How severe was the demographic impact of COVID-19 in the first two years of the pandemic?  Many articles published in recent years have sought to answer this question by comparing different national experiences. We ourselves contributed to this effort by publishing two articles examining excess mortality across a large number of European regions in 2020, and then in 2020 and 2021 (Bonnet at al. 2024a,b). However, understanding what makes COVID-19 unique requires not only international comparisons, but also a longer-term historical perspective to compare its burden with those of other longevity crises. 

In a recent article, we assessed the historical and spatial burden of COVID-19 in France by comparing it to four major longevity crises since 1900: the Spanish flu epidemic (1918–1919), the Hong Kong flu epidemic (1968–1969), and the heatwaves of 1911 and 2003 (Bonnet and Camarda 2025). Our study uses annual death and population counts for 90 French departments drawn from the French Human Mortality Database, combined with statistical modeling techniques, to estimate excess mortality related to each crisis.

ASYLL, or age-standardized years of life lost

To compare these events across space and time, the choice of metric is crucial; we used age-standardized years of life lost (ASYLL) as the central indicator of our study. Widely used in public health, this metric quantifies the total years of life lost due to premature death, and can be applied to estimate the burden of specific crises. One of its main advantages is that it enables meaningful comparisons between different populations by relying on a common age structure. Another key benefit is that it can be summed across multiple years, allowing researchers to capture the full impact of a pandemic that spans more than a single year – something that is not possible with other measures, like drops in life expectancy. Finally, it provides a more nuanced picture of the mortality burden than the number of deaths, by considering the age at which deaths occur.

Ranking mortality crises

Our findings show that COVID-19 led to an average of 9 years of life lost per 1,000 inhabitants in France over 2020–2021. How high is this in historical terms? Measuring in ASYLL per 1,000 population,

• the 2003 heatwave caused about 2.5 years of life lost,

• the 1968–69 Hong Kong flu between 5 and 7, 

• COVID-19 about 9 (as mentioned above),

• the 1911 heatwave close to 24, and 

• the Spanish flu nearly 100. 

These figures help contextualize the scale of COVID-19’s mortality impact. Despite its global scope and the societal disruption it caused, the demographic burden of COVID-19 in France remains about 10 times lower than that of the most devastating crisis of the 20th century, the Spanish flu epidemic (excluding the two world wars, to be sure).

The age profile of deaths varied markedly between crises. Excess mortality related to COVID-19 and the 2003 heatwave was heavily concentrated among adults aged 60 and over. In contrast, the Spanish flu disproportionately affected children and young adults. Finally, the heavy toll of the 1911 heatwave was largely driven by infant deaths, in many cases stemming from contaminated milk. These age patterns partly explain why the overall burden of the COVID-19 pandemic, as measured by ASYLL, appears lower than that of other longevity crises, especially those of the first half of the 20^th century: premature deaths at younger ages result in more years of life lost due to the higher remaining life expectancy at those ages, an outcome that would also emerge with other metrics, though ASYLL captures it more directly.

Geographical distribution

As Figure 1 shows, in each of the five cases considered, striking regional contrasts emerged, with some crises showing widespread effects across the country and others confined to specific areas.

• The Spanish flu was remarkably uniform in its spread, affecting all French regions with comparable intensity, although slightly less in the north-west of the country.

• The 1911 heatwave, in contrast, affected northern and south-eastern France especially, with particularly severe losses in the Lozère department (84 years of life lost per 1,000 population).

• The 2003 heatwave had the strongest impact in the central-western part of the country, including departments such as Creuse, Indre and Loir-et-Cher (about 10 years of life lost per 1,000 population).

• COVID-19 stands out for its highly fragmented geography. Mortality was particularly severe in the Paris region (Île-de-France) and eastern regions bordering Belgium, Germany, and Switzerland (about 20 years of life lost per 1,000 population). Parts of western France experienced little to no excess mortality, especially in 2020 (Bonnet et al. 2024b).

This geographical heterogeneity is not simply a product of random variation; it underscores the crucial role of regional and local dynamics in shaping health outcomes, particularly during a global pandemic. National-level aggregates often conceal significant internal disparities, which public authorities must recognize in order to better allocate resources and design effective interventions. In our article (Bonnet and Camarda 2025), we found that the 2003 heatwave showed the highest territorial diversity, followed by COVID-19; the Spanish flu, on the other hand, had the most geographically uniform impact on mortality. The marked variability in the impact of the 2003 heatwave is unsurprising, given that mortality rates were highest in areas where peak temperatures were most extreme, especially in the central-west region. In contrast, the notable regional disparities observed during the COVID-19 pandemic are likely attributable to stringent social distancing measures implemented during the first wave (Spring 2020), which limited the virus’s spread across the national territory, protecting regions in the west and south especially. In comparison, the minimal social distancing measures during the Spanish flu epidemic were compounded by the depleted state of the health system in the aftermath of the First World War that exacerbated the pandemic’s impact. Additionally, the population’s familiarity with death in the post-war era further hindered effective containment efforts, allowing the virus to spread uniformly across the French regions. 

Conclusion

Placing the COVID-19 pandemic within a century-long perspective shows that its impact – while unprecedented in terms of media coverage and social disruption – is part of a longer history of unevenly distributed longevity crises. 

References

Bonnet, F., Camarda, C.G. (2025). Contextualizing the Global Burden of COVID‐19 Pandemic: A Historical and Geographical Exploration of Excess Mortality in France, 1901–2021. Population and Development Review.

Bonnet, F., Grigoriev, P., Sauerberg, M., Alliger, I., Mühlichen, M., Camarda, C.G. (2024a). Spatial variation in excess mortality across Europe: a cross-sectional study of 561 regions in 21 countries. Journal of Epidemiology and Global Health, 14(2), 470-479. 

Bonnet, F., Grigoriev, P., Sauerberg, M., Alliger, I., Mühlichen, M., Camarda, C.G. (2024b). Spatial disparities in the mortality burden of the covid-19 pandemic across 569 European regions (2020-2021). Nature Communications, 15(1), 4246. 

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Emergency Contraception Pills (ECPs) first appeared in the early 1990s, with the first ECP being approved by the US Food and Drug Administration in 1999 for use on prescription. Over the last decades, new ECPs have emerged and become increasingly widespread in both developed and developing countries. The most widely used ECPs are based on two different active ingredients:

1) levonorgestrel (LNG), a synthetic progestogen commonly known as the morning-after pill, that can prevent pregnancy if taken within 48 hours of sexual intercourse, and 

2) ulipristal acetate (UPA), which can inhibit conception if taken within 120 hours (5 days) of unprotected sex. 

