Epidemiology and Psychiatric Thirty-day suicidal thoughts and behaviours in Sciences the Spanish adult general population during cambridge.org/eps the first wave of the Spain COVID-19 pandemic P. Mortier1,2 , G. Vilagut1,2, M. Ferrer1,2,3, I. Alayo1,2,4, R. Bruffaerts5, Original Article P. Cristóbal-Narváez6,7 , I. del Cura-González8, J. Domènech-Abella6,7, M. Felez-Nobrega6,7, B. Olaya6,7, J. I. Pijoan9,10, E. Vieta11, V. Pérez-Solà3,7,12, Cite this article: Mortier P et al (2021). Thirty- day suicidal thoughts and behaviours in the R. C. Kessler13, J. M. Haro6,14,15, J. Alonso1,2,16 and on behalf of the MINDCOVID Spanish adult general population during the first wave of the Spain COVID-19 pandemic. Working group Epidemiology and Psychiatric Sciences 30, e19, 1 1–11. https://doi.org/10.1017/ Health Services Research Unit, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques, Barcelona, Spain; 2CIBER S2045796021000093 Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; 3Universitat Autònoma de Barcelona (UAB), Barcelona, Spain; 4Pompeu Fabra University, Barcelona, Spain; 5Center for Public Health Psychiatry, Universitair Psychiatrisch Received: 18 November 2020 Revised: 8 February 2021 Centrum, KU Leuven, Leuven, Belgium; 6Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain; 7 Accepted: 9 February 2021 CIBER de Salud Mental (CIBERSAM), Madrid, Spain; 8Research Unit Primary Care Management, Madrid Health Service. (REDISSEC). Universidad Rey Juan Carlos, Madrid, Spain; 9Clinical Epidemiology Unit, Hospital Keywords: Universitario Cruces/ OSI EEC, Bilbao, Spain; 10CIBER Epidemiología y Salud Pública (CIBERESP), Spain; 11Hospital suicide; COVID-19; pandemic; epidemiology; Clínic, University of Barcelona, IDIBAPS, (CIBERSAM), Barcelona, Spain; 12Parc de Salut Mar PSMAR, Barcelona, risk factors; Spain Spain; 13Department of Health Care Policy, Harvard Medical School, Boston, MA, USA; 14CIBER Salud Mental (CIBERSAM), Madrid, Spain; 15Universitat de Barcelona (UB), Barcelona, Spain and 16Department of Experimental Author for correspondence: and Health Sciences, Pompeu Fabra University, Barcelona, Spain Philippe Mortier, E-mail: pmortier@imim.es Abstract Aims. To investigate the prevalence of suicidal thoughts and behaviours (STB; i.e. suicidal ideation, plans or attempts) in the Spanish adult general population during the first wave of the Spain coronavirus disease 2019 (COVID-19) pandemic (March−July, 2020), and to investigate the individual- and population-level impact of relevant distal and proximal STB risk factor domains. Methods. Cross-sectional study design using data from the baseline assessment of an obser- vational cohort study (MIND/COVID project). A nationally representative sample of 3500 non-institutionalised Spanish adults (51.5% female; mean age = 49.6 [S.D. = 17.0]) was taken using dual-frame random digit dialing, stratified for age, sex and geographical area. Professional interviewers carried out computer-assisted telephone interviews (1–30 June 2020). Thirty-day STB was assessed using modified items from the Columbia Suicide Severity Rating Scale. Distal (i.e. pre-pandemic) risk factors included sociodemographic variables, number of physical health conditions and pre-pandemic lifetime mental disorders; proximal (i.e. pandemic) risk factors included current mental disorders and a range of adverse events-experiences related to the pandemic. Logistic regression was used to investigate individ- ual-level associations (odds ratios [OR]) and population-level associations (population attributable risk proportions [PARP]) between risk factors and 30-day STB. All data were weighted using post-stratification survey weights. Results. Estimated prevalence of 30-day STB was 4.5% (1.8% active suicidal ideation; n = 5 [0.1%] suicide attempts). STB was 9.7% among the 34.3% of respondents with pre-pandemic lifetime mental disorders, and 1.8% among the 65.7% without any pre-pandemic lifetime mental disorder. Factors significantly associated with STB were pre-pandemic lifetime mental disorders (total PARP = 49.1%) and current mental disorders (total PARP = 58.4%), i.e. major depressive disorder (OR = 6.0; PARP = 39.2%), generalised anxiety disorder (OR = 5.6; PARP = 36.3%), post-traumatic stress disorder (OR = 4.6; PARP = 26.6%), panic attacks (OR = 6.7; PARP = 36.6%) and alcohol/substance use disorder (OR = 3.3; PARP = 5.9%). Pandemic- related adverse events-experiences associated with STB were lack of social support, interper- © The Author(s), 2021. Published by Cambridge University Press. This is an Open sonal stress, stress about personal health and about the health of loved ones (PARPs 32.7– Access article, distributed under the terms of 42.6%%), and having loved ones infected with COVID-19 (OR = 1.7; PARP = 18.8%). Up to the Creative Commons Attribution licence 74.1% of STB is potentially attributable to the joint effects of mental disorders and adverse (http://creativecommons.org/licenses/by/4.0/), events−experiences related to the pandemic. which permits unrestricted re-use, distribution Conclusions. STB at the end of the first wave of the Spain COVID-19 pandemic was high, and and reproduction, provided the original article is properly cited. large proportions of STB are potentially attributable to mental disorders and adverse events −experiences related to the pandemic, including health-related stress, lack of social support and interpersonal stress. There is an urgent need to allocate resources to increase access to adequate mental healthcare, even in times of healthcare system overload. Study registration number. NCT04556565 2 P. Mortier et al. Apart from STB prevalence, we also investigate a range of rele- Introduction vant STB risk factors for individual- and population-level impact. The coronavirus disease 2019 (COVID-19) pandemic poses Distal risk factors (i.e. referring to the pre-pandemic period) include unprecedented challenges worldwide. In line with concerns sociodemographic variables (Franklin et al., 2017; Carrasco-Barrios about a potential increase in psychopathology related to the pan- et al., 2020), physical health conditions (Franklin et al., 