Australia is one of the world’s major migrant-receiving countries. More than three in ten residents were born overseas (Australian Bureau of Statistics, 2025) and migration has, for decades, played a central role in shaping the country’s workforce, cities, families and cultural life. It has also influenced the health profile of the population. One of the widely documented findings in migration research is the so-called “healthy migrant effect”: migrants often arrive in their destination country in better health and tend to live longer than people born in the destination country (Anikeeva et al., 2010, Cunningham et al., 2008).

Healthy migrant? At first, yes

Australia broadly fits this pattern, but only in part. In our recent study of life expectancy and healthy life expectancy among overseas-born and Australian-born populations, migrants exhibited a clear longevity advantage (Huang et al., 2026). In 2016, life expectancy at birth was 84.8 years for the overseas-born population, compared with 82.4 years for the Australian-born population (Table 1). Similar differences were evident in 2006 and 2011: Australia’s migrant population does indeed live longer than the Australian-born population.

The reasons for this advantage are not difficult to understand. Migration is often a selective process: healthier individuals are more likely to migrate, and Australia’s migration system has long incorporated health checks and, particularly in recent decades, a strong emphasis on skilled migration (Martinez et al., 2015). Part of the observed advantage may also reflect so-called “salmon bias”, whereby migrants in poor health return to their country of origin and are therefore less likely to be captured in destination-country statistics. In addition, many migrants bring with them protective health behaviours, family networks and community ties that can contribute to better health, particularly in the early years after arrival. 

When longer lives do not mean healthier lives

Living longer does not necessarily mean living healthier. A person may live longer, but spend more of those years with disability, chronic illness or limitations in daily life. This is why healthy life expectancy matters, and this is where the story becomes more complex. In 2016, although overseas-born Australians enjoyed more healthy years at younger ages, this advantage diminished steadily through adulthood and reversed in later life. By ages 55–64, the overseas-born population had lower healthy life expectancy than the Australian-born population. At older ages, this produced a notable divergence: migrants retained a survival advantage yet experienced a disadvantage in health. Globally, immigrants spend a lower share of their life in good health: 91.7% vs 92.5% for natives (Table 1).

Between 2006 and 2016, life expectancy increased for both overseas-born and Australian-born Australians, but healthy life expectancy barely moved. Among overseas-born Australians, for instance, life expectancy at birth rose from 83.4 to 84.8 years, while healthy life expectancy increased only from 77.5 to 77.7 years, and, in comparative terms, things worsened for immigrants (Figure 1). Longer lives were not accompanied by equally large gains in healthy years.

The gendered aspect of migrant ageing

The most striking differences were observed among women. Overseas-born women lived longer than overseas-born men, but they spent a smaller proportion of life in good health. Compared with Australian-born women, their healthy-life advantage disappeared earlier than it did for men. By age 35, overseas-born women had a lower healthy life expectancy than Australian-born women, while overseas-born men retained an advantage over Australian-born men across almost the whole life course.

This matters because migrant women may face a double burden (Donato et al., 2014). Some arrive through family rather than employment pathways, and may therefore be less positively selected in terms of health and labour market advantage. After arrival, they may experience language barriers, unpaid caregiving responsibilities, weaker labour-market attachment, limited social networks, and difficulties accessing culturally appropriate healthcare. These pressures may accumulate over time and become visible in health outcomes in later life.

Discussion

Our findings suggest that migrants’ longer lives should not be taken as evidence that their health is secure. In Australia, the central challenge is increasingly about healthspan, not just lifespan. A multicultural ageing society requires services that recognise the intersecting influences of language, culture, gender, migration history and social disadvantage. This includes culturally safe primary care, stronger efforts in chronic disease prevention, better support for older migrant women, and improved data to identify which migrant communities are most at risk.

The healthy migrant effect remains real, but it is neither permanent nor evenly distributed. Australia’s migrants live longer: the next task is to ensure that those longer lives are also lived in good health. 

References

ANIKEEVA, O., BI, P., HILLER, J. E., RYAN, P., RODER, D. & HAN, G.-S. 2010. The health status of migrants in Australia: a review. Asia Pacific Journal of Public Health, 22, 159-193.

