Epidemiology and Psychiatric Changes in depression and suicidal ideation Sciences under severe lockdown restrictions during the cambridge.org/eps first wave of the COVID-19 pandemic in Spain: a longitudinal study in the general population Original Article J. L. Ayuso-Mateos1,2,3 , D. Morillo1,2,3 , J. M. Haro2,4 , B. Olaya2,4 , Cite this article: Ayuso-Mateos JL, Morillo D, E. Lara1,2,3 and M. Miret1,2,3 Haro JM, Olaya B, Lara E, Miret M (2021). Changes in depression and suicidal ideation 1 Department of Psychiatry, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS- under severe lockdown restrictions during the Princesa), Madrid, Spain; 2Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, first wave of the COVID-19 pandemic in Spain: CIBERSAM, Madrid, Spain; 3Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain and 4Parc a longitudinal study in the general population. Sanitari Sant Joan de Déu, Universitat de Barcelona, Sant Boi de Llobregat, Barcelona, Spain Epidemiology and Psychiatric Sciences 30, e49, 1–9. https://doi.org/10.1017/ S2045796021000408 Abstract Received: 9 February 2021 Aims. To assess whether there is a change in the prevalence of depression and suicidal idea- Revised: 24 May 2021 tion after the strict lockdown measures due to the first wave of the COVID-19 pandemic in Accepted: 25 May 2021 Spain; and to assess which are the factors associated with the incidence of a depressive episode or suicidal ideation during the lockdown. Keywords: Depression; population survey; risk factors; Methods. Data from a longitudinal adult population-based cohort from the provinces of suicide Madrid and Barcelona were analysed (n = 1103). Structured face-to-face home-based inter- views (pre-pandemic) and telephone interviews were performed. Both depression and suicidal Author for correspondence: ideation were assessed through an adaptation of the Composite International Diagnostic Elvira Lara, E-mail: elvira.lara@uam.es Interview (CIDI 3.0). A variety of validated instruments and sociodemographic variables including age, sex, educational level, occupational status, home quietness, screen time, resilience, loneliness, social support, physical activity, disability, economic situation and COVID-19-related information were also considered. Population prevalence estimates and multivariable logistic regressions were computed. Results. Overall, prevalence rates of depression and suicidal ideation did not change signifi- cantly from before to after the COVID-19 outbreak. However, the rates of depression among individuals aged 50+ years showed a significant decrease compared to before the pandemic (from 8.48 to 6.41%; p = 0.01). Younger individuals (odds ratio (OR) = 0.97 per year older; 95% confidence interval (CI) = 0.95–0.99) and those feeling loneliness (OR = 1.96; 95% CI = 1.42–2.70) during the lockdown were at an increased risk of developing depression during the confinement. Resilience showed a protective effect against the risk of depression (OR = 0.46; 95% CI = 0.32–0.66) and suicidal ideation (OR = 0.33; 95% CI = 0.16–0.68), whereas individuals perceiving social support were at a lower risk of developing suicidal thoughts (OR = 0.35; 95% CI = 0.18–0.69). Conclusions. Continuous reinforcement of mental health preventive and intervening measures during and in the aftermath of the crisis is of global importance, particularly among vulnerable groups who are experiencing the most distress. Future research should strive to evaluate the long-term effects of the COVID-19 crisis on mental health. Introduction The COVID-19 outbreak and the policies to prevent its spread have disrupted the daily living of the population. The evidence regarding the mental health consequences of the confinement due to the COVID-19 pandemic in the general population is inconclusive (O’Connor et al., 2020; Bueno-Notivol et al., 2021; Faust et al., 2021; Prati and Mancini, 2021; Tanaka and © The Author(s), 2021. Published by Okamoto, 2021; van der Velden et al., 2021). Cambridge University Press. This is an Open The lockdown in Spain was one of the most restrictive in Europe (García-Esquinas et al., Access article, distributed under the terms of 2021). The Government imposed a State of Alarm starting on 15th March that established a the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), national lockdown that included the imposition of distancing measures such as the closure of which permits unrestricted re-use, distribution non-essential