ECPs are not recommended as ordinary contraception, but only in case of contraceptive error (condom breakage, forgetting the daily pill…) or after unprotected sex. The side effects of ECPs are modest, and they have few contraindications. There is general consensus in the scientific literature that neither LNG nor UPA are abortive drugs, as they prevent fertilization by inhibiting or delaying ovulation.

The diffusion of ECPs in Italy

ECPs became available in Italian pharmacies in 2006 (LNG) and 2012 (UPA). Between 2012 and 2021, the diffusion of ECPs in Italy increased considerably: from 2.2 per 100 women aged 15–49 in 2012 to 5.5 in 2021. This level is similar to those estimated in other European countries such as France (6.2% in 2016), and the Netherlands (5.0% in 2017), although lower than the United Kingdom (33% in 2014), where ECPs have always been free of charge (ECEC 2024). However, the increase was not linear over time: while sales remained stable at around 340,000 single doses per year in the three years 2012-14, there was a continuous increase over the following five years, reaching more than 588,000 in 2019. Sales then fell in 2020, only to rise again the following year. 

The spread of ECPs in Italy increased significantly when UPA (in March 2015) and LNG (in May 2016) became available over the counter, without a prescription (Dalla-Zuanna, Giraldo & Loghi 2025). Sales rose from 30,000 to 40,000 single-dose packs per month from 2014 to 2016, compared to the three-year period of 2012-14 when the number of doses sold remained constant (Figure 1). The estimated number of unwanted pregnancies prevented thanks to ECP rose from about 10,000 in 2014 to some 16,000 in 2016, and 21,500 in 2021, helping to make the abortion rate in Italy one of the lowest in Europe. This increase in sales, and in particular the preference for UPA – whose sales have steadily exceeded those of LNG since early 2021 – was also bolstered by UPA becoming available as an over-the-counter drug for minors without a prescription in October 2020.

The effect of COVID-19 lockdown

During the strict COVID-19 lockdown of March-May 2020, sales of emergency contraceptive pills in Italy decreased by 38% compared to the same quarter in 2019, with a 61% reduction in April 2020, the month of strict and complete lockdown, compared to April 2019 (Figure 2). Nonetheless, conceptions during the lockdown also declined significantly, especially those resulting in an abortion and those among women below age 25. This result suggests that

a) ECPs are widely used by non-cohabiting partners, and 

b) ECPs have played a non-marginal role in the second Italian contraceptive revolution, in particular by reducing unwanted pregnancies among singles, who are frequent condom users and/or exposed to the risk of unprotected sexual intercourse.

The importance of ECPs sales and dissemination strategies

Although there are no recent nationally representative surveys on the diffusion of contraception in Italy, data on oral contraceptive sales and some surveys on particular groups suggest that the drop in conception and abortion in Italy in the first decades of the twenty-first century has mainly been due to the decline in coitus interruptus as a contraceptive method, and the spread of condoms (especially for casual and juvenile sexual intercourse) and the daily pill (particularly in stable couple relationships). However, the contribution of ECPs has grown steadily since liberalization, and it may continue to do so, as there is considerable room for improvement, for example, by bridging current geographical disparities: 6.4 doses of ECP per 100 women aged 15-49 were sold annually in 2019‒2021 in Liguria, compared with just 3.6 in Sicily. Unfortunately, little is known about the socio-demographic characteristics of ECP users (social class, age, relationship status…) or the reasons for using this method (condom breakage, forgetting the daily pill, direct contraception…). 

Based on the probabilities of conception for a single act of sexual intercourse followed by the use of LNG or UPA – as estimated by Glasier et al. (2010) – ECPs alone are much less effective in preventing conception than other contraceptive methods. The annual probability of becoming pregnant with perfect use is 1% for the daily pill, 0. 6% for the IUD, 2% for condoms. In contrast, for a woman who has sex once a week (about 52 times a year), without avoiding her fertile days, and without any other hormonal contraceptive method, the annual probability of conception jumps to 68% for LNG and 51% for UPA, if she uses only ECPs. Moreover, misconceptions about the contraceptive efficacy of ECPs are widespread in Italy: 49% of a sample of Italian women aged 16-45 interviewed by Nappi et al. (2014) “agreed” or “strongly agreed” with the statement “ECP is 100% effective if taken the next day”. This proportion was higher than, for, instance, in France (43%), Spain (42%), Germany (37%), and the UK (35%).

What policy indications can be drawn from these figures? It would be judicious to maintain the current policy of selling ECPs in pharmacies, which guarantees interaction with the pharmacist who is required by law to warn users of the limited contraceptive power of ECPs and their unsuitability as an ordinary contraceptive method. It would also be useful to extend LNG without a medical prescription to minors, as was done for UPA in October 2020. The free distribution of emergency contraception may not be desirable, because it could lead to improper use as a substitute for more effective methods, although the current prices are high: €21.50 and €28.90, respectively, for a single dose of the most popular commercial brands of LNG and UPA at the end of 2024.

Extensive information campaigns are needed, both on the high risk of pregnancy in case of unprotected intercourse and on the use of ECPs. No such information campaign has ever been launched in Italy, despite evidence that the main reason for inappropriate or poor use of ECPs (and of contraception in general) is insufficient knowledge about the risk of pregnancy, available contraception, and reproductive health more generally. 

References

Dalla-Zuanna G., A. Giraldo and M. Loghi (2025) “Ten years of emergency contraception in Italy (2012‒21) in the context of the second contraceptive revolution”, Popolazione e Storia, https://popolazioneestoria.it/article/view/1696

ECEC – European Consortium for Emergency Contraception (2024) https://www.ec-ec.org/about-ecec/aims/ visited 22nd February 2024.

Glasier A.F. et al. (2010) “Ulipristal acetate versus levonorgestrel for emergency contraception: a randomised non-inferiority trial and meta-analysis”, The Lancet, 375, 9714, 13-19 February 2010, 555‒562.

Nappi R.E., P.L. Abascal, D. Mansour, T. Rabe, R. Shojai and The Emergency Contraception Study Group (2014) “Use of and attitudes towards emergency contraception: A survey of women in five European countries”, The European Journal of Contraception & Reproductive Health Care, 19, 2, 93‒101.

