, the composition of these years with care needs varies by countries (see Table 1). Individuals from Brazil and Colombia spend more years with multimorbidity but without chronic conditions. Meanwhile, Mexicans, Spaniards and Portuguese spend more years with one chronic condition without care needs. This emphasises that healthcare needs and (multi)morbidity are not necessarily related to increasing social care needs.

Between healthcare and social care: the challenges ahead

Individuals from Portugal and Spain, especially women, are expected to live longer with healthcare and social care needs. This reflects their longer life expectancy, but also the fact that men in the Latin American countries under scrutiny tend to have shorter lives, probably due to the impact of amenable causes.

Comparing countries that have experienced demographic and epidemiological transitions in heterogeneous ways helps to highlight the differing consequences of population ageing in different contexts. Moreover, by studying the relationship between healthcare and social care needs, debates on healthy life expectancies can be expanded to consider how the experience of (multi)morbidity and other unhealthy states affects individuals’ lives. 

References

Alvarez, J.-A., Aburto, J. M., & Canudas-Romo, V. (2020). Latin American convergence and divergence towards the mortality profiles of developed countries. Population Studies, 74(1), 75–92. https://doi.org/10.1080/00324728.2019.1614651

Beltrán-Sánchez, H., Soneji, S., & Crimmins, E. (2015). Past, Present, and Future of Healthy Life Expectancy. Cold Spring Harb Perspect Med., 11.

Calazans, J. A., & Queiroz, B. L. (2020). The adult mortality profile by cause of death in 10 Latin American countries (2000–2016). In Revista Panamericana de Salud Publica/Pan American Journal of Public Health (Vol. 44). Pan American Health Organization. https://doi.org/10.26633/RPSP.2020.1

Calderón-Jaramillo, M., Spijker, J., Rentería, E., & Correia Alves, L. (2026). Between curing and caring: years of life expectancy with care needs (YLCN) in Ibero-American countries. Genus, 82(1), 1. https://doi.org/10.1186/s41118-025-00279-8

Lam, A., Keenan, K., Cezard, G., Kulu, H., & Myrskylä, M. (2023). Inequalities in disability-free and disabling multimorbid life expectancy in Costa Rica, Mexico, and the United States. https://doi.org/10.4054/MPIDR-WP-2023-002

Saito, Y., Robine, J.-M., & Crimmins, E. M. (2014). The methods and materials of health expectancy. Statistical Journal of the IAOS, 30, 209–223. https://doi.org/10.3233/SJI-140840

Shen, T., & Payne, C. F. (2023). Disability and morbidity among US birth cohorts, 1998–2018: A multidimensional test of dynamic equilibrium theory. SSM – Population Health, 24, 101528. https://doi.org/10.1016/j.ssmph.2023.101528

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Avoidable mortality comprises amenable deaths that should not occur with timely and effective healthcare, and preventable deaths that could be avoided through effective public health measures. It is widely used to assess health system performance and population health inequalities in Europe (Mühlichen et al., 2023; OECD & Eurostat, 2022). Over recent decades, avoidable mortality has declined substantially, reflecting advances in prevention, treatment, and living conditions. However, large disparities persist among European nations, and further gains in life expectancy could be achieved if unnecessary and untimely deaths were reduced (Hrzic & Vogt, 2024).

The limits of national narratives

Most comparative studies across Europe focus on national averages. While useful, this perspective risks obscuring important differences within countries and overlooking similarities between regions located on different sides of national borders. Health systems are organised nationally, but many determinants of health, such as labour markets, environmental exposures, infrastructure, and demographic composition, are shaped at regional level, not necessarily demarcated by national borders.

To unravel these dynamics, in a recent study (Stroisch et al. 2026) we analysed trends in avoidable mortality across 581 districts in 10 European countries, including both new and old EU member states, between 2002 and 2019. By examining districts rather than countries, we aimed to capture spatial patterns that national-level analyses cannot reveal.

Beyond national averages

While avoidable mortality has generally declined across most European regions, persistent geographic disparities remain, as shown in Figure 1. By 2017-2019, Eastern European districts continued to exhibit the highest rates of avoidable mortality for both men and women. However, these elevated mortality rates are not confined to Eastern Europe alone: regions of northeastern Germany and southern Belgium also exhibit high levels of avoidable mortality, challenging simplistic East–West dichotomies. 

When hotspots for amenable and preventable mortality are examined separately, these patterns become even more evident (Figure 2). Germany is a good illustration of this complexity: despite its robust healthcare system, several regions persistently exhibit high levels of amenable mortality, placing the country in an intermediate position between Western and Eastern Europe. This aligns with previous research indicating that longevity in Germany is stagnating and lagging behind that of other high-income nations (Jasilionis et al., 2023).

Spatial patterns of avoidable mortality extend across internal EU borders, with cross-border clusters of both high and low mortality (hotspots and coldspots, respectively). A long-term hotspot of preventable mortality among women spans the French–Belgian border region, affecting districts on both sides despite differences in healthcare systems and national policies. This region’s challenges stem from shared socioeconomic issues such as deindustrialisation, demographic decline, and disparities in healthcare access (Bonnet & d’Albis, 2020; Otavova et al., 2024). In contrast, a persistent cross-border coldspot spans parts of Spain, France, Switzerland, Austria, and northern Italy, where avoidable mortality remains consistently low. This contradicts the notion that health outcomes are solely determined by national healthcare systems. These areas are united not just by geography but also by ongoing economic prosperity, robust healthcare infrastructure, and cultural practices that actively promote well-being.

