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. 2023 Feb 16;328:72–80. doi: 10.1016/j.jad.2023.02.042

What factors explain the changes in major depressive disorder symptoms by age group during the COVID-19 pandemic? A longitudinal study

Aina Gabarrell-Pascuet a,b,c,d,⁎, Tibor V Varga e, María Victoria Moneta a,b,c, José Luis Ayuso-Mateos c,f,g, Elvira Lara c,f,g, Beatriz Olaya a,b,c, Josep Maria Haro a,b,c, Joan Domènech-Abella a,b,c,h
PMCID: PMC9933581  PMID: 36806591

Abstract

Background

Data collected during the COVID-19 pandemic suggest an increase in major depressive disorder (MDD) among younger adults. The current study aims to assess the association of age groups and MDD risk before and during the COVID-19 pandemic and quantify the effect of potential mediating variables such as loneliness, social support, resilience, and socioeconomic factors.

Methods

A representative sample of Spanish adults was interviewed before (2019, N = 1880) and during (2020, N = 1103) the COVID-19 pandemic. MDD was assessed using the CIDI, loneliness through the UCLA scale, social support through the OSSS-3, resilience with the 6-BRS, and worsened economic circumstances and unemployment through a single question. Mixed-models were used to study changes in MDD by age group. Regression models were constructed to quantify the association between age and potential mediators, as well as their mediating effect on the association between age group and MDD.

Results

Among the younger age cohorts (18-29 and 30-44 years) the probability of having MDD during the pandemic increased from 0.04 (95 % CI: 0.002-0.09) to 0.25 (0.12-0.39) and from 0.02 (-0.001-0.03) to 0.11 (0.04-0.17), respectively. Some 36.6 % of the association between age and risk of MDD during the pandemic was explained by loneliness (12.0 %), low resilience (10.7 %), and worsened economic situation (13.9 %).

Limitations

Reliance on self-report data and generalizability of the findings limited to the Spanish population.

Conclusions

Strategies to decrease the impact of a pandemic on depressive symptoms among young adults should address loneliness, provide tools to improve resilience, and enjoy improved financial support.

Keywords: Depression, Loneliness, Resilience, Economic situation, Young, COVID-19 pandemic

Abbreviations: MDD, Major Depressive Disorder; COVID-19, Coronavirus disease; CIDI, Composite International Diagnostic Interview

Graphical abstract

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1. Introduction

The COVID-19 pandemic is a global pandemic of coronavirus disease (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) (Yousefi et al., 2020). The pandemic has aggravated mental health problems, including depressive disorder symptoms in the general population (Chen et al., 2021; Li et al., 2020; Vindegaard and Benros, 2020; Xiong et al., 2020).

Depression affects approximately 280 million people, equivalent to 3.8 % of the world's population (WHO, 2021). Major depressive disorder (MDD) is among the leading causes of years lost to disability (James et al., 2018) and has been associated with diminished quality of life, medical morbidity, and worse physical and cognitive health (Ferrari et al., 2013; Herrman et al., 2002).

Several studies carried out during the COVID-19 pandemic report higher prevalence of depression among younger adults (Beutel et al., 2021; Morin et al., 2021; Nwachukwu et al., 2020; Varma et al., 2021; Xiong et al., 2020). The psychological, social, and economic effects of the pandemic may impact each age group differently. Therefore, it is critical to identify potential mediating factors that explain what makes younger adults more vulnerable to depression in a pandemic context, so that consistent public health and social measures can be developed accordingly.

Loneliness and low social support are predictors of depression (Cacioppo et al., 2010; Gariépy et al., 2016; Santini et al., 2015), and are of particular concern in the COVID-19 pandemic context. During the first year of the pandemic, public health and social measures to contain COVID-19's spread were based on physical distancing and stay-at-home orders, which may have led to changes in loneliness (Ernst et al., 2022).

Pre-pandemic data identified older adult populations at higher risk for loneliness, social isolation, and social network reduction due to age-related changes and life events affecting social relationships (e.g., losing a partner, moving to a nursing home, functional decline, a disease diagnosis, etc.) (Mikkelsen et al., 2019). Following the socioemotional selectivity theory (Carstensen, 1993), although social contact declines across adulthood, social goals change and the close and emotionally satisfying relationships prevail, and these may have remained more stable during the pandemic. In contrast, young people rely more on frequent and diverse social interactions which might have been more greatly affected by social restrictions (Carstensen, 1993; Nicolaisen and Thorsen, 2017). Studies carried during the COVID-19 pandemic identified younger adults as a high-risk group for loneliness and lower social support (Bu et al., 2020; Lee et al., 2020; Losada-Baltar et al., 2021; Varga et al., 2021; Varma et al., 2021).

Resilience is based on how people respond to challenges and adversities, and it has been negatively correlated with depression during the pandemic (Killgore et al., 2020; Ran et al., 2020). Older adults usually present a more stable and settled lifestyle, while young adults are still going through a critical period of interpersonal development, education, and career building, which makes them more vulnerable to economic crises and adverse experiences (Lee et al., 2020). Older adults have been found to be more resilient (Gooding et al., 2012; Losada-Baltar et al., 2021; Varma et al., 2021), especially with respect to emotional regulation ability and problem solving (Gooding et al., 2012).

Finally, the risk of depression in developed countries is associated with lower socioeconomic status (Rojas-García et al., 2015). The economic adversities caused by the COVID-19 pandemic, such as unemployment, low income, and financial strain, might exacerbate mental health problems in the short and long term (Liu et al., 2021; Margerison-Zilko et al., 2016). During the first weeks of lockdown in Spain, the unemployment rate for young adults (16-29 years) more than doubled, compared with the population aged 30 to 64 years (Injuve, 2020a). Thus, the global economic effects of the pandemic have impacted age groups differently, which may partially explain the mental health disorder increase among the younger.

Longitudinal studies comparing pre-pandemic data with pandemic data in representative samples of the general population are essential to determine the factors that account for increases in depression among younger populations.

We aimed to assess the association of age with changes in MDD risk between before and during the COVID-19 pandemic in two Spanish regions, and to quantify the effects of potential mediating variables such as loneliness, social support, resilience, and socioeconomic factors, on the observed associations.

2. Methods

2.1. Study design

We used data from the ‘Edad con Salud’ 2019 cohort (C19) (Edad con Salud, 2022; Lara et al., 2022), a representative sample of the noninstitutionalized adult population (18+ years) of Barcelona and Madrid, the two largest provinces in Spain. The C19 baseline data was collected between 2019 and 2021, and 3002 adults were interviewed. To achieve an appropriate representation of the Barcelona and Madrid populations, a stratified multistage clustered area probability method was used considering sex, age group, and municipality of residence. Trained professional interviewers administered structured face-to-face interviews with the use of computer-assisted personal interviewing (CAPI). Further details about the collection procedure can be found elsewhere (Lara et al., 2022).