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The context: COVID-19 in Latin America 

Latin America was one of the world regions most affected by the COVID-19 pandemic, accounting for 8.5% of the global population but 27% of all COVID-19 deaths by mid-2022 (Larrain et al. 2023). With a dramatic increase in the number of deaths, especially in Brazil, the region was declared the epicenter of the pandemic in May 2020 by the World Health Organization. The rapid spread of the virus throughout the region resulted in some of the highest levels of excess mortality worldwide in the year 2020. For example, in Peru, Ecuador, and Mexico, the numbers of observed deaths were 153%, 80% and 61% above the expected levels for the year 2020, respectively (Karlinsky and Kobak 2021). This metric, known as P-score, measures the relative change in total mortality by comparing the observed number of deaths against the expected number under “normal” conditions. Lima et al. (2021) estimate that in 2020, 10.9 years of life were lost to the pandemic in Peru, 7.9 years in Ecuador and 5.5 years in Mexico, despite a younger (but rapidly ageing) population age structure than in most other world regions. Social inequalities have been pointed up as important determinants of the severe impact of the COVID-19 pandemic in Latin America (Cid and Marinho 2022). 

While the death toll increased massively in most Latin American countries during 2020, the first year of the pandemic, it declined instead in Uruguay, as well as in a handful of other countries around the world (Australia, New Zealand and Norway; Karlinsky and Kobak 2021). Yet, mandatory lockdown was not decreed in Uruguay; instead, the authorities called upon citizens to adhere to social distancing, which they called “responsible freedom”. In addition, several non-pharmaceutical measures were implemented by the government, guided by a group of scientific experts.

Mortality changes in Uruguay in 2020-2022

In a recently published study (Torres et al. 2024), we showed that, in comparison with the year 2019, life expectancy increased in Uruguay in 2020, for both sexes, by almost 10 months. The relatively few COVID-19 fatalities of that year, concentrated mainly in the month of December (Figure 1), were not enough to offset the general survival improvements of the country, mainly driven by mortality reductions among people of ages 65+, possibly benefiting from the battery of preventive measures implemented in the first year of the pandemic. 

Conversely, 2021 and 2022 showed large peaks in the estimated number of excess deaths as well as COVID-19 deaths. The annual P-scores for those years rose to almost 19% and 13% respectively. These figures, although much less dramatic than those of countries like Peru, Ecuador and Mexico in 2020, as mentioned above, were unprecedented in Uruguay. With respect to the year 2019, Uruguayan life expectancy declined by about 2 years in 2021, dropping from 81.14 to 79.21 years for females and from 73.43 to 71.37 years for males. The 2022 losses were smaller but still substantial, especially for females (–1.34 years compared to 2019, vs. –0.66 years for males). 

The worst months were April and May of 2021 (autumn in the southern hemisphere), when most excess deaths can be attributed to COVID-19. The following, milder mortality peak is observed in January and February of 2022 (summer), which contrasts with the experience of other countries where COVID-19 mortality was lowest in summer and highest in winter (Torres et al. 2023). This result could be related to the relaxation of mobility restrictions in Uruguay during the summer holidays. A third, smaller peak is observed in June 2022 (winter), when most excess deaths were related to causes other than COVID-19. 

In comparison with other selected countries, Uruguay’s monthly excess mortality profile during the first pandemic year more closely resembles the paths followed by countries like Australia and New Zealand, which also had a deficit of deaths in 2020 (Karlinski and Kobak 2021). However, 2021 tells a different story, with a pattern more similar to that of its neighbors, Argentina and Brazil. 

Why the shift from a deficit to an excess of deaths in Uruguay?

The success of Uruguay during the first pandemic year has been attributed, in part, to the joint efforts of the scientific community and the authorities. The latter were receptive and rapidly implemented the recommendations of the former, especially in 2020 (Taylor 2021). Furthermore, the testing, tracing and isolating strategy was particularly effective in 2020, when most people took social distancing and other official recommendations very seriously, even though lockdown was never mandatory in the country. 

Starting in May 2021, however, due in part to the increase in vaccination coverage and in part to the general fatigue with the restrictions that was beginning to surface in society, the battery of preventive measures was progressively relaxed. Too early, maybe, considering that the new “Gamma variant” – which originated in Brazil – was in circulation in the first months of that year. Furthermore, the “pool” of vulnerable people may have been larger than usual in 2021, thanks to the lives saved in 2020, mainly among people of advanced age.

References

Karlinsky, A., Kobak, D. (2021). Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset. elife, 10, e69336.

Cid C., Marinho M.L. (2022). Dos años de pandemia de COVID-19 en América Latina y el Caribe: reflexiones para avanzar hacia sistemas de salud y de protección social universales, integrales, sostenibles y resilientes. Documentos de Proyectos (LC/TS.2022/63), Santiago, Comisión Económica para América Latina y el Caribe (CEPAL).

Larrain, N., Di Paolantonio, G., Raitzik Zonenschein, T., Naik, R., Guanais, F. (2023), “The impact of the COVID-19 pandemic on Latin American and Caribbean healthcare systems”, in Health at a Glance: Latin America and the Caribbean 2023, OECD Publishing, Paris, https://doi.org/10.1787/1dd81269-en.

Lima, E. E., Vilela, E. A., Peralta, A., Rocha, M., Queiroz, B. L., Gonzaga, M. R., Piscoya-Díaz, M., Martinez-Folgar, K., García-Guerrero, V., Freire, F. H. (2021). Investigating regional excess mortality during 2020 COVID-19 pandemic in selected Latin American countries. Genus, 77(30).

Taylor L. Why Uruguay lost control of COVID. Nature 2021; 595(7865): 21.

Torres, C., García, J., Meslé, F., Barbieri, M., Bonnet, F., Camarda, C. G., … & Robine, J. M. (2023). Identifying age-and sex-specific COVID-19 mortality trends over time in six countries. International Journal of Infectious Diseases, 128, 32-40.

Torres, C., Prieto Rosas, V., De Armas, G., Paredes, M. (2024). The impact of the COVID-19 pandemic on mortality in Uruguay from 2020 to 2022. Demographic Research, 51, 911-926.

