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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For nearly half the population, health and wellbeing fluctuates across the menstrual cycle, but their cyclical health experience remains absent from the surveys we use to understand population health. As a result, what we know about gender differences in health and wellbeing may be incomplete and misunderstood.

Menstrual health: measured but missed

Menstrual cycles affect mood, energy, pain, and daily functioning for a substantial share of the population across several decades of their life course. Around 38% of menstruating women report being unable to perform all their regular daily activities during menstruation, with consequences extending to work productivity, academic performance, and social participation (Schoep et al., 2019; Leon-Larios et al., 2024). Menstrual irregularity is also associated with longer-term health conditions including cardiovascular disease and metabolic disorders (Attia et al., 2023), making the menstrual cycle not just a reproductive marker but a crucial health indicator (Vollmar et al., 2025).

In early 2026, we conducted small-scale exploratory qualitative research in the Netherlands to inform and interpret the findings of the survey review, including an in-person focus group discussion with three participants aged 24 who currently menstruated, and an online in-depth interview with one participant aged 49 with perimenopausal experience. Both interviews were conducted in English and lasted approximately 60 minutes. Participants were recruited through personal networks. 

Although our sample is small and not representative, its results seem clear: menstrual cycles matter for women, but are ignored, or at least underplayed, by society as a whole. As a 49-year-old participant in our research described: “I even tried at some point to start moving important appointments to a date that would not fall within my PMS (premenstrual syndrome) phase, because I just thought if I have an important meeting, right then, it will just mess it up”. Another, aged 24, put it simply: “I cry more before my period and then I don’t know why I’m that sad if I don’t look at my tracker. And then when I get my period I’m like, that’s why I was sad”.

These are not marginal experiences. They are widespread, consequential, and entirely invisible in the data we use to understand population health.

What surveys currently capture and what they miss

A review of major population health and demographic surveys, including the European Social Survey, the Generations and Gender Survey, the WHO World Health Survey Plus, the Socio- Economic Panel and the Demographic and Health Survey reveals a consistent pattern. All include single-item measures of self-rated health and life satisfaction. None capture menstrual cycle phase, cycle regularity, or symptom burden as health-relevant variables.Where menstruation does appear in these surveys, it is framed as a reproductive marker, e.g. age at first menstruation, menopause onset, or contraceptive use to prevent pregnancy. The health dimension of menstruation, how it shapes daily functioning, mood, and energy across the cycle is not present.

This matters because these health measures are collected at a single moment in time. For women who menstruate that moment is not neutral. As one participant in our research reflected: “When you get those questionnaires and they ask, ‘how are you?’, I find it sometimes difficult to answer because it’s so cyclical. In one week, I would feel super energetic and like the old me, and if I would have to answer that questionnaire in a week that it’s not going well, then it really would be quite low”.

Cyclical health variation is one factor that could contribute to observed gender gaps in self-reported health in ways that have not been examined. Without knowing where a woman is in her cycle or how much menstrual symptoms affect her daily life, a health rating captured on a high-symptom day may say more about the timing of the survey than about her general health. This may help explain why women report poorer self-rated health on average than men, though this gap narrows when health covariates are controlled (Zajacova et al., 2017). This suggests that unmeasured factors may be contributing to what looks like a stable gender difference. It should be noted, however, that gender differences in self-rated health persist beyond menopause, suggesting that menstrual cycle variation is one contributing factor among several, rather than a complete explanation of the gender health gap.

A small addition with significant potential

The good news is that this is a solvable problem. Menstrual cycle characteristics can be captured through brief, accessible self-reported items in large-scale studies. Women can and do report their cycle status, symptom frequency and the impact of symptoms on daily activities and these reports are both feasible and informative at population scale (Ali et al., 2026; Dhonnabháin & Wallace, 2025).

Two brief items, one asking where a respondent places themselves in their menstrual cycle at the moment of interview and one asking how often menstrual symptoms have affected their daily activities in the past month, would be enough to begin addressing this concern. They require no medical diagnosis, no clinical knowledge, and minimal survey time.

When asked about the idea, participants in our research were unambiguous: “This is such an easy thing to include, it’s one question and then you would be able to adjust for it as a variable. That does not seem scientific, to exclude half the population”.

The policy relevance is also clear. Spain became the first European country to introduce statutory menstrual sick leave in 2023, and similar provisions exist in several countries across Asia and Africa (Boletí, 2023). Evaluating whether such policies reduce symptom burden and improve daily functioning requires population-level data that current surveys simply cannot provide.

Menstrual health has long been treated as a topic solely for women’s health, studied separately in specific research, outside the mainstream of population science. But cyclical health variation does not exist in isolation. It intersects with work, education, relationships, and social participation in ways that only become visible when measured alongside the full range of variables that general population surveys capture. Growing policy attention, like Spain’s introduction of menstrual sick leave in 2023, reflects a broader recognition that this is a population-level issue. A few simple questions could make a previously invisible health experience finally countable and comparable.