For the present analyses, we only considered those participants that were interviewed before the COVID-19 pandemic broke out – between June 17, 2019 and March 14, 2020 – and who could answer the questionnaire without a proxy respondent (N = 1880, pre-pandemic data, termed T1). During the pandemic these participants were re-contacted to carry out a shorter survey with COVID-19-specific questions and mental health follow-up screenings. 1103 participants responded to this survey (during pandemic data, termed T2), showing a response rate of 58.7 %. These interviews were also performed by professional lay interviewers with computer-assisted telephone interviews (CATI) between May 21 and June 30, 2020.

2.2. Ethics statement

The authors declare that all procedures involved in this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Ethical approvals were obtained from the ethics review committees of Parc Sanitari Sant Joan de Déu (PIC-129-17), Barcelona, and Hospital Universitario La Princesa (register n°: 2801), Madrid. Informed consent was obtained from all participants at the two time points.

2.3. Measures

2.3.1. Main study variables

The current study focuses on major depressive disorder (MDD), which is characterized by a period of at least 2 weeks during which there is either depressed mood or the loss of interest or pleasure in nearly all activities (World Health Organization, 1993). MDD in the previous 12 months for the pre-pandemic interviews and in the previous 30 days in the interviews carried out during the pandemic were assessed with an adapted version of the Composite International Diagnostic Interview (CIDI 3.0) (Kessler and Üstün, 2004), a comprehensive, fully structured interview designed to be used by trained lay interviewers with algorithms based on the definition of depression and criteria of the International Classification of Diseases (ICD-10) (World Health Organization, 1993). Algorithms based on the ICD-10 for the assessment of a depressive episode require the following criteria to be fulfilled: (i) at least two of the following three symptoms are present: depressed mood, loss of interest, and decreased energy; (ii) an additional symptom or symptoms (i.e., loss of confidence and self-esteem, unreasonable feelings of self-reproach or guilt, recurrent thoughts of death, suicide, or any suicidal behavior, complaints or evidence of diminished ability to think or concentrate, change in the psychomotor activity, sleep disturbance of any type, and change in appetite with corresponding weight change) are present, having at least four symptoms in total; (iii) symptoms should last for at least 2 weeks; (iv) criteria for hypomanic or manic episode at any time in the individual's life have been discarded; and (v) the episode is not attributable to any psychoactive substance use or to any organic mental disorder.

Age at T1 was categorized into four groups: 18–29 years, 30–44 years, 45–64 years, and 65 years or older.

Loneliness was measured through the Spanish version of the three-item University of California, Los Angeles (UCLA) loneliness scale (Hughes et al., 2004; Sancho et al., 2020; Trucharte et al., 2021). Responses were summed up to generate a total score from 3 to 9, with a higher score indicating greater feelings of loneliness.

Social support was assessed using the Oslo Social Support Scale (OSSS-3) (Kocalevent et al., 2018). The total score was obtained by adding the responses of the three items and ranging them from 3 to 14, with higher values representing stronger levels of social support. Social support was categorized as low (3-8), moderate (9-11), and high (12-14) (Kocalevent et al., 2018).

Resilience was measured through the Spanish adaptation of the 6-item Brief Resilience Scale (BRS) (Rodríguez-Rey et al., 2016; Smith et al., 2008), which is designed to measure the ability to bounce back or recover from stress. It is a self-report scale with a 5-point response scale ranging from 1 (strongly disagree) to 5 (strongly agree). The responses were added up and divided by six, yielding a score from 1 to 5, with a higher score indicating a greater degree of resilience. The total score was dichotomized using a cut-off of ≥3 to determine normal/high vs. low resilience (Smith et al., 2013).

Finally, socioeconomic factors (i.e., worsened economic situation and unemployment) were assessed through direct questions. We asked participants if their economic situation worsened due to the COVID-19 pandemic and its consequences, and if the participants became (temporarily or permanently) unemployed due to the COVID-19 pandemic.

All variables were time-variant, so measures at T1 and T2 were considered in the analyses, except for the socioeconomic factors and resilience, which were only asked about in T2.

2.3.2. Covariates

All covariates were based on baseline data and were selected based on previous studies including variables with a statistically significant relationship with MDD during the COVID-19 pandemic; these were sociodemographic variables such as self-reported sex, age, educational level, partner status, physical and mental health-related variables, and socioeconomic indicators (González-Sanguino et al., 2020; Palgi et al., 2020). We also included province of residence as a covariate since the COVID-19 cases and the public health and social measures differed slightly between these two provinces (Villalonga, 2022).

General health was assessed with a validated metric composed of 45 items, comprising questions related to impairments in body functions, limitations in activities of daily living (ADL) and instrumental ADL, and a set of evaluations about cognitive functioning and walking speed. The health metric score ranges from 0 to 100, with higher values indicating a better health status (Sanchez-Niubo et al., 2020).

2.4. Statistical analysis

The statistical analyses were adjusted to the stratified study design. Post-stratification corrections were made to weights to adjust for the population distribution obtained from the national census (INE, 2022) and for survey non-response.

The study sample was characterized by descriptive analyses, which included weighted means and standard errors for continuous variables, and weighted proportions and unweighted frequencies for categorical variables. Differences between individuals with MDD in T1 and T2 were assessed with Student's t-test for general health and loneliness, and with χ2 tests for sex, age, educational level, partner status, province of residence, social support, resilience, worsened economic situation, and unemployment.

A mixed-effects logistic regression model was constructed to study changes in MDD depending on age group comparing pre-pandemic and during the pandemic data (T1 and T2). The model used age-group as a fixed factor, time point (T1 or T2) as a within-participants repeated factor, and participants ID as a random factor. The model tested the interaction between age group and time point with MDD (both before and during pandemic) as the outcome. The model was adjusted for sex, education, partner status, province of residence, and health. To interpret our results, probabilities for MDD depending on age and stratified by time point were calculated through margins (Williams, 2012). Control variables were centred at mean according to their distribution in the sample.