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In 2020 and 2021, drastic measures were taken to combat the COVID-19 pandemic, contain the spread of the virus and limit the number of deaths. The efficacy of these measures, initially hard to evaluate (Caporali et al 2022), can be better assessed today, as reliable estimates of deaths caused by this pandemic are now being published by major research institutes in scientific journals and by international organizations. In a report issued in May 2024, for instance, the World Health Organization (WHO) estimated that life expectancy dropped by 1.8 years between 2019 and 2021 globally, erasing a decade of progress.

COVID-19-related excess mortality

These estimates are derived from what is known as excess mortality, which represents the difference between observed mortality during the pandemic years and the mortality that would have been expected in its absence. Excess mortality can be quantified using different indicators, such as the number of excess deaths. However, comparing this indicator between countries of different sizes and age structures can be misleading. Another informative metric is the loss of life expectancy at birth, calculated globally by organizations like WHO.

The regular calculation, publication, and dissemination of excess mortality indicators serve as crucial tools for comparing the pandemic’s impact across different countries at national level. However, the pandemic did not have a homogeneous impact at subnational level, primarily due to the varying confinement strategies implemented to curb the spread of the virus. This variability underscores the importance of quantifying these indicators at a more granular geographical level to highlight the pandemic’s impact in the regions most severely affected. Detailed geographical analysis of excess mortality can thus localize the effect of the pandemic and help tailor response strategies accordingly.

Mortality in Europe at subnational level: 2020 and 2021

In a series of studies published in 2024, we first proposed an innovative method to calculate excess mortality at a fine regional level (Bonnet and Camarda 2024), then used this method to estimate the levels of excess mortality in 561 European regions covering 21 countries in 2020 (Bonnet et al 2024a). We finally expanded our geographical scope to 569 regions covering 25 European countries, differentiating between the two pandemic years, 2020 and 2021 (Bonnet et al. 2024b). These estimates are the outcome of a thorough process involving the collection of data from a wide array of national statistical institutes. Here, we present our findings based on loss of life expectancy at birth for both sexes combined.

Figure 1 depicts the spatial distribution of the estimated life expectancy losses in 2020. Losses were highest in northern Italy and central Spain, with values close to 4 years in the regions of Bergamo and Cremona, 3.5 years in Piacenza, and 3 years in the Spanish regions of Segovia, Ciudad Real, Cuenca, and Madrid. They are even higher when considering only men (not shown here), who were more severely affected by the pandemic: life expectancy loss is close to 5 years in Cremona and 4.5 years in Bergamo. Losses in 2020 were more moderate, though still  dramatic, in eastern Europe (notably in Poland), eastern Sweden, and northern and eastern France. In the latter, the Paris region and areas near the German border showed the highest values, ranging from 1.5 to 2 years. Conversely, the losses were much lower in other geographical areas, such as southern Italy, much of Scandinavia and Germany, and western France, where life expectancy remained close to what would have been expected in the absence of the pandemic.

Figure 2 presents the estimated life expectancy losses in 2021. At first glance, the regions affected by excess mortality were not the same as in 2020. The most significant losses were predominantly in eastern Europe. More broadly, among the regions where life expectancy losses exceed 2 years, we find 61 of the 73 Polish regions, 12 of the 14 Czech regions, all eight Hungarian regions, seven of the eight Slovak regions, but only one Italian region and one Spanish region, even though these two countries were also heavily impacted in 2020. Moreover, the life expectancy losses were much greater in Germany in 2021 than in 2020, especially in the eastern part of the country where values often exceed 1.5 years. In southern Saxony, Halle, and Lusatia, losses were nearly 2 years. Conversely, the lowest values were observed in Spain and Scandinavia. In France, life expectancy losses were more uniform than in 2020, generally ranging between 0 and 1.5 years, with the highest loss in the Parisian suburbs (Seine-Saint-Denis) where it reached 1.5 years (2 years for men).

Ranking European regions

Combining the data from both years, an overall assessment of the mortality impact of COVID-19 is possible (Bonnet et al 2024b). The regions where mortality increased the most in 2020 and 2021 are Pulawy, Bytom, and Przemyski in south-eastern Poland, and Kosice and Presov in eastern Slovakia. More broadly, eastern European regions predominate among the top 50 regions, which include 36 Polish regions, six Slovak regions, two Czech regions, one Hungarian region, and the two Lithuanian regions. Notably, the Italian regions of Cremona, Bergamo, and Piacenza complete this panel, ranging between the 15th and 30th places. In France, Seine-Saint-Denis ranks 81^st, while all other French regions rank below the 100th place.

Conclusions

In conclusion, analyzing the impact of a crisis like the COVID-19 pandemic on longevity at a fine geographical scale is essential, as within-country disparities can be significant. This was notably true for Italy in 2020 (between the South and the North), and for Germany in 2021 (between the west and the east). Our study highlighted the severe impact of the pandemic in certain European regions, where the loss of life expectancy exceeded 3 years. The most affected regions shifted between 2020 and 2021, moving from areas with traditionally high life expectancy (northern Italy, central Spain, Greater Paris) to those with traditionally low life expectancy (Eastern Europe). France was relatively spared compared to the rest of Europe except for Seine-Saint-Denis. The coming years will be crucial for determining whether life expectancy levels return to their long-term trajectory or whether the pandemic has caused a structural impact in certain regions.

References

Caporali, A., Garcia, J., Couppié, É., Poniakina, S., Barbieri, M., Bonnet, F., … & Torres, C. (2022). The demography of COVID-19 deaths database, a gateway to well-documented international data. Scientific Data, 9(1), 93.

Bonnet, F., & Camarda, C. G. (2024). Estimating subnational excess mortality in times of pandemic. An application to French départements in 2020. PLoS ONE, 19(1), e 0293752. 

Bonnet, F., Grigoriev, P., Sauerberg, M., Alliger, I., Mühlichen, M., & Camarda, C. G. (2024a). Spatial variation in excess mortality across Europe: a cross-sectional study of 561 regions in 21 countries. Journal of Epidemiology and Global Health, 1-10.

Bonnet, F., Grigoriev, P., Sauerberg, M., Alliger, I., Mühlichen, M., & Camarda, C. G. (2024b). Spatial disparities in the mortality burden of the covid-19 pandemic across 569 European regions (2020-2021). Nature Communications, 15(1), 4246.

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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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Now that they are growing old, have US baby boomers fulfilled the aspirations towards more positive and equitable life circumstances that characterized their youth, especially through the Civil Rights movement? One way to address this question is to focus on life expectancy, the number of remaining years that an average person can expect to live, and to analyze racial disparities in length of life. 