customer-facing businesses and educational institutions (Real Decreto, 463/ and reproduction, provided the original article 2020). In order to avoid the saturation of the intensive care units, theses initial measures is properly cited. were strengthened with another decree from the Government on 29th March (Real Decreto, 10/2020). A period of 5 weeks started in which citizens were only allowed to leave their homes for essential work, to buy food and other staple products or for emergencies. On 4th May, citizens were first authorised to leave their homes to exercise or walk, for a maximum of 1 h a day, under strict conditions. From 10th May to 21st June, a progressive de-escalation 2 J. L. Ayuso‐Mateos et al. of confinement measures led to the so-called ‘new normality’ in the Clinical Research Ethics Review Committees of Parc Sanitari which Spaniards were allowed to attend their jobs, gather in Sant Joan de Déu (Barcelona) and Hospital Universitario La small groups, and move between provinces as long as they com- Princesa (Madrid). All participants provided informed consent. plied with safe distancing and face-covering requirements. Some participants were unable to respond first-hand due to During the first wave of the COVID-19 pandemic, several physical and/or mental limitations, and thus a relative or studies have investigated its mental health consequences in the co-habitant answered in their name. Only first-hand respondents Spanish adult population (Balanzá-Martínez et al., 2020; to both interviews were included in these analyses; therefore, out Garcia-Fernandez et al., 2020; Gonzalez-Sanguino et al., 2020; of a sample of 1935 participants, 54 proxy respondents were dis- Justo-Alonso et al., 2020; Pérez et al., 2020; Planchuelo-Gomez carded, making a sample of 1881 participants in the pre-measure. et al., 2020; Cecchini et al., 2021; Losada-Baltar et al., 2021; A total of 778 were excluded from the post-measure (81 partici- Mortier et al., 2021; Valiente et al., 2021). Overall, these studies pants did not provide recontact information, 110 participants have shown a general worsening in mental health throughout could not be contacted, 9 were deceased, 39 were responded by the confinement, with prevalence estimates ranging from 9 to a proxy respondent, 329 either rejected to respond to the post- 46% among those reporting data on depressive symptoms. measure telephone interview or aborted it before finishing and Younger age, being female, being a healthcare worker, low 210 had unspecified incidents), so the final post-measure sample income, prior mental disorders, loneliness, and substance use comprised of 1103 participants. appeared as the strongest factors associated with mental health problems. However, the validity of these findings may be Measures somewhat hindered by at least one of the following drawbacks: (i) non-probabilistic sampling approaches or convenience samples Depression was assessed with an adapted version of the evaluated through online surveys, which increases the risk of Composite International Diagnostic Interview (CIDI; Kessler selection bias; (ii) cross-sectional design or lack of information and Ustün, 2004). An algorithm following the ICD-10 criteria on the pre-pandemic period, which does not allow for a proper was used to diagnose depression in the previous 12 months assessment of the determinants of the observed changes in mental (World Health Organization, 1993). For the post-measure inter- health indicators and (iii) assessment of dimensional measures of view, an abbreviated version was used, and the items were adapted psychological distress only. to ask for a 30-day time span in order to account for an onset while The study aims to assess whether there is a change in the the lockdown measures were in effect. The assessment algorithm in prevalence of depression and suicidal ideation after the strict lock- the pre-measure was adapted to use the same item set as in the down measures during the first wave of the COVID-19 pandemic post-measure. Suicidal ideation comprised a single item asking in Spain; and to assess which are the factors associated with the whether the participant had had suicidal thoughts in the previous incidence of a depressive episode or suicidal ideation during the 12 months/30 days, for the pre- and post-measure periods, lockdown. Our analysis is based on an adult population-based respectively. cohort from the provinces of Madrid and Barcelona, which was The following