2017) and demic (Vindegaard and Benros, 2020), representative population- pre-pandemic mental disorders (Nock et al., 2015; Franklin et al., based studies (i.e. not using snowball sampling) from the Czech 2017; O’Connor et al., 2020). Proximal risk factors (i.e. referring Republic, the UK and the US found high prevalence of suicidal to the pandemic period) are current mental disorders (Fitzpatrick thoughts and behaviours (STB) during the pandemic, i.e. suicidal et al., 2020; Iob et al., 2020); health-related factors, including ideation range 4.6–18% and suicidal behaviour range 0.1–4.9% COVID-19 infection (Iob et al., 2020; Winkler et al., 2020) and (Bryan et al., 2020; Czeisler et al., 2020; Fitzpatrick et al., 2020; health-related stress (Winkler et al., 2020; Ammerman et al., Gratz et al., 2020; Iob et al., 2020; O’Connor et al., 2020; 2021); financial factors (Fazel and Runeson, 2020), including Winkler et al., 2020; Ammerman et al., 2021). Mental health COVID-19-related financial stress or job loss (Gratz et al., 2020; experts have therefore urged governments to invest resources in Winkler et al., 2020); and interpersonal factors, including interper- mental health and suicide prevention strategies (Gunnell et al., sonal stress and lack of social support (O’Connor and Kirtley, 2018; 2020; Moutier, 2020; Wasserman et al., 2020). To guide interven- Carrasco-Barrios et al., 2020). tion planning, research is needed to provide reliable STB preva- lence estimates and to quantify the population-level impact of relevant risk factors (Christensen et al., 2016). The latter can be Methods achieved by calculating population attributable risk proportions Study design, population and sampling (PARP; Krysinska and Martin 2009), i.e. estimates of potential reductions in STB when eliminating risk factors in the population. This study is explanatory in purpose, and risk factors are therefore Despite their great value in prioritising prevention interventions conceptualised as causal factors, i.e. factors that, when manipu- (Christensen et al., 2016), no study to date provided PARP for lated (e.g. through interventions), may change the outcome STB during the COVID-19 pandemic. (Schooling and Jones, 2018). However, this is an exploratory Spain was hit particularly hard by the COVID-19 pandemic. observational study without experimental manipulation, using a The first COVID-19 case in Spain was confirmed on 31 January cross-sectional study design. No temporality between the risk fac- 2020. On 14 March, a state of alarm was declared, including tors and the outcome can be established, and therefore, no causal national lockdown restrictions requiring citizens to stay at home inference can be made. except to purchase food and medicines, or to go to work. A baseline survey of a cohort of general population adults was Schools and all non-essential shops and businesses were closed. conducted as part of the MIND/COVID project (MIND/COVID, As from 28 April, all activity in non-essential sectors was banned. 2020). The target population consisted of non-institutionalised Between the beginning of March and mid-April, daily new cases Spanish adults (i.e. aged 18 years or older) without Spanish language were >2000/day, peaking on 27 March (10 141 cases; WHO barriers. Professional interviewers carried out computer-assisted COVID-19 Dashboard). During the last two weeks of March, telephone interviews (1–30 June 2020) in a sample drawn using daily hospitalisations were >2000/day (RENAVE, 2020). Daily dual-frame random digit dialing (n = 3500). Mobile numbers were deaths peaked on 1 April (913 deaths) with 29 080 cumulative generated through an automated system and landline numbers deaths by the end of May (WHO COVID-19 Dashboard). The were selected from an internal database maintained by the survey healthcare system nearly collapsed during April−May due to company to ensure that all Spanish geographical areas were lack of healthcare resources (RENAVE, 2020). By the time the adequately represented. Up to seven calls at different times of the situation stabilised in early July, Spain had the eighth highest day and days of the week were attempted to each number. The dis- number of cases (i.e. 249 659), and the fifth highest COVID tribution of the interviews was planned according to quotas in terms death rate (i.e. 60.7/100 000) in the world (Roser et al., 2020). of age groups, sex and autonomous community (National Institute In this report we investigate 30-day STB during the first wave of Statistics in Spain, July 2019). A total of 138 656 numbers were of the COVID-19 pandemic (March−June 2020) in the Spanish sampled, with a final split of 71% mobile and 29% landline tele- adult general population. Spain traditionally has low rates of phones; 45 002 numbers were non-eligible (i.e. non-existing num- STB (12-month estimates 0.7–0.9%; Miret et al., 2014) and suicide bers [ 43 120], numbers of enterprises [984], numbers of persons (3.5–9.0/100 000; Alfonso-Sánchez et al., 2020), but this might with Spanish language barriers [444], fax numbers [268] and num- have changed due to the particularly severe toll of COVID-19 bers belonging to quota that were already completed [186]) and 72 in Spain in terms of number of cases and deaths, healthcare sys- 428 had unknown eligibility (i.e. no contact was made after the seven tem overload and a prolonged period of national lockdown. No attempted calls), resulting in a cooperation rate (i.e. the proportion previous data exist on STB prevalence during the pandemic in of all cases interviewed of all eligible units ever contacted) of 16.5%. Spain other than from an online survey (March 2020) based on Ethical approval was provided by the Parc de Salut Mar non-representative snowball sampling (Sáiz et al., 2020). Clinical Research Ethics Committee (protocol 2020/9203/I) and Representative estimates could contribute to the ongoing debate by Parc Sanitari Sant Joan de Déu, Barcelona, Spain (PIC as to whether STB have increased during the pandemic. Here, 86-20). Participants were fully informed about the objectives we present data from a nationally representative sample assessed and