Trends in American longevity and physical disability

In 1970, life expectancy at birth in the US was approximately eight years longer for Whites than for Blacks: 71.7 vs 64.1 years, respectively. By 2010 this racial gap had narrowed to slightly less than four years: 78.9 vs. 75.1 years, respectively. More recent data are difficult to compare because of the pervasive effects of the COVID-19 pandemic, but national vital statistics suggest that some progress seems to be underway.

Both survival increase and convergence are good news, of course, but it is also important to consider if these additional years are lived in good health, both in general and by subgroup. For instance, let us consider active life expectancy at age 65, or remaining years of life past the 65th birthday expected to be lived without a disability. Between the 1980s and 2010s, it increased by 2.8 years for White Americans (from 12.2 to 15.0 years) and 2.2 years for Black Americans (from 9.8 to 12.0 years). This means that the percentage of remaining years of life expected to be lived without a disability remained approximately constant, but at different levels: 76% for White people but only 67% for Blacks (Freedman & Spillman, 2016). In other words, increases in life expectancy were accompanied by an equivalent number of healthy and unhealthy years, and racial disparities remained unchanged.

Racial disparities in long and happy living 

While life expectancy and active life expectancy are key objective indicators of quality of life, at high levels of development, additional indicators of well-being can also be considered. One such indicator links this survival to subjective well-being, a facet of societal progress that has recently garnered wide-spread attention (Layard, 2010). Until now, few researchers have tried to determine happy life expectancy, i.e., how many years an individual can expect to live happily. This is precisely what we did in a recent study, breaking our results down by physical disability status, sex, and race (Bardo and Cummings 2023) based on representative data from the 2010 and 2012 waves of the Health and Retirement Study (n = 16,614) of the U.S. population aged 50 years and older. 

The emergence of health problems is, of course, a normal and anticipated part of the aging process, and, within limits, worsening health is not a strong determinant of happiness in later life, because individuals assess their quality of life based on their expectations and ability to adapt or adjust to changing circumstances. However, the development of a disability reflects a major life change, with clear and negative consequences for both quality of life and longevity. For example, at age 50, White Americans with a disability can expect to live happily for 50% fewer years of their remaining life than their able-bodied peers. The negative influence of disability on happy life expectancy is exacerbated for Black Americans who experience a 60% reduction in remaining happy years of life when faced with a disability. These findings portray the compounding effects of cumulative (dis)advantage and circumstances that people with multiple intersecting identities (e.g., Black, male, disability) uniquely experience (Figure 1).

Unfortunately, in the USA, racial disparities in key resources that promote health, happiness, and longevity remain as wide today as they were in the 1970s, as, for example, in the case of the Black-White gap in median household income. Do socioeconomic differences account for racial disparities in happy life expectancy? As our analyses indicate, they do for healthy Black women but not for healthy Black men. The reasons behind this unexpected result are not self-evident, and merit further analysis. 

Conclusion

There is no simple solution for achieving equity in opportunities to live a long, healthy and happy life. We focused on later life outcomes, but these are the consequences of cumulative chronic exposure to various race-related forms of stress and discrimination experienced throughout life, including uneven access to high quality primary/secondary education and safe housing in childhood, unequal access to quality jobs and affordable and safe housing during adulthood, and likely the high risk of imprisonment for young Black males (Gilbert et al., 2016).

It is high time that America turned attention towards the disparities in length and quality of life that persist in the land where the pursuit of life, liberty, and happiness is a supposedly unalienable right.

References 

Bardo, A.R., Cummings, J.L. (2023) Life, Longevity, and the Pursuit of Happiness: The Role of Disability in Shaping Racial and Sex Disparities in Living a Long and Happy Life. Population Research and Policy Review. 42, 72.

Freedman, V. A., & Spillman, B. C. (2016). Active life expectancy in the older US population, 1982–2011: Differences between blacks and whites persisted. Health Affairs, 35(8), 1351–1358.

Gilbert, K. L., Rashawn, R., Siddiqi A., Shetty, S., Baker, E.A., Elder, K. & Griffith, D.M. (2016). Visible and invisible trends in black men’s health: pitfalls and promises for addressing racial, ethnic, and gender inequities in health.  Annual Review of Public Health, 37, 295-311.

Layard, R. (2010). Measuring subjective well-being. Science, 327, 534-534.

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The COVID-19 pandemic had a major impact on life expectancy at birth in the United States. In 2020 it fell by 1.8 years, but the drop varied markedly by race and ethnicity, with the Hispanic/Latino (henceforth “Hispanic”) population losing substantially more than other race and ethnicity groups (Arias and Xu, 2022). Before the pandemic, the Hispanic population was characterized by better survival chances than the majority (non-Hispanic White) population, despite having lower levels of educational attainment, higher levels of poverty, and lower access to health insurance and quality care. This advantage, known as the “Hispanic Mortality Paradox”, was almost completely cancelled out by the pandemic, declining from 3.1 years in 2019 to 0.5 years in 2020 (Arias and Tejada-Vera, 2023). 

The terms “Hispanic” or “Latino” were created in the US to classify persons from Spanish speaking countries and, as a result, they mask the great diversity of this population (Arias, et al. 2020). Hispanic people have origins in countries representing diverse cultural, economic, social, and political characteristics, and have distinct histories of immigration and patterns of assimilation in the US. Prior to the pandemic this diversity was also evident in differences in life expectancy at birth by country of origin (Arias and Tejada-Vera, 2023). To better understand why the pandemic disproportionately affected the Hispanic population, we examined changes in life expectancy at birth between 2019 and 2020 and the impact of changes in age and cause-specific mortality in Hispanic sub-groups across different countries of origin. 

Decline in life expectancy

Figure 1 shows life expectancy at birth in 2019 and 2020 by Hispanic sub-group. The figure highlights differences in both levels and losses in the period, particularly among males. The overall loss of life expectancy among the Hispanic population was 4.5 years for males and 3.1 years for females, ranging from 0.6 (Other Hispanic) to 6.7 years (Central and South American) among males and from 0.6 (Other Hispanic) to 3.6 years (Mexican) among females. 