covariates were also measured: age, sex, educa- evaluated before the pandemic and once again after the tion level, whether the participant lived alone (both before and COVID-19 lockdown. during the lockdown), whether the participant had co-habited/ was co-habiting with a relative isolated by COVID-19, whether the participant had been/was concerned about a relative/friend Method infected by COVID-19, whether the participant had been infected with COVID-19 and its severity, whether the participant had Sample and recruitment enough quietness at home to get proper rest, whether the house- Non-institutionalised adults (i.e. 18+ years old) from the regions hold economic situation had worsened due to the COVID-19 of Madrid and Barcelona participated in this study. These consti- emergency, whether the participant had been unemployed due tute the refreshment sample of the Edad con Salud project (agein- to the COVID-19 emergency, time a day spent in front of screens gandhealth.com) (Miret et al., 2014). They were recruited during the lockdown (working and non-working), pre- and post- following a multistage stratified design consisting of: (i) a random measure levels of physical activity according to an abbreviated sample of municipalities (sampling probability proportional to version of the Global Physical Activity Questionnaire version 2 population size); (ii) a random sample of census units from (GPAQ-2) (Armstrong and Bull, 2006), and the following scales: each municipality; (iii) a random sample of households within post-measure score in the Brief Resilience scale (Rodríguez-Rey each census track, and assigned to one of two age groups: 18– et al., 2016), pre- and post-measures of social support measured 49 or 50+ (the second one oversampled). For each household, with the OSLO3 Social Support scale (Dalgard et al., 2006), pre- individuals in the assigned age group were invited to participate; and post-measures of loneliness measured with the UCLA loneli- the response rate was 68.0%. Sampling weights were generated for ness scale (Hughes et al., 2004), and post-measure of disability the sample to be representative of the target population, according assessed with the 12-item World Health Organization Disability to the population distribution obtained from the National Assessment Schedule (WHODAS 2.0) (Luciano et al., 2010). Institute of Statistics. The Brief Resilience Scale was taken from the validated version Participants were interviewed at their homes between 17 June by Rodríguez-Rey et al. (2016), whereas the rest of them have 2019 and 14 March 2020 (pre-COVID measure). They were been validated in the original in English (as referenced) and reached out again between 21 May 2020 and 30 June 2020 to were adapted for their use in the Edad con Salud cohort study. respond to a telephone interview (post-COVID measure). All of them had internal consistency indices (i.e. Cronbach’s α) Trained interviewers conducted the pre- and post-measure inter- above 0.70, except for the OSLO3 Social Support scale, which reli- views, using a Computer-Assisted Personal and Telephonic ability was moderate (α = 0.653) and low (α = 0.386) in the pre- Interviewing system, respectively. Protocols were approved by and post-measures, respectively. Epidemiology and Psychiatric Sciences 3 Data analysis in depression between the included and the excluded samples (x21 = 0.03, p value = 0.860, ϕc = 0.000). Regarding suicidal idea- Sample descriptive statistics were computed for depression, sui- tion, the excluded sample differed significantly from the included cidal ideation and all the covariates. Attrition in the pre-measure one (0.64 v. 2.18%, respectively; x21 = 7.07, p value = 0.008); the sample was analysed for differences in sociodemographics and the effect size was also negligible though (ϕc = 0.052). The descriptive two outcome variables: sex, depression and suicidal ideation were statistics for both outcome variables and the covariates for the tested with the χ 2-test; bias-corrected Cramér’s V (ϕc) was com- sample included in the analysis are shown in Table 1. puted as a measure of effect size. For age, a two-sample T-test was performed, with Hedges’ g as a measure of effect size. Prevalence estimates – population-wise and disaggregated by Prevalence rates sex and age (grouped in 18–29, 30–49 and 50+ year-olds) – Estimated prevalence rates are given in Table 2. For depression, it were computed for depression and suicidal ideation in both mea- increased from 7.78% in the pre- to 9.82% in the post-measure. sures. The differences between both measures were tested with a According to the McNemar’s test, the difference was not signifi- weighted McNemar’s test of symmetry, using the complete data. cant (x21 = 1.53, p value = 0.216). When considering the differen- Bonferroni correction was applied variable-wise to the disaggre- tiated age groups, the difference was more prominent for the gated estimates. 