procedures of the study prior to providing oral consent. near the end of the first wave of the Spain COVID-19 pandemic (June 2020). By that time, daily new cases were <500/day, and Measures restrictions were lifted to obtain a ‘new normality’, including social distancing, obligatory wearing of masks and limited cap- A modified version of selected items from the Columbia Suicide acity in shops, bars and restaurants. Severity Rating Scale (Posner et al., 2011) assessed STB in the Epidemiology and Psychiatric Sciences 3 past 30 days, i.e. dichotomous items that assessed passive suicidal Dohrenwend et al., 1978) assessing stress related to personal ideation (‘wish you were dead or would go to sleep and never wake health and to potential COVID-19 infection. up’), active suicidal ideation (‘have thoughts of killing yourself’), Factors related to the health of loved ones included: having suicide plans (‘think about how you might kill yourself [e.g. taking loved ones infected with COVID-19 (including type of loved pills, shooting yourself] or work out a plan of how to kill yourself’) one infected and severity of infection of most affected loved and suicide attempt (‘make a suicide attempt [i.e. purposefully one); and stress related to loved ones’ health, i.e. a summary hurt yourself with at least some intent to die]). For analyses, scale [0–4] of two 5-level Likert-type adapted PLES items (ran- three dichotomies were created: “any STB” (i.e. having any of ging from ‘none’ to ‘very severe’) assessing stress related to the the four STB outcomes), “passive suicidal ideation only” and health of loved ones and to loved ones potentially getting infected “active suicidal ideation, plan or attempt”, with no STB as the ref- with COVID-19 (Dohrenwend et al., 1978). erence level’. Financial factors included: a significant loss of income and/or Distal risk factor domains included in this study are socio- [temporary] unemployment due to COVID-19 (two items demographic variables, number of physical health conditions recoded into a dichotomy); and financial stress, i.e. a summary and type and number of pre-pandemic lifetime mental disorders. scale [0–4] of two 5-level Likert-type adapted PLES items (ran- Proximal risk factor domains are type and number of current ging from ‘none’ to ‘very severe’) assessing financial stress and mental disorders as well as four risk factor domains considering stress related to loss of job or income due to COVID-19 adverse events-experiences related to the pandemic: personal (Dohrenwend et al., 1978). health-related factors, factors related to the health of loved ones, Interpersonal factors included: interpersonal stress, i.e. a sum- financial factors and interpersonal factors. mary scale [0–4] of four 5-level Likert-type adapted PLES items Socio-demographic variables included age (i.e. 18–29/30–49/ (ranging from ‘none’ to ‘very severe’) assessing stress related to 50–64/65 < years), sex, nationality (i.e. non-Spanish nationality romantic relationships, family relationships, other problems of or both Spanish and non-Spanish nationality, v. Spanish nation- loved ones and getting along with people at work (Dohrenwend ality only), marital status (i.e. single, divorced or legally separated, et al., 1978); and lack of social support using the reverse scaled widowed and married), living with a partner, pre-pandemic level [0–4] Oslo Social Support Scale (Dalgard, 1996). of income (i.e. <570/570–799/800–1049/1050–1299/1300–1549/ 1550–1799/1800–2199/2200–2699/2700–3599/3600–4499/4500– Analysis 5999/6000 < euros), having children in care, having elderly people or people with a disability in care and work status (i.e. essential All analyses were conducted using SAS version 9.4 (SAS Institute service worker, non-essential service worker, not working). Inc, 2013). Post-stratification weights were used to match the Physical health conditions were assessed using a 7-item check- sample to the distribution of the Spain adult general population list (Sangha et al., 2003) including respiratory diseases (not pro- according to age groups, sex and geographic region. Item-level voked by coronavirus), cardiovascular diseases, diabetes, cancer, missing data were minimal (median 0.09% [IQR 0.03–0.25%]; see chronic hepatic diseases, immunological diseases and ‘other’. A also online Supplementary Table 1) and addressed using single sum score was created and categorised into ‘none’, ‘exactly one’, multivariable imputation by chained equations (van Buuren, 2018). ‘exactly two’ and ‘three or more’. STB prevalence was estimated for the entire sample and strati- Pre-pandemic lifetime mental disorders were assessed using a fied by distal risk factors and current mental disorders. Logistic checklist based on the Composite International Diagnostic regression was used to estimate individual-level associations Interview (CIDI; Kessler and Üstün 2004) that screens for depres- (odds ratios [OR] with 95% CI) of the risk factors with the sion, bipolar disorder, anxiety, panic attacks, alcohol and drug use three STB outcomes. To deal with data sparseness, penalised max- problems and ‘other’ mental disorders. A sum score was created imum likelihood estimation (Firth-type estimation) was used and categorised into ‘none’, ‘exactly one’ and ‘two or more’. (Allison, 2012). Population-level associations, i.e. population Current mental disorders were assessed using well-validated attributable risk proportions (PARP % [S.E.]; Krysinska and screener scales, i.e. two-week major depressive disorder (PHQ-8; Martin 2009) were calculated using simulation methods based cutoff score 10; Kroenke et al., 2009), two-week generalised anx- on the logistic regression equations. PARP is the proportion of iety disorder (GAD-7; cutoff score 10; Newman et al., 2002), the cumulative predicted value of an outcome explained statistic- 30-day panic attacks (adapted CIDI screening scale item; ally by specific predictor variables. If the odds ratios from the Kessler et al., 2013), 30-day post-traumatic stress disorder logistic regression equations represent causal effects of the risk (4-item short form of the PCL-5; cutoff score 7; Zuromski