References

Ali, Z. E., Wainwright, E., Murugesu, S., Moraru, S., Lawrie, L., Getreu, N., & O’Neill, H. C. (2026) How well do women understand their menstrual cycles? Insights from 383,085 UK based women. Reproductive Health.

Attia, G. M., Alharbi, O. A., & Aljohani, R. M. (2023). The impact of irregular menstruation on health: a review of the literature. Cureus, 15(11).

Boletín Oficial del Estado. Ley Orgánica 1/2023, de 28 de febrero, por la que se modifica la Ley Orgánica 2/2010, de 3 de marzo, de salud sexual y reproductiva y de la interrupción voluntaria del embarazo. España, núm. 51, 1 de marzo de 2023. https://www.boe.es/buscar/act.php?id=BOE-A-2023-5364

Dhonnabháin, B. N., & Wallace, H. (2025). Exploring the role of menstrual perceptions on health, well- being, and daily functioning. Women’s Health, 21, 17455057251399893.

Leon-Larios, F., Silva-Reus, I., Puente Martínez, M. J., Renuncio Roba, A., Ibeas Martínez, E., Lahoz Pascual, I., … & Quílez Conde, J. C. (2024). Influence of menstrual pain and symptoms on activities of daily living and work absenteeism: a cross-sectional study. Reproductive Health, 21(1), 25.

Schoep, M. E., Nieboer, T. E., van der Zanden, M., Braat, D. D., & Nap, A. W. (2019). The impact of menstrual symptoms on everyday life: a survey among 42,879 women. American journal of obstetrics and gynecology, 220(6), 569-e1.

Vollmar, A. K. R., Mahalingaiah, S., & Jukic, A. M. Z. (2025). The menstrual cycle: a vital sign across the lifespan. The Lancet Obstetrics, Gynaecology, & Women’s Health, 1(2), e141-e145.

Zajacova, A., Huzurbazar, S., & Todd, M. (2017). Gender and the structure of self-rated health across the adult life span. Social Science & Medicine, 187, 58-66.

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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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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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For years, scholars have been puzzled by a peculiar paradox: migrants often arrive in better health than the locals they join – only to see that advantage erode over time. Newly arrived immigrants tend to be healthier than the native-born, even when they are poorer or less educated. Yet the longer they stay, the more their health advantage declines.

The prevailing wisdom attributes this to “positive selection” and “unhealthy assimilation.” Only the most capable – or healthiest – are willing or able to migrate. Once settled, exposure to discrimination, stress, new diets, sedentary lifestyles, and limited access to healthcare gradually wears away their edge.

But what about refugees – those who did not choose to leave? Can people fleeing war and persecution still be described as “positively selected”? And if not, should we expect their health to follow the same arc as other immigrants?

In a recent study, I explored these questions for asylum seekers and refugees (AS&Rs) in Germany, the European country that absorbed the largest share of individuals displaced by conflicts in Syria, Iraq, and Afghanistan during the 2010s (Ferrara 2025). Using data from the German Socio-Economic Panel, a large longitudinal survey, I compared the health of refugees with other immigrants arriving around the same time and with native-born Germans. The findings challenge some comfortable assumptions in migration and health research.

A tale of two migrations

The academic worlds of “migration studies” and “refugee studies” have often operated in parallel. Most migration research focuses on economic migrants, and its conclusions are frequently generalized to other groups. Yet refugees may differ in crucial ways.

Standard explanations for immigrants’ health advantage rely heavily on selectivity: moving countries requires resources, initiative, and often physical stamina. Those who go abroad are therefore atypical of the populations they leave behind.

Refugees, by contrast, are forced to move – often abruptly and under dire circumstances. Conventional wisdom suggests they should be less positively selected than other migrants, and thus start off in worse health. Yet recent work complicates that picture. Studies find that refugees in Germany were among the most educated coming from their origin countries (Spörlein et al., 2020). Reaching Germany from a war-torn region is no easy feat. It requires navigating dangerous routes, evading legal obstacles like the EU’s Dublin Regulation, and often paying smugglers. Those who make it are, in a sense, doubly selected: first by circumstance, then by endurance.

Refugees also face additional hurdles compared with other migrants, which may cause their health to deteriorate. They are more likely to experience traumatic events before and during their journeys, women especially, and often have limited access to health care in the initial period after arrival.

Challenging the health paradox

Using survey data, I analyzed the health of AS&Rs and other immigrants arriving in Germany in the first half of the 2010s. The former mostly included individuals from Syria, Iraq, and Afghanistan, while the latter mostly included migrants from Eastern Europe. Their health trajectories were compared with those of native Germans.