18–29 (increasing from 5.32 to 14.17%) and the 30–49 (increasing To model the risk of incidence after the lockdown, the cases from 7.96 to 11.99%) groups, but none of them was significant. with depression or suicidal ideation in the pre-measure periods However, a significant decrease from 8.48 to 6.41% was observed were filtered out from the dataset for its corresponding analysis. in the 50+ group (x21 = 6.46, p value = 0.011). For suicidal ideation, Then, we performed a weighted logistic regression model on the the prevalence rate estimate increased from 1.56% in the pre- to post-measure. All covariates stated in section ‘Measures’ were ini- 2.78% in the post-measure, but this difference was not significant tially considered. In the case of suicidal ideation, the pre- and (x21 = 1.39, p value = 0.239). After Bonferroni correction, none of post-measures of depression were also considered as covariates. the disaggregated estimates was significant either. The following procedures were applied for fitting the models: first, in order to archive better numerical convergence, all interval-level variables were standardised, and categorical covari- Risk of depression after the lockdown ates that yielded complete separation (Albert and Anderson, The final regression model for depression was fit with a sample 1984) were discarded. Covariates were tested individually with size of 940. Its covariates are given in Table 3, along with their univariate weighted logistic regression models and the Rao and odds ratios (ORs). The coefficient for age was found to be signifi- Scott (1984) likelihood-ratio test (without Bonferroni correction, cant, along with the post-measures of Loneliness and Resilience. in order to decrease type-II error risk). Among the significant The OR for age was 0.971 (z = −3.17, p value = 0.002); for covariates, the ordinal ones were tested for non-linearity with each year, the risk of developing depression was expected to the Wald test, comparing the general model with a model with decrease by 2.9%. For Loneliness (Post), the OR was 1.957 (z = 4.08, the linear term only. Whenever the test was non-significant, p value <0.001), which means that an increase of 1 standard devi- only the linear term was included. Afterwards, a multivariate ation (S.D.) in the post-measure of the UCLA Loneliness Scale was weighted logistic model was fit with all the significant covariates. associated with an increase of 95.7% in the OR of receiving a posi- A backward-step procedure was then run, dropping covariates tive diagnosis of depression in the post-measure. In the case of according to the Akaike information criterion statistic. Resilience (Post), the OR was 0.460 (z = −4.22, p value <0.001), Demographic variables sex and age were fixed, excluding them meaning that an increase of 1 S.D. in the post-measure of the from dropping. In the model of suicidal ideation, the measures Brief Resilience Scale was associated with a decrease of 54.0% in of depression were also fixed. Finally, the resulting model was the OR of receiving a positive diagnosis of depression in the refit to the subset of complete cases in the covariates selected post-measure. by the backward-step procedure. As the procedure may select a different subset of covariates for each model, the number of com- plete cases may also differ. Risk of suicidal ideation after the lockdown A significance level of α = 0.05 was used throughout. All sig- This model was fit with a sample size of 951; its covariates and nificance tests were performed applying Bonferroni correction their coefficients (as ORs) are shown in Table 4. After for multiple comparisons (unless stated otherwise). All the ana- Bonferroni correction, the significant covariates were the post- lyses were conducted in R v. 4.0.3 (R Core Team, 2019). measures of Social support and Resilience. The OR of Social sup- Package survey v. 4.0 (Lumley, 2004) was used to fit the models. port was 0.352 (z = −3.04, p value = 0.002). This implies that an increase of 1 S.D. in