factors under study, PARP can be interpreted as the expected pro- et al., 2019) and 30-day alcohol and substance use disorders portional reduction in STB prevalence if STB risk factors were (CAGE-AID; cutoff score 2; Hinkin et al., 2001). A sum score eradicated from the population. was created and categorised into ‘none’, ‘exactly one’ and ‘two Since causal relationships between the included risk factors are or more’. largely unknown, we refrained from constructing fully adjusted Personal health-related factors included: a history of multivariable models to avoid the risk of overadjustment bias COVID-19 infection (positive test and/or medical diagnosis) (Schisterman et al., 2009). A first series of analyses considered and/or having been in isolation or quarantine related to the distal risk factor domains. We estimated bivariable individual- COVID-19 (three items recoded into a dichotomy); number of level associations of socio-demographic variables and number of close contacts (<1 m) when working outside of home (0–100); physical health conditions with each of the STB outcomes. perceived inefficiency of available protective equipment at work Next, we estimated individual- and population-level associations (4-level Likert-type item ranging from ‘sufficient’ to ‘completely of pre-pandemic lifetime mental disorders with STB using separ- insufficient’); and personal health-related stress, i.e. a summary ate models for each type of disorder and for number of disorders, scale [0–4] of two 5-level Likert-type items (ranging from ‘none’ each time adjusting for sociodemographic variables and number to ‘very severe’) adapted from the Peri Life Events Scale (PLES; of physical health conditions (i.e. the remaining distal risk 4 P. Mortier et al. Table 1. Prevalence of 30-day STB in the Spanish adult general population during the first wave of the Spain COVID-19 pandemic (n = 3500) Among those without any Among those with any pre-pandemic lifetime mental pre-pandemic lifetime mental Full sample (n = 3500) disorder (n = 2274) disorder (n = 1226) na %a S.E. a na %a S.E. a na %a S.E. a No STB 3340 95.5 0.4 2233 98.2 0.3 1107 90.3 0.9 Any STB 160 4.5 0.4 41 1.8 0.3 119 9.7 0.9 Passive suicidal ideation only 96 2.7 0.3 27 1.1 0.2 69 5.7 0.7 Active suicidal ideation, plan or attempt 64 1.8 0.2 14 0.7 0.2 50 4.0 0.6 - Active suicidal ideation only 20 0.6 0.1 3 0.1 0.1 17 1.5 0.4 - Suicide plan, no attempt 39 1.1 0.2 8 0.4 0.1 31 2.4 0.4 - Suicide attempt 5 0.1 0.1 3 0.1 0.1 2 0.1 0.1 Abbreviations: S.E. = standard error; STB = STB. a Number of observations (n) are unweighted; proportions (%, S.E.) are weighted. factors). Unadjusted (bivariable) models are shown in the Associations of distal risk factors with STB Supplement. We also calculated a total PARP representing the Bivariable associations of socio-demographic variables and num- joint effects of type and number of pre-pandemic lifetime mental ber of physical health conditions with STB are shown in Table 2. disorders using a model including seven dummy variables indi- Risk factors consistently associated with all three STB outcomes cating the seven types of disorders plus one dummy variable indi- were pre-pandemic level of income (OR = 0.9), having work cating having two or more disorders (see Nock et al., 2015 for a (ORs = 0.3–0.5) and having three or more physical health condi- detailed discussion of type-number models), again adjusting for tions (ORs = 3.9–5.3). Unique associations with active suicidal the remaining distal risk factors. ideation, plan or attempt included being aged 30 or more (ORs A second series of analyses considered the proximal risk factors. = 0.3–0.5), being single (OR = 2.2), being divorced or legally sepa- We estimated individual- and population-level associations of rated (OR = 3.0) and living with a partner (OR = 0.3). Unique proximal risk factors with STB using separate models for each associations with passive suicidal ideation included being female proximal risk factor, each time adjusting for distal risk factors. (OR = 2.5), being widowed (OR = 2.2), having elderly persons or Unadjusted (bivariable) models are shown in the Supplement. persons with a disability in care (OR = 1.9) and having exactly Next, we calculated total PARPs representing the joint effects of two physical health conditions (OR = 4.0). the risk factors belonging to each separate proximal risk factor Adjusted associations of pre-pandemic lifetime mental disor- domain, each time adjusting for all distal risk factors. Similar to ders with STB are shown in Table 3 (see online Supplementary the total PARP for pre-pandemic lifetime mental disorders, the Table 5 for unadjusted analyses). Almost all pre-pandemic disor- total PARP for current mental disorders consisted of a type- ders were significantly associated with all three STB outcomes number model including five dummy variables indicating the five (ORs = 2.7–21.0; PARPs = 2.8–48.7%), including comorbidity of types of disorders plus one dummy variable indicating having disorders (ORs = 6.2–10.9; PARPs = 31.2–56.1%). Associations two or more disorders, adjusting for distal risk factors. We also cal- were generally stronger with active suicidal ideation, plan or culated the total PARP representing the joint effects of all four risk attempt than with passive ideation only. Individual-level impact factor domains considering pandemic adverse events−experiences, was particularly high for bipolar disorder (ORs = 9.3–21.0) and the total PARP representing the joint effects of all five proximal while population-level impact was highest for depression and anx- risk factor domains, adjusting for distal risk factors. iety (PARPs = 33.9–48.7%). About 49.1% of any STB is potentially attributable to the joint effects of all pre-pandemic lifetime mental Results disorders. Thirty-day prevalence of STB Associations of proximal risk factors with STB Estimated prevalence of any 30-day STB was 4.5% in the total sample, 9.7% among the 34.3% of respondents with pre- Adjusted associations of proximal risk factors with 30-day STB pandemic lifetime mental disorders and 1.8% among the are presented in Table 4 (see online