The COVID-19 pandemic disrupted life expectancy trends in most high-income countries. In Denmark, while overall life expectancy returned to 2019 levels after the initial two pandemic years (2020 and 2021), this pattern was not observed across all the socioeconomic strata of the Danish population. Indeed, contrary to what many people outside the Nordic countries believe, large social inequalities in life expectancy persist in the Nordic countries (Brønnum-Hansen et al. 2021). In a recent publication (Strozza et al. 2024), using Danish registry data from 2014 to 2021 for all individuals aged 30 years and over, we investigated whether the COVID-19 pandemic magnified social inequalities in life expectancy in Denmark, and examined what causes of death contributed most to the observed changes. 

Life expectancy changes during the COVID-19 pandemic

Life expectancy can be calculated in different years and at various ages. Table 1, for instance, shows life expectancy at 30 years at the beginning of the period and for the first two pandemic years by sex and income quartile, used as a proxy of socioeconomic status (SES).

Life expectancy is a snapshot of mortality conditions, and not a forecast of any individual’s lifespan. Therefore, its decline does not represent the number of years that will be deducted from the lifespans of individuals; rather, it provides a comparable measure of the impact of the mortality shock generated by the pandemic. 

Table 1 shows yearly changes in life expectancy by sex and income quartile for the five years prior to the pandemic (2014-2019) and the initial two pandemic years (2020-2021).

Before the pandemic, yearly female life expectancy gains followed a social gradient. In other words, life expectancy was increasing faster for women in the highest socioeconomic groups than in the lowest. However, the confidence intervals shown in Figure 1 suggest that these results should be interpreted with caution. This was the case, to some extent, for men too, with the exception of the highest income group, where survival improvements were relatively limited. However, the differences in life expectancy changes were small, and the gap in life expectancy between the highest and lowest income groups remained large.

Figure 1 illustrates the Danish COVID-19 success story of 2020, when small gains in life expectancy were observed in the overall population (Aburto et al. 2021), as well as for most sub-populations, possibly due to the timing of policy interventions. However, the situation changed in 2021, and life expectancy losses were observed for the overall population (Schöley et al. 2022) and for most sub-populations.

Focusing on 2021, we observe a social gradient in life expectancy changes for both men and women. Among women, all sub-populations experienced a loss in life expectancy, with those in the highest income group experiencing the smallest loss, less than two and a half months, compared to those in the lowest income group, who lost almost six months. Among men, those in the highest income groups experienced gains in life expectancy, while those in the lowest income groups experienced losses, the extent of which spanned from of almost four months gained to almost four months lost between 2020 and 2021. To summarize, inequalities in life expectancy increased across socioeconomic groups for both women and men. While the confidence intervals for the 2020 results mostly overlap, in 2021 this is the case for women but not for men, at least not when the top and the bottom quartiles are compared.

Causes of death contributing to changes in life expectancy

COVID-19 was not the only factor that contributed to changes in life expectancy over this period. Mortality from other leading causes of death changed too, illustrating socioeconomic inequalities in health caused indirectly by the pandemic. 

Figures 2 and 3 show cause-specific contributions to changes in life expectancy at age 30 by income quartile for women and men, respectively. Note that individual year-on-year changes include random variability, and thus require caution in their interpretation. The positive trend in 2020 was due to a decrease in cardiovascular disease and cancer mortality across income quartiles, which counterbalanced the negative contributions of COVID-19. The smaller losses among women in the highest income group in 2021 were explained by decreases in cancer mortality and a smaller negative contribution of COVID-19 and residual causes. For those in the lowest income group, cancer mortality increased slightly, contributing to small negative changes in life expectancy. Slightly higher COVID-19 mortality, together with a high contribution from residual causes and infectious diseases accounted for the rest. Analyzing changes in other causes of death, in conjunction with COVID-19 deaths, gives a more complete picture of the indirect impacts of the pandemic.

Among men, reductions in cardiovascular disease and cancer mortality also counteracted the emergence of COVID-19 in 2020, as they did for women, although other causes, such as accidents, deaths caused by suicide, drugs, or alcohol abuse, and deaths due to respiratory diseases had negative effects on changes in life expectancy in the lowest income groups. In 2021, cardiovascular disease and cancer mortality continued to decrease in high-income groups, leading to positive contributions to changes in life expectancy, while COVID-19 had practically no impact. Low-income groups, on the other hand, experienced increased mortality from COVID-19 as well as from respiratory diseases, infectious diseases, and other residual causes. For both women and men, this shows how the direct and indirect effects of COVID-19 contributed to the increasing life expectancy gap across socioeconomic groups. 