the post-measure of the Oslo-3 Social Results Support Scale was associated with a decrease of 64.8% in the OR of reporting having suicidal ideation in the post-measure. Sample descriptives The OR for Resilience was 0.334 (z = −3.01, p value = 0.003), Participants with data in both measures differed from the ones implying a decrease of 66.6% for an increase of 1 S.D. in the post- excluded in the post-measure in sex (x21 = 15.80, p value <0.001) measure of the Brief Resilience Scale. and age (t1467.34 = 5.59, p value <0.001): the proportion of men excluded (48.8%) was relatively higher than the ones included Discussion (39.6%), and the participants excluded were older (mean = 59.6, S.D. = 19.8) than the ones included (mean = 54.8, S.D. = 16.4). The current study is the first to assess changes in mental health However, the effect size was negligible for sex (ϕc = 0.089), and during the first wave of the COVID-19 pandemic in Spain by small for age (g = 0.270). No significant differences were found using a population-based cohort. Overall, our results did not 4 J. L. Ayuso‐Mateos et al. Table 1. Sociodemographic and health characteristics before and after the confinement Variable Pre-confinement Post-confinement p value* Age, mean (S.D.) 54.82 (16.35) Sex (female), n (%) 666 (60.38%) Education level, n (%) Less than primary 97 (8.79%) Primary 283 (25.66%) Secondary 466 (42.25%) Tertiary 257 (23.30%) Depression, n (%) 99 (8.98%) 87 (7.89%) 0.327 Suicidal ideation, n (%) 24 (2.20%) 23 (2.09%) 0.853 a Resilience, mean (S.D.) 3.51 (0.63) Living alone, n (%) 163 (14.78%) 131 (11.88%) <0.001 Social support, mean (S.D.)a 78.34 (17.91) 76.20 (16.41) <0.001 a Loneliness, mean (S.D.) 12.07 (25.13) 12.69 (23.97) 0.489 b COVID-19 co-habitant, n (%) 43 (3.90%) COVID-19 concern, n (%)c 268 (24.30%) COVID-19 infection, n (%)d Not infected 1093 (99.09%) Infected 7 (0.63%) Infected and hospitalised 3 (0.27%) a Disability, mean (S.D.) 8.72 (13.43) Working screen time (h), mean (S.D.) 3.93 (2.16) Non-working screen time (h), mean (S.D.) 1.23 (2.62) Home quietness, n (%) 1030 (93.64%) Economy worsened, n (%) 331 (30.15%) Unemployed, n (%) 179 (16.29%) Physical activity, n (%) Low 353 (32.09%) 910 (82.50%) <0.001 Moderate 384 (34.91%) 76 (6.89%) <0.001 High 363 (33.00%) 117 (10.61%) <0.001 n, number of participants; S.D., standard deviation. *p values correspond to a paired-sample T-test for the quantitative variables, and a McNemar’s test of symmetry for the categorical ones. a These variables are measured in a 0–100 scale. b Co-habited/ing with relative isolated by COVID-19. c Concerned about relative/friend infected by COVID-19. d Severity of COVID-19 infection. show significant differences in the prevalence of depression or Among the studies tracking longitudinal changes in mental suicidal ideation from before to after the COVID-19 outbreak. health from before to during the pandemic, some of them have Indeed, the prevalence rate of depression in our study was similar showed increases in the prevalence rate of depression and suicidal to those reported in other Spanish and European studies in ideation (Daly et al., 2020; Niedzwiedz et al., 2021; Novotny et al., non-epidemic circumstances (Paykel et al., 2005; Gutiérrez- 2020; Pierce et al., 2020; Planchuelo-Gomez et al., 2020; Winkler Rojas et al., 2020). Interestingly, the rates of depression among et al., 2020; McGinty et al., 2020b) whereas others did not report individuals aged 50+ years showed a significant decrease compared differences above pre-pandemic levels (Kwong et al., 2020; van to pre-pandemic. The study findings also found that younger der Velden et al., 2020) or even decreased estimates (van der individuals and those feeling loneliness exhibited a significant Velden et al., 2021). It is worth noting that all but one of the pre- increase in the risk of developing depression. Resilience showed a vious studies measured psychological distress or depressive symp- protective effect against the risk of depression and suicidal ideation, toms. In addition, according to a meta-analysis of longitudinal while individuals perceiving social support were at a lower risk of studies investigating the psychological impact of the COVID-19 developing suicidal thoughts. pandemic, the initial effect of lockdowns on mental health is Epidemiology and Psychiatric Sciences 5 Table 2. Prevalence rate estimates in the pre- and post-measures of depression and suicidal ideation, for the population and