Age-specific variation in the probability of dying

The COVID-19 pandemic increased the age-specific probability of dying during 2020, compared to 2019, differently by age and across the Hispanic sub-groups (Figure 2). For example, Central and South American males experienced probabilities of dying up to 2.5 times greater in 2020 than in 2019, concentrated in the working ages (30 to 70 years), while Cuban females experienced increases close to two times greater, concentrated in the oldest ages (75 and older).

The impact of age and cause-specific changes in mortality on the decline in life expectancy

We used a decomposition technique to explore the effects of changes in age-specific mortality on changes in life expectancy between 2019 and 2020 (see Arias and Tejada-Vera, 2023 for details). For all groups, the effects of increases in mortality below age 20 were minimal. The effects in working ages (20-69) were greater among males across all groups and, conversely, the effects in the oldest ages (70 and older) were greater among females in all groups. There were important differences across Hispanic sub-groups. Among males, increases in mortality at working ages (20-69) were largest for Mexicans (57.4%) and smallest for Cubans (39.5%). Conversely, increases at the oldest ages (70 and older) ranged from 41.3% for Mexicans to 58.4% for Cubans.

Among females, increases in mortality at working ages were smallest for Other Hispanics and Cubans (6.8% and 17.9%, respectively). The largest effects of increased mortality at the oldest ages occurred among Cubans and Other Hispanics (81.4% and 87.1%, respectively).

Figure 3 presents the contributions of increases in the five causes of death with the largest impact on the decline in life expectancy between 2019 and 2020. For all groups, increases in mortality due to COVID-19 had the largest effect on life expectancy loss and its impact was greater among the Hispanic population than among the majority population. There was variation within the Hispanic population, however. The percent contributions of increases in mortality due to COVID-19 were smallest for Puerto Rican (53.6%) and Cuban (55.3%) males compared to Central and South American (74.2%) and Mexican (72.5%) males. Among females, the impact of COVID-19 mortality ranged from 41.8% among Cubans to 77.5% and 98.2% among Central and South Americans and Other Hispanics, respectively.

Conclusions

The impact of the COVID-19 pandemic on life expectancy in the Hispanic population varied by country of origin and sex. This differential impact was also linked to differences in the age distribution of excess mortality due to COVID-19 and in the relative impact of COVID-19 in comparison to other causes of death. These results suggest that for some groups, the excess burden of COVID-19 mortality was an outcome of the over-representation of some Hispanic sub-groups in essential industries with higher risks of exposure to the virus (Riley et al., 2021). For other groups, it may have been the over representation of multigenerational households where older adults were at greater risk of intergenerational transmission of the virus (Riley et al., 2021). These results highlight the importance of population disaggregation in studies of the disproportionate impact of the COVID-19 pandemic on the Hispanic population in the US.

References

Arias E, Johnson NJ, Tejada-Vera B (2020). Racial disparities in mortality in the adult Hispanic population. SSM – Population Health 11.

Arias E, Xu JQ (2022). United States life tables, 2020. National Vital Statistics Reports 71 (1). National Center for Health Statistics.

Arias E, Tejada-Vera B (2023). Differential impact of the COVID-19 pandemic on excess mortality and life expectancy loss within the Hispanic population. Demographic Research 48, 339-352.

Do DP, Frank R. (2021). Using race- and age-specific COVID-19 case data to investigate the determinants of the excess COVID-19 mortality burden among Hispanic Americans. Demographic Research 44, 699-718.

Riley AR, Chen YH, Matthay EC, Glymour MM, Torres JM, Fernandez A, Bibbins-Domingo K (2021). Excess mortality among Latino people in California during the COVID-19 pandemic. SSM-Population Health 15

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Introduction

The COVID-19 pandemic has caused enormous psychological, social, and economic harm worldwide. Public health measures adopted to fight the spread of the virus, such as limitations of social and physical contacts, may have exacerbated loneliness and social isolation among younger and older adults. Loneliness is a state of emotional distress arising from a discrepancy between desired and actual social interactions. It should not be confused with social isolation, as not all individuals with limited social interactions feel lonely.

Several studies suggest that older adults, despite a higher risk of social isolation, were more resilient to loneliness during the COVID-19 outbreak than younger people (Arpino et al., 2020; Luchetti et al., 2020). However, research is needed to correctly identify who felt lonely during the COVID-19 pandemic and who felt lonely because of the COVID-19 pandemic.

Who feels lonely?

To provide a clearer picture of this issue, we exploited data collected by the Survey of Health, Ageing and Retirement in Europe (SHARE) before and during the COVID-19 outbreak. More specifically, we analysed data collected in the eighth wave of SHARE, carried out in 2019/2020 (Börsch-Supan, 2022a), the first SHARE Corona survey, carried out in 2020 (Börsch-Supan, 2022b), and the second SHARE Corona survey, carried out in 2021 (Börsch-Supan, 2022c).

In these waves, the same question (“How often do you feel lonely?”) was asked with three possible answers:

1) Often
2) Some of the time
3) Hardly ever or never.

Figure 1 displays the distribution of self-reported loneliness before and during the COVID-19 pandemic, selecting only the respondents who participated in all of these waves (n=31,250). It shows that the proportion of individuals never (or hardly ever) reporting loneliness was very large (more than two thirds of respondents) and declined over time. However, while this comparison highlights who feels lonely in different periods of time, it cannot be conclusive about the role of the pandemic in causing loneliness.

To further investigate this issue, we exploited the additional question asked in both SHARE Corona surveys: “Has that been more so, less so or about the same [as before the outbreak of Corona/than during the first wave]?”. Here, the possible answers were:

1) Less so
2) About the same
3) More so. 

Unfortunately, this question was asked only to those who reported feeling lonely “often” or “some of the time” in the first SHARE Corona survey and to all respondents (whatever their feeling) in the second Corona survey, so the results cannot be compared. Moreover, they should be interpreted with caution since these are self-assessments of loneliness status and answers may suffer from individual heterogeneity in reporting feelings.

A different view: changes in loneliness

To identify those who felt lonely because of the COVID-19 pandemic, we focused on respondents who reported a change with respect to their pre-pandemic status, although even this strategy may not be flawless, as for some of these respondents the change may be due to other reasons (e.g., the loss of a loved one). However, this approach has the advantage of removing individual heterogeneity and allows us to identify both deterioration and improvement of self-reported loneliness resulting from different reactions to the measures put in place to contain the virus.