Several regression models were created to assess the association between age groups and potential mediating factors at T2 (i.e., social support, loneliness, resilience, worsened economic situation, and unemployment). Models were adjusted for the same control variables as the mixed model. For dichotomous outcomes (resilience, worsened economic situation, and unemployment), logistic regression models were constructed, whereas for the ordinal variables (social support and loneliness) ordered logistic regression models were employed. Models with loneliness and social support as an outcome included loneliness and social support at T1 as covariates, respectively. From these regression models, probabilities for each potential mediation factor depending on age were calculated through margins (Williams, 2012). In the case of social support and loneliness, we calculated the probabilities of not reporting any loneliness symptoms (i.e., UCLA loneliness score = 3) and reporting a low social support (i.e., OSSS-3 score < 9). Control variables were centered at mean according to their distribution in the sample.

To assess the mediating role of potential mediators (i.e., social support, loneliness, resilience, worsened economic situation, and unemployment) in the associations between age group and MDD, mediational analyses were performed using the Karlson-Holm-Breen (KHB) method (Breen et al., 2013), which divides the total effect of a variable into a direct and an indirect (i.e., mediational) effect. The mediation models' outcome was MDD at T2 and the models were adjusted for sex, educational level, partner status, province of residence, general health, mediating factors at T1 (except from resilience), and MDD at T1. The KHB “disentangle” option was applied to have a more detailed description of the mediational effects, as it shows how much of the difference between the total and the indirect effect is contributed by each mediator.

Stata statistical software was used to execute all the analyses.

3. Results

The overall characteristics of the study sample and the specific characteristics of those participants with major depressive disorder (MDD) before (T1) and during the pandemic (T2) are shown in Table 1 . The sample consisted of a nearly balanced proportion of females and males in both waves. The best represented age group was those between 45 and 64 years (30 % at T1), followed by those between 30 and 44 years (28 % at T1). The other two age groups (18-29 and 65+ years) represented around 21 % of the sample each. Slightly more than half of the sample had a higher-secondary (29 %) or tertiary (24 %) education level at T1, and 53 % were married or with a civil partner. The sample was almost equally distributed between Barcelona (52 %) and Madrid (48 %) and presented a mean general health of 74.8 out of 100. At baseline, 3.4 % of the study sample reported MDD and during the pandemic the MDD prevalence increased to 9.5 %. Participants with MDD at T2 were younger, mainly from Madrid province, with a higher educational level, better general health, and lower social support, when compared to participants with MDD at T1.

Table 1.

Characteristics of the whole study sample and of individuals with major depressive disorder (MDD) at T1 (pre-pandemic) and T2 (during the pandemic).

Characteristic Total sample T1 (N = 1880) Total sample T2 (N = 1103) MDD at T1 (n = 68, 3.36 %) MDD at T2 (n = 87, 9.50 %)
Sex, n(%)
Male 817 (47.74) 437 (46.92) 22 (36.78) 28 (41.10)
Female 1063 (52.26) 666 (53.08) 46 (63.22) 59 (58.90)



Age, n(%)
18-29 203 (21.49) 111 (17.97) 6 (18.29) 18 (27.00)
30-44 263 (27.61) 163 (25.64) 5 (15.11) 18 (30.72)
45-64 750 (30.43) 525 (38.38) 32 (44.54) 38 (34.25)
65+ 664 (20.47) 304 (18.02) 25 (22.07) 13 (8.03)



Education, n(%)
Less than primary 262 (8.76) 98 (6.23) 14 (12.07) 8 (7.06)
Primary 508 (21.10) 282 (20.93) 24 (30.76) 16 (14.59)
Lower-secondary 283 (17.50) 172 (16.45) 9 (19.05) 18 (23.49)
Higher-secondary 453 (29.12) 294 (30.90) 12 (19.89) 32 (35.81)
Tertiary 374 (23.52) 257 (25.49) 9 (18.23) 13 (19.04)



Partner status, n(%)
Not married/cohabiting 307 (25.89) 181 (24.77) 10 (24.61) 21 (31.69)
Married/civil partner 1100 (52.80) 639 (52.66) 37 (43.55) 42 (42.48)
Cohabiting 80 (6.62) 62 (8.11) 2 (5.71) 7 (10.81)
Separated/divorced 182 (8.14) 110 (7.94) 8 (16.66) 11 (10.56)
Widowed 211 (6.56) 111 (6.52) 11 (9.47) 6 (4.46)



Province of residence, n(%)
Barcelona 976 (52.33) 547 (49.86) 54 (82.25) 39 (46.46)
Madrid 904 (47.67) 556 (50.14) 14 (17.75) 48 (53.54)



Health (0−100), mean(SD) 74.84 (0.50) 74.01 (0.62) 50.09 (2.61) 65.72 (2.49)



Social support, n(%)
Low 154 (7.86) 70 (7.25) 4 (6.22) 13 (12.18)
Moderate 617 (34.31) 397 (41.65) 23 (36.83) 42 (51.77)
High 1037 (57.82) 523 (51.09) 39 (56.95) 28 (36.05)



Loneliness (3-9), mean(SD) 3.67 (0.04) 3.80 (0.05) 5.91 (0.36) 5.42 (0.22)



Resilience, n(%)
Low – 164 (15.42) – 47 (55.58)
Normal/high – 939 (84.58) – 40 (44.42)



Worsened economic situation, n(%)
No – 767 (65.16) – 42 (45.82)
Yes – 331 (34.84) – 45 (54.18)



Unemployment, n(%)
No – 920 (79.28) – 65 (69.84)
Yes – 179 (20.72) – 22 (30.16)
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Unweighted frequencies and weighted percentages are displayed for categorical variables, and weighted means with standard deviation (SD) for continuous variables. Some variables did not include all cases due to missing values.

Table 2 reports the mixed-model results showing a statistically significant interaction between age group and time point (T1 vs. T2) with MDD risk as the outcome. This interaction reveals that the MDD risk of the younger age groups (18–29 and 30–44 years) between T1 and T2 increased in a statistically significantly manner, while MDD odds remained stable among older age groups (45–64 and 65+ years). Fig. 1 represents the probability of having MDD according to the age group and time point. Among the younger age cohorts (18–29 and 30-44 years), the probability of having MDD increased from 0.04 (95 % CI 0.002–0.09) to 0.25 (95 % CI 0.12–0.39), and from 0.02 (95 % CI −0.001–0.03) to 0.11 (95 % CI 0.04–0.17), respectively.

Table 2.

Mixed logistic regression model of the association between age group and major depressive disorder (MDD) at T1 (before the pandemic) and T2 (during the pandemic).

