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Supplementary Material 1

Supplementary Material 2

The aim of this study is to investigate the following research questions: 1. What is the overall effectiveness of any psychotherapy in reducing suicidal thoughts and behavior? 2. Which participant characteristics moderate the overall effects of psychotherapy? 3. Which intervention characteristics moderate their effectiveness? In addition, the analyses will be performed in subgroups of interest: different types of psychotherapy and specific psychiatric patient groups. In addition, the analyses will be performed in subgroups of interest (e.g. different types of psychotherapy and specific psychiatric patient groups). This study will be conducted in collaboration with the Australian National Health and Medical Research Council Centre for Research Excellence for Suicide Prevention (CRESP) and the Black Dog Institute and Australian National University, Australia. Two researchers will run systematic search using the databases PubMed, Embase, PsycINFO, Web of Science, Scopus, and The Cochrane Central Register of Controlled Trials. Index and free terms, such as ‘suicide’, ‘self-kill’, ‘self-poison’, ‘RCT’, ‘randomised’, ‘clinical trials’, and ‘psychotherapy’ will be used. Studies of interest will also be extracted from the reference lists of relevant articles. Study extraction and selection will be carried out in the Covidence software. We are studying suicidal thoughts and behavior, i.e. outcomes of suicidal ideation and/or suicidal thoughts, further suicide attempt, death by suicide where applicable, or self-harm leading to hospitalization. We will exclude non-suicidal self-injury (i.e. clearly without the intention to die), as this has been shown to be a distinct phenomenon. We also exclude self harm that did not result in hospitalization. The control conditions to be studied are waiting list, treatment as usual (medication use is allowed), placebo, psychological placebo (supportive therapy), sham version of the intervention. Studies will be excluded when they measure suicidal ideation with only one or few item(s) from a scale developed for a different construct, such as depression or only report on non-suicidal self-injury (i.e. clearly without the intention to die) as this has been shown to be a distinct phenomenon), they were conducted in an inpatient setting, and the participants (or at least the majority of them) were under 18 years old. The authors of eligible studies will be contacted to obtain the IPD of baseline and follow-up measurements.The main analyses will be conducted with post-treatment outcomes.If enough data is available on follow-up measures, additional analyses will be performed for these outcomes.An email requesting the raw data will be sent to the corresponding authors of the articles.If no author has responded 9 weeks after the first email request or if the contacted authors indicate that they do not have access to the data, data will be considered unavailable.When requested, we will also draft a written agreement on data sharing, management, and use.The data will be stored at the server of the VU and is owned by SURE-NET. The IPD-MA will be performed in R package ipdmeta in one stage.Analyses will be performed according to the intention-to-treat principle. Multiple imputations will be conducted within studies under the missing at random assumption. This method will generate 100 imputed datasets using data available across studies (if available, predictors for imputation will be at least baseline suicidality scores, age, sex, and psychiatric disorder).In addition, we will run analyses with complete cases only to examine the differences in results between imputed and complete cases.A multilevel linear regression analysis will be used to assess the effect of the intervention on standardized suicidal ideation scores post-treatment, while controlling for baseline suicidal ideation.Patient-level data will be treated as level 1, and study-level data as level 2.In case of a cluster RCT design, we will add the cluster as a third level. We will use a random effects model to estimate summary intervention effects and to capture unobserved heterogeneity between