Tables 1 and 2 summarise the results obtained with our strategy. More than 70% of the older respondents show no change in loneliness with respect to the 2019 evaluation (the total percentages in the main diagonal). This does not mean that they did not feel lonely, but rather that their reported level of loneliness did not change during the pandemic period. Indeed, it is interesting to note that about 2.7% of the respondents (combining the two surveys) rated themselves as often lonely both before and during the COVID-19 outbreak.

The proportion of individuals who reported any type of deterioration of their status (14.5% and 16.7%, respectively, during the first and the second COVID-19 waves) is somewhat low and comparable with the proportion of those reporting improvement (13.2% during the first wave and 12.4% in the second wave). Moreover, only about 2% of respondents (slightly more in the second wave) reported a strong increase in their feelings of loneliness.

Summing up, this brief discussion can help to identify people whose feelings of loneliness really changed during the COVID-19 outbreak. In future research, we will investigate the characteristics of those who did not report any change. Our conjecture is that they represent a group of individuals whose feelings were barely affected by the pandemic, and this could provide a first step towards studying resilience to the COVID-19 crisis among older people.

References

Arpino B, Mair CA, Quashie NT, Antczak R (2022). Loneliness before and during the COVID-19 pandemic – are unpartnered and childless older adults at higher risk? European Journal of Ageing 19, 1327-1338

Börsch-Supan A (2022a). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 8. Release version: 8.0.0. SHARE-ERIC. Data set. DOI: 10.6103/SHARE.w8.800

Börsch-Supan A (2022b). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 8. COVID-19 Survey 1. Release version: 8.0.0. SHARE-ERIC. Data set. DOI: 10.6103/SHARE.w8ca.800

Börsch-Supan A (2022c). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 9. COVID-19 Survey 2. Release version: 8.0.0. SHARE-ERIC. Data set. DOI: 10.6103/SHARE.w9ca.800

Luchetti M, Lee JH, Aschwanden D, Sesker A, Strickhouser JE, Terracciano A, Sutin AR (2020). The trajectory of loneliness in response to COVID-19. American Psychologist 75, 897-908

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Research prior to the COVID-19 outbreak identified being unpartnered or childless as risk factors for loneliness among older adults (Dahlberg et al. 2022; Fokkema et al. 2012). The COVID-19 pandemic might have affected loneliness among kinless older adults differently from the rest of the older population. With the aim of fighting the spread of the virus, several mitigation policies and recommendations were implemented at national, regional, and local levels. These mitigation strategies, which were especially stringent during the first phases of the pandemic and varied across countries, included avoidance of public social spaces, minimizing physical contact with others, “stay-at-home” orders, and full physical isolation, especially for older adults, and those with health issues in particular.

Kinlessness, loneliness and the COVID-19 pandemic

The “physical distancing” imposed by these strategies also produced “social distancing”, i.e. reduced social contacts, at least face-to-face (Arpino et al. 2021). Thus, anti-COVID policies may have exacerbated risks of loneliness, among older adults especially (Dahlberg 2021; van Tilburg et al. 2021). These consequences may have been more serious for those lacking close kin, such as unpartnered or childless older adults. For example, the types of contacts that are more common in networks of kinless older adults, such as extended family and non-family ties, may have been more vulnerable to disruption during the pandemic compared to partner and child ties. 

On the other hand, linkages between parenthood, partnership, and loneliness are complex and may have been particularly nuanced during the pandemic. For example, older parents who are accustomed to frequent in-person contact with their children may have experienced an uptick in loneliness when child contact was disrupted. Childless older adults, on the other hand, might be more accustomed to less contact and may therefore have experienced less disruption. 

Loneliness before and during the pandemic in Europe

In a recent study (Arpino et al. 2022), we examined whether unpartnered and childless older adults (aged 50 years and over) were more likely to report loneliness compared to those with these family ties, and whether the gap in loneliness between those with and without these ties increased during the COVID-19 pandemic. Our analyses are based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE). We used pre-COVID data from wave 8, which started in October 2019 but was suspended in all countries in March 2020 due to the COVID-19 outbreak. We also used data from the SHARE Corona Survey 1 (SCS1) implemented between June and August 2020 to collect information on individuals’ behaviours and conditions during the pandemic.

Our analyses show that before the pandemic, when asked “Have you felt lonely recently?”, unpartnered and childless older adults were more likely to report loneliness compared to those with these family ties (Table 1, M1a-M2a). This remained true even when both factors were considered together, although the effect of childlessness on loneliness was smaller (Table 1, M3a). Older adults lacking one tie only (unpartnered parents or partnered childless) were at greater risk of loneliness compared to those who had both ties (Table 1, M4a).

During the pandemic, the associations between family ties and loneliness remained stable, with no notable change in the effects of being childless or unpartnered on loneliness before versus during the pandemic (Table 1, M1b-M3b). However, in contrast to the pre-COVID period, considering the combination of being childless and unpartnered made no difference (Table 1, M4b): In fact, in this case, the average marginal effect (AME) of parenthood is very similar for partnered and unpartnered individuals; similarly, the AME of partnership does not change significantly by parenthood. As a result, the effects of being unpartnered and childless on loneliness became more independent during the pandemic.

Feeling more lonely during the pandemic

Our second outcome examines whether people reported feeling more lonely during the pandemic. We found that unpartnered individuals were more likely to report feeling lonelier during the COVID-19 pandemic compared to those in a partnership. Childless individuals, on the other hand, were not at higher risk of reporting feeling lonelier compared to parents (Table 1, M1c-M3c). The effect of being unpartnered was slightly stronger among childless individuals, but the difference in the impact of being unpartnered on loneliness between childless and parent individuals was not statistically significant (Table 1, M4c).

Changes and stability in feeling lonely compared to before the pandemic

Our third outcome was obtained by combining the answers to the question “Have you felt lonely recently?” asked of respondents who participated in both the regular SHARE wave 8 and the SCS1. This analysis (not presented in Table 1) highlights that unpartnered individuals were more likely to “have become lonely” during the pandemic, while childless and unpartnered individuals were less likely to “exit” loneliness if they felt lonely before the pandemic.

Conclusions

We found that both before and during the pandemic, lacking close kin (especially a partner) was associated with a higher risk of feeling lonely among older Europeans. However, “kinless” older adults (unpartnered and childless) were not lonelier than those who lacked only one of these ties. This may signal that kinless individuals have developed a range of resources and different coping strategies to manage the lack of close kinship ties, such as a rich network of friends (Mair, 2019).