Non-AS&R immigrants followed the familiar script. They started off healthier than Germans across most indicators, but their advantage faded with time.

Asylum seekers and refugees, however, did not fit neatly into the Immigrant Health Paradox. Two to three years after arrival, they reported better physical and self-rated health than both natives and other immigrants, but this advantage declined over time. Conversely, they had markedly worse mental health upon arrival, which improved significantly as they settled.

The initially strong physical health of refugees may reflect the extreme selectivity of those who reach Germany. Surviving arduous journeys across deserts and seas, and enduring long waits in refugee camps, likely discourages the less robust. Their poorer mental health is less surprising: war, flight, and uncertainty leave lasting scars, compounded by protracted asylum procedures, legal limbo, and restricted access to psychological care. Yet the modest improvement in mental health over time signals resilience. Stabilization – securing legal status, housing, and family reunification – appears to sustain mental well-being, countering the “unhealthy assimilation” narrative that dominates immigrant health studies.

Implications for research and policy

These findings underscore that the health trajectories of migrants are not uniform. Refugees’ experience challenges simple assumptions about selection and assimilation. Future research should treat refugees and economic migrants as analytically distinct groups, exploring how pre-migration adversity, migration pathways, and post-migration conditions jointly shape health outcomes.

For policy, the message is clear: timely access to healthcare, legal protection, and social integration programs can significantly influence refugee health. Mental health support, in particular, can foster resilience and mitigate the long-term consequences of trauma. Recognizing heterogeneity among migrants is not merely an academic exercise – it is central to designing effective public health and social policies in an era of unprecedented displacement.

References

Ferrara, Alessandro. 2025. “A Refugee Health Paradox? Self-Reported Health Trajectories of Refugees and Immigrants in Germany”. Population and Development Review, https://doi.org/10.1111/padr.70027.

Spörlein, Christoph, Cornelia Kristen, Regine Schmidt, and Jörg Welker. 2020. “Selectivity Profiles of Recently Arrived Refugees and Labour Migrants in Germany.” Soziale Welt 71 (1–2): 54–89. https://doi.org/10.5771/0038-6073-2020-1-2-54.

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Background

There are good reasons to expect that health problems may lead to lower fertility. Individuals who are exhausted because of health problems or who struggle with daily functioning may not want as many children as others, and those with particularly severe conditions may be less likely to enter partnerships. Additionally, some mental or physical diseases or the treatments for them may reduce fecundity. However, there are also mechanisms that could lead to a relatively large number of children, or quite early childbearing, among individuals with health issues. For example, to the extent that health problems make schooling more difficult, this could increase fertility, especially among women. So far, demographers who study high-income countries have taken little interest in the possible importance of health as a fertility determinant. 

Most women and men of reproductive age are in good health, but some suffer, for example, from chronic physical diseases with a severe impact on their lives, and in recent decades there have been indications of a quite sharp rise in depression, anxiety and other mental disorders among teenagers and young adults in many high-income countries (Krokstad et al. 2022; Zhou et al. 2025). Has this increase contributed negatively to fertility? This question is especially relevant for the Nordic countries, where fertility was higher than in most other rich countries for many years but has declined more markedly since 2009 (Eurostat 2025). 

Associations between mental disorders and fertility in Norway

In a recent analysis (Kravdal, Flatø and Torvik 2025), we used data from Norwegian registers for the years 2008-2018 to estimate whether individuals who had consulted a general practitioner or a specialist for a mental disorder in a certain year were less likely to have a child two years later than individuals who had not consulted. We found that women with depression had reduced first-, second- and third-birth rates (Table 1). According to a simulation where the calendar year was set to 2018, these lower birth rates correspond to a reduction in completed fertility from 1.60 births per woman (close to Norway’s total fertility rate in 2018) among those without any of the mental disorders under study, to 1.34 among those who – hypothetically – have depression throughout their reproductive period (Table 1, column 4). Depression is even more strongly linked to lower fertility among men: the corresponding numbers for them are 1.41 and 0.90. 

The associations between anxiety and fertility were of similar magnitude, while fertility was even lower among individuals with bipolar disorder, eating disorder or personality disorder. The simulated completed fertility was lowest among women and men with schizophrenia: 0.36 and 0.16, respectively. However, to the extent that individuals with mental disorders enter parenthood, many of the estimates suggested that they did so at a relatively early age.