Conclusions

Disparities in life expectancy across income groups in Denmark increased after the initial two pandemic years. The well-established mortality gaps were exacerbated by the dual impact of the pandemic, both directly through COVID-19 mortality, and indirectly through greater social inequalities in mortality from other causes of death. Our findings emphasize the critical need for more focused interventions for socially disadvantaged sub-groups during health emergencies. These interventions should not be restricted to the cause of the crisis itself, but are key to preventing a further widening of the gap between the most and least vulnerable sub-groups. Ultimately, this approach will foster a more equitable response to health crises.

References

Aburto JM, Schöley J, Kashnitsky I, Zhang L, Rahal C, Missov TI, et al. Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries. Int J Epidemiol. 2021;51(1):63-74. https://doi.org/10.1093/ije/dyab207 

Brønnum-Hansen H, Östergren O, Tarkiainen L, Hermansen Å, Martikainen P, van der Wel KA, et al. Changes in life expectancy and lifespan variability by income quartiles in four Nordic countries: a study based on nationwide register data. BMJ Open. 2021;11(6): e048192. doi.org/10.1136/jech-2020-214108

Schöley J, Aburto JM, Kashnitsky I, Kniffka MS, Zhang L, Jaadla H, et al. Life expectancy changes since COVID-19. Nat Hum Behav. 2022;6(12):1649–59. doi.org/10.1038/s41562-022-01450-3

Strozza, C., Vigezzi, S., Callaway, J., and Aburto J.M. (2024). The impact of COVID-19 on life expectancy across socioeconomic groups in Denmark. Population Health Metrics 22, 3. doi.org/10.1186/s12963-024-00323-3

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What are life expectancy and lifespan inequality?

Life expectancy at birth is a measure of the mean level of mortality. Simply put, it expresses the average number of years a newborn is expected to live given the mortality conditions at a point in time. Because it is an average, life expectancy does not show differences in length of life among individuals; these differences can be substantial, however. To fully understand mortality at the population level, life expectancy can be complemented by lifespan inequality, which measures variation in ages at death, capturing differences in lifespans. Lifespan inequality has been described as the most fundamental of all inequalities, because every other type of inequality is conditional upon being alive (van Raalte, Sasson, & Martikainen, 2018). Its level and evolution can tell us much about how equitably health improvements are allocated in a society.

At the individual level, lifespan inequality gauges the unpredictability of the timing of death. For example, using the standard deviation of ages at death (SD) as a measure of lifespan inequality, it was considerably more difficult to predict how long a newborn in Italy would live in 1920 (SD = 31.6 years) than it was in 2017 (SD = 12.4 years) because in 1920 deaths were more widely spread across different ages (Figure 1).

How does life expectancy relate to lifespan inequality?

In 1920, 13.3% of females born in Italy died before their first birthday and less than 10% of them were expected to reach the age of 82. In contrast, 99.7% of female babies born in 2017 survived to their first birthday and almost 70% of them are expected to be alive in 2100 (Figure 1). This exceptional progress in reducing mortality has raised life expectancy at birth from 46.4 years to almost 85 years over the last century. At the same time, life chances have become more equitable, i.e. lifespan inequality has decreased (Figure 2).

Higher levels of life expectancy usually correspond to lower levels of lifespan inequality (Figure 2 and e.g. Smits and Monden 2009). However, historically, this has not always been the case. In certain countries, lifespan inequality has been shown to stall or even increase during specific periods despite improvements in life expectancy, or vice versa. In Venezuela, for instance, between 1996 and 2013 life expectancy increased (from 69.4 to 71 years), but lifespan inequality also increased (SD from 20.7 to 21.7 years) because progress in reducing mortality at almost all ages, including infant mortality, was counteracted by a rise in violent deaths concentrated among young males (García & Aburto, 2019).

A recent article shows that changes in both life expectancy and lifespan inequality can be expressed as rates of progress in reducing mortality (Aburto et al 2020). For example, in 1920’s Italy, the biggest gains in life expectancy were achieved if progress was concentrated in infancy. By contrast, in today’s Italy, reducing infant death rates by 5% leads to the same gain in life expectancy as reducing mortality at age 79 by 5%. Lifespan inequality is reduced significantly if lives are saved at infancy but increases if lives are saved at older ages (Figure 3). This has potential implications for the relationship between life expectancy and lifespan inequality. In contemporary societies, where rising life expectancy is often attributable to improvements in old-age mortality, increases in lifespan inequality may become more frequent.

Societal implications of increased lifespan inequality

Lifespan inequality is important because people making major life decisions are influenced by the mortality experience of those around them. Subjective beliefs about one’s lifespan have been shown to affect decisions such as whether and when to invest in education, save, buy a house, or retire (e.g. Bloom et al., 2006).