AUSTRALIAN BUREAU OF STATISTICS. 2025. Australia’s population by country of birth [Online]. Available: https://www.abs.gov.au/statistics/people/population/australias-population-country-birth/latest-release [Accessed 4 June 2024].

CUNNINGHAM, S. A., RUBEN, J. D. & NARAYAN, K. V. 2008. Health of foreign-born people in the United States: a review. Health & place, 14, 623-635.

DONATO, K. M., PIYA, B. & JACOBS, A. 2014. The double disadvantage reconsidered: Gender, immigration, marital status, and global labor force participation in the 21st century. International Migration Review, 48, 335-376.

HUANG, G., GUO, F., TAKSA, L., CHENG, Z., LIU, L., ZIMMERMANN, K. F., TANI, M. & FRANKLIN, M. 2026. The ‘Healthy Migrant Effect’and Ageing: Life Expectancy and Healthy Life Expectancy of the Migrant and Native-born Populations in Australia. Social Indicators Research, 182, 38.

MARTINEZ, J. N., AGUAYO-TELLEZ, E. & RANGEL-GONZALEZ, E. 2015. Explaining the Mexican-American health paradox using selectivity effects. International Migration Review, 49, 878-906.

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While China successfully leveraged a favorable age structure to fuel decades of economic growth, this age-based demographic dividend is now weakening due to rapid population aging and sustained low fertility. Policy responses have largely focused on reversing fertility decline through pro-natalist measures, yet existing evidence suggests that such interventions have only limited effects (Gauthier & Gietel-Basten, 2025).

This raises a broader question: if the contribution of the age structure to economic growth is diminishing, what factors can sustain future development? To address this, it is useful to distinguish between two dimensions of the workforce. The first is captured by the age support ratio (ASR), which reflects the balance between the working-age population and dependents. The second is related to the skills embodied in the workforce. To measure this second dimension, we introduce the task-based skill ratio (TSR), an index that captures the composition of jobs in terms of the tasks they require, ranging from low- to high-skill activities.

The key challenge, therefore, is to understand how these two dimensions interact. Can improvements in the skill composition of the labor force compensate for the decline in the age-based demographic dividend? And to what extent can skill upgrading sustain economic growth in the context of continued demographic aging?

China provides a particularly relevant case to examine these questions. Its rapid transition from a labor-abundant economy to an aging society, combined with significant investments in education and structural economic transformation, offers a unique opportunity to observe how the sources of economic growth evolve. Insights from this transition are not only important for China, but also for other countries at different stages of the demographic transition.

A skill-based perspective

In a recent study (Gu et al., 2026), we constructed the TSR using an approach comparable in spirit to the ASR, but focused on skills rather than age. Building on the framework of the United States Occupational Information Network, occupations were decomposed into detailed work activities, which were then assigned skill intensities. These task-based scores were aggregated to the city level to produce a consistent measure of local skill composition. This makes the TSR directly comparable to the ASR: both are aggregate indicators defined at city level, both vary over time, and both can be linked empirically to economic outcomes such as per capita GDP. However, they capture different dimensions of the labor force: quantity versus quality. Empirically, we found that the ASR in China reached a turning point around 2010 and has since declined, reflecting demographic aging. In contrast, the TSR has continued to rise, particularly in eastern megacities, indicating sustained improvements in the skill composition of the workforce. 

We then estimated both the independent and joint effects of the ASR and TSR on economic performance, and projected the levels of TSR required to offset future declines in the ASR under different demographic scenarios from 2025 to 2100.

Synergy between age and skills

Both the ASR and the TSR have a positive effect on per capita GDP, but since around 2010, the TSR has become the stronger and more consistent driver of economic performance. Our results also show that expanding the working-age population alone is not sufficient to generate growth. In cities with low skill levels, a larger labor force yields limited economic benefits. By contrast, improvements in skill composition can sustain growth even as the advantage based on age structure declines. This complementarity weakens with population aging. As the workforce ages, the overall contribution of the ASR declines, and the adaptability of the labor force becomes more constrained. Older workers, on average, face greater challenges in adjusting to changing task requirements, which reinforces the shift from a quantity-based to a skill-based driver of economic growth.