MDD odds ratio (95 % CI)
Time point
T1 (pre-pandemic) Ref.
T2 (pandemic) 1.48 (0.72, 3.05)



Age (years)
65+ Ref.
45–64 2.04 (1.08, 3.86)⁎
30–44 1.55 (0.52, 4.65)
18–29 4.57 (1.47, 14.21)⁎⁎



Time × agea
T1 × 65+ Ref.
T2 × 45-64 1.22 (0.50, 2.95)
T2 × 30-44 5.12 (1.40, 18.75)⁎
T2 × 18-29 4.94 (1.43, 17.05)⁎
Intercept 0.44 (0.05, 4.32)
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a

Interaction term. 95 % CI: confidence interval. Ref. = category of reference (Odds Ratio = 1.00). Model adjusted for sex, educational level, partner status, province of residence, and health.

⁎

p < 0.05.

⁎⁎

p < 0.01.

Fig. 1.

Fig. 1

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Probability of having major depressive disorder (MDD) by age group and time point.

NOTE: Predicted margins from mixed model of Table 2. Control variables were centered at mean according to their distribution in the study sample

Fig. 2 shows probabilities for low social support, not being lonely, low resilience, worsened economic situation, and unemployment by age group. The younger groups (18–29 and 30–44 years) demonstrate a higher probability of being lonely, having low social support, and presenting low resilience. They were more likely to have a worsened economic situation and unemployment due to the COVID-19 pandemic. These potential mediation variables were included in the mediation models alone and with different variables to test all possible combinations. Table 3 presents the combination of variables accounting for the highest mediation percentage, which included loneliness (12.0 %), low resilience (10.7 %), and worsened economic situation (13.9 %), together explaining 36.6 % of the association between age group and risk for MDD during the COVID-19 pandemic.

Fig. 2.

Fig. 2

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Probabilities of potential risk factors for MDD by age group.

Probabilities with 95 % confidence interval obtained through margins calculation from logistic regression models. In the case of social support and loneliness, ordered logistic regression models were constructed and the probabilities of not reporting any loneliness symptoms (i.e., UCLA loneliness score = 3) and reporting a low social support (i.e., OSSS-3 score < 9) were calculated. All models were adjusted for sex, educational level, partner status, province of residence, health, and loneliness and social support at T1. Probabilities were calculated with covariates centered at mean according to their distribution in the study sample.

Table 3.

Association between age groups and major depressive disorder (MDD) at T2, with loneliness, resilience, and economic situation at T2 as mediators (KHB method).

Coefficient (95 % CI) % Mediated
Mediation model
Total −0.80 (−1.27, −0.32)⁎⁎⁎
Direct −0.51 (−0.97, −0.05)⁎
Indirect: −0.29 (−0.46, −0.12)⁎⁎⁎ 36.55 %
Loneliness −0.10 (−0.17, −0.03) 11.96 %
Resilience −0.09 (−0.19, 0.02) 10.67 %
Economic situation −0.11 (−0.21, −0.01) 13.93 %
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CI: confidence interval. All models were adjusted for sex, educational level, partner status, province of residence, health, and MDD and loneliness at T1.

⁎

p < 0.05.

⁎⁎⁎

p < 0.001.

4. Discussion

To the best of our knowledge, this is the first study to examine the association between age and changes in MDD risk before and during the COVID-19 pandemic, as well as potential mediating factors in the association based on longitudinal data. Our results confirm the increase in depression during the pandemic when compared with pre-pandemic data among young adults (18–44 years) and specifically, among the youngest (18–29 years). Regarding the studied potential mediating variables (i.e., social support, loneliness, resilience, worse economic situation, and unemployment), the younger population had a higher probability of having worse outcomes. More than one third of the relationship of being younger and having a higher probability of MDD during the pandemic was explained by loneliness, worsened economic situation, and resilience, which are modifiable factors that could be targeted to reduce MDD among this vulnerable age-group.

In line with our results, several studies have found young adults to be at higher risk of depression during pandemic (Beutel et al., 2021; Morin et al., 2021; Nwachukwu et al., 2020; Varma et al., 2021). Bu et al. (2020) examined the loneliness trajectories and predictors during the COVID-19 pandemic and found that the odds of being in a higher loneliness cluster were greater in an inverse dose-response pattern with age, i.e. younger adults were at higher risk of loneliness compared to older adults, which was also confirmed by other studies (Varga et al., 2021). Loneliness is a well-known predictor of mental health disorders, but it has usually been reported and studied among old-age populations. Our results indicate that loneliness explained part of the association between younger age and higher MDD, which underlines the necessity of a deeper evaluation of potential interventions targeting feelings of loneliness among young adults. Conversely, social support was not a significant mediator variable in the association between age group and MDD, which could be explained by the fact that the pandemic has had a greater impact on subjective factors of social relationships like loneliness, rather than on objective factors such as the available social support. In addition, previous studies have reported that loneliness is a mediating variable in the association between social support and mental health (Gabarrell-Pascuet et al., 2022; Santini et al., 2016) and a recent meta-analysis on the association of social support and loneliness with mental health during the pandemic, found that social support had a weak association with depressive symptoms, while the association with loneliness was moderate (Gabarrell-Pascuet et al., 2023). Therefore, when it comes to mental health, social relationships in an objective sense might not be as important as the perception one has of them.

Resilience results are also coherent with prior studies done during the pandemic (Ran et al., 2020). Resilience acts as a protective factor for depression and is usually stronger among older adults, who in our sample had lower probabilities of having low resilience. In contrast, younger adults had a higher probability of having low resilience, which mediated part of the association between age and MDD risk, so younger adults were more prone to report MDD. Resilience is dynamic and can be trained, so interventions promoting the development of resilience among youth might prevent the long-term mental health effects of the COVID-19 pandemic and future hazards.

Older adults were less affected by the economic impact of the pandemic and had more means to deal with them. In Spain, the official retirement age is 67, so most of the sample of the 65+ age group was retired, and consequently less affected by the layoffs. On the other hand, the higher unemployment due to the COVID-19 pandemic and worsened economic situation among younger adults might be explained by the fact that they usually work in more vulnerable sectors, such as tourism, catering, services, trade, and manufacture, with seasonal or temporary contracts, and are hired by private companies or small businesses, which were more vulnerable during the pandemic (Injuve, 2020b). Moreover, young people and short-term workers benefited less during the pandemic from the temporary work suspension, known as ERTE in Spain. The rates of re-entry into active employment of those affected by ERTE were lower for young people. Worsened economic situation due to the pandemic explains the association between age and MDD, while unemployment does not. This could be explained by young people having financial struggles despite being employed, as they might have kept their jobs but with lowered financial benefits (e.g., reduction in both their working hours and their salaries) (Arce, 2021). Moreover, the progressive delay in the age of emancipation and the fall in the rate of home ownership among young people in Spain have left many young adults economically dependent on their parents' employment situation. Finally, we must also bear in mind that financial hardships, in addition to fostering depressive symptoms, can prevent accessibility to adequate mental health care.