These findings lead to the central conclusion that national boundaries are not always the most meaningful demarcation lines for explaining health outcomes. Local socioeconomic conditions, historical development paths, cultural and linguistic similarities, and shared regional contexts can play an important role. Regions with similar economic structures and demographic profiles often exhibit similar mortality patterns, even when located in different countries.

The need for area-specific health policies

These results have important implications for population research and health policy. First, they highlight the limits of national benchmarking. Improvements in national averages can coexist with stagnation or deterioration in specific areas. Without a regional perspective, such inequalities are liable to remain invisible. Furthermore, growing disparities in avoidable mortality within countries raise critical questions regarding the equitable and effective provision of healthcare services.

Second, the persistence of cross-border hotspots suggests that some health challenges cannot be addressed effectively within national frameworks alone. Regions facing similar structural disadvantages may benefit from coordinated transnational approaches, particularly in border areas where population dynamics, labour markets, and healthcare utilisation are closely intertwined. At the same time, cross-border coldspots offer opportunities for best-practice learning. Regions that consistently achieve low levels of avoidable mortality do so in different institutional settings, indicating that favourable outcomes are not necessarily tied to a single health system model. Identifying and transferring successful practices from these regions could support more equitable health improvements across Europe.

Finally, for European societies facing population ageing and exploding healthcare expenditures, reducing avoidable mortality is a high priority. Our findings suggest that achieving this goal requires targeted area-based interventions. Health policies should be regionally sensitive, and, where appropriate, cross-border in scope, complementing national health strategies but not replacing them.

References

Bonnet, F., & d’Albis, H. (2020). Spatial Inequality in Mortality in France over the Past Two Centuries. Population and Development Review, 46(1), 145–168. https://doi.org/10.1111/padr.12318

Hrzic, R., & Vogt, T. (2024). The contribution of avoidable mortality to life expectancy differences and lifespan disparities in the European Union: A population-based study. The Lancet Regional Health – Europe, 46, 101042. https://doi.org/10.1016/j.lanepe.2024.101042

Jasilionis, D., Van Raalte, A. A., Klüsener, S., & Grigoriev, P. (2023). The underwhelming German life expectancy. European Journal of Epidemiology, 38(8), 839–850. https://doi.org/10.1007/s10654-023-00995-5

Mühlichen, M., Lerch, M., Sauerberg, M., & Grigoriev, P. (2023). Different health systems – Different mortality outcomes? Regional disparities in avoidable mortality across German-speaking Europe, 1992–2019. Social Science & Medicine, 329, 115976. https://doi.org/10.1016/j.socscimed.2023.115976

OECD & Eurostat. (2022). Avoidable mortality: OECD/Eurostat lists of preventable and treatable causes of death (January 2022 version). https://www.oecd.org/health/health-systems/Avoidable-mortality-2019-Joint-OECD-Eurostat-List-preventable-treatable-causes-of-death.pdf

Otavova, M., Masquelier, B., Faes, C., van den Borre, L., Vandeninden, B., de Clercq, E., & Devleesschauwer, B. (2024). Trends in socioeconomic inequalities in cause-specific premature mortality in Belgium, 1998–2019. BMC Public Health, 24(1), 470. https://doi.org/10.1186/s12889-024-17933-z

Stroisch, S., Mühlichen, M., Grigoriev, P., & Vogt, T. (2026). Spatial Differences in Avoidable Mortality Across 581 European Districts, 2002–2019. European Journal of Population, 42(5), 21. https://doi.org/10.1007/s10680-025-09761-7

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Mortality does not occur evenly throughout the year. Deaths follow clear seasonal patterns, rising during cold winters and flu epidemics and sometimes during periods of extreme heat (Rau, 2007). Despite this regularity, evidence on seasonal mortality and its impact on life expectancy remains very limited. In fact, life expectancy is typically measured on an annual basis, hiding the role of recurring seasonal mortality peaks. As Europe’s population ages and climate-related risks intensify, understanding how seasonal mortality shapes life expectancy has become increasingly urgent. In a recent paper (Marinetti et al. 2025), we examined how seasonal mortality continued to shape life expectancy across 20 European countries in the early 21^st century, before the outbreak of COVID-19.

A substantial and persistent burden

To assess how much the seasons matter when studying mortality patterns, we compared observed mortality with a scenario without seasonal spikes: no winter flu epidemics or summer heatwaves. The results reveal a visible and surprisingly stable and pattern. Across Europe, men lost around 1.1 years of life expectancy annually because of seasonal mortality, while women lost about 0.8 years (Figure 1). This translates to roughly 400,000 premature deaths each year across the 20 countries studied. 

Seasonal mortality, therefore, is not a marginal or rare phenomenon. It is a recurring and predictable pattern that interacts strongly with population ageing: deaths at ages 65 and above account for between 70 and 90 per cent of its overall impact. Perhaps most striking is what did not change. These figures remained remarkably stable across two decades, even in years without major health crises. The seasonal burden persisted despite Europe becoming wealthier, healthier, and better equipped to cope with severe weather.