Three phases of the demographic-skill transition

The demographic foundations of economic growth in China have undergone a clear transition across three distinct phases (Fig. 1). First, the age-based dividend phase (2000 to 2005) was characterized by rapid growth driven primarily by the expansion of the working-age population. Market reforms and the relaxation of household registration constraints facilitated large-scale labor reallocation from agriculture to industry, allowing China to fully exploit its demographic advantage.

Second, the transition phase (2005 to 2010) marked a turning point. During this period, the contribution of the ASR declined substantially, while the influence of the TSR increased. Third, the skill-based dividend phase (after 2010) saw a fundamental shift in the drivers of economic growth. As labor supply growth slowed and population aging intensified, the marginal effect of the ASR continued to weaken. At the same time, industrial upgrading and rising human capital led the TSR to become the dominant source of growth. Overall, Fig. 1 illustrates that the demographic dividend has not disappeared. Rather, its underlying mechanism has evolved from an extensive model based on labor supply to an intensive model driven by skill accumulation and productivity gains.

Projections and policy implications

To quantify the TSR enhancement required to counteract aging pressure, we constructed a dynamic projection based on the United Nations low fertility variant for the period between 2025 and 2100 (Fig. 2). We selected this low variant because recent fertility trends in China fall below medium-level projections, which makes it a realistic reflection of population dynamics. We then established three adjustment scenarios to evaluate future policy options:

1) Redefining the working-age population: We extended the upper boundary of the working-age population to 69 years and 74 years to simulate delayed retirement policies.

2) Age-dynamic scenario, assuming that the marginal contribution coefficient of the ASR (1) grows at an annual rate of 1% (i.e. becomes stronger over time).

3) Skill-dynamic scenario, assuming that the marginal contribution coefficient of the TSR (2) grows at an annual rate of 1%.

Projections of the compensatory TSR required up to the year 2100 indicate that delayed retirement can partially alleviate the effects of the declining age support ratio. Extending the upper boundary of the working age to 69 or 74 years dilutes the negative impact of age-structure changes by expanding the labor supply, which creates a substantial window for adjustment in the medium term. However, adjusting the labor supply provides only a temporary buffer and cannot reverse the long-term upward trend of ageing. 

The projections highlight that the evolution of economic parameters exerts a decisive influence on future outcomes and, not surprisingly, that combining the enhancement of the TSR with a moderate extension of working life offers the most viable path to navigate the challenges of advanced aging. Economic development relies less on favorable age structures and increasingly depends on the skill composition of the workforce, making skill upgrading a central policy priority.

References

Gauthier A. H. & Gietel-Basten S. (2025). Family policies in low fertility countries: Evidence and reflections, Population and Development Review, 51(1), 125-161. https://doi.org/10.1111/padr.12691

Gu H., Wu Y., Marois G., Lutz W. & Niu T. (2026). China’s demographic dividend has moved from age-based labor supply to skill-based productivity, Proceedings of the National Academy of Sciences, 123 (15) e2532906123. https://doi.org/10.1073/pnas.2532906123

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Education and the dementia divide

For decades, researchers have observed a striking pattern: people with more education are less likely to develop dementia. Across countries, cohorts, and populations, schooling consistently appears to protect cognitive health in old age. But a few important questions have remained surprisingly underexplored: How exactly does dementia risk decline across the educational spectrum? Is the benefit concentrated among those who complete high school or college? Are there diminishing returns at higher levels? Or does every additional year of schooling matter?
Using nationally representative data on Americans aged 65 and older, my colleagues and I examined how dementia risk changes with each additional year of education (Cha, Farina and Hayward 2025). The answer is both simple and powerful: dementia risk declines steadily with each year of schooling (Figure 1).

From as little as six years of education through advanced schooling, the pattern is remarkably linear: every additional year is associated with a lower risk of dementia. The protective effect does not plateau after high school, nor does it operate only at degree milestones. Education appears to function as cumulative exposure: more years mean more protection.
This finding reinforces what many scholars have suggested but rarely quantified so clearly: educational exposure itself matters, not just credentials. At the population level, the implications are substantial: even modest increases in average educational attainment can translate into large reductions in dementia cases among older adults.