Supplementary Table 6 for un- 65.7% without any pre-pandemic lifetime mental disorder adjusted analyses). Almost all current mental disorders were sig- (Table 1; see online Supplementary Table 2 for unweighted nificantly associated with all three STB outcomes (ORs = 3.3–11.7; and unimputed estimates). Around 40% of those reporting PARPs = 5.9–58.2%), including comorbidity of disorders (ORs = any 30-day STB reported active suicidal ideation, plans or 9.4–36.5; PARPs = 40.7–68.7%). Considering personal health- attempts (1.8% in total); five respondents (0.1%) reported a sui- related factors, we found that the perceived inefficiency of cide attempt. Sample characteristics are shown in Table 2 (col- protective equipment at work was consistently associated umns 2 and 3; see online Supplementary Table 3 for unweighted with STB (ORs = 1.7–2.0; PARPs = 6.5–6.6%), while personal and unimputed estimates). STB estimates stratified by distal risk health-related stress was uniquely associated with active suicidal factors and current mental disorders are shown in online ideation, plan or attempt (OR = 1.7; PARP = 52.6%). Factors Supplementary Table 4. related to the health of loved ones were uniquely associated with Epidemiology and Psychiatric Sciences 5 Table 2. Associations of distal risk factors (sociodemographic variables and number of physical health conditions) with 30-day STB (n = 3500) Passive Any STB ideation Active suicidal ideation, (n = 160) only (n = 96) plan or attempt (n = 64) % (S.E.) or Med na (S.E.) (IQR)a OR (95% CI)b OR (95% CI)b OR (95% CI)b Sociodemographic variables Agec 65 years or more 622 23.7 (0.8) 0.7 (0.5–1.2) 1.2 (0.6–2.2) 0.3 (0.2–0.7)* 50–64 years 1127 25.8 (0.7) 0.7 (0.4–1.0) 0.9 (0.5–1.6) 0.5 (0.2–0.9)* 30–49 years 1305 36.0 (0.8) 0.5 (0.3–0.8)* 0.6 (0.3–1.1) 0.5 (0.2–0.9)* 18–29 years 446 14.6 (0.6) (ref) (ref) (ref) Being female 1962 51.5 (0.9) 1.8 (1.3–2.4)* 2.5 (1.6–3.9)* 1.1 (0.7–1.8) Having non-Spanish nationality or both (vs. 331 9.4 (0.5) 1.1 (0.7–1.9) 1.3 (0.7–2.5) 0.9 (0.4–2.2) Spanish only) Marital status Single 1180 35.3 (0.8) 1.4 (1.0–2.0) 1.0 (0.6–1.6) 2.2 (1.3–3.9)* Divorced or legally separated 319 8.3 (0.5) 2.2 (1.3–3.6)* 1.8 (0.9–3.5) 3.0 (1.4–6.6)* Widowed 195 6.7 (0.5) 1.8 (1.0–3.3)* 2.2 (1.1–4.2)* 1.2 (0.4–3.9) Married 1806 49.7 (0.9) (ref) (ref) (ref) Living with a partner 2272 63.1 (0.8) 0.5 (0.4–0.7)* 0.7 (0.5–1.1) 0.3 (0.2–0.5)* Having children in care 1195 31.1 (0.8) 0.7 (0.5–1.0) 0.6 (0.4–1.0) 0.9 (0.5–1.5) Having elderly persons or persons with a 489 14.4 (0.6) 1.5 (1.0–2.2) 1.9 (1.2–3.0)* 1.0 (0.5–2.0) disability in care Pre-pandemic level of income (scaled 1–12) 5.5 (0.1) (3.1–7.7) 0.9 (0.8–0.9)* 0.9 (0.8–0.9)* 0.9 (0.8–1.0)* Work status Working, essential service worker 937 24.7 (0.7) 0.4 (0.2–0.6)* 0.4 (0.3–0.8)* 0.3 (0.1–0.7)* Working, no essential service worker 769 20.9 (0.7) 0.4 (0.3–0.7)* 0.4 (0.2–0.7)* 0.5 (0.2–1.0)* Not working 1794 54.5 (0.9) (ref) (ref) (ref) Number of physical health conditions Three or more 89 2.7 (0.3) 4.3 (2.3–8.2)* 3.9 (1.7–9.0)* 5.3 (2.2–12.7)* Exactly two 295 8.4 (0.5) 3.1 (2.0–4.8)* 4.0 (2.4–6.8)* 1.8 (0.8–4.1) Exactly one 997 28.6 (0.8) 1.3 (0.9–1.9) 1.2 (0.7–2.0) 1.5 (0.9–2.7) None 2119 60.3 (0.9) (ref) (ref) (ref) Abbreviations: OR = odds ratio; CI = confidence interval; IQR = interquartile range; Med = median; S.E. = standard error; STB = suicidal thoughts and behaviours. a Number of observations (n) are unweighted; proportions (%, S.E.) and medians (Med, IQR) are weighted. b Bivariable models were used, i.e. a separate logistic regression model was created for each risk factor. c Mean age = 49.6 years [standard deviation = 17.0] (weighted estimate). * Indicate statistically significant results (α = 0.05). passive suicidal ideation (ORs = 1.2–1.6; PARPs = 20.0–31.3%). related to the health of loved ones (total PARP = 42.2%) and to Detailed analyses (online Supplementary Table 7) show that personal health (total PARP = 34.2%). Up to 69.7% of any STB is having a partner, child or parent infected (OR = 4.2 [95%CI potentially attributable to the joint effects of pandemic adverse 1.8–10.0]), or having a loved one with severe COVID-19 symp- events-experiences, while up to 74.1% of STB is potentially attribut- toms (OR = 3.1 [95%CI 1.5–6.4]) were also associated with active able to the joint effects of all five proximal risk factor domains. suicidal ideation, plan or attempt. No significant associations were Except for the health of loved ones domain, all risk factor domains found with financial factors. Interpersonal stress was consistently were more strongly associated with active suicidal ideation, plan or associated with STB (ORs = 1.4–1.9; PARPs = 30.7–50.0%) while attempt, than with passive suicidal ideation. lack of social support was uniquely associated with active suicidal ideation, plan or attempt (OR = 2.6; PARP = 68.3%). Discussion Table 5 shows that large proportions of any 30-day STB are potentially attributable to interpersonal factors (total PARP = 62.4%), Using data from a representative sample of general adults, 30-day followed by current mental disorders (total PARP = 58.4%), factors prevalence of STB during the first wave of the Spain COVID-19 6 P. Mortier et al. Table 3. Associations of distal risk factors (pre-pandemic lifetime mental disorders) with 30-day STB (adjusted analyses; n = 3500) Active suicidal ideation, plan or Any STB (n = 160) Passive ideation only (n = 96) attempt (n = 64) PARP % PARP % PARP % na % (S.E.)a OR (95% CI)b (S.E.)b OR (95% CI)b (S.E.)b OR (95% CI)b (S.E.)b Pre-pandemic lifetime mental disorders Depression 490 13.6 (0.6) 5.4 (3.8–7.6)* 38.1 (4.6)* 4.7 (3.0–7.2)* 34.0 (6.1)* 6.7 (4.0–11.2)* 44.5 (7.1)* Bipolar disorder 55 1.6 (0.2) 14.3 (7.8–26.3)* 11.3 (2.5)* 9.3 (4.2–20.8)* 7.6 (2.8)* 21.0 (9.7–45.5)* 16.7 (4.6)* Panic attacks 199 5.5 (0.4) 5.6 (3.7–8.6)* 18.7 (3.4)* 4.7 (2.7–8.1)* 14.0 (4.2)* 7.5 (4.2–13.2)* 25.8 (6.1)* Anxiety 1052 29.3 (0.8) 3.2 (2.3–4.4)* 39.7 (6.0)* 2.7 (1.8–4.1)* 33.9 (7.9)* 3.9 (2.4–6.6)* 48.7 (9.4)* Alcohol use 37 1.0 (0.2) 5.1 (2.1–12.4)* 3.5 (1.5)* 4.5 (1.4–14.5)* 2.8 (1.7)* 6.2 (2.0–19.0)* 5.2 (2.9)* problems Drug