Winter remains the dominant threat

Every winter, cold weather and respiratory infections significantly increase mortality rates, with a life expectancy loss of about 0.55 years on average (Figure 2). This effect is particularly strong among older people, whose immune systems are more fragile and who are more vulnerable to complications such as pneumonia or heart failure (Analitis et al., 2008). The winters associated with strong influenza epidemics (2004-2005, 2009-2010, and 2014-2015) correspond closely to the sharpest annual drops in life expectancy in our data (Figure 1).

Yet, seasonal impacts vary widely across countries, even in recent years (Figure 3). In Portugal, Bulgaria, and Spain, winter alone reduces life expectancy by 0.6 to 0.85 years annually. By contrast, Northern European countries such as Sweden and Finland, despite facing colder winters, show much smaller seasonal losses, likely reflecting better-insulated homes, strong health systems, and widespread flu vaccination coverage (Füssel, 2010; Hajat & Kosatky, 2010)

It’s worth noting that heatwaves have also recently captured public attention, and for good reason. Europe’s extreme summer temperatures in recent years have caused thousands of deaths (Ballester et al., 2023). Yet our findings show that winter still has the greatest toll on life expectancy across Europe. Even the devastating 2003 heatwave reduced life expectancy at birth in France and Spain (the most affected countries) by only 0.3 years, which is less than half of winter’s typical impact. Although climate change will likely raise heat-related mortality in summer, winter is unlikely to cease being a major threat. This points to a “double burden” of seasonal mortality, rather than an offset through warmer winters, as some have suggested.

Little progress, growing challenges

One might expect modern medicine, improved living standards, and stronger public health systems to reduce the seasonal burden on life expectancy. Yet we find little evidence of such progress. In most countries, seasonal losses remained almost unchanged over the 20-year period we studied. In a few cases, including Poland, Slovakia, Germany (men), and Sweden (women), the seasonal burden even appears to have increased slightly. This lack of improvement is concerning. Seasonal mortality is neither random nor unpredictable. Much of it is linked to risks that are well known and, to a large extent, preventable. 

Meanwhile, two trends threaten to amplify this challenge. Europe is ageing rapidly: by 2070, about one in three Europeans will be aged 65 or older (European Commission, Directorate General for Economic and Financial Affairs, 2023), and this is precisely the age group most vulnerable to seasonal mortality shocks. At the same time, climate change is expected to increase the frequency and intensity of temperature extremes, from winter cold spells to summer heatwaves. Together, these trends suggest that seasonal mortality could become an even more pressing public health challenge in the coming decades.

Targeted and timely public policies 

Seasonal mortality is predictable and largely preventable, yet it continues to claim hundreds of thousands of lives across Europe each year. Without stronger preparedness and targeted protection for vulnerable populations, the seasonal toll on life expectancy is unlikely to decline. The challenge is clear: winter preparedness must improve through early warning systems, strengthened vaccination campaigns, adequate heating subsidies, better home insulation, and efficient healthcare systems, especially in Southern and Eastern Europe, where the burden is greatest, and for older age groups. As the European populations age more rapidly and climate extremes intensify, addressing seasonal mortality is not just about improving current life expectancy; it is about preventing a growing public health crisis.

References

Analitis, A., Katsouyanni, K., Biggeri, A., Baccini, M., Forsberg, B., Bisanti, L., Kirchmayer, U., Ballester, F., Cadum, E., Goodman, P. G., Hojs, A., Sunyer, J., Tiittanen, P., & Michelozzi, P. (2008). Effects of Cold Weather on Mortality: Results From 15 European Cities Within the PHEWE Project. American Journal of Epidemiology, 168(12), 1397–1408. https://doi.org/10.1093/aje/kwn266

Ballester, J., Quijal-Zamorano, M., Méndez Turrubiates, R. F., Pegenaute, F., Herrmann, F. R., Robine, J. M., Basagaña, X., Tonne, C., Antó, J. M., & Achebak, H. (2023). Heat-related mortality in Europe during the summer of 2022. Nature Medicine, 29(7), 1857–1866. https://doi.org/10.1038/s41591-023-02419-z

European Commission, Directorate General for Economic and Financial Affairs. (2023). The 2024 ageing report: Underlying assumptions and projection methodologies. Publications Office. https://data.europa.eu/doi/10.2765/960576

Füssel, H.-M. (2010). How inequitable is the global distribution of responsibility, capability, and vulnerability to climate change: A comprehensive indicator-based assessment. Global Environmental Change, 20(4), 597–611. https://doi.org/10.1016/j.gloenvcha.2010.07.009

Hajat, S., & Kosatky, T. (2010). Heat-related mortality: A review and exploration of heterogeneity. Journal of Epidemiology & Community Health, 64(9), 753–760. https://doi.org/10.1136/jech.2009.087999

Marinetti, I., Jdanov, D. A., Jasilionis, D., Nepomuceno, M., Islam, N., & Janssen, F. (2025). Seasonality in mortality and its impact on life expectancy levels and trends across Europe. Journal of Epidemiology and Community Health, jech-2024-223050. https://doi.org/10.1136/jech-2024-223050

Rau, R. (2007). Seasonality in human mortality: A demographic approach. Springer.