Do diplomas matter, and why?

Although the overall pattern is linear, we also asked whether earning a high school diploma or college degree provides an additional “step-change” benefit beyond years of schooling alone. The answer is nuanced.
Among men and White Americans, obtaining a high school diploma is associated with an extra reduction in dementia risk beyond the steady year-by-year decline. In other words, completing high school appears to confer additional cognitive protection for these groups.
However, this step-change effect is not observed among women or Black Americans: for them, dementia risk declines steadily with each year of education, and the diploma itself does not appear to deliver an extra boost beyond accumulated schooling.
Educational credentials historically translated into different labor market and social opportunities across gender and racial groups. For mid-20th-century cohorts, the generations now entering older age, a high school diploma yielded especially strong economic returns for White men. These economic and occupational advantages may have produced downstream health benefits.
By contrast, women and Black Americans often faced discrimination in labor markets and unequal returns to credentials. A diploma may not have translated into equivalent occupational or income advantages, potentially limiting additional health benefits tied to credentials.
Importantly, this does not mean education is less protective for women or Black Americans. The linear decline in dementia risk is present across all groups. In fact, the year-by-year protective slope is at least as strong, if not stronger, among Black adults. What differs is the additional boost from crossing credential thresholds.
These patterns remind us that education operates both as cognitive exposure and as a social institution embedded within systems of inequality.

What this means for public health

Dementia remains one of the most pressing public health challenges in aging societies. Effective treatments are limited, and prevention strategies are urgently needed.
Education is increasingly recognized as one of the most important social determinants of dementia risk. But discussions often focus on “low” versus “high” education, implicitly treating schooling as a categorical variable. Our findings suggest this framing misses something crucial. The protective association of education is not confined to completing high school or college. It accumulates year by year. Each additional year of schooling is associated with a lower risk of dementia later in life.
This has several implications. First, expanding access to education, even incremental improvements, may yield long-term cognitive health benefits at the population level. The gains do not depend solely on achieving degree milestones.
Second, historical inequalities in educational opportunity continue to shape dementia disparities decades later. Cohorts educated during periods of racial segregation and gender discrimination carry those structural disadvantages into old age.
Third, as educational attainment continues to rise, especially among women and racial minority groups, the shape of the association may evolve. Future cohorts may experience different credential effects than those observed today.
Finally, these findings highlight the importance of understanding how education works. Is the benefit driven by cognitive development in childhood and adolescence? By occupational complexity in adulthood? By income and health behaviors? Or by some combination?
While education is unlikely to be a simple causal lever, it is undeniably a powerful marker of life-course advantage. In a society seeking ways to reduce dementia burden, schooling stands out not merely as an early-life investment, but as a long-term protective factor whose effects echo across decades. Every year counts.

References

Cha, H., Farina, M. P., & Hayward, M. D. (2025). How does the risk of dementia change with each additional year of education? Demography, 12231508. https://doi.org/10.1215/00703370-12231508

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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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Population change is one of the defining stories of our time. Longer lives and smaller families are reshaping our societies in ways that no previous generation has experienced. These trends are often portrayed as a crisis – a “demographic winter” or an impending burden on future generations. Yet there is another way to see them. Population ageing is also a testament to human progress: we live longer, healthier, and safer lives than ever before. Demographic change is not merely a challenge to be managed, but also a success to be valued and an opportunity to be shaped.

This e-book is grounded in a positive demographic perspective – a viewpoint centred on change and adaptation, echoing key recommendations from the World Health Organization, the United Nations, and the European Commission. As emphasised by the Italian Association for Population Studies (AISP), positive demography does not mean overlooking the real challenges posed by population ageing, low fertility, or social inequality. Rather, it invites us to shift our gaze from fear and alarm towards understanding and action. It encourages us to view demographic transformation as a new phase in the history of our societies: not better or worse than before, but different, and rich in possibilities for renewal.