Finally, it is important to bear in mind that interventions to target these modifiable factors in practice are not simple to currently apply and even less so in contexts such as the pandemic. However, successful initiatives have been carried out in these contexts. In the case of addressing loneliness, there are many psychosocial interventions that have shown to be effective in reducing feelings of loneliness (Veronese et al., 2021), and some of them could be feasible in a pandemic context through telematic means (Hickin et al., 2021). Regarding resilience, there have been promising online interventions to increase psychological resilience in response to the pandemic (Wang et al., 2021; Zhang et al., 2022). Currently, psychological care in Spain is suffering from an overflow, so public health measures to increase the number of visits each person receives and the professional-to-patient ratio are needed to improve mental health care and compensate the saturation of these services (Ballescà et al., 2022). In addition, it is important that policies consider the social complexity that influences mental health, offering shared strategies outside the strictly health field, such as from social services, employment offices, or educational centres, focusing also on the socioeconomic effects of the pandemic. It is necessary to assess the cost-utility of these interventions and strategies in order to evaluate which aspects should be prioritized.

The high rates of depression during the COVID-19 pandemic among young adults, together with its expected long-term consequences, highlight the need for understanding the potential factors that may have contributed to the increase. The present study was intended to contribute to improved understanding of their role and influence, together with the identification of vulnerable age-groups, which can help in the design and implementation of public health strategies and psychological and social interventions that directly address these mediating factors.

4.1. Limitations and strengths

The strengths of this study include its large sample size and heterogeneity, including good stratification across all major socio-demographic groups. Moreover, the study was based on pre-pandemic and during-pandemic assessments of the same population, using the same questionnaires, which enables the comparability of various factors. The study controls for the main confounding factors and assesses the study variables with a range of validated scales. Nevertheless, these results must be interpreted in light of several limitations. First, our data are based on self-reported measurements, so reporting or recall bias could be present, particularly in the COVID-19 pandemic context, which could distort participants' perceptions and increase the chances for these biases. However, in our study recall periods were short and well-defined, which minimizes recall bias. The main study outcome (MDD) was measured with the CIDI 3.0 by lay interviewers who received a specific training (Lara et al., 2022) and, although they often lack clinical experience, the outcome screening was done by researchers according to an algorithm combining criteria based on the ICD-10. Moreover, a previous study found no evidence for systematic bias in the diagnostic threshold for depression by the CIDI 3.0 (Haro et al., 2006). Second, the pre-pandemic data were collected through face-to-face interviews, while the pandemic data were collected through telephone interviews due to the restrictions that prevented in-person contacts. This methodological difference could also be linked to differences regarding social desirability bias. Third, socioeconomic factors were measured with single-item and non-validated direct questions, which limit the reliability of these constructs; nonetheless the assessed constructs were unidimensional and clearly defined, overcoming part of the bias that could be associated with single-item measures. Moreover, several articles about the impact of the COVID-19 pandemic on mental health used similar measures to assess changes in financial or employment situation due to the pandemic (Codagnone et al., 2020; Zajacova et al., 2020), which allows comparability between studies. Fourth, our study population was from the two largest provinces of Spain, leaving out participants from rural areas that have been found to be protective against feelings of loneliness during the pandemic (Bu et al., 2020); this could have been an additional factor to consider in our analysis. Finally, the generalizability of our results is limited to the period between 2019 and the initial months of the pandemic in Spain. Future longitudinal studies with longer follow-up periods in Spain (e.g., it is planned to reinterview the cohort of the present study in 2023) and from other countries should investigate to what extent the detected differences remain in the medium and long term and whether they occur in other countries that applied different policies to control the pandemic, with different cultural perceptions, and socioeconomic conditions.

5. Conclusions

The results of our study show that loneliness has been an important explanatory factor for the increase in mental health problems among young adults during the pandemic. The younger population has also been affected by the socioeconomic consequences of the pandemic to a greater extent and have shown lower psychological resilience to stressors. Over the coming months and years, we will assess whether the impact of the pandemic on mental health remains, and we will study the need and possibility of implementing strategies focused on the detected risk factors.

Role of the funding source

This study was supported by the EU Horizon 2020 Framework Program for Research and Innovation (635,316 [ATHLOS Project]), and by The Joint Programming Initiative “More Years, Better Lives—The Potential and Challenges of Demographic Change.” It was funded by the “Acciones de Programación Conjunta Internacional 2016” program (PCIN-2016-118) of the Spanish Research Agency (AEI) of the Spanish Ministry of Science and Innovation, by the European Community's Seventh Framework Program (FP7/2007 2013) under agreement number 223071 (COURAGE in Europe), by the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009-1010), and by the Instituto de Salud Carlos III-FIS research grants (PS09/00295, PS09/01845, PI12/01490, PI13/00059, PI16/00218, PI16/01073, and PI16/00177). Projects PI12/01490, PI13/00059, PI16/00218, PI16/01073, and PI16/00177 have been co-funded by the European Union European Regional Development Fund “A Way to Build Europe”. The study was also supported by the Instituto de Salud Carlos III Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). Aina Gabarrell-Pascuet's work is supported by the Secretariat of Universities and Research of the Generalitat de Catalunya and the European Social Fund (2021 FI_B 00839). Joan Domènech-Abella and Elvira Lara have a “Juan de la Cierva” research contract awarded by the Spanish Ministry of Science and Innovation (MCIU: FJC2019-038955-I and IJC2019-041846-I, respectively). Tibor V. Varga is funded by the Department of Public Health, University of Copenhagen.

CRediT authorship contribution statement

Aina Gabarrell-Pascuet: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Tibor V. Varga: Methodology, Formal analysis, Writing – review & editing. María Victoria Moneta: Data curation. José Luis Ayuso-Mateos: Project administration, Funding acquisition. Elvira Lara: Writing – review & editing, Funding acquisition, Project administration. Beatriz Olaya: Project administration, Funding acquisition. Josep Maria Haro: Writing – review & editing, Supervision, Project administration, Funding acquisition. Joan Domènech-Abella: Conceptualization, Methodology, Formal analysis, Investigation, Writing – review & editing, Supervision, Project administration.

Conflict of interest

The authors declare no conflict of interests.

Acknowledgements

The authors would like to express special gratitude to all the participants for their generous contribution, which made this study possible. The authors thank Thomas Yohannan for the help in English language editing.

Data availability statement

Data will be made available upon request made to the corresponding author.