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Life tables are the most precise instruments available to demographers for assessing overall mortality levels and trends in a population. Unfortunately, their construction is data-demanding: for both genders, and for each age x (from 0 to over 100), death risks (or rates) must be calculated. This requires a numerator (deaths) and a denominator (population), and results in more than 200 cells. If the observed population is small, random effects can easily predominate in one or more of these cells, producing unpredictable (and unreliable) results – even setting aside omissions, age misclassifications, and other forms of error. To be sure, a variety of methods exist to limit these distortions, but even they have their limits and can be stretched only so far. Is any analysis possible beyond that point?

Data, method and purpose

This question arose in our case. Exploiting an existing Istat database at municipal level (with a reconstruction of the series ensuring constant boundaries from 2002 to 2018 – a rare case indeed), we aimed to study small-scale mortality over the period across Italy’s almost 8,000 local administrative units (LAUs), some of them very small (down to 30 residents in the worst case). For that purpose, we needed a reliable and comparable measure of mortality, but also one that was not too demanding in terms of data input.

We therefore opted for the standardised mortality ratio (SMR), which is simply the ratio between observed and expected deaths. The former were available, thanks to the aforementioned Istat database, while the latter were calculated by applying a standard (national) mortality schedule to the local populations, broken down by gender and age.

Our SMR passed several preliminary tests, performing more than satisfactorily at the national level (Figure 1), as well as at the regional and provincial levels (NUTS2 and NUTS3, respectively – not shown here). Encouraged by this, we decided to apply it to Italy’s municipalities, the smallest possible territorial level (De Santis et al. 2025).

Our main curiosities (… oops, sorry. We meant: our main research questions) were:

1) How large were survival inequalities at the municipal level in Italy between 2002 and 2018?

2) How did these inequalities evolve over time?

3) Was any significant breakpoint detectable during this period?

4) Which contextual factors were most strongly associated with poor survival outcomes at the municipal level?

Main results

Figure 2 allows us to address our first three questions. Small-scale mortality variability was low in Italy during the period under examination (2002–2018): the coefficient of variation (i.e. the ratio between the standard deviation and the mean) ranged between 12% and 15% (panel a). On the negative side, it showed a slight upward trend, indicating somewhat greater relative heterogeneity (i.e. inequality) towards the end of the period.

No major breakpoints emerged over the 17 years we examined. Panel b of Figure 2 indicates that, of the total variance in LAU mortality, around 80% was attributable to within-region differences, and only about 20% to between-region differences. In other words, the frequently voiced concerns about wider inter-regional disparities following the regionalisation of the Italian health system – initiated in 1992 – appear to have been overstated, at least in terms of mortality. (It should be noted, however, that mortality is not the only metric by which health system performance should be assessed, but in our case, no other measures were available.)

Here too, however, a note of caution is warranted: although modest (around 20%, as noted), the between-region component of variability showed an upward trend. In other words, regions were more diverse at the end of the period than they had been at the beginning. Unfortunately, with our data, it is not possible to determine how much of this trend is attributable to policy choices (i.e. regionalisation of the health system) and how much to other factors.

Maps and correlations

Figure 3 visualises the distribution of small-scale mortality, averaged over the period 2002–2018. As is often the case in such analyses, the researchers’ hope of discerning a clear pattern is somewhat frustrated – particularly by the fact that male and female mortality do not appear to follow the same geographical distribution.

Lower mortality, especially among females, is typically observed in eastern Italy (Trentino–Alto Adige, the southern part of Veneto, Emilia-Romagna, Marche), in the centre (Umbria and Tuscany), and in southern Sardinia. Conversely, poorer survival conditions are found in south-western Italy (particularly the area around Naples and in Sicily), in the municipalities surrounding the capital city Rome (though not in Rome itself), and – somewhat unexpectedly – in numerous municipalities in the “affluent” North-West (Liguria, Valle d’Aosta, Piedmont, and parts of Lombardy), as well as in several municipalities along the river Po (running eastwards from Turin to just below Venice). Another area of concern can be identified in the north-east, covering parts of Friuli–Venezia Giulia and the northern municipalities of Veneto.

Regression results clearly indicate that mortality during the period was higher in municipalities characterised by greater “fragility” (an ad hoc index developed by Istat) and lower affluence (Table 1). They also show clear evidence of spillover effects between adjacent municipalities: mortality is geographically correlated (as captured by SAR models – neighbouring municipalities tend to exhibit similar levels), and so are the residuals (SEM models), suggesting that unobserved factors (e.g. air pollution, local policies, etc.) influence areas that extend beyond individual municipal boundaries.

Other potentially relevant variables – among the relatively few available in our database – did not yield consistent results. For instance, we expected mortality to be higher in municipalities with smaller and older populations, or those located in mountainous areas. This was not the case, or at least not consistently so: results varied by gender and proved difficult to interpret, at least for us.

Funding

We gratefully acknowledge financial support from:

– PRIN 2022 research project “The pre-Covid-19 stall in life expectancy in Italy: looking for explanations”, (n°2022CENE9F, PRIN 2022 call DD n.104 of 02/02/2022, funded by the European Union – NextGenerationEU)

– PRIN2022-PNRR research project “Foreign population and territory: integration processes, demographic imbalances, challenges and opportunities for the social and economic sustainability of the different local contexts (For.Pop.Ter)” [P2022 WNLM7], funded by European Union – Next Generation EU, component M4C2, Investment 1.1. 