Italy stands at the forefront of this transition. It is among the oldest countries in the world, with one in four citizens aged 65 or over and one of the highest life expectancies globally. Fertility remains very low, and regional differences continue to shape demographic realities across the country. Yet Italy also provides an exceptional setting in which to explore the future of ageing societies. Its achievements in longevity, vibrant local welfare systems, and long tradition of family solidarity make it a “natural laboratory” for studying how to age well in an ageing society. The Italian case therefore offers lessons not only for Europe, but also for many countries now undergoing similar demographic shifts.

It is within this context that the Age-It Research Programme – Ageing Well in an Ageing Society was established. Supported by the National Recovery and Resilience Plan and the Italian Ministry of University and Research, Age-It represents one of the largest and most ambitious research initiatives on ageing ever launched in Europe. With an investment exceeding €115 million and a broad interdisciplinary partnership, Age-It brings together experts in demography, medicine, biology, economics, psychology, engineering, and the social sciences. Its aim is not only to understand the causes and consequences of population ageing, but also to design practical, evidence-based solutions for individuals, families, and institutions.

Age-It is organised into ten thematic research centres, known as “Spokes”, each devoted to a key challenge of ageing: from the demographic foundations of longevity, fertility, and migration, to the biology of ageing, the complexity of multimorbidity, and the sustainability of care systems. Other Spokes address the silver economy and the role of older adults in the workforce, the cultural and political dimensions of ageing societies, and the development of innovative technologies for health and inclusion. Cross-cutting initiatives also focus on education and lifelong learning, data management, and the transfer of research findings into public policy.

The central message of Age-It is clear: ageing should not be seen solely as a cost, but as a shared opportunity to promote well-being, equity, and sustainability. By building bridges between scientific research and policymaking, the programme seeks to foster a holistic vision of ageing, one that combines social innovation with practical action. The challenge is not merely to adapt to demographic change, but to shape it, aligning demographic trends with the broader goals of sustainable development and intergenerational justice.

This e-book draws on Age-It’s research agenda and presents a selection of the most pressing population challenges facing Italy and other ageing societies today. It explores the demography of ageing, including fertility (Ch. 2 and 3), longevity and health (Ch. 4), and migration dynamics (Ch. 5 and 6); the growing focus on frailty and resilience in later life, examining how to assess and mitigate vulnerability (Ch. 7); the demography of care, analysing how families, institutions, and communities can respond to this growing issue (Ch. 8); the silver economy, where older adults play active roles as workers, consumers, and innovators (Ch. 9); and the question of intergenerational justice, concerning fairness between younger and older generations (Ch. 10).

Each of these topics is discussed with particular attention to their policy implications. The goal is to translate demographic knowledge into actionable insights for a sustainable ageing agenda. This involves rethinking traditional indicators – such as the old-age dependency ratio – which too often convey an overly pessimistic picture, and instead developing measures that better capture improvements in health, education, and life expectancy. It also entails identifying strategies that combine adaptation – preparing for the realities of an ageing population – with mitigation – addressing the underlying drivers of demographic imbalance, such as persistently low fertility or unequal access to care.

The positive demography perspective promoted by Age-It thus offers a new way forward. It recognises that demographic change is not destiny, but a domain for policy action. Just as societies have successfully adapted to industrialisation, urbanisation, and globalisation, they can also adapt to population ageing. The key lies in foresight, inclusiveness, and a steadfast commitment to fairness across generations.

Ultimately, the story of ageing is the story of humanity’s success – and its next great challenge. With the right policies and a shared vision, it can also become the story of our renewal. This e-book invites readers to engage with that challenge, exploring how a positive and sustainable approach to demographic change can help build societies that truly enable everyone to age well – today and tomorrow.

Acknowledgements

We acknowledge funding from the National Recovery and Resilience Plan, Next Generation EU, Project Age-It: “Ageing Well in an Ageing Society”. The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.
Age-It: Ageing Well in an Ageing Society

References

Alderotti, G., Cozzani, M., Barbi, E., De Santis, G., Mezzanzanica, M., Miglio, R., Paterno, A., & Vignoli, D. (in press). Positive demography: Changing the perspective on population aging from the Age-It Research Program. The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences.