References

  1. Arce Ó. La crisis del COVID-19 y su impacto sobre las condiciones económicas de las generaciones jóvenes. 2021. https://transparencia.gob.es/transparencia/transparencia_Home/index/MasInformacion/Informes-de-interes/Economia/Covid19_Economia_Jovenes.html
  2. Ballescà R., Martínez Roig A., Guarino F., Garcia G., Belchi I., Chavanel Pastor Fapaes Catalunya Javier González J., Planagumà L., Ribas M., Isabel Fernández Servei Inclusiu M., Ruiz de Gauna R., Cusco Segarra R., Jiménez R. 2022. La salut mental de la infància i l’adolescència a Catalunya: situació actual, mancances i propostes. September 13. [Google Scholar]
  3. Beutel M.E., Hettich N., Ernst M., Schmutzer G., Tibubos A.N., Braehler E. Mental health and loneliness in the german general population during the COVID-19 pandemic compared to a representative pre-pandemic assessment. Sci. Rep. 2021;11(1):14946. doi: 10.1038/S41598-021-94434-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Breen R., Karlson K.B., Holm A. Total, direct, and indirect effects in logit and probit models. Sociol. Methods Res. 2013;42(2):164–191. doi: 10.1177/0049124113494572. [DOI] [Google Scholar]
  5. Bu F., Steptoe A., Fancourt D. Loneliness during a strict lockdown: trajectories and predictors during the COVID-19 pandemic in 38,217 United Kingdom adults. Soc. Sci. Med. 2020;265 doi: 10.1016/J.SOCSCIMED.2020.113521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cacioppo J.T., Hawkley L.C., Thisted R.A. Perceived social isolation makes me sad: 5-year cross-lagged analyses of loneliness and depressive symptomatology in the Chicago health, aging, and social relations study. Psychol. Aging. 2010;25(2):453–463. doi: 10.1037/a0017216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Carstensen L.L. University of Nebraska Press; 1993. Motivation for social contact across the life span: A theory of socioemotional selectivity.https://psycnet.apa.org/record/1993-98639-005 [PubMed] [Google Scholar]
  8. Chen P.J., Pusica Y., Sohaei D., Prassas I., Diamandis E.P. An overview of mental health during the COVID-19 pandemic. Diagnosis. 2021;8(4):403–412. doi: 10.1515/DX-2021-0046/ASSET/GRAPHIC/J_DX-2021-0046_FIG_003.JPG. [DOI] [PubMed] [Google Scholar]
  9. Codagnone C., Bogliacino F., Gomez C., Charris R., Montealegre F., Liva G., Lupiañez-Villanueva F., Folkvord F., Veltri G.A. Assessing concerns for the economic consequence of the COVID-19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United Kingdom. PLOS ONE. 2020;15(10) doi: 10.1371/JOURNAL.PONE.0240876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Edad con Salud Envejecer con salud. 2022. http://edadconsalud.com/?lang=en
  11. Ernst M., Niederer D., Werner A.M., Czaja S.J., Mikton C., Ong A.D., Rosen T., Brähler E., Beutel M.E., Ernst C. Loneliness before and during the COVID-19 pandemic: a systematic review with meta-analysis. Am. Psychol. 2022 doi: 10.1037/AMP0001005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Ferrari A.J., Charlson F.J., Norman R.E., Patten S.B., Freedman G., Murray C.J.L., Vos T., Whiteford H.A. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10(11) doi: 10.1371/JOURNAL.PMED.1001547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gabarrell-Pascuet A., Moneta M.V., Ayuso-Mateos J.L., Miret M., Lara E., Haro J.M., Olaya B., Domènech-Abella J. The effect of loneliness and social support on the course of major depressive disorder among adults aged 50 years and older: a longitudinal study. Depress. Anxiety. 2022;39(2):147–155. doi: 10.1002/DA.23236. [DOI] [PubMed] [Google Scholar]
  14. Gabarrell-Pascuet A., García-Mieres H., Giné-Vázquez I., Moneta M.V., Koyanagi A., Haro J.M., Domènech-Abella J. The Association of Social Support and Loneliness with symptoms of depression, anxiety, and posttraumatic stress during the COVID-19 pandemic: a meta-analysis. Int. J. Environ. Res. Public Health. 2023;20(4):2765. doi: 10.3390/ijerph20042765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gariépy G., Honkaniemi H., Quesnel-Vallée A. Social support and protection from depression: systematic review of current findings in Western countries. Br. J. Psychiatry. 2016;209(4):284–293. doi: 10.1192/BJP.BP.115.169094. [DOI] [PubMed] [Google Scholar]
  16. González-Sanguino C., Ausín B., Castellanos M.Á., Saiz J., López-Gómez A., Ugidos C., Muñoz M. Mental health consequences during the initial stage of the 2020 coronavirus pandemic (COVID-19) in Spain. Brain Behav. Immun. 2020;87:172–176. doi: 10.1016/j.bbi.2020.05.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gooding P.A., Hurst A., Johnson J., Tarrier N. Psychological resilience in young and older adults. Int. J. Geriatr. Psychiatry. 2012;27(3):262–270. doi: 10.1002/GPS.2712. [DOI] [PubMed] [Google Scholar]
  18. Haro J.M., Arbabzadeh-Bouchez S., Brugha T.S., de Girolamo G., Guyer M.E., Jin R., Lepine J.P., Mazzi F., Reneses B., Vilagut G., Sampson N.A., Kessler R.C. Concordance of the composite international diagnostic interview version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO world mental health surveys. Int. J. Methods Psychiatr. Res. 2006;15(4):167. doi: 10.1002/MPR.196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Herrman H., Patrick D.L., Diehr P., Martin M.L., Fleck M., Simon G.E., Buesching D.P. Longitudinal investigation of depression outcomes in primary care in six countries: the LIDO study. Functional status, health service use and treatment of people with depressive symptoms. Psychol. Med. 2002;32(5):889–902. doi: 10.1017/S003329170200586X. [DOI] [PubMed] [Google Scholar]
  20. Hickin N., Käll A., Shafran R., Sutcliffe S., Manzotti G., Langan D. The effectiveness of psychological interventions for loneliness: a systematic review and meta-analysis. Clin. Psychol. Rev. 2021;88 doi: 10.1016/J.CPR.2021.102066. [DOI] [PubMed] [Google Scholar]
  21. Hughes M.E., Waite L.J., Hawkley L.C., Cacioppo J.T. A short scale for measuring loneliness in large surveys: results from two population-based studies. Res. Aging. 2004;26(6):655–672. doi: 10.1177/0164027504268574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. INE . Instituto Nacional Estadística; 2022. Cifras oficiales de población resultantes de la revisión del Padrón municipal.https://www.ine.es/en/ [Google Scholar]