use problems 50 1.6 (0.2) 5.5 (2.7–11.3)* 5.2 (1.7)* 6.8 (2.7–17.1)* 5.2 (2.2)* 4.4 (1.7–11.4)* 5.4 (3.0) (illicit drugs and/ or medication) Other 30 0.8 (0.2) 3.0 (1.1–8.7)* 1.7 (1.1) 2.7 (0.7–10.9) 1.3 (1.3) 3.8 (1.0–14.1)* 2.7 (1.8) Number of disorders Two or more 488 13.5 (0.6) 8.0 (5.4–12.1)* 41.5 (4.5)* 6.2 (3.7–10.5)* 31.2 (5.6)* 10.9 (6.0–19.7)* 56.1 (7.3)* Exactly one 738 20.9 (0.7) 2.8 (1.8–4.3)* 15.8 (4.0)* 3.6 (2.2–6.0)* 24.0 (5.7)* 1.5 (0.7–3.3) 4.1 (5.2) None 2274 65.7 (0.8) (ref) (ref) (ref) (ref) (ref) (ref) Total PARPc 49.1 (5.4)* 48.3 (6.9)* 55.0 (8.5)* Abbreviations: OR = odds ratio; CI = confidence interval; PARP = Population Attributable Risk Proportion; S.E. = standard error; STB = suicidal thoughts and behaviours. a Number of observations (n) are unweighted; proportions (%, S.E.) are weighted. b Adjusted models were used, i.e. a separate logistic regression model was created for each type of pre-pandemic lifetime mental disorder and for the number of pre-pandemic lifetime mental disorders, each time adjusting for sociodemographic variables and for number of physical health conditions. c Total PARP considers the joint effects of type and number of pre-pandemic lifetime mental disorders, i.e. a model including seven dummy variables indicating the seven types of disorders plus one dummy variable indicating having two or more disorders, adjusting for sociodemographic variables and for number of physical health conditions. * Indicate statistically significant results (α = 0.05); for PARP, statistical significance is based on the percentile bootstrap confidence interval. pandemic was estimated at 4.5%. Population-level impact analysis in the pre-pandemic Spanish population (0.7–0.9%; Miret et al., of risk factors showed that approximately three quarters of STB is 2014), even though lower than in representative population-based potentially attributable to mental disorders and adverse events studies in other countries conducted during the COVID-19 pan- −experiences related to the pandemic, including health-related demic: the Czech Republic (11.9%; Winkler et al., 2020), the UK stress, lack of social support and interpersonal stress. Above all, (8.2–18.0%; Iob et al., 2020; O’Connor et al., 2020) and the US these findings highlight the need for interventions that increase (4.6–15%; Bryan et al., 2020; Czeisler et al., 2020; Fitzpatrick access to adequate mental healthcare. et al., 2020; Gratz et al., 2020; Ammerman et al., 2021). Several limitations of this study need mentioning. First, the Pre-pandemic studies also documented higher 12-month STB lack of a pre-pandemic reference point of STB precludes direct prevalence in countries such as the US (4.3%; SAMHSA, 2018) pre−post comparisons of STB prevalence. Second, the observa- and the UK (5.4%; APMS, 2014) v. Spain. Apart from methodo- tional cross-sectional study design limits causal inference of the logical differences (e.g. different STB measures), higher degrees of identified associations of risk factors with STB. Prospective stud- collectivism in Spain are likely to explain the lower STB in Spain ies are needed, which could also use a structural approach (e.g. relative to other countries (Eskin et al., 2020). direct acyclic graphs) to control for as much confounding as pos- Despite our study lacking a direct pre-pandemic reference sible according to prior causal knowledge without introducing point of STB prevalence, two observations suggest a potential overadjustment bias. Third, poststratification weights were based increase of STB during the pandemic. First, although ∼75% of on three sociodemographic variables only, and the survey cooper- all STB was found among the roughly one-third of the population ation rate was low (16.5%), which could have led to non-response with a pre-pandemic history of psychopathology, STB among bias. Scarce evidence from previous studies suggests that mental respondents without pre-pandemic mental disorders was substan- disorders are higher among survey non-respondents (Kessler tial, i.e. 1.8%. Second, we found that 74.1% of STB is potentially et al., 1994), suggesting underestimation of STB in our study. attributable to mental disorders and adverse events−experiences Fourth, the assessment of mental disorders relied on self-report related to the pandemic. This estimate is in line with the PARP screener scales (current disorders) and a CIDI checklist (pre- that can be indirectly calculated when comparing the pre- pandemic lifetime disorders); it should be stressed that this assess- pandemic STB estimate from Miret et al. (2014), i.e. 0.7–0.9%, ment is inferior to face-to-face clinical assessments. with our STB estimate of 4.5%, i.e. PARP = (4.5−[0.7–0.9])/4.5 Thirty-day STB prevalence in this study (4.5%; about 1 in 22 = 80.0–84.4% (Fleiss, 1979). Since no data on suicides during adults) is substantially higher than reliable 12-month estimates the Spain COVID pandemic are currently available, it is Epidemiology and Psychiatric Sciences Table 4. Associations of proximal risk factors with 30-day STB (adjusted analyses; n = 3500) Active suicidal ideation, plan or Any STB (n = 160) Passive ideation only (n = 96) attempt (n = 64) % (S.E.) or Med (S.E.) PARP % PARP % PARP % na (IQR)a OR (95% CI)b (S.E.)b OR (95% CI)b (S.E.)b OR (95% CI)b (S.E.)b Positive screens for current mental disorders Major depressive disorder 407 11.2 (0.5) 6.0 (4.1–8.8)* 39.2 (5.0)* 3.8 (2.4–6.1)* 27.4 (6.7)* 11.7 (6.5–20.8)* 58.2 (7.4)* Generalised anxiety disorder 395 10.9 (0.5) 5.6 (3.8–8.1)* 36.3 (4.8)* 3.7 (2.3–5.9)* 25.4 (6.6)* 10.5 (5.9–18.7)* 54.0 (7.6)* Post-traumatic stress disorder 342 9.5 (0.5) 4.6 (3.1–6.8)* 26.6 (4.3)* 4.2 (2.6–6.8)* 25.7 (5.5)* 4.8 (2.7–8.5)* 28.1 (7.2)* Panic attacks 357 9.8 (0.5) 6.7 (4.5–10.0)* 36.6 (4.6)* 5.2 (3.2–8.5)* 29.9 (6.0)* 8.9 (4.9–16.2)* 47.3 (7.8)* Alcohol or substance use disorder 94 2.8 (0.3) 3.3 (1.7–6.6)* 5.9 (2.6)* 1.3 (0.5–3.8) 1.8 (2.3) 5.0 (2.1–11.5)* 11.3 (5.0)* Number of positive screens Two or more 426 11.8 (0.6) 15.6 (9.7–25.0)* 51.5 (4.3)* 9.4 (5.4–16.4)* 40.7 (5.8)* 36.5 (16.1–82.5)* 68.7 (6.2)* Exactly one 418 11.8 (0.6) 7.1 (4.3–11.8)* 18.8 (3.7)* 