Leeson, G. W. (2017). Realizing the potentials of ageing. Journal of Population Ageing, 10, 315–321.

Natale, F., Bernini, A., Seiger, F., & Kajander, N. (2024). Old age does not start at 65 (JRC137524). European Commission.

United Nations, Department of Economic and Social Affairs. (2023). World social report 2023: Leaving no one behind in an ageing world. United Nations.

Vignoli, D., & Paterno, A. (Eds.). (2025). Rapporto sulla popolazione – Verso una demografia positiva [Towards a positive demography]. Bologna: il Mulino.

Vignoli, D., Albertini, M., Chiatti, C., Aimaretti, G., Boccuzzo, G., Boffo, V., Brugiavini, A., Cavallo, F., Cenci, S., Cherubini, A., Cincotti, F., D’Adda di Fagagna, F., Ferrarese, C., Galasso, V., Galeotti, E., Graziani, A., Iaccarino, G., Lattanzio, F., Lucifora, C., & Petrucci, A. (in press). Aging well in an aging society: Italy at the forefront of global aging and the Age-It Research Program. The Journals of Gerontology: Series B, Psychological Sciences and Social Sciences.

Vignoli, D., Barbi, E., & Paterno, A. (2024). La demografia dell’invecchiamento: Una lettura positiva [The demography of ageing: A positive view]. Rivista il Mulino, 73(528), 12–30.

Vignoli, D., Paterno, A., & Stranges, M. (2025). Population report 2025: Towards a positive demography. Executive summary. Population Europe.

World Health Organization. (2015). World report on ageing and health. World Health Organization.

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Between February and April 2024, I conducted a qualitative study in six purposively selected communities in Ghana’s Greater Accra Region—Dansoman, Osu, Adenta (urban), Sege, Korle Gonno, and Bawjiase (rural/peri-urban). Using focus group discussions and semi-structured interviews, 52 older adults aged 60 and above were engaged. Participants were recruited through the Social Security & National Insurance Trust Pensioners Association and interviewed in English or local languages. 
The study sought to understand the lived experiences of older people ageing alone or at risk of doing so. Thematic analysis followed Clarke and Braun’s (2013) six-phase process, and findings were interpreted through three sociological lenses (“social determinants of health”, “contemporary life course perspective”, and “feminist gerontology”), to allow for gender-sensitive and structural insight into ageing in solitude.

An emerging but under-researched problem

In Ghana, the share of the population aged 60 and over had risen to 6.4% by 2021 and is projected to double by 2050. Yet this quiet demographic shift has received little structured policy or scholarly attention. While the ageing population grows, the number of older persons living alone is also rising, a reality that challenges the long-standing norms of communal and intergenerational living in African societies (Aboderin & Hoffman 2015; Apt 2012). Most studies on ageing in Ghana and sub-Saharan Africa have focused primarily on poverty, chronic illness, or pension coverage, overlooking the emotional, spiritual, and psychosocial implications of living and ageing in solitude (Cattell 2001; Mba 2010). Widowhood, youth out-migration, and the breakdown of extended family systems have quietly restructured daily life for many older adults, but these patterns remain insufficiently documented or addressed. As a result, existing social protection and health policies fail to account for the relational dimensions of ageing, such as loneliness, anxiety, and social disconnection, especially among those ageing in place with no regular household support.

Lived realities of solitary ageing in Ghana

In urban Accra, a 74-year-old woman lives alone in a single-room compound house. “Weeks pass without a visitor”, she explained. Her children have migrated, and her spouse died ten years ago. Her situation reflects the new reality; declining fertility, rising longevity, and fragmented kinship networks are leaving many older adults, especially women, to age alone. Women are disproportionately affected. They tend to outlive men, experience higher rates of widowhood, and lack formal pensions or income streams. “I don’t need much”, said a 72-year-old woman in Bawjiase. “But I need to know someone remembers I am here.” Men also expressed deep emotional strain. A 68-year-old widower in Adenta shared, “I talk to myself sometimes. It is the only way to break the silence”. Even those with informal work or community visibility felt emotionally disconnected. Solitary ageing in Ghana is thus not merely a matter of residence, but a deeper experience of being socially, spiritually, and emotionally forgotten.