* The views and opinions expressed are only those of the authors and may not reflect those of the financing institutions.

References

De Santis G., Benassi F., Carboni G., Maltagliati M. (2025). Local mortality patterns in Italy at the beginning of the 21st century. Genus, https://doi.org/10.1186/s41118-025-00262-3

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Excess mortality in the United States (US) is significantly higher than in other countries at similar levels of socioeconomic development (National Research Council, 2013). The US is also disadvantaged in terms of morbidity and, more generally, the prevalence of risk factors for major diseases (Chowdhury, 2023). In a recent article, we measured the contribution of multi-morbidity at death to the mortality gap between the US and three other high-income countries with comparable data, namely France, Italy, and Spain, using multiple cause of death data for 2017 (Barbieri et al., 2025). 

An original approach to identify multi-morbidity contribution to death

Unlike most previous studies on multi-morbidity based on survey data, we used information from routinely collected vital statistics data on mortality. This approach has the advantage of providing widespread, systematic, cost-efficient, and highly comparable information on multi-morbidity at death, given that all high-income countries now use unified definitions and the same data collection instruments, i.e. the World Health Organization recommended death certificate with all causes of death coded to the International Classification of Diseases.

Causes of death are listed on two different sections of the certificate. In Part 1, certifiers report all the medical causes involved in the sequence of health conditions that led directly to death. In Part 2, they record all other causes that were not part of the sequence(s) listed in Part 1, but that nonetheless contributed to death by influencing the course of the morbid process, for instance by eroding an individual’s overall health status. These conditions are called associated causes of death. Our study analyzed these multiple causes of death, which include all the causes and conditions reported on both sections of the death certificate, to classify deaths according to the type of morbid process.

Using all death certificates in the year 2017 (the most recent for which we could access the data in all four study countries at the time of the analysis), we grouped deaths into three categories, depending on whether the morbid process could be considered as 1) simple, 2) multi-morbid, or 3) ill-defined. Following Grippo et al. (2024), we identified all the originating causes, i.e. the conditions that initiated a chain of morbid events. Our definitions are as follows:

• Simple: processes that characterize deaths with a single originating cause and no associated cause listed on Part 2 of the death certificate;

• Multi-morbid: processes that characterize deaths with more than one originating cause or with associated causes; and

• Ill-defined: processes that characterize deaths with no well-defined causes, meaning that the death certificate did not list any known cause of death or only symptoms, signs or poorly informative conditions (e.g. “respiratory arrest”) (Figure 1).

We calculated mortality rates by sex and age for each of the three types of morbid processes in the four study countries and assessed the contribution of multi-morbidity to excess mortality by sex and age group and to the gap in life expectancy at birth between the US and the three comparison countries.

The major contribution of multi-morbidity to US excess mortality

Our analysis demonstrates that multi-morbid processes contribute disproportionately to the large mortality and life expectancy gaps between the US on the one hand, and France, Italy and Spain on the other, for both men and women. In 2017, life expectancy at birth was 78.7 years for both sexes combined in the US, much lower than in the other three countries (82.4 years in France, 82.7 years in Italy, and 83.0 years in Spain). Multi-morbid processes contributed 51% of the gap in life expectancy at birth with Italy, 73% of the gap with Spain, and 75% of the gap with France (Figure 2). This is all the more striking because multi-morbid processes in the US contribute to a smaller share of the age-standardized death rate (46%) than simple processes (50%; the remaining 4% being attributed to ill-defined processes). This result suggests that the US disadvantage arises disproportionately from the excess mortality linked to complex, multi-condition health profiles.

As regards overall mortality, the US disadvantage in multi-morbidity is particularly concentrated among working-age adults (20 to 64-year-olds). In this age group, most of the loss is attributable to multi-morbid processes. Among men, this type of process accounted for 63% of the difference with Italy, 77% with Spain, and 88% with France; among women, the corresponding figures were 59%, 66%, and 71%.

Between one fifth and one quarter of all deaths would have been averted in the US if the country had experienced the mortality rates of Italy (20%), France (23%), or Spain (23%), with a very large proportion attributable to multi-morbid processes. Excess US deaths from multi-morbid processes represented 17–19% of the total number of deaths in the US, depending on the comparison country, versus 4–11% for those resulting from simple morbid processes. The average number of years of life lost by Americans whose deaths would have been averted if they had experienced the mortality risks of their more advantaged peers was found to be 23 years with respect to the mortality rates of France (27 years for men and 20 years for women), 27 years with respect to Spain (31 years for men and 24 years for women), and 30 years with respect to Italy (32 years for men and 28 years for women). These large values reflect the fact that excess US deaths tend to be concentrated at relatively young adult ages, when the potential for additional years of life is high.

Implications

Multi-morbidity creates multiple challenges for medical infrastructures which, in most countries, are still designed to manage a single disease with a highly segmented system of care. Multi-morbidity is associated with increased mortality risks, disability, reduced functional status, poor quality of life, complicated treatment options, and adverse drug events due to incompatibilities, calling for coordinated and comprehensive health care (Fortin et al., 2007). The disproportionate burden of multi-morbidity at death in the US certainly explains in part why the US spends, on average, 60% more per inhabitant on health care than other high-income countries. Because each disease comes with its own risk factors and determinants, the high prevalence of multi-morbidity in the US suggests that no single factor is likely to explain the gap with peers. It is thus unlikely that a unique intervention, program, or policy will be sufficient to close the life expectancy gap between the US and other countries. In the meantime, the disproportionate burden of multi-morbidity in the US will continue to complicate efforts to sustain progress in longevity and to improve health and quality of life of the general population. 