It is important to note that these observed associations reflect the effects of mental disorders on fertility – through a variety of causal pathways – but may also be a result of individual and societal characteristics that influence not only the probability of developing a mental disorder but also fertility. We carried out additional analyses controlling for partnership, education and income. These factors may be joint determinants of mental health and fertility, and may also mediate the causal effects of mental disorders on fertility. Additionally, we controlled for unobserved characteristics shared by siblings, by comparing siblings with and without mental disorders. These steps made the associations between mental disorders and fertility weaker, but many of them were still significant. The remaining associations may partly reflect causal effects of mental disorder on fertility, operating through factors other than the above-mentioned control variables. For example, women and men with mental health problems may fear that raising a child will be too burdensome, and therefore not want a child, or another child. However, the remaining associations may also be linked to characteristics unique to one sibling, such as experiences early in life, that have implications for both mental health and fertility. It is hard to imagine a statistical analysis that is entirely free from this kind of confounding. 

The contribution to the national total fertility rate

In our paper (Kravdal, Flatø and Torvik 2025), we also showed that Norwegian fertility would have been higher in 2008-2018 in a hypothetical scenario without mental disorders, but not by much: only 0.05 extra children per woman, (depression and anxiety, the most prevalent conditions, are the main drivers of reduced fertility in our simulation). In other words, if we disregard the fact that the causal effects of mental disorders on fertility are likely smaller than the estimated associations, and if we assume hypothetically that the prevalence of the disorders increases from zero to the overall level observed in 2008-2018, fertility will be reduced by 0.05. Clearly, the increase in the actual prevalence from 2009 up to now is different from this, partly because the data do not capture all cases of mental disorders, and especially not the least severe. Everything considered, it is hard to believe that the rise in mental disorders has contributed more than modestly to the half-a-child reduction of the total fertility rate in Norway since 2009. 

Conclusion

The apparent increase in mental disorders is undoubtedly a challenge. Poor health reduces people’s wellbeing, partly because of its adverse effects on their education and income, which in turn have implications for society. Our analysis of Norwegian data shows that individuals with mental disorders also have fewer children than others, although the increasing prevalence of these problems is unlikely to have influenced the recent fertility decline in any significant way. In settings with less generous welfare policies, mental health may be more strongly linked to birth rates. All in all, this suggests that mental disorders probably deserve more attention from a demographic perspective than they have received thus far. 

References 

Eurostat (2025). Fertility statistics. Fertility statistics – Statistics Explained – Eurostat 

Kravdal, Ø., Flatø, M. & Torvik, F.A. (2025). Fertility among Norwegian women and men with mental disorders. European Journal of Population, online first  

Krokstad, S., Weiss, D.A., Krokstad, M.A., Rangul, V., Kvaløy, K., Ingul, J.M., Bjerkeset, O., Twenge, J. & Sund, E.R. (2022). Divergent decennial trends in mental health according to age reveal poorer mental health for young people: repeated cross-sectional population-based surveys from the HUNT Study, Norway. BMJ Open, 12(5), e057654.

Zhou, W., Hei, B., Liu, Z., Liu, Y., Ding, Z., & Li, M. (2025). Global temporal trends in depression incidence among women of childbearing age: A 30-year analysis and projections to 2030. Social Science & Medicine, 372, 118005.

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China has made notable progress in public health over recent decades, marked by a significant rise in healthy life expectancy, which, in the last 30 years, has risen by nearly 8 years, reaching 68.4 years in 2019 (Wang et al., 2020). However, these gains are tempered by the ongoing challenges posed by an aging population, now exceeding 264 million individuals aged 60 and over. Among them, more than 40 million older adults face difficulties with self-care, and the number of years spent living with illness or disability continues to rise as life expectancy extends. This, understandably, is a cause of great concern in China.

China’s patriarchal culture and dual urban-rural structure have long limited access to medical and healthcare resources for females and rural residents. Consequently, they tend to spend more years in poor health compared to their male and urban counterparts (Luo et al., 2016). With the overall health of the population improving in recent years, how have sex and urban-rural disparities in healthy life expectancy changed?

Health expectancy in China from 2010 to 2020

To address these concerns, in a recent study (Guo, Mei and Jiang, 2025) we examined life expectancy in four health states—good, fair, poor, and disabled—using data from the Chinese Population Census. Figure 1 illustrates the change in life expectancy across health states in China. The results show an increase in life expectancy in both good and fair health: at the age of 60, they increased from 9.63 and 7.82 years in 2010 to 11.66 and 8.41 years in 2020, respectively. Additionally, the proportion of life spent in good health rose across all age groups. By 2020, 60-year-old individuals could expect to spend nearly 48% of their remaining life in good health. However, there are concerns about disabled life expectancy, which has also increased, both in years (from 0.76 to 0.97 years for a 60-year-old over the same period), and as a proportion of remaining life.