Solitary ageing as a public health concern

Elders living alone face heightened risks of depression, nutritional neglect, untreated illness, and even premature death. Many are not enrolled in targeted programmes, and neighbours may remain unaware of their isolation until a crisis occurs. Shame, pride, or fear of burdening others often prevents older adults from seeking help. This growing vulnerability calls for recognition of solitary ageing as a public health issue, not only in terms of material deprivation but of psychosocial decline.

Policy gaps and a tentative look beyond Ghana

Ghana’s National Ageing Policy offers useful protections, but implementation is weak. Community nurses are under-resourced, and social protection schemes such as LEAP¹ often fail to reach those living alone. Few local governments have elder-sensitive housing or visitation systems. While faith-based groups and pensioner associations offer some relief, their impact is small and uncoordinated. Ageing alone is not only about economic deprivation but also about disconnection. Where ageing was once a shared, communal journey, it is now becoming an individual and often silent struggle. The absence of children, regular contact, and supportive neighbours exacerbates both the physical and emotional decline that comes with age. While this study is focused on Ghana, similar patterns may be emerging elsewhere in sub-Saharan Africa. Urbanisation, outmigration, longer survival, and the weakening of traditional familial structures are continent-wide trends. Although contexts vary, the warning signs are clear: the solitary elder is not an isolated case, but a signal of changing times.

Conclusions

Solitary ageing is no longer a distant or marginal issue. It is a pressing demographic reality that demands urgent, targeted, and humane responses. As urbanisation, outmigration, increased longevity, and the weakening of traditional kinship structures continue to erode intergenerational cohabitation, more older adults are left to navigate the final stages of life in profound isolation. This phenomenon must be named, measured, and addressed. Governments, local assemblies, faith institutions, and civil society must act decisively. Routine elder outreach, intergenerational community programmes, and inclusive housing policies must reflect the emerging profile of older adults who age without daily familial contact. Technology and social innovations must also be harnessed not as substitutes for human touch, but as bridges to connection. As one 73-year-old woman in urban Accra said, “I am old, but I have not died. My ears still want to hear a human voice”. That voice must now be society’s, not echoing in pity or policy promises, but responding with a consistent, compassionate presence. Ageing in solitude should not be Ghana’s quiet legacy. 

Footnote

¹The Livelihood Empowerment Against Poverty (LEAP) programme is Ghana’s national cash transfer initiative targeting extremely poor households. Beneficiaries include the elderly poor (65+), orphans, persons with severe disabilities, and vulnerable pregnant women. In addition to modest bimonthly stipends, LEAP enrollees receive free access to Ghana’s National Health Insurance Scheme. Despite its potential, many isolated older adults remain unreached due to registration gaps and weak community-level implementation.

References

Aboderin, I., & Hoffman, J. (2015). Families, intergenerational care and ageing in sub-Saharan Africa: Policy challenges and responses. International Social Work, 58(2), 275–290. https://doi.org/10.1177/0020872813517320

Apt, N. A. (2012). Ageing in Ghana: Meeting the challenges of a changing society. Accra: Sub-Saharan Publishers.

Clarke, V. & Braun, V. (2013) Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120-123

Cattell, M. G. (2001). African widows, culture and social change: Case studies from Kenya. The International Journal of Ageing and Human Development, 52(4), 321–337. https://doi.org/10.2190/C7G2-8TNN-W7E2-0GVJ

Mba, C. J. (2010). Population ageing in Ghana: Research gaps and the way forward. Journal of Ageing Research, 2010, 672157. https://doi.org/10.4061/2010/672157

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Most advice about claiming a Social Security old-age pension in the U.S. says: “Wait to claim, and you’ll receive higher monthly payments” (Meyer and Reichenstein, 2010). But there’s one crucial factor often left out of the conversation: no one knows how long they will live (Edwards and Tuljapurkar, 2005). This uncertainty, which we call demographic risk, fundamentally changes how we should think about retirement decisions.