disaggregated by sex and group age Depression Suicidal ideation Segment Pre (S.D.) Post (S.D.) p value Pre (S.D.) Post (S.D.) p value Total 7.78% (0.81%) 9.82% (1.19%) 0.216 1.56% (0.38%) 2.78% (0.66%) 0.239 Sex Male 6.28% (1.14%) 8.90% (1.86%) 0.375 1.17% (0.51%) 3.11% (1.14%) 0.110 Female 9.13% (1.13%) 10.54% (1.53%) 0.384 1.91% (0.54%) 2.52% (0.79%) 0.964 Age 18–29 5.32% (1.88%) 14.17% (4.12%) 0.071 2.21% (1.24%) 4.09% (2.37%) 0.878 30–49 7.96% (1.66%) 11.99% (2.51%) 0.064 0.93% (0.59%) 3.31% (1.39%) 0.018 50+ 8.48% (0.86%) 6.41% (0.99%) 0.011 1.85% (0.42%) 1.87% (0.55%) 0.243 S.D., standard deviation. Table 3. Logistic regression model of depression after the confinement in Table 4. Logistic regression model of suicidal ideation after the confinement in participants without depression before the confinement participants without suicidal ideation before the confinement Term OR (95% CI) z p value Term OR (95% CI) z p value (Intercept) 0.26 (0.09–0.75) −2.48 0.013 (Intercept) 0.00 (0.00–0.02) −5.60 <0.001 Age (Pre) 0.97 (0.95–0.99) −3.17 0.002 Age (Pre) 0.95 (0.89–1.02) −1.40 0.161 Sex 0.74 (0.34–1.63) −0.75 0.455 Sex 0.35 (0.08–1.51) −1.40 0.161 Resilience (Post) 0.46 (0.32–0.66) −4.22 <0.001 Depression (Pre) 5.06 (1.01–25.30) 1.98 0.049 Social support (Pre) 0.77 (0.51–1.18) −1.20 0.229 Depression (Post) 1.83 (0.27–12.56) 0.62 0.538 Loneliness (Post) 1.96 (1.42–2.70) 4.08 <0.001 Resilience (Post) 0.33 (0.16–0.68) −3.01 0.003 Disability (Post) 1.26 (0.84–1.91) 1.11 0.269 Social support 0.35 (0.18–0.69) −3.04 0.002 (Post) Working screen 0.90 (0.80–1.02) −1.69 0.092 time (h) Disability (Post) 1.30 (0.86–1.95) 1.25 0.211 Home quietness 0.42 (0.19–0.90) −2.22 0.026 Physical activity (Pre) (Ref. Low) Physical activity 0.76 (0.49–1.18) −1.22 0.224 Moderate 0.45 (0.08–2.39) −0.94 0.348 (Pre)a High 2.61 (0.76–8.96) 1.52 0.129 OR, odds ratio; CI, confidence interval; Resilience, Brief Resilience Scale; Social support, OSLO3 Social Support Scale; Loneliness, UCLA Loneliness Scale; Disability, 12-item WHO OR, odds ratio; CI, confidence interval; Resilience, Brief Resilience Scale; Social support, Disability Assessment Schedule Physical activity (GPAQ-2 abbreviated). OSLO3 Social Support Scale; Disability, 12-item WHO Disability Assessment Schedule a Included as linear covariate. Physical activity (GPAQ-2 abbreviated). relatively small, with no evidence of significant increase in suicide an initial drop in suicide deaths from February to June 2020, risk (Prati and Mancini, 2021). then followed by an increase during the second wave (July to The first emotional reactions may represent feelings of fear, October 2020). Similarly, the Spanish Statistical Office revealed anger or sadness in response to an unprecedented situation rather that suicide remained the leading cause of external death during than a mental disorder. More fine-grained analyses have showed the first months of 2020. However, there was a drop of 8.8% as that mental health problems remained stable or declined through- compared with the same period in 2019 (Spanish Statistical out the initial lockdown period (Bryan et al., 2020; Chandola Office, 2021). Initial declines in suicidal behaviours are not unex- et al., 2020; Daly et al., 2020; Gonzalez-Sanguino et al., 2020; pected and may be explained by reduced stress derived from Hyland et al., 2020; Somma et al., 2020; Wang et al., 2020; workplaces and social interactions, government financial support McGinty et al., 2020a; van der Velden et al., 2021), which and limited access to lethal means (Tanaka and Okamoto, 2021). would be consistent with the notion of a progressive adjustment Specific groups appear to be disproportionately affected by the for managing and overcoming stressful events. Nevertheless, it COVID-19 pandemic. That is the case of young individuals, who should be noted that these are findings at a very early stage of may encounter more difficulties coping with certain unexpected the COVID-19 outbreak and different conclusions may hold for life-changing events, consistent with the view of resilience as a the comparisons among rate estimates of mental health condi- life-span process (Portella Fontes and Liberalesso Neri, 2015). tions in the mid- and long-terms. In this regard, Tanaka and In addition, the adverse economic and social consequences of Okamoto (2021) examined whether suicide mortality changed the pandemic might have a greater impact on them (e.g. during the pandemic using high-frequency data covering