Healthy life expectancy depends on two key factors: mortality rates and good health prevalence. Our analysis shows that the increase in healthy life expectancy is largely driven by improvements in health (Figure 2). For instance, healthy life expectancy at age 60 rose by 2.03 years between 2010 and 2020. Of this, good health prevalence improvements contributed 1.23 years, while reduced mortality accounted for 0.80 years. Meanwhile, the rise in disabled life expectancy is primarily due to reduced mortality rates. Disabled life expectancy increased by 0.21 years, broken down as +0.24 years driven by a reduction in mortality, offset by –0.03 years due to a slight drop in disability rates. 

Sex and residence disparities

Sex and urban-rural disparities are two dimensions of China’s health divide. From 2010 to 2020, the gender gap in healthy life expectancy narrowed (Table 1). In 2010, males outlived females in good health, with healthy life expectancies of 8.71 years for males and 8.06 years for females. By 2020, however, females saw a substantial improvement, surpassing males to reach a healthy life expectancy of 11.71 years at age 60, compared with 11.61 years for males. Yet, despite this, the proportion of life spent in good health is smaller for females than for males. In 2020, 60-year-old females could expect to live 44.96% of their remaining years in good health, compared with 51.42% for males. Besides, females consistently have a longer disabled life expectancy than males. At age 60, females are expected to live 1.18 years of life in a disabled state, compared to 0.76 years for males.

Between 2010 and 2020, trends in health expectancy diverged between urban and rural areas. Urban residents saw improvements at both ends of the health spectrum, with increases in life expectancy driven entirely by gains in good health and disabled life expectancy. Meanwhile, life expectancy in intermediate health states (fair and poor) declined. In 2020, at the age of 60, urban residents could expect to live 13.19 years in good health and 1.05 years in a disabled state. In rural areas, gains in total life expectancy were largely driven by increases in good and fair health, while disabled life expectancy fell. In 2020, at age 60, rural residents could expect to live 10.07 years in good health and 0.90 years in a disabled state. 

Urban-rural disparities  in both good health and disabled life expectancy, have persisted and even widened over the past decade. In 2010, urban residents spent 2.44 more years in good health and 0.09 more years in a disabled state compared to their rural counterparts. By 2020, these gaps had expanded to 3.12 years and 0.15 years, respectively. Urban residents benefit from better access to healthcare, which contributes to superior health conditions and lower mortality rates. Approximately 80% of the gap in good health life expectancy (2.45 years) is attributable to health effects. Meanwhile, the higher disabled life expectancy among urban residents is largely a result of their lower mortality rates.

Policy implications

Over the past decade, China has seen a notable increase in healthy life expectancy, accompanied by a modest rise in disabled life expectancy. Gender disparities in health expectancy have narrowed, driven especially by a significant rise in healthy life expectancy among women. While urban-rural gaps in overall life expectancy have decreased, the divide in healthy life expectancy and disabled life expectancy between the two groups has expanded. These shifts highlight the urgent need for the Chinese government to implement policies that not only capitalize on the gains in health expectancy but also address the growing demand for long-term care, especially for females and older adults in rural areas.

References

Guo, Y., Mei, Li, and Jiang, Q. 2025. Health expectancies among older adults in China: Results from the 2010 and 2020 population censuses. Demographic Research 52(11), 351-368. doi:10.4054/DemRes.2025.52.11.

Luo, H., Wong, G.H.Y., Lum, T.Y.S., Luo, M.X., Gong, C.H., and Kendig, H. (2016). Health expectancies in adults aged 50 years or older in China. Journal of Aging and Health 28(5): 758–774. doi:10.1177/0898264315611663.

Wang, H.D., Abbas, K.M., Abbasifard, M., […] and Advani, S.M. (2020). Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: A comprehensive demographic analysis for the Global Burden of Disease Study 2019. Lancet – 396(10258): 1160–1203. doi:10.1016/S0140-6736(20)30977-6. 

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When parents or parents-in-law experience a decline in health, adult children often step in to provide care. This new role can profoundly affect their plans for the future, including the decision to start or expand their own families. In a recently published study (Lazzari and Zurla 2024), we explored how Australian men and women expecting to have children changed their plans after becoming care givers to their parents. 

The issue is especially important today as many people are delaying parenthood and, by the time they are ready to start a family, their parents will be older and more likely to need care. 

Balancing caregiving and family plans

When a parent’s health deteriorates, adult children may take on caregiving duties that are both emotionally and physically demanding (Fortinsky et al. 2007). If becoming a caregiver happens at a time when they are planning to have children, it could negatively affect those plans. Two main reasons explain this link. First, caring for an elderly parent takes time and energy, leaving less room to focus on other goals, like building a family. Second, many people rely on their parents for childcare once they have children. If their parents are unwell, this support may no longer be available, leading them to revise their fertility plans.

Previous research in Europe has shown that the informal childcare provided by grandparents often encourages their adult children to have kids (e.g., Aassve et al. 2012; Rutigliano 2024; Rutigliano & Lozano, 2022; Tanskanen & Rotkirch 2014). This is likely true also in Australia, the setting of this study, where grandparents are the most common childcare providers (Baxter 2022). 