  23. Injuve I.de la J. Juventud en riesgo: análisis de las consecuencias socioeconómicas de la COVID-19 sobre la población joven en España. Informe 1. 2020. http://www.injuve.es/observatorio/formacion-empleo-y-vivienda/estudio-consecuencias-economicas-covid-19-en-la-juventud
  24. Injuve I.de la J. Juventud en riesgo. Análisis de las consecuencias socioeconómicas de la COVID-19 sobre la población joven en España. Informe 2. 2020. http://www.injuve.es/observatorio/formacion-empleo-y-vivienda/consecuencias-economicas-covid-19-en-la-juventud-informe-2
  25. James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A., Abdollahpour I., Abdulkader R.S., Abebe Z., Abera S.F., Abil O.Z., Abraha H.N., Abu-Raddad L.J., Abu-Rmeileh N.M.E., Accrombessi M.M.K., Murray C.J.L.… Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1789–1858. doi: 10.1016/S0140-6736(18)32279-7/ATTACHMENT/DB6E3413-74DC-43AE-B7CC-CA155C28589E/MMC2.PDF. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kessler R.C., Üstün B.B. The world mental health (WMH) survey initiative version of the World Health Organization (WHO) composite international diagnostic interview (CIDI) Int. J. Methods Psychiatr. Res. 2004;13(2):93. doi: 10.1002/MPR.168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Killgore W.D.S., Taylor E.C., Cloonan S.A., Dailey N.S. Psychological resilience during the COVID-19 lockdown. Psychiatry Res. 2020;291 doi: 10.1016/J.PSYCHRES.2020.113216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kocalevent R.D., Berg L., Beutel M.E., Hinz A., Zenger M., Härter M., Nater U., Brähler E. Social support in the general population: standardization of the Oslo social support scale (OSSS-3) BMC Psychol. 2018;6(1):31. doi: 10.1186/s40359-018-0249-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lara E., Miret M., Olaya B., Caballero F., Morillo D., Moneta V., Maria Haro J., Ayuso-Mateos J.L. Cohort profile: the spanish longitudinal study on ageing and health (Edad con Salud) Int. J. Epidemiol. 2022 doi: 10.1093/IJE/DYAC118. [DOI] [PubMed] [Google Scholar]
  30. Lee C.M., Cadigan J.M., Rhew I.C. Increases in loneliness among young adults during the COVID-19 pandemic and association with increases in mental health problems. J. Adolesc. Health. 2020;67(5):714. doi: 10.1016/J.JADOHEALTH.2020.08.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Li J., Yang Z., Qiu H., Wang Y., Jian L., Ji J., Li K. Anxiety and depression among general population in China at the peak of the COVID-19 epidemic. World Psychiatry. 2020;19(2):249–250. doi: 10.1002/WPS.20758. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Liu S., Haucke M.N., Heinzel S., Heinz A. Long-term impact of economic downturn and loneliness on psychological distress: triple crises of COVID-19 pandemic. J. Clin. Med. 2021;10(19):4596. doi: 10.3390/JCM10194596. 2021, Vol. 10, Page 4596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Losada-Baltar A., Jiménez-Gonzalo L., Gallego-Alberto L., Pedroso-Chaparro M.D.S., Fernandes-Pires J., Márquez-González M. “We’re staying at home”. Association of self-perceptions of aging, personal and family resources and loneliness with psychological distress during the lock-down period of COVID-19. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 2021;76(2):E10–E16. doi: 10.1093/GERONB/GBAA048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Margerison-Zilko C., Goldman-Mellor S., Falconi A., Downing J. Health impacts of the great recession: a critical review. Curr. Epidemiol. Rep. 2016;3(1):81. doi: 10.1007/S40471-016-0068-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mikkelsen A.S.B., Petersen S., Dragsted A.C., Kristiansen M. Social interventions targeting social relations among older people at nursing homes: a qualitative synthesized systematic review. Inquiry (United States) 2019;56 doi: 10.1177/0046958018823929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Morin C.M., Bjorvatn B., Chung F., Holzinger B., Partinen M., Penzel T., Ivers H., Wing Y.K., Chan N.Y., Merikanto I., Mota-Rolim S., Macêdo T., de Gennaro L., Léger D., Dauvilliers Y., Plazzi G., Nadorff M.R., Bolstad C.J., Sieminski M., Espie C.A.… Insomnia, anxiety, and depression during the COVID-19 pandemic: an international collaborative study. Sleep Med. 2021;87:38. doi: 10.1016/J.SLEEP.2021.07.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Nicolaisen M., Thorsen K. What are friends for? Friendships and loneliness over the lifespan - from 18 to 79 years. Int. J. Aging Hum. Dev. 2017;84(2):126–158. doi: 10.1177/0091415016655166. [DOI] [PubMed] [Google Scholar]
  38. Nwachukwu I., Nkire N., Shalaby R., Hrabok M., Vuong W., Gusnowski A., Surood S., Urichuk L., Greenshaw A.J., Agyapong V.I.O. COVID-19 pandemic: age-related differences in measures of stress, anxiety and depression in Canada. Int. J. Environ. Res. Public Health. 2020;17(17):1–10. doi: 10.3390/IJERPH17176366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Palgi Y., Shrira A., Ring L., Bodner E., Avidor S., Bergman Y., Cohen-Fridel S., Keisari S., Hoffman Y. The loneliness pandemic: loneliness and other concomitants of depression, anxiety and their comorbidity during the COVID-19 outbreak. J. Affect. Disord. 2020;275(April):109–111. doi: 10.1016/j.jad.2020.06.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Ran L., Wang W., Ai M., Kong Y., Chen J., Kuang L. Psychological resilience, depression, anxiety, and somatization symptoms in response to COVID-19: a study of the general population in China at the peak of its epidemic. Soc. Sci. Med. 2020;262 doi: 10.1016/J.SOCSCIMED.2020.113261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Rodríguez-Rey R., Alonso-Tapia J., Hernansaiz-Garrido H. Research on translations of tests: reliability and validity of the brief resilience scale (BRS) spanish version. Psychol. Assess. 2016;28(5):e101–e110. doi: 10.1037/PAS0000191. [DOI] [PubMed] [Google Scholar]
  42. Rojas-García A., Ruiz-Perez I., Rodríguez-Barranco M., Gonçalves Bradley D.C., Pastor-Moreno G., Ricci-Cabello I. Healthcare interventions for depression in low socioeconomic status populations: a systematic review and meta-analysis. Clin. Psychol. Rev. 2015;38:65–78. doi: 10.1016/J.CPR.2015.03.001. [DOI] [PubMed] [Google Scholar]