the unemployment, changes in daily routines and social dynamics). entire Japanese population. The authors found that there was Unsurprisingly, the scientific literature has provided broad 6 J. L. Ayuso‐Mateos et al. evidence suggesting a high proportion of individuals with or at data on the mental health effects of the pandemic across the higher risk of depressive symptoms or psychological distress dur- whole population and vulnerable groups, together with the devel- ing the COVID-19 outbreak within this group (Daly et al., 2020; opment, assessment and refinement of driven strategies to address Gonzalez-Sanguino et al., 2020; Hyland et al., 2020; Kwong et al., its psychological, social and neuroscientific aspects. 2020; Niedzwiedz et al., 2021; O’Connor et al., 2020; McGinty et al., 2020a; Valiente et al., 2021). Prior studies have repeatedly documented the intimate link between loneliness and depression Strengths and limitations (van den Brink et al., 2018; de la Torre-Luque et al., 2019; This research has an important number of strengths. First, the use Lee et al., 2021). The consistency of results among other of an adult population-based cohort following a probabilistic COVID-19-related research is also noteworthy (Chandola et al., sampling approach. Moreover, this sample comprises subjects of 2020; Creese et al., 2020; Gonzalez-Sanguino et al., 2020; all educational levels and age ranges, as compared to recent pub- Kantor and Kantor, 2020; Novotny et al., 2020; Palgi et al., lished studies that tend to over-represent highly educated people 2020; van der Velden et al., 2021). Even though little is known and under-represent the oldest population. Second, this study is yet about the mechanisms underlying this association, there is one of the few including a baseline evaluation of the participants evidence that loneliness may compromise emotion processing some months before the pandemic outbreak. Third, data from our and regulation, can lead to decreased cognitive function, and study were collected through structured face-to-face home-based alter metabolic, endocrine and immune responses (Hawkley and interviews and telephone interviews, unlike most prior studies, Cacioppo, 2010; Lara et al., 2019; de la Torre-Luque et al., relying on web-based surveys instead. Fourth, we used a standar- 2021), all of which have been associated with depression. In our dised assessment tool providing a clinical diagnosis of major current situation, the risk of loneliness over depression is expected depression, whereas the majority of previous research assessed to be heightened. In the opposite corner, the identification of the depressive symptoms through screening tests or non-validated protective effect of resilience on depression and suicidal ideation instruments. Finally, we used a large variety of validated instru- accords with recent reports (Killgore et al., 2020; Lenzo et al., ments and sociodemographic variables to cover a broad-ranging 2020; Novotny et al., 2020; Ran et al., 2020; Cenat et al., 2021). research of potentially vulnerable groups. Our findings need to Resilience is the process of effectively coping with uncertainty be also interpreted in the context of its shortcomings. As with and hardship. Although this finding may be well-suited for all COVID-19-related research, the current study is limited by a designing interventions to mitigate the risk of depression and sui- short follow-up period, which reduced the power to evaluate cidal thinking, it remains to be further investigated who are these the effects of the confinement on depression and suicidal behav- resilient people and what factors characterise resilience (Huisman iour. However, ours is an ongoing project that will provide infor- et al., 2017). Furthermore, social support is among the best well- mation to a more comprehensive understanding of the changes in documented variables to influence suicidal behaviour (Calati mental health in the mid- and long-terms. We also acknowledge et al., 2019; Hegerl and Heinz, 2019). Early research has also pro- that some measures were collected retrospectively through self- posed a similar association between social support and suicidal report, which may be affected by recall or reporting bias, espe- ideation in the context of COVID-19 (Bryan et al., 2020; cially for the longer recall period. Finally, as this survey did not Fitzpatrick et al., 2020; Gratz et al., 2020; Papadopoulou et al., intend to generate clinical