Parental caregiving may also have positive effects by prompting psychological changes that positively influence fertility plans. For instance, adult children who became caregivers may feel more motivated to continue the family line or to give their parents the joy of becoming grandparents (Rackin & Gibson-Davis 2022).

Fertility expectations decline after caregiving begins 

In our study, we examined these connections using survey data from Australia collected between 2001 and 2021. We focused on a sample of men and women of reproductive age who had positive childbearing expectations and analyzed how these expectations evolved over time. We used a difference-in-differences model which compared changes in fertility expectations between two groups: a treatment group consisting of individuals who became caregivers and a control group consisting of similar individuals who did not become caregivers. If becoming a parental caregiver affects fertility expectations, we would expect to see a divergence in the trajectories of the two groups after individuals in the treatment group take on caregiving responsibilities. Moreover, if these two groups have similar trajectories before the event, this suggests that they would have followed comparable patterns if not for their differing experiences with caregiving. 

Results are represented in Figure 1. The x-axis indicates the time (in years) relative to the onset of becoming a parental caregiver. The dashed gray line indicates where we would expect the regression coefficients to fall if caregiving had no effect on fertility expectations. The figure indicates that becoming a caregiver results in an immediate and substantial decline in expectations, and that this negative effect intensifies over time. On average, fertility expectations decrease by approximately 7% within the first two years and by 20% three years after the caregiving responsibility begins. 

In a separate analysis, we explored how these results vary based on whether caregivers are childless or already have one child. No relevant difference emerges between the two groups in the first two years following the caregiving event. However, after three years, parents with one child experience a more pronounced decline in fertility expectations. This pattern could be attributed to stricter birth timelines among parents. Typically, the interval between the first and second child is 2 to 4 years, and missing this window may lead to a larger drop in expectations compared to individuals with no children.

Implications 

While previous research has identified many factors that can lead people to revise their fertility plans, the role played by parental caregiving responsibilities has not been specifically investigated. In this study, we shed light on this link by presenting evidence that an increase in caregiving responsibilities toward parents is likely to have a negative impact on fertility expectations. 

Although grandparents can serve as an important source of support for their children planning to start a family, their declining health may present obstacles to family formation. Since health tends to deteriorate with age, individuals who delay family formation are more likely to face the caregiving needs of their aging parents when planning their own families. This may make parental caregiving an increasingly important factor in understanding childbearing choices and suggests that public interventions supporting adult children in their caregiving roles could help alleviate some of the pressures when making childbearing decisions.

References

Aassve A., Arpino, B., & Goisis, A. (2012a). Grandparenting and mother’s labour force participation: A comparative analysis using the Generations and Gender Survey. Demographic Research, 27(3): 53-84.

Fortinsky, R. H., Tennen, H., Frank, N., & Affleck, G. (2007). Health and Psychological Consequences of Caregiving. In C. M. Aldwin, C. L. Park, & A. Spiro III (Eds.), Handbook of health psychology and aging (pp. 227–249). The Guilford Press.

Lazzari, E. & Zurla, V. (2024). The Effect of Parental Caregiving on the Fertility Expectations of Adult Children. European Journal of Population, 40 (online first).

Rackin, H., & Gibson-Davis, C.M. (2022). Familial deaths and first birth. Population and Development Review, 48(4): 1027-1059.

Rutigliano, R. (2024). Do grandparents really matter? The effect of regular grandparental childcare on the second-birth transition. European Sociological Review, 40(5), 772-785.

Rutigliano, R., & Lozano, M. (2022). Do I want more if you help me? The impact of grandparental involvement on men’s and women’s fertility intentions. Genus, 78: 13.

Tanskanen, A.O., & Rotkirch, A. (2014). The impact of grandparental investment on mothers’ fertility intentions in four European countries. Demographic Research, 31: 1-26.

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Why should family ties influence mental health?

With population ageing and declining fertility rates, there is growing concern about childless older adults, who are considered more vulnerable to social isolation as they leave the workforce and face reduced independence – factors linked to mental health risks. 

An extensive review of the existing literature reveals two relevant but scarcely overlapping research streams. The former examines the mental health consequences of childlessness in later life, typically comparing childless older adults with parents, but often overlooking the fact that many parents are also grandparents. The latter focuses instead on grandparenthood, comparing older individuals with or without grandchildren, while excluding childless individuals altogether. This gap in the literature has limited our understanding of the role family status plays in mental health in old age, and scientific research has yet to reach a consensus on whether ageing without children or grandchildren affects mental health, in what sense and by how much. 