A potential weakening or reversal of the negative relationship between lifespan inequality and life expectancy means that, while people live longer on average, individuals also face greater uncertainty about when they will die. Studies have shown that such increased uncertainty within a society is likely to be concentrated among those from disadvantaged socio-economic groups. For example, in Finland, between 1971 and 2014, lifespan inequality increased among subgroups with low educational attainment and low income but decreased among more advantaged groups (van Raalte et al., 2018). This represents a “double burden” for lower socio-economic groups experiencing not only shorter lifespans but also higher uncertainty compared to the more advantaged groups.

Diverging patterns in lifespan inequality across socioeconomic groups may have important consequences for the perpetuation of social inequalities. For instance, faced with greater uncertainty about their own lifespan, individuals from disadvantaged socio-economic backgrounds risk making unfavorable financial decisions that might further reduce their financial resources later in life. Lifespan inequality is also likely to create uncertainty about the timing of inheritances and financial bequests among family members. If this uncertainty is concentrated among lower socio-economic groups, poorer individuals may not only receive smaller transfers from their parents and grandparents than the rich; they may also be less able to anticipate the timing of those transfers. Given the importance of these transfers for the recipients’ socio-economic conditions, higher lifespan inequality may strengthen the transmission of disadvantage across generations, reducing intergenerational social mobility.

The significance of including lifespan inequality in policy and research

Increasing the average age at death alone is not enough to guarantee a more equitable distribution of the length of life. An important message of recent research is that policies and interventions can affect this relationship by reducing mortality at ages that matter the most (Aburto et al., 2020). In terms of policy interventions, one key question to ask is how different types of healthcare investments, such as preventive as opposed to end-of-life care, may relate to inequality in lifespans. It is also important to understand how the strength of the relationship between life expectancy and lifespan inequality varies across societies and periods characterized by different levels of social inequality. There is reason to believe that more equal or socially cohesive societies may be more effective at reducing lifespan inequality, for example by reducing violent crime or providing better access to preventive healthcare across all socio-economic groups. These are promising avenues for future research linking demographic phenomena to the broader societal context in which they take place, with important implications for social policy.

*The authors thank Jim Oeppen for thoughtful comments and suggestions.

References

Aburto, J. M., Villavicencio, F., Basellini, U., Kjærgaard, S., & Vaupel, J. W. (2020). Dynamics of life expectancy and life span equality. Proceedings of the National Academy of Sciences.

Bloom, D. E., Canning, D., Moore, M., & Song, Y. (2006). The effect of subjective survival        probabilities on retirement and wealth in the United States. NBER Working Paper, 12688.

García, J., & Aburto, J. M. (2019). The impact of violence on Venezuelan life expectancy and lifespan inequality. International journal of epidemiology.

Smits, J., & Monden, C. (2009). Length of life inequality around the globe. Social Science & Medicine, 68(6), 1114-1123.

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

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Homicide rates have declined in other parts of the world but remain high in Latin America and the Caribbean (LAC). Most victims are adolescents and young adults: there were over half a million homicides among males aged 15–29 between 2005 and 2015. Brazil and Mexico, the countries with the biggest populations, account for the highest absolute number of homicides. In relative terms, El Salvador and Honduras had staggering rates of 204 and 105 homicides per 100 000 males, respectively, in 2015 (Figure 1). In contrast, Chile and Cuba had rates of 5 and 8 homicides per 100 000 persons, respectively, in that year, comparable to the rate of 8 per 100 000 persons in the USA (United Nations Office on Drugs Crime, 2019). In our new research we analyzed the effect of homicides on changes in life expectancy between 2005-2009 and 2010-2014 and quantified their effect on the mortality gap between 23 countries in LAC and high-income countries in recent years (Canudas-Romo & Aburto, 2019).    

Homicides, particularly among young men, turn out to be a major explanatory factor of the gap in life expectancy in LAC countries with respect to high-income countries. In fact, homicide rates in the region are much higher than suicide rates, in contrast to the rest of the world (Briceño-León, Villaveces, & Concha-Eastman, 2008). To put this in perspective, males in Honduras lose over six years of life when compared to developed countries, and women two years.

Our study also underscores the disparities in levels of violence in the LAC. Males in Honduras, El Salvador, Colombia, Venezuela and Guatemala lose over two years of life expectancy due to homicides when compared to developed countries, while Chile, Cuba and Uruguay exhibit levels similar to those in developed regions.

LAC countries have seen progress in most other major causes of death, such as cancer and cardiovascular disease, but homicide, particularly among young men, is still impacting people’s lives so much that progress in life expectancy has practically stalled in some countries (Figure 2). The life expectancy forerunners of longevity in the region are also the group of countries with the lowest burden of homicide mortality.