diagnoses for all mental disorders, 2021). For instance, Gratz and colleagues (2020), having analysed some individuals presenting for example bipolar disorder or data from a nationwide community sample of 500 adults from 45 schizophrenia may have been included in our analytical sample. states, claimed that it is not loneliness but an absence of belong- ingness and significant connections that accounts for the associ- ation of the lockdown to a greater suicide risk. In this sense, Conclusions Joiner’s Interpersonal Theory of Suicide (Joiner et al., 2005) pro- posed that the lack of social connectedness may lead to a poten- This pandemic has put at the forefront the imperative of taking tially lethal suicidal attempt. More recently, Klonsky and May care for others, particularly among vulnerable groups who are (2015) suggested that there is a three-step process towards suicidal experiencing the most distress. Altogether, our results point to attempts where connectedness protects against the escalation of the value of the social factors as strongly associated with mental ideation among individuals suffering both psychological pain health conditions, with loneliness and social support maybe and hopelessness. representing different risk pathways. Promoting sense of connect- It will take time to know what the ultimate impact of the edness, experiences of companionship, and meaningful relation- COVID-19 outbreak is on mental health. The psychological toll ships show promise in mental health prevention, especially in of the pandemic is unquestionable, but the reality is complex. times of physical distancing and lockdowns. Future research As the pandemic persists, its consequences are predicted to grad- should strive to evaluate the long-lasting effects of the ually appear, including rising unemployment, financial loss, COVID-19 crisis on mental health. reduced participation, or inadequate supplies derived from signifi- Availability of data and materials. Data supporting the findings of this cant cuts in spending on social and health care. The effects on study are available upon a reasonable request. mental conditions are expected to stay and peak later, with varia- tions across populations and nations (Brooks et al., 2020; John Acknowledgements. E. L.’s work is supported by the Sara Borrell postdoc- et al., 2020; Ayuso-Mateos et al., 2021). Continuous reinforce- toral programme (CD18/00099) from the Instituto de Salud Carlos III (Spain) ment of preventive and intervening mental health measures dur- and co-funded by European Union (ERDF/ESF, ‘Investing in your future’). The authors sincerely appreciate the generous contribution of all the partici- ing and in the aftermath of the crisis is thus of global importance. pants, which made this work possible. In this regard, a position paper detailed several mental health research priorities in response to the demands of COVID-19 Financial support. This work was funded by the Instituto de Salud Carlos (Holmes et al., 2020). These include the collection of high-quality III (J. L. A. M., grant numbers PI16/00218 and PI19/00150), (J. M. H., grant Epidemiology and Psychiatric Sciences 7 number PI16/01073 and PI19/00088), (M. M., grant number PI19/00235), (B. O., symptoms in old age: relationships with sociodemographic and gran number PI19/00103), the Centro de Investigación Biomédica en Red de health-related factors. Journal of Affective Disorders 246, 329–337. Salud Mental (CIBERSAM), and by the European Union’s Horizon 2020 de la Torre-Luque A, Lara E, de la Fuente J, Rico-Uribe LA, Caballero FF, under Grant Agreement No 101016127. Lopez-Garcia P, Sanchez-Niubo A, Bobak M, Koskinen S, Haro JM and Ayuso-Mateos JL (2021) Metabolic dysregulation in older adults with depres- Conflict of interest. None. sion and loneliness: the ATHLOS study. Psychoneuroendocrinology 123, 104918. Ethical standards. The protocols were approved by the Clinical Research Faust JS, Shah SB, Du C, Li SX, Lin Z and Krumholz HM (2021) Suicide Ethics Review Committees of both Parc Sanitari Sant Joan de Déu deaths during the COVID-19 stay-at-home advisory in Massachusetts, (Barcelona), and Hospital Universitario La Princesa (Madrid). All participants March to May 2020. JAMA Network Open 4, e2034273. provided written informed consent for their participation and the treatment of Fitzpatrick KM, Harris C and Drawve G (2020) How bad is it? Suicidality in their personal data. 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