Family ties are often viewed as vital for emotional support and social connections. Relationships with children are important in later life: close and supportive ties can enhance psychological well-being, while conflictual relationships may have the opposite effect. Additionally, family members often exchange time and resources, with adult children providing care for elderly parents, and parents offering financial support or care to grandchildren. Being a grandparent may also provide a sense of purpose and fulfilment. Nevertheless, studies show that childless individuals often compensate for the lack of family ties with other support networks, including relatives, friends, and volunteer activities.

Three insights for studying family ties in later life

In a recent study (Brini & Zanasi 2024), we introduced three innovative approaches to understanding the relationship between family ties and mental health in later life.

First, we differentiated between three (not just two) categories of older adults: people without children, parents without grandchildren, and grandparents, thus providing a more nuanced range of family situations and ties than the traditional dichotomous classifications mentioned earlier.

Second, we adopted a life-course approach, because family ties may affect well-being differently at different ages (Elder et al. 2003). This approach also highlights the role of social expectations relative to the normative timing of transitions (Furstenberg, 2005) that shape the perception of these transitions – whether as “early”, “on-time”, or “late” – and influence self-image. For instance, becoming a grandparent at a young age may exacerbate depressive symptoms by intensifying feelings of ageing, while becoming a grandparent at a socially expected age may boost self-esteem and help protect against depression. The timing of transitions also brings attention to situations of role overlap and competing obligations (Leopold & Skopek, 2015). Younger grandparents may experience stress from juggling work, grandchild care, and caregiving duties toward both adult children and ageing parents, and this may diminish the psychological benefits of grandparenthood. Conversely, older grandparents, often retired, face fewer overlapping roles and can better enjoy the experience of having grandchildren.

Third, we used advanced statistical models to control for selection effects – factors influencing both future family status and psychological well-being over the life course. This approach ensures reliable estimates of the causal effects of family ties on mental health.

Data and methods

We analysed data from eight waves of the Survey of Health, Ageing and Retirement in Europe (SHARE), conducted between 2004 and 2020, covering 17 European countries. Mental health is measured using the Euro-D scale for depressive symptoms. We categorized individuals into three family status groups: childless adults, parents without grandchildren (grandchildless), and grandparents. To examine the relationship between family status and mental health, we used Inverse Probability Weighted Regression Adjustment (IPWRA) (Petersen et al., 2006). This approach makes the groups comparable by weighting observations based on the likelihood of having a particular family status given a set of confounding characteristics, and thus ensures a reliable estimate of the Average Treatment Effect (ATE).

Results

Our findings (Figure 1) show that depressive symptoms (Euro-D scores) increase with age for men. Among men aged 50–59, childless individuals (grey bars) report an average of almost 2 depressive symptoms, rising to 2.5 symptoms at ages 80–89. However, no significant differences are observed between grandchildless men (black circles) and grandfathers (black diamonds), except for men aged 70–79, where the grandchildless report 0.22 fewer symptoms and grandfathers 0.14 fewer symptoms than childless men. The results for women, not reported here, mirror these findings, with no significant differences across any age group.

Beyond family: broader determinants of well-being

The results from our analyses reveal no clear psychological advantage to being a grandparent compared to being a parent without grandchildren or being childless, across all age groups and for both genders, with the exception of men aged 70–79, among whom fathers (with or without grandchildren) perform slightly better than childless men, in terms of mental health.

These findings seem to suggest that psychological well-being in later life is more influenced by life experiences and socioeconomic conditions than by family status. Therefore, public policies addressing demographic challenges must go beyond traditional family structures, promoting healthy lifestyles, social support, and economic resources from early life, regardless of family composition.

References

Brini, E., & Zanasi, F. (2024). (Grand) childlessness and depression across men and women’s stages of later life. Journal of Family Studies, 30(3), 365-396. https://doi.org/10.1080/13229400.2023.2230970

Elder, G. H., Johnson, M. K., & Crosnoe, R. (2003). The emergence and development of life course theory. In J. T. Mortimer & M. J. Shanahan (Eds.), Handbook of the life course. Handbooks of sociology and social research (pp. 3–19). Springer US. https://doi.org/10.1007/978-0-306-48247-2_1

Furstenberg, F. F. (2005). Non-normative life course transitions: Reflections on the significance of demographic events on lives. Advances in Life Course Research, 10, 155–172. https://doi.org/10.1016/S1040-2608(05)10005-7

Leopold, T., & Skopek, J. (2015). The demography of grandparenthood: An international profile. Social Forces, 94(2), 801–832. https://doi.org/10.1093/sf/sov066

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Non-communicable diseases (NCDs) loom large in the global health landscape, posing formidable challenges to public health in both developed and developing nations (Lozano et al. 2012). Among these, cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, exerting a significant impact on human lives and healthcare systems. In 2017, for instance, approximately 73% of global deaths, totaling 41 million, were attributed to NCDs, with CVDs accounting for a staggering 17.8 million deaths, representing 31.8% of overall global mortality (GBD 2018).