References

Barbieri, M., Désesquelles, A., Egidi, V., Frova, L., Grippo, F., Meslé, F., Pappagallo, M., & Trias-Llimós, S. (2025). Multi-Morbidity at Death and the US Disadvantage in Mortality: M. Barbieri et al. European Journal of Population, 41(1), 28.

Chowdhury, S. R., Das, D. C., Sunna, T. C., Beyene, J., & Hossain, A. (2023). Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine, 57.

Fortin, M., Soubhi, H., Hudon, C., Bayliss, E. A., & Van den Akker, M. (2007). Multimorbidity’s many challenges. Bmj, 334(7602), 1016–1017.

Grippo, F., Frova, L., Pappagallo, M., Barbieri, M., Trias-Llimós, S., Egidi, V., Meslé, F., & Désesquelles, A. (2024). Beyond the underlying cause of death: an algorithm to study multi-morbidity at death. Population Health Metrics, 22(1), 36.

National Research Council, & Committee on Population. (2013). US health in international perspective: Shorter lives, poorer health.

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Introduction 

Around the world, health policy agendas aim to reduce mortality disparities across population subgroups (Marmot 2007). One way of doing this is through educational expansion (through increases in compulsory education, literacy rates, and school enrolment). In Western countries, education became compulsory between the second half of the nineteenth century and the early twentieth century, although significant reforms occurred after 1945 (Viarengo 2007). This resulted in an increase in the average number of years of formal education from 6.2 to 11.9 in Europe between 1950 and 2015 (Lee and Lee 2016).

Educational expansion influences health and mortality trends through three potential mechanisms: it improves general knowledge about health, treatment adherence, and the adoption of better health behaviours (Sørensen et al. 2015); it facilitates the adoption of important social policy changes that indirectly affect health, like the implementation of national health systems (Mackenbach 2017); and last, it creates positive health selection, increasing, in particular, the number and share of older adults with tertiary education (Enroth et al. 2022).

Previous studies have estimated that between 16% and 40% of life expectancy increases in recent years can be attributed to educational expansion (Luy et al. 2019, Enroth et al. 2022). However, the role of educational expansion in determining trends in lifespan variation remains unknown. While life expectancy informs us about the average length of life, lifespan variation provides information on the variability in the age at death (van Raalte et al, 2018). Studying both metrics simultaneously enables a more thorough understanding of longevity. Moreover, we do not know if the contribution of educational expansion to time trends in life expectancy and lifespan variation has been changing over time, and the direction of these possible changes. 

Trends in life expectancy and lifespan variation (1975-2015)

In a recent paper (Zazueta et al. 2025), we examined the contributions of educational expansion and mortality trends by educational attainment group to national trends in both life expectancy and lifespan variation. We covered England & Wales (E&W), Finland, and Italy (Turin), using individually linked mortality data by educational level (low, middle, high) by single years of age for those aged 30 and over, and by single calendar year for the period 1975-2015. This unique dataset is immune from the so-called numerator/denominator bias that affected previous studies because of the use of different, unlinked data sources for mortality by educational level and population by educational level.

Between 1975 and 2015, in all the countries examined, the proportion of individuals with middle and high education increased over time, while the share of those with low educational attainment declined from 76.8% in 1975 to 32.5% in 2015. For the different populations by sex, remaining life expectancy at age 30 increased over time, while lifespan variation declined (Figure 1A and 1B). However, levels and trends differed substantially by educational level. For life expectancy, the highly educated had higher levels and a faster rate of increase than the low educated. The low educated also exhibited an increasing trend in lifespan variation for males and for Finnish females, with increases in lifespan variation of 0.3 years and 0.7 years for males and females, respectively, between 1975 and 2015. 

The impact of educational expansion on longevity trends

For the three countries and for both sexes, educational expansion contributed positively to the increase in life expectancy and the reduction of lifespan variation over the study period (Figure 2). Between 1975 and 2015, the contribution of educational expansion to the overall life expectancy increase of 7.5 years was 15.3% on average, ranging from 10% for Italian females to 20.9% for Finnish females. For lifespan variation declines, the contribution of educational expansion ranged from 12.2% (British females) to 80.8% (Italian females). Both results highlight the contribution of expanding education to global population health.

The contribution of educational expansion to the increase in life expectancy increased over time (Figure 3A). Between 1975 and 1985, this contribution was modest, averaging less than 10%, whereas between 2005 and 2015, it was 28.4% on average. The same pattern occurred for lifespan variation (Figure 3B). This result indicates that educational expansion will likely influence future mortality progress.

The impact of mortality trends by education on longevity trends

Across both sexes and the different welfare regimes, the main driver of national longevity trends proved to be the mortality trends among the low-educated, especially for females in E&W and Italy (Turin) (Figures 2 and 3). Mortality changes among low-educated groups at younger ages seem to be an important driver of changes at the national level. The members of this group have relatively high mortality, partially deriving from their lifestyles, e.g. high alcohol consumption in Finland (Zazueta et al. 2024).