While “physical distancing,” “lockdown,” and “stay-at-home” mitigation measures provided protection from COVID-19, the restrictions on face-to-face interactions and public spaces for socializing also removed key sources of social integration for those who were already more likely to spend time alone (e.g., the unpartnered). As the population of unpartnered and childless older adults grows globally, future public health strategies should seek a balanced mitigation approach that also considers the consequences of isolation, particularly for those who are already at higher risk for loneliness.

References

Arpino, B., Pasqualini, M., and Bordone, V. (2021) Physically distant but socially close? Changes in non-physical intergenerational contacts at the onset of the COVID-19 pandemic among older people in France, Italy and Spain. European Journal of Ageing, 18, 185–194.

Arpino B., Mair C. Quashie N., and Antczak R. (2022) Loneliness Before and During the COVID-19 Pandemic: Are Unpartnered and Childless Older Adults at Higher Risk? European Journal of Ageing. 19, 1327–1338.

Dahlberg L (2021) Loneliness during the COVID-19 pandemic. Aging & Mental Health, 25(7): 1161–1164.

Dahlberg L, McKee KJ, Frank A, Naseer M (2022) A systematic review of longitudinal risk factors for loneliness in older adults. Aging & Mental Health, 26(2): 225–249.

Fokkema, T., De Jong Gierveld, J., & Dykstra, P.A. (2012). Cross-national differences in older adult loneliness. The Journal of Psychology, 146(1-2), 201-228.

Mair, Christine A. (2019). “Alternatives to Aging Alone?: ‘Kinlessness’ and the Importance of Friends across European Contexts.” Journal of Gerontology: Social Sciences 74(8):1416-1428.

Van Tilburg, T.G., Steinmetz, S., Stolte, E., van der Roest, H., & de Vries, D.H. (2020). Loneliness and Mental Health During the COVID-19 Pandemic: A Study Among Dutch Older Adults. Journal of Gerontology: Social Sciences, 76(7), e249-e255

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Introduction

The Covid-19 pandemic has once again highlighted the importance of accurately counting deaths, and of classifying them by age, sex, place of residence and cause, so that health officials and decision-makers can identify health threats and high-risk populations (Sakia et al., 2023; Sankoh et al., 2020).

India, the second most populated country of the world, enacted its Registration of Birth and Death (RBD) act in 1969, mandating universal registration within 21 days of a birth or death. Today, however, things are still far from perfect. For instance, in 2020, there were around 5.6 million and 4.6 million deaths, respectively, among Indian males and females according to United Nations estimates (United Nations, 2022), but the Office of Registrar General of India (2022) “missed” some 6% of female deaths in that year.

This calls for an understanding of the determinants of death registration in India. This is the topic of our recent study (Saikia et al., 2023) based on 84,390 individuals interviewed during the National Family Health Survey, 2019-21 (NFHS-5) (IIPS & ICF, 2021). Note, however, that we relied on interviewees’ reports of family deaths and their registration in the previous year, without checking death certificates: errors of various types are therefore possible. Besides, part of the data collection took place in the post-Covid period. Due to the government compensation scheme for Covid-related deaths, higher-than-normal death registration is likely to have occurred.

Death registration level varies by place of residence, region, state and district

Between 2019 and 2021, according to our sample data, slightly less than 71% of deaths were registered, on average, with higher rates in urban than in rural areas (83% vs 66%, respectively). In the latter, most adults are employed in informal sectors such as farming, construction work and fishing, which seldom provide social security (e.g. survivor’s pension). Low registration levels may reflect a lack of motivation, few incentives and poor access to death registration services. 

Death registration coverage varies geographically, and is particularly low in the Northeast (64%), East (57%), and Central (56%) Indian regions (Figure 1). 

Death registration level vary by demographic and socioeconomic characteristics

Registration levels are higher for deaths at older ages, e.g. about 78% at 50-64 years, and 72% at 65-98 years (Figures 2 and 3). Conversely, they are very low for children: merely 35% at 0-4 years. Sex differences are large: around 74% of male deaths are registered versus 66% of female deaths. This difference is linked, among other things, to two factors: 

1) a lower proportion of women employed in the formal sector (see above) and

2) a higher proportion of accidental deaths among males. These are usually subject to police investigation, which increases the likelihood of registration (Adair et al., 2021).

Death registration was lower: 

1) among the low-educated: 63% and 71% when the household head was, respectively, illiterate or had just primary education,

2) among Muslims (65%) than Hindus (71%),

3) among scheduled castes (or STs: 67%) than other castes (77%),

4) among poorer households (52% in the first income quintile; 87% in the fifth). 

Conclusion 

Death registration levels vary considerably by sex, age, place of residence and regions in India, and are influenced by socioeconomic variables. Periodic awareness programs may therefore be needed among vulnerable subgroups and in disadvantaged districts. Providing financial assistance for funeral rites, education loans to orphans, and social security to the deceased’s family members after reporting a death to a civil authority would probably prove helpful in increasing death registration coverage.

References

Adair T, Gamage USH, Mikkelsen L, Joshi R. 2021. Are there sex differences in completeness of death registration and quality of cause of death statistics? Results from a global analysis. BMJ Glob Health. 6(10):e006660. doi:10.1136/bmjgh-2021-006660

International Institute for Population Sciences (IIPS) and ICF 2021. National Family Health Survey (NFHS-5), 2019-21. IIPS: Mumbai, India. 

Niti Aayog – SDG India Index. 2021.; 2021.

Saikia N, Kumar K, Das B. 2023. Death registration coverage 2019–2021, India. Bulletin of the World Health Organization. Feb 1; 101(2):102-10. doi: http://dx.doi.org/10.2471/BLT.22.288889 

Sankoh O, Dickson KE, Faniran S, et al. 2020. Births and deaths must be registered in Africa. Lancet Glob Health. 8(1):e33-e34. doi:10.1016/S2214-109X(19)30442-5.

Setel PW, Macfarlane SB, Szreter S, et al. 2007. A scandal of invisibility: making everyone count by counting everyone. The Lancet. 370(9598):1569-1577. doi:10.1016/S0140-6736(07)61307-5

United Nations. Department of Economic and Social Affairs, Population Division 2022. World Population Prospect. 2022.

Source fugure 1: https://figshare.com/articles/figure/Death_Registration_Level_by_Sex/21724049

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