5.7 (3.2–10.1)* 21.1 (5.1)* 10.3 (4.1–25.8)* 14.9 (4.8)* None 2656 76.4 (0.7) (ref) (ref) (ref) (ref) (ref) (ref) Personal Health History of COVID-19 infection or isolation/ 600 16.5 (0.6) 0.8 (0.5–1.4) −2.9 (2.7) 0.7 (0.4–1.4) −3.6 (3.5) 1.0 (0.5–2.0) 0.9 (4.1) quarantine for COVID-19 Number of close contacts (<1 m) when 0.0 (0.2) (0.0–0.0) 1.0 (1.0–1.0) 1.1 (1.1) 1.0 (1.0–1.0) 2.0 (1.5) 1.0 (1.0–1.0) 0.6 (2.8) working outside of home Perceived inefficiency of protective 0.0 (0.1) (0.0–0.0) 1.9 (1.4–2.4)* 6.5 (2.0)* 1.7 (1.3–2.4)* 6.6 (2.5)* 2.0 (1.4–2.9)* 6.4 (3.3) equipment at work (scaled 0–4) Stress about personal health (scaled 0–4) 0.7 (0.0) (0.0–1.6) 1.4 (1.2–1.6)* 32.9 (8.7)* 1.2 (1.0–1.5) 16.0 (12.3) 1.7 (1.4–2.2)* 52.6 (11.9)* Health of loved ones Having loved ones infected with COVID-19 1606 45.0 (0.9) 1.7 (1.2–2.3)* 18.8 (6.6)* 1.6 (1.1–2.5)* 20.0 (8.7)* 1.6 (0.9–2.7) 16.0 (11.0) Stress about health loved ones (scaled 0–4) 1.7 (0.0) (0.8–2.6) 1.2 (1.1–1.4)* 32.7 (11.2)* 1.2 (1.0–1.5)* 31.3 (15.0)* 1.2 (1.0–1.5) 30.5 (18.1) Financial factors Significant income loss or (temporarily) 1445 39.8 (0.8) 1.1 (0.7–1.5) 1.9 (6.9) 1.2 (0.8–1.9) 6.7 (8.9) 0.9 (0.5–1.6) −3.1 (12.1) unemployed due to COVID-19 Stress about financial situation (scaled 0–4) 0.9 (0.0) (0.0–2.1) 1.1 (1.0–1.3) 14.9 (10.0) 1.1 (0.9–1.3) 10.9 (12.8) 1.2 (1.0–1.5) 22.0 (16.7) Interpersonal factors Interpersonal stress (scaled 0–4) 0.6 (0.0) (0.0–1.4) 1.6 (1.3–1.9)* 38.9 (6.7)* 1.4 (1.2–1.8)* 30.7 (9.5)* 1.9 (1.4–2.4)* 50.0 (9.7)* Lack of social support (scaled 0–4) 0.8 (0.0) (0.4–1.3) 1.7 (1.4–2.1)* 42.6 (9.2)* 1.3 (0.9–1.7) 14.4 (15.5) 2.6 (1.9–3.6)* 68.3 (10.0)* Abbreviations: OR = odds ratio; CI = confidence interval; IQR = interquartile range; Med = median; PARP = Population Attributable Risk Proportion; S.E. = standard error; STB = suicidal thoughts and behaviours. a Number of observations (n) are unweighted; proportions (%, S.E.) and medians (Med, IQR) are weighted. b Adjusted models were used, i.e. a separate logistic regression model was created for each proximal risk factor, each time adjusting for distal risk factors. * Indicate statistically significant results (α = 0.05); for PARP, statistical significance is based on the percentile bootstrap confidence interval. 7 8 P. Mortier et al. Table 5. Population-level associations of proximal risk factor domains with 30-day STB (n = 3500) Passive ideation Active suicidal ideation, plan or Any STB (n = 160) only (n = 96) attempt (n = 64) PARP % (S.E.)a PARP % (S.E.)a PARP % (S.E.)a Positive screens for current mental disordersb 58.4 (3.9)* 45.6 (5.3)* 73.1 (5.0)* Adverse events-experiences related to the pandemic Personal health 34.2 (8.6)* 18.1 (12.2) 54.4 (11.4)* Health of loved ones 42.2 (11.3)* 41.6 (14.6)* 38.6 (20.1) Financial factors 14.2 (10.4) 13.7 (13.8) 18.1 (18.0) Interpersonal factors 62.4 (8.1)* 37.9 (14.8)* 81.4 (7.0)* c Total all adverse events-experiences 69.7 (8.5)* 55.6 (14.2)* 83.0 (10.5)* Total all proximal risk factor domainsd 74.1 (7.7)* 56.4 (14.3)* 86.4 (8.5)* Abbreviations: PARP = Population Attributable Risk Proportion; S.E. = standard error; STB = suicidal thoughts and behaviours. a Adjusted models were used, i.e. a separate logistic regression model was created to estimate the joint effects of all STB risk factors belonging to the proximal risk factor domain under consideration, each time adjusting for distal risk factors. b PARP considers the joint effects of type and number of current mental disorders, i.e. one model including five dummy variables indicating the five types of disorders plus one dummy variable indicating having two or more disorders, adjusting for distal risk factors. c Total PARP considers the joint effects of all adverse events-experiences related to the pandemic, adjusting for distal risk factors. PARPs of separate adverse events-experiences domains do not add up to this total PARP because of the multifactorial aetiology of STB, i.e. one given risk factor can be part of multiple risk factor combinations and causal pathways leading to STB. d Total PARP considers the joint effects of all proximal STB risk factors, adjusting for distal risk factors. PARPs of separate proximal risk factor domains do not add up to this total PARP because of the multifactorial aetiology of STB, i.e. one given risk factor can be part of multiple risk factor combinations and causal pathways leading to STB. * Indicate statistically significant results (α = 0.05) based on the percentile bootstrap confidence interval. impossible to investigate whether the potential increase in STB during this time of healthcare system overload. The fact that that our study suggests is reflected in increased suicide rates. nearly one out of 20 Spanish adults screened positive for STB Studies from other countries suggest that suicide rates either points to the need for multi-stage routine suicide risk screening remained unchanged during the first wave of the pandemic (e.g. programmes in healthcare systems (Bahraini et al., 2020), and Greece, Vandoros et al., 2020; the US, Faust et al., 2021; for augmenting the availability of crisis response helplines, safety Australia, Coroners Court of Victoria, 2020; China, Qi et al., planning and evidence-based treatments for STB (Gunnell et al., 2020) or decreased (e.g. Norway, Qin and Mehlum 2020; the 2020; Moutier, 2020; Wasserman et al., 2020). In addition, one UK, Office for National Statistics, 2020; Germany, Radeloff out of four Spanish adults screened positive for current mental et al., 2021; Peru, Calderon-Anyosa and Kaufman 2021). A recent disorders and roughly 58% of STB is potentially attributable to study from Japan found evidence for the so-called honeymoon these disorders. Our findings also suggest an important role of effect, i.e. a delayed increase in suicide rates after an initial drop pre-pandemic mental disorders with roughly 49% of STB being (Tanaka and Okamoto, 2021). Taken together, this warns for potentially attributable to disorders with onset before the pan- the adverse effects of the pandemic on current and future popu- demic. Given the high population-level burden these findings