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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Introduction

Depression is one of the most common mental disorders affecting older people. It often coexists with other medical conditions and physical impairments (Blazer, 2003), and affects women more than men.

Later-life depression is characterized by a broad range of symptoms. The identification of substantial and meaningful subgroups of depressive symptom profiles has important implications for research, public health policies and clinical practice, offering scope for developing customized intervention and treatment programmes.

A range of screening methods are available for detecting depressive disorders and can be divided into two broad categories: tools to collect information on diagnostic criteria that best reflect diagnoses of mental disorders; and tools to measure more generic factors (psychological distress) and the presence of certain symptoms or emotional disorders, with scores and cut-off points (usually just one) to classify individuals with or without depression or mental health disorders.

Going beyond dichotomisation

A more nuanced classification based on the number and type of reported items in a depression scale would be preferable to a simple dichotomisation of ‘depressed’ versus ‘non-depressed’.

To this end, we tried an alternative approach based on two statistical techniques:

1. a latent class analysis to estimate the individual probability of belonging to a specific category (with respect to risk of depression) and then
2. a factor analysis, to reduce the number of clusters (Gennaro et al., 2021).

We analysed data collected in the sixth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE), carried out in 2015 (Börsch-Supan, 2017).

We focused on the 12 items of the EURO-D scale of depressive symptoms, developed by the EURODEP Concerted Action Programme: depressed mood, pessimism, wishing death, guilt, sleep, interest, irritability, appetite, fatigue, concentration, enjoyment, tearfulness (Table A1). EURO-D is a symptom-oriented scale, with scores ranging between 0 and 12. Scores above 3 are customarily assumed to reveal depression (Prince et al., 1999).

In our sample (64,716 individuals aged 50 years and over, living in 18 countries: Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Italy, Israel, Luxembourg, Poland, Portugal, Slovenia, Spain, Sweden and Switzerland) about 27% of respondents (20% of men, and 33% of women) are depressed according to the EURO-D scale (Table 2).

There is substantial heterogeneity in the number of reported symptoms: some are frequent, such as depressed mood, sleep impairment, irritability, fatigue and (only for women) tearfulness, while some are rare, such as suicidality and guilt (Figure 1). Among women classified as non-depressed by EURO-D, only one-fourth reported no symptoms at all, a proportion that rises to about one-third among men.

Main results

Our more refined approach leads to the identification of seven (main) categories of depressive and emotional states, from very low risk of depression to extreme depression (Table 3). These groups  are characterised by different combinations of symptoms. As the attribution of individuals to one of the seven clusters is guided by probabilistic criteria, our solution does not necessarily group individuals with the same number of symptoms: rather, it identifies common patterns in these symptoms.

About 40% of the people in our sample are at a very low risk of depression: they present a similar combination of symptoms, in particular between zero and two reported items at most. The last three categories are characterised by individuals with a high probability of reporting several depressive and emotional problems. One-third of respondents fall into the high-risk-of-depression category, i.e. they are not depressed, strictly speaking, but might well become so in the future. Ex-post analyses of the composition of this group reveal that unhealthy respondents are overrepresented (for instance, almost 60% of them report two or more chronic diseases), as are women (more than 60%).

A large proportion of individuals classified as depressed under the EURO-D system falls into one of the clusters with some risk of depression (Figure 2). Under our approach, however, a non-trivial proportion of older adults classified as non-depressed by EURO-D are identified as being at some risk of depression. Almost all respondents in the three most severe depression categories are also classified as depressed by the EURO-D scale. Gender differences are particularly evident.

As a first step, we applied our approach to cross-sectional data only. However, given the dynamic nature of depression, it would be very interesting to follow developmental trajectories or patterns of change over time. This should be possible with our approach, using Latent Class Growth Models as a first step. Hopefully, then, more on this in the near future.

References

Blazer DG (2003). Depression in late life: review and commentary. Journals of Gerontology 58A, 249–265

Börsch-Supan A (2017). Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 6. Release version 6.0.0. SHARE-ERIC (Data set). doi:10.6103/SHARE.w6.600

Gennaro C, Paccagnella O, Zaninotto P (2021). A model-driven approach to better identify older people at risk of depression. Ageing & Society 41, 339-361

Prince M, Reischies F, Beekman AT, Fuhrer R et al. (1999). Development of the EURO-D scale – a European Union initiative to compare symptoms of depression in 14 European centres. British Journal of Psychiatry 174, 330–338

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