In our recent study (Jiang et al., 2025), we show that this risk doesn’t go away, even for people who are well-informed and make rational plans. And importantly, we find that many people who claim their Social Security pension early, often seen as making a poor choice, may actually be acting in a very reasonable way.

The hidden risk of delaying benefits

At least in theory, Social Security pensions are designed to be actuarially fair: people who claim later get more per year but collect for fewer years. If everyone lived exactly to their average life expectancy, then claiming early or late would make no difference in lifetime benefits (Shoven and Slavov, 2014).

But no one lives exactly to the average, even among people with the same income, education, or health; lifespans vary widely, and that variation matters. We call this irreducible demographic risk: the uncertainty in how long you will live, even if you know everything else about your situation. Think of pension claiming like investing. Delaying can boost your monthly benefit, but it also means you’re betting on living longer. If you die earlier than expected, you may get little, or nothing at all.

We built a simplified model that captures the essential actuarially fair features of the U.S. Social Security system. To isolate the effect of lifespan uncertainty, we focused on two risks: dying before claiming and living only a short time after. We measured this using the coefficient of variation, the standard deviation divided by the mean. It captures how much lifetime benefits vary relative to the average, and helps compare risk across groups with different life expectancies. The result is clear: delaying increases financial risk, especially for males (Figure 1, left panel) or people with lower incomes (Figure 1, right panel).

Why early claiming can be a rational choice

About half of Americans start claiming a Social Security pension at the earliest possible age, 62 years. Some experts call this irrational but our model suggests otherwise. For people with limited savings or uncertain health, claiming early can help reduce risk. It ensures they receive at least some benefits, even if they don’t live long. Others may choose a middle ground, claiming a few years later to balance risk and return. There’s no one-size-fits-all answer. What matters is how much uncertainty you can afford to carry.

Using U.S. mortality data, we compared outcomes for people in different income groups. Those in the bottom 20% of income not only live shorter lives on average, but also face greater uncertainty in how long they will live (see the right panel of Figure 1). That means delaying benefits is riskier for them, because there’s a higher chance they won’t live long enough to benefit from the delay. Policies that encourage everyone to delay claiming may unintentionally harm those with fewer resources (Isaacs and Choudhury, 2017), by pushing them toward risk they cannot bear.

To ensure our results weren’t just a fluke, we tested them under a range of scenarios: 

• People with different levels of risk aversion

• Couples with different age gaps and claiming strategies

• Analytical and simulation-based approach

No matter how we built the model, demographic uncertainty stayed front and center. Even rational, informed people face this unavoidable risk, and it gets larger the longer they wait to claim.

Policy implications: one size does not fit all

Many retirement planning tools and policies assume that people can plan based on average life expectancy. But our research shows that average predictions can be dangerously misleading, especially for those at the bottom of the income ladder. If policymakers want to support fair and effective retirement decisions, they must account for this irreducible risk. Encouraging delayed claiming across the board could widen existing inequalities in retirement outcomes.

You can save, plan, and optimize. But you can’t predict how long you’ll live, and that unpredictability has real financial consequences. For millions of Americans, especially those in poorer health or with lower incomes, claiming their Social Security pension early isn’t a mistake: it’s a rational response to risk. Understanding this can help us build more equitable and realistic policies for an aging society.

References

Jiang, S., Zuo, W., Guo, Z., & Tuljapurkar, S. (2025). When to claim a pension: the effect of uncertainty in ages at death. Genus, 81(5). https://doi.org/10.1186/s41118-025-00243-6

Edwards, R. D., & Tuljapurkar, S. (2005). Inequality in life spans and a new perspective on mortality convergence. Population and Development Review, 31(4), 645–674. 

Shoven, J. B., & Slavov, S. N. (2014). Recent changes in the gains from delaying Social Security. Journal of Financial Planning, 27(3), 32–41. 

Isaacs, K. P., & Choudhury, S. (2017). The growing gap in life expectancy by income: Recent evidence and implications for the social security retirement age. Congressional Research Service.

Meyer, W., & Reichenstein, W. (2010). Social security: When to start benefits and how to minimize longevity risk. Journal of Financial Planning, 23(3), 49–59.

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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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