lation mental health and urges policy makers to implement effect- indicate, innovative methods such as remote tele-mental health ive prevention intervention strategies (Moutier, 2020). services (Wasserman et al., 2020) or E-health interventions The strong associations of mental disorders and interpersonal (Torok et al., 2020) are needed to support and complement trad- factors with pandemic STB that our study documented are in line itional mental healthcare, not only during the pandemic, but also with pre-pandemic studies: a meta-analysis on STB risk factors in beyond (Vieta et al., 2020). the general European population found a pooled OR = 7.4 for Our study also highlights the need for interventions support- affective disorders, OR = 4.3 for anxiety disorders, OR = 1.5–2.5 ing interpersonal relationships in the population. In line with pre- for substance use and OR = 2.6 for low social support. Our find- vious research on prosocial behaviour under stress (Barzilay et al., ings also confirm pandemic STB studies that found health-related 2020), stress related to the health of loved ones was higher than worries a significant risk factor (e.g. OR = 1.43 with 30-day sui- personal health-related stress, and having loved ones infected cide risk; Winkler et al., 2020). We now show that both stress with COVID-19 – but not a personal COVID-19 infection – about personal health and the health of loved ones is associated was strongly associated with STB. Furthermore, we found that with 30-day STB. In contrast to previous studies (Gratz et al., tackling interpersonal stress and lack of social support in the 2020; Iob et al., 2020; Winkler et al., 2020), however, no signifi- population could potentially reduce STB up to ∼62%. Taken cant associations were found with having a COVID-19 diagnosis together, these findings support previous calls (Gunnell et al., or with financial factors. Given the scarce evidence available to 2020; Moutier, 2020; Wasserman et al., 2020) to increase date, more research is needed on the relationship between adverse community-level support for those living alone, to enable regular events−experiences related to the pandemic and STB. (digital) check-ins by relatives and friends, to provide access to Our findings support recently proposed prevention frame- support by those experiencing health-related stress, family pro- works targeting adverse mental health during and after the blems or domestic violence, as well as to implement public health COVID-19 pandemic (Gunnell et al., 2020; Moutier, 2020; strategies to promote stress resilience and cohesion. In conclusion, Wasserman et al., 2020). Above all, the results urge policy-makers our findings together with emerging evidence worldwide call for a to increase access to adequate mental healthcare in Spain, even strong coordinated response from policy-makers, health care Epidemiology and Psychiatric Sciences 9 professionals and the scientific community to overcome current Rosa Pla, Nieves Plana, Coro Pérez-Aznar, Beatriz Pérez-Gómez, Víctor and prevent future adverse mental health related to the Pérez-Solà, Aurora Pérez-Zapata, José Ignacio Pijoan, Elena Polentinos- COVID-19 pandemic. Castro, Beatriz Puértolas, María Teresa Puig, Álex Quílez, María Jesús Quintana, Antonio Quiroga, David Rentero, Cristina Rey, Cristina Rius, Supplementary material. The supplementary material for this article can Carmen Rodríguez-Blázquez, M. José Rojas-Giraldo, Yamina Romero- be found at https://doi.org/10.1017/S2045796021000093. Barzola, Gabriel Rubio, Pedro Ruiz, Mercedes Rumayor, Margarita Sáenz, Jesús Sánchez, Ignacio Sánchez-Arcilla, Ferran Sanz, Cònsol Serra, Victòria Data. The de-identified participant data as well as the study protocol, statis- Serra-Sutton, Manuela Serrano, Sílvia Solà, Sara Solera, Miguel Soto, tical analysis plan and data dictionaries used for this study are available as Alejandra Tarragó, Natividad Tolosa, Mireia Vázquez, Margarita Viciola, from publication and upon reasonable request from the corresponding author Eduard Vieta, Gemma Vilagut, Wouter Voorspoels, Sara Yago-González, (PM; pmortier@imim.es) as long as the main objective of the data-sharing Jesús Yáñez-Sánchez, Yolanda Zapico, Luís María Zorita, Iñaki Zorrilla, and request is replicating the analysis and findings as reported in this paper (with- Saioa L. Zurbano. out investigator support), after approval of a proposal and with a signed data access agreement. Acknowledgements. The authors would like to sincerely thank Puri Barbas References and Franco Amigo for the management of the MINDCOVID project. Adult Psychiatric Morbidity Survey (APMS). Survey of Mental Health and Financial support. This study was supported by the Instituto de Salud Wellbeing, England, 2014. NHS Digital, 2016 Available at (https://digital. Carlos III, Ministerio de Ciencia e Innovación/FEDER (grant number nhs.uk/data-and-information/publications/statistical/adult-psychiatric- COV20/00711), (PM, grant number ISCIII, CD18/00049), (grant number morbidity-survey/adult-psychiatric-morbidity-survey-survey-of-mental-health- ISCIII, FI18/00012), (VPS, grant number PI19/00236); Ayudas para la and-wellbeing-england-2014). Accessed 21 January 2021. Formación de Profesorado Universitario, Ministerio de Ciencia, Innovación Alfonso-Sánchez JL, Martin-Moreno JM, Martinez IM and Martinez AA y Universidades (grant number FPU15/05728); Generalitat de Catalunya (2020) Epidemiological study and cost analysis of suicide in Spain: over (grant number 2017SGR452). The funding institutions had no role in the 100 years of evolution. Archives of Suicide Research 24, S356–S369. design, analysis, interpretation or submission of publication of the data. No Allison PD (2012) Binary logistic regression: Details and options. In Logistic payment was made for writing this article by a pharmaceutical company or Regression Using SAS®: Theory and Application, 2nd Edn., Cary, NC: SAS other agency. Corresponding authors had full access to all the data in the Institute Inc, p. 57. study and the final responsibility for the decision of submitting for publication. 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