Economic and health consequences of violence

Excess mortality from homicides can have transcendental social and economic consequences in a population. For instance, the increase in the proportion of the population of working age (also known as the demographic dividend, which is shaped through a transition from high to low rates of mortality and fertility) represents a window of opportunity to boost a country’s development. However, the potential to integrate the surplus work force is jeopardized by the high number of young deaths. Crime has direct and indirect adverse effects on economic efficiency. It directly affects the productivity of those who are deceased, incapacitated or incarcerated, but also generates social inefficiencies by reducing investment (Soares & Naritomi, 2010) and causing major displacement in the region (Csete et al., 2016).

As a social determinant of health, violence can have unquantifiable consequences on population health. For example, women, children and the elderly face a higher burden of non-fatal physical, sexual and psychological consequences of abuse. In Venezuela, for instance, homicides have increased lifespan inequality (García & Aburto, 2019). Exposure to violence can increase the likelihood of young people perpetrating gun violence, and heightens the risk of depression, alcohol abuse, suicidal behavior, and psychological problems, among other detrimental consequences over the life course (World Health Organization, 2014). Moreover, living in constant fear of extreme violence is disruptive to the normal functioning of health and social services, education and civic participation (Canudas-Romo, Aburto, García-Guerrero, & Beltrán-Sánchez, 2017).

Violence prevention

The ongoing levels of violence in the region are not recent phenomena. Mexico and Central and South America have suffered an enormous burden of drug-related violence associated with efforts of criminal groups to protect their illicit markets, often against armed police or military forces. In Mexico, for example, the rise of homicides began in 2006 with the so-called War on Drugs aimed at combating drug cartels. The rise in violence was such that life expectancy gains were reversed between 2000 and 2010 (Aburto, Beltrán-Sánchez, García-Guerrero, & Canudas-Romo, 2016). In Brazil, homicides together with accidents are the third leading cause of death for the total population and the main cause of death among young adults (Reichenheim et al., 2011), and the homicide risk is ten times higher than in most developed countries. In Venezuela, homicides increased steadily and more than doubled between 1995 and 2009 (from 20.3 to 49.0 homicides per 100 000 people) due to the social and economic upheavals related to political conflict (García & Aburto, 2019).

Violence prevention should be targeted at the individual, family, and community levels. Previous evidence suggests that school-based efforts, mental health and child welfare programs, educational programs and placement of graduates in jobs, self-employment, and continuing education, together with programs aimed at reducing alcohol consumption have been successful in mitigating the diffusion of violence (World Health Organization, 2014). In Central and South America, political will is essential. Policies built around drug prohibition or severe repression have not worked and military involvement should be phased out wherever possible since it exacerbates violence.

In our view, there is a clear need to rethink approaches and to bring the region together so as to reduce the high levels of homicide mortality and to prevent its spread to other parts of LAC. It is important to recognize and correct the damage to health and human rights that previous policies have caused in the region.

References

Aburto, J. M., Beltrán-Sánchez, H., García-Guerrero, V. M., & Canudas-Romo, V. (2016). Homicides in Mexico reversed life expectancy gains for men and slowed them for women, 2000–10. Health Affairs, 35(1), 88-95.

Briceño-León, R., Villaveces, A., & Concha-Eastman, A. (2008). Understanding the uneven distribution of the incidence of homicide in Latin America. International Journal of Epidemiology, 37(4), 751-757.

Canudas-Romo, V., & Aburto, J. M. (2019). Youth lost to homicides: disparities in survival in Latin America and the Caribbean. BMJ Global Health, 4(2), e001275.

Canudas-Romo, V., Aburto, J. M., García-Guerrero, V. M., & Beltrán-Sánchez, H. (2017). Mexico’s epidemic of violence and its public health significance on average length of life. Journal of Epidemiology and Community Health, 71(2), 188-193.

Csete, J., Kamarulzaman, A., Kazatchkine, M., Altice, F., Balicki, M., Buxton, J., . . . Goulão, J. (2016). Public health and international drug policy. The Lancet, 387(10026), 1427-1480.

García, J., & Aburto, J. M. (2019). The impact of violence on Venezuelan life expectancy and lifespan inequality. doi:10.1093/ije/dyz072.

Reichenheim, M. E., De Souza, E. R., Moraes, C. L., de Mello Jorge, M. H. P., Da Silva, C. M. F. P., & de Souza Minayo, M. C. (2011). Violence and injuries in Brazil: the effect, progress made, and challenges ahead. The Lancet, 377(9781), 1962-1975.

Soares, R. R., & Naritomi, J. (2010). Understanding high crime rates in Latin America: The role of social and policy factors. In The economics of crime: Lessons for and from Latin America (pp. 19-55): University of Chicago Press.

United Nations Office on Drugs Crime. (2019). Intentional Homicide Victims. Retrieved 17/04/2019, from UNODC

World Health Organization. (2014). Global status report on violence prevention 2014: World Health Organization.

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