Conclusion 

Educational expansion results not only in longer lives, but also in more equal lifespans. Luckily, educational levels have improved over time, and should keep on doing so, which will likely (and positively) influence future mortality progress. In the recent past, the main driver of mortality progress at the national level was mortality change among the low-educated, which also contributed to reducing the variability of lifespan in selected populations, making them a primal focus of health policies. 

Funding

This work is financed by the Netherlands Organisation for Scientific Research (NWO) as part of the research programme “Forecasting future socio-economic inequalities in longevity: the impact of lifestyle ‘epidemics’”, under grant no. VI.C.191.019. See: www.futurelongevitybyeducation.com.

References

Enroth, L., Jasilionis, D., Németh, L., Strand, B. H., Tanjung, I., Sundberg, L., … & Brønnum-Hansen, H. (2022). Changes in socioeconomic differentials in old age life expectancy in four Nordic countries: the impact of educational expansion and education-specific mortality. European Journal of Ageing, 19(2), 161‒173.

Lee, J. W., & Lee, H. (2016). Human capital in the long run. Journal of development economics, 122, 147-169.

Luy, M., Zannella, M., Wegner-Siegmundt, C., Minagawa, Y., Lutz, W., & Caselli, G. (2019). The impact of increasing education levels on rising life expectancy: a decomposition analysis for Italy, Denmark, and the USA. Genus, 75(1), 11.

Mackenbach, J. P. (2017). Nordic paradox, Southern miracle, Eastern disaster: persistence of inequalities in mortality in Europe. The European Journal of Public Health, 27(suppl_4), 14‒17.

Marmot, M. (2007). Achieving health equity: from root causes to fair outcomes. The Lancet, 370(9593), 1153-1163.

Sørensen, K., Pelikan, J. M., Röthlin, F., Ganahl, K., Slonska, Z., Doyle, G., … & Brand, H. (2015). Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). The European journal of public health, 25(6), 1053‒1058.

Van Raalte, A. A., Sasson, I., & Martikainen, P. (2018). The case for monitoring life-span inequality. Science, 362(6418), 1002‒1004.

Viarengo, M. (2007). An historical analysis of the expansion of compulsory schooling in Europe after the Second World War (LSE Working Paper, No. 97/07). London, UK: Department of Economic History, London School of Economics.

Zazueta-Borboa, J. D., Van Hemelrijck, W. M., Zengarini, N., Sizer, A., Kunst, A. E., Martikainen, P., & Janssen, F. (2024). Long-term trends in educational inequalities in alcohol-attributable mortality, and their impact on trends in educational inequalities in life expectancy. Frontiers in public health, 12, 1355840. https://doi.org/10.3389/fpubh.2024.1355840 

Zazueta-Borboa, J. D., Basellini, U., Zagheni, E., van Wissen, L., Martikainen, P., & Janssen, F. (2025). The Contribution of Educational Expansion to Trends in Life Expectancy and Lifespan Variation in England and Wales, Finland, and Italy (Turin). Demography, 62(5), 1689‒1715. doi: https://doi.org/10.1215/00703370-12270715.

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How long can one expect to live? Behind this seemingly simple question lies one of the most important indicators of a country’s socio-economic development (Olshansky et al 2024). Life expectancy at birth reflects more than the average length of life: it encapsulates a population’s health status, living conditions and social inequalities in longevity.

In 2024, France ranked among the countries with the highest life expectancy worldwide, reaching 80 years for males and 85 years and 9 months for females, according to INSEE. But national averages can hide regional differences. In Paris, for example, life expectancy was 82 years for males and 86 years and 9 months for females, two years and one year higher, respectively, than the national averages.

Paris hasn’t always been a haven for healthy life, however. Just 150 years ago, life in the French capital was dramatically shorter. Figure 1 charts life expectancy at age one (excluding infant mortality, which was very high and poorly recorded in Paris at the time) between 1872 and 2019 for France as a whole (in black) and for Paris (in red). In 1872, a Parisian boy who reached his first birthday could expect to live another 43 years and 6 months, and a Parisian girl, 44 years and 10 months.

Less well known is the fact that life expectancy in Paris remained lower than the national average for much of modern history. At the end of the 19th century, the gap reached 10 years for males and 8 years for females, a phenomenon known in the literature as the “urban penalty”, reflecting the deadly combination of crowding, poor sanitation, and high exposure to infectious diseases (Woods 2003). It wasn’t until the early 1990s for females and the 2000s for males that Paris overtook the rest of France.

Reconstructing a century of urban mortality

In a recent study, we sought to understand how Paris transitioned from a capital of early death to one of the world’s longevity leaders (Bonnet et al 2025). The data to answer this question existed, but were scattered across municipal archives. Moreover, cause-of-death statistics were difficult to use due to changes in classifications and the high cost of collection.

For this study, we compiled and harmonized a new dataset of cause-specific mortality in Paris between 1890 and 1949. This unprecedented resource – now publicly available (https://osf.io/wcyft/) – enabled us to trace the key drivers behind the city’s rapid improvements in life expectancy during the first half of the 20th century (the period between the two red vertical lines in Figure 1), when its trajectory began to converge with the rest of France. To analyse the evolution of social and spatial inequalities in mortality, we also collected data for selected infectious diseases at the district level (across all 80 neighbourhoods of present-day Paris).