  43. Sanchez-Niubo A., Forero C.G., Wu Y.-T., Giné-Vázquez I., Prina M., de La Fuente J., Daskalopoulou C., Critselis E., de La Torre-Luque A., Panagiotakos D., Arndt H., Ayuso-Mateos J.L., Bayes-Marin I., Bickenbach J., Bobak M., Caballero F.F., Chatterji S., Egea-Cortés L., García-Esquinas E., Haro J.M.… Development of a common scale for measuring healthy ageing across the world: results from the ATHLOS consortium. Int. J. Epidemiol. 2020 doi: 10.1093/ije/dyaa236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sancho P., Pinazo-Hernandis S., Donio-Bellegarde M., Tomás J.M. Validation of the University of California, Los Angeles loneliness scale (version 3) in spanish older population: an application of exploratory structural equation modelling. Aust. Psychol. 2020;55(3):283–292. doi: 10.1111/ap.12428. [DOI] [Google Scholar]
  45. Santini Z.I., Koyanagi A., Tyrovolas S., Mason C., Haro J.M. The association between social relationships and depression: a systematic review. J. Affect. Disord. 2015;175:53–65. doi: 10.1016/J.JAD.2014.12.049. [DOI] [PubMed] [Google Scholar]
  46. Santini Z.I., Fiori K.L., Feeney J., Tyrovolas S., Haro J.M., Koyanagi A. Social relationships, loneliness, and mental health among older men and women in Ireland: a prospective community-based study. J. Affect. Disord. 2016;204:59–69. doi: 10.1016/j.jad.2016.06.032. [DOI] [PubMed] [Google Scholar]
  47. Smith B.W., Dalen J., Wiggins K., Tooley E., Christopher P., Bernard J. The brief resilience scale: assessing the ability to bounce back. Int. J. Behav. Med. 2008;15(3):194–200. doi: 10.1080/10705500802222972. 2008 15:3. [DOI] [PubMed] [Google Scholar]
  48. Smith B.W., Epstein E.M., Ortiz J.A., Christopher P.J., Tooley E.M. The Foundations of Resilience: What Are the Critical Resources for Bouncing Back from Stress? 2013. pp. 167–187. [DOI] [Google Scholar]
  49. Trucharte A., Calderón L., Cerezo E., Contreras A., Peinado V., Valiente C. Three-item loneliness scale: psychometric properties and normative data of the spanish version. Curr. Psychol. 2021;1–9 doi: 10.1007/s12144-021-02110-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Varga T.V., Bu F., Dissing A.S., Elsenburg L.K., Bustamante J.J.H., Matta J., van Zon S.K.R., Brouwer S., Bültmann U., Fancourt D., Hoeyer K., Goldberg M., Melchior M., Strandberg-Larsen K., Zins M., Clotworthy A., Rod N.H. Loneliness, worries, anxiety, and precautionary behaviours in response to the COVID-19 pandemic: a longitudinal analysis of 200,000 Western and Northern Europeans. Lancet Reg. Health - Europe. 2021;2 doi: 10.1016/J.LANEPE.2020.100020/ATTACHMENT/40857BD8-A439-4353-82FF-5AD1D12C4A0C/MMC6.XLSX. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Varma P., Junge M., Meaklim H., Jackson M.L. Younger people are more vulnerable to stress, anxiety and depression during COVID-19 pandemic: a global cross-sectional survey. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2021;109 doi: 10.1016/J.PNPBP.2020.110236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Veronese N., Galvano D., D’Antiga F., Vecchiato C., Furegon E., Allocco R., Smith L., Gelmini G., Gareri P., Solmi M., Yang L., Trabucchi M., de Leo D., Demurtas J. Interventions for reducing loneliness: an umbrella review of intervention studies. Health Soc. Care Community. 2021;29(5):e89–e96. doi: 10.1111/HSC.13248. [DOI] [PubMed] [Google Scholar]
  53. Villalonga C. Madrid y Catalunya: una pandemia, dos gestiones. La Vanguardia. 2022. https://stories.lavanguardia.com/vida/20210311/36010/medidas-covid-madrid-catalunya
  54. Vindegaard N., Benros M.E. COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain Behav. Immun. 2020;89:531–542. doi: 10.1016/J.BBI.2020.05.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Wang K., Goldenberg A., Dorison C.A., Miller J.K., Uusberg A., Lerner J.S., Gross J.J., Agesin B.B., Bernardo M., Campos O., Eudave L., Grzech K., Ozery D.H., Jackson E.A., Garcia E.O.L., Drexler S.M., Jurković A.P., Rana K., Wilson J.P., Moshontz H. A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic. Nat. Hum. Behav. 2021;5(8):1089–1110. doi: 10.1038/s41562-021-01173-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. WHO . World Health Organization; 2021. Depression.https://www.who.int/news-room/fact-sheets/detail/depression September 13. [Google Scholar]
  57. Williams R. Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects: Doi:10.1177/1536867X1201200209. 12(2) 2012. pp. 308–331. [DOI] [Google Scholar]
  58. World Health Organization The ICD-10 classification of mental and behavioural disorders : diagnostic criteria for research. 1993. https://apps.who.int/iris/handle/10665/37108
  59. Xiong J., Lipsitz O., Nasri F., Lui L.M.W., Gill H., Phan L., Chen-Li D., Iacobucci M., Ho R., Majeed A., McIntyre R.S. Impact of COVID-19 pandemic on mental health in the general population: a systematic review. J. Affect. Disord. 2020;277:55–64. doi: 10.1016/J.JAD.2020.08.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Yousefi B., Valizadeh S., Ghaffari H., Vahedi A., Karbalaei M., Eslami M. A global treatments for coronaviruses including COVID-19. J. Cell. Physiol. 2020;235(12):9133–9142. doi: 10.1002/JCP.29785. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Zajacova A., Jehn A., Stackhouse M., Choi K.H., Denice P., Haan M., Ramos H. Mental health and economic concerns from march to may during the COVID-19 pandemic in Canada: insights from an analysis of repeated cross-sectional surveys. SSM - Popul. Health. 2020;12 doi: 10.1016/J.SSMPH.2020.100704. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Zhang N., Yang S., Jia P. Cultivating Resilience During the COVID-19 Pandemic: A Socioecological Perspective. Doi:10.1146/Annurev-Psych-030221-031857. Vol. 73. 2022. pp. 575–598. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data will be made available upon request made to the corresponding author.

Articles from Journal of Affective Disorders are provided here courtesy of Elsevier

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