CVD: A global but unequally distributed problem

The prevalence of CVD-related mortality is not evenly distributed across the globe, with over three-quarters of CVD deaths occurring in low- and middle-income countries (WHO 2020). Despite ongoing efforts to mitigate the risk factors associated with CVDs, such as tobacco use, sedentary lifestyles, and poor diet, these conditions continue to affect populations, particularly in regions with socioeconomic disparities and limited access to healthcare.

Understanding the complex interplay of factors contributing to CVD mortality requires a nuanced examination of demographic, socioeconomic, behavioral, environmental, and physiological determinants. Several risk factors have been identified, including, among others, population aging, income inequality, urbanization, high blood pressure, high blood cholesterol levels, and tobacco use (Okwuosa et al. 2016; Roth et al. 2020; WHO 2020). While much of the existing research on CVD mortality has focused on developed countries, there is a growing recognition of the importance of understanding global trends and impacts also in low- and middle-income countries. These regions often face unique challenges, including limited healthcare infrastructure, inadequate access to preventive measures, and disparities in education and income levels, all of which contribute to the burden of CVDs.

In a recent study (Baptista and Queiroz 2022), we assessed and juxtaposed several prevalent models utilized in mortality and health research, with the objective of determining whether national CVD mortality rates among the adult population (aged 30 years and above) correlate with the attributes of neighboring countries from 2013 to 2017. Their results highlighted persistent disparities in the crude mortality rate from cardiovascular disease (CMRCVD) across continents (Figure 1).

We also investigated the relationship between CVD mortality rates and GDP per capita (Figure 2, top left), urbanization (top right), schooling (bottom left), and cigarette use (bottom right) through bivariate maps. In the Americas, 66% of the countries are in the lowest quantile of CVD mortality rates and only two (Guyana and Haiti) are in the highest. Here, countries farther away from the equator tend to perform better, and so do most of the countries in Oceania except for Papua New Guinea which exhibits high CVD mortality rates along with low GDP per capita and urbanization levels, and where tobacco use is emerging as a significant risk factor. In Asia, there is heterogeneity in CVD mortality rates, reflecting the continent’s diverse socioeconomic, cultural, and demographic landscape. CVD mortality rates are higher in European countries, particularly those of Eastern Europe, and also in Africa, where, however, regional variation is particularly strong. Although Africa is still lagging in the epidemiological transition, the prevalence of chronic non-communicable diseases (NCDs), such as cardiovascular diseases, has increased in recent years, while the occurrence of communicable diseases has decreased. 

Tackling the issue

Our findings underscore the importance of a coordinated global response to address the growing burden of CVDs. Policymakers, healthcare practitioners, and researchers must collaborate to implement evidence-based strategies that prioritize their prevention, early detection, and treatment. Moreover, efforts to strengthen health systems, improve data collection and surveillance, and address the social determinants of health are paramount for achieving sustainable progress in reducing CVD mortality rates worldwide. Aggregated country-level data may obscure regional variations within countries, highlighting the need for more granular analyses. Additionally, data quality and availability pose challenges, particularly in regions with limited healthcare infrastructure and inadequate vital statistics systems. 

Efforts to address CVD mortality require a multifaceted approach that considers the broader social, economic, and environmental determinants of health. Investments in healthcare infrastructure and education, poverty alleviation, and public health initiatives are essential for reducing the burden of CVDs globally. Furthermore, targeted interventions aimed at mitigating known risk factors, such as tobacco control policies, promotion of healthy lifestyles, and access to affordable healthcare, are critical for stemming the tide of CVD-related mortality.

References

Baptista, E.A. & Queiroz, B.L. (2022). Spatial analysis of cardiovascular mortality and associated factors around the world. BMC Public Health 22, 1556 (2022). DOI: 10.1186/s12889-022-13955-7

GBD (Global Burden of Disease Collaborative Network). Global Burden of Disease Study 2017 (GBD 2017) Cause-Specific Mortality 1980–2017. Seattle: Institute for Health Metrics and Evaluation (IHME); 2018.

Lopez, A.D. & Adair, T. (2019). Is the long-term decline in cardiovascular-disease mortality in high-income countries over? Evidence from national vital statistics. Int J Epidemiol. 2019;48(6):1815–1823

Lozano, R. et al. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012;2012(380):2095–128.

Okwuosa, I.S. et al. (2016). Worldwide disparities in cardiovascular disease: challenges and solutions. Int J Cardiol. 2016;202:433–40.

Roth, G.A. et al. (2020). Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982–3021.

WHO (World Health Organization). Cardiovascular diseases (CVDs). 2020.

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