Nearly 25 years gained in life expectancy, 80% due to infectious disease decline

Between 1890 and 1950, life expectancy at age one in Paris increased by nearly 25 years. What drove this remarkable progress? Figure 2 breaks down the gain by major causes of death for both males and females. Red colours represent infectious diseases – tuberculosis, diphtheria, measles, bronchitis, and pneumonia – which were the leading causes of death in Paris during the Belle Époque.

The conclusion is striking: infectious disease decline alone accounts for almost 20 years, or 80% of the total life expectancy gain (25 years). Tuberculosis was the primary driver. Its rapid decline after World War I contributed 8 years of life expectancy gain for males and 6 for females. Respiratory infections (bronchitis and pneumonia) added another 5 years, while diphtheria (particularly deadly among children) declined sharply in the 1890s thanks to improvements in drinking water infrastructure, contributing around 2.5 years.

Cardiovascular diseases and cancers, on the other hand, played only a marginal role before 1950. Their effects appeared later and, in the case of cancer (especially among males), even slightly slowed progress in life expectancy. Pregnancy-related mortality among women had a limited impact.

The evolution of social inequalities in mortality 

The battle against tuberculosis reveals another critical dimension: stark social and spatial disparities in mortality. Using the reconstructed cause-of-death data across Paris’s 80 districts, we mapped mortality rates from tuberculosis around 1900. These rates were calculated as the number of deaths per 100,000 inhabitants.

Figure 3 highlights massive inequalities within the city. The highest rates, often exceeding 400 deaths per 100,000, were concentrated in the east and south of Paris. The three most affected neighbourhoods were Saint-Merri (nearly 900), Plaisance (850), and Belleville (just under 800). In contrast, the western neighbourhoods, such as Champs-Élysées, Europe, and Chaussée-d’Antin, had rates closer to 100.

These spatial differences directly reflected the social inequalities of the time. Using early 20th-century rent data, we identified the ten richest (black triangles) and ten poorest (black circles) neighbourhoods. A stark social divide emerges – an affluent central and western Paris versus working-class eastern periphery – that mirrors the geography of tuberculosis mortality.

Figure 4 traces the evolution of these gaps between 1893 and 1950. At the end of the 19th century, the poorest neighbourhoods had tuberculosis mortality rates over 600 per 100,000, 3.5 times higher than those in the richest districts. The gap widened until World War I, when mortality fell faster in wealthier areas. By 1910, poor districts had rates 4.5 times higher. It was only in the interwar period that conditions improved significantly in disadvantaged neighbourhoods. By the late 1930s, the gap had halved, and after World War II, even the poorest neighbourhoods fell below the symbolic threshold of 100 deaths per 100,000, reached by the Champs-Élysées district 50 years earlier.

A public health transformation, and lessons for today

The progressive eradication of infectious diseases drove the spectacular gains in life expectancy observed from the Belle Époque through to World War II. The pace was unprecedented, with nearly six months gained per year. The fight against tuberculosis was at the heart of this journey toward modernity. Its decline also helped reduce deep social inequalities: in the worst-affected periods, tuberculosis mortality in the poorest districts was three to four times higher than in the wealthiest ones.

The determinants of this sharp decline in mortality remain the subject of debate. However, they can be broadly grouped into three main categories. The first relates to investments in public health infrastructure. The gradual connection of homes to modern sanitation systems likely contributed to the reduction of waterborne infectious diseases, as already emphasized by Kesztenbaum and Rosenthal (2017). Meanwhile, the establishment of the Casier Sanitaire (Health Registry) at the turn of the 20th century may have helped reduce airborne disease mortality – particularly from tuberculosis – by identifying high-risk buildings.

The second category involves medical innovations. The introduction of the anti-diphtheria serum and the BCG vaccine against tuberculosis, among other developments, significantly reshaped the public health landscape. However, antibiotics, discovered later, played only a marginal role in reducing mortality before 1950.

The third category reflects broader economic and social transformations. The first half of the 20th century saw strong economic growth, improved living conditions, and a marked decline in income inequality. In addition, the expansion of transportation networks facilitated food supply from rural areas, contributing to better nutrition. 

Life expectancy in Paris has continued to rise in recent times, albeit more slowly: another 20 years were gained between 1950 and 2019, making it the longevity capital it is today. The social and spatial dynamics that have led to this remarkable result are the object of our current research.

References

Bonnet, F., Torres, C., Kesztenbaum, L. and Meslé, F. (2025) Life and Death in a Changing City: Mortality Patterns and Inequalities in Paris, 1890–1949. Population and Development Review. https://doi.org/10.1111/padr.70031 (Data can be accessed here: https://osf.io/wcyft/)

Kesztenbaum, L., & Rosenthal, J. L. (2017) Sewers’ diffusion and the decline of mortality: The case of Paris, 1880–1914. Journal of Urban Economics, 98, 174-186. https://doi.org/10.1016/j.jue.2016.03.001

Olshansky, S.J., Willcox, B.J., Demetrius, L. et al. (2024) Implausibility of radical life extension in humans in the twenty-first century. Nat Aging 4, 1635–1642. https://doi.org/10.1038/s43587-024-00702-3

Woods, R. (2003) Urban-Rural Mortality Differentials: An Unresolved Debate. Population and Development Review, 29: 29-46. https://doi.org/10.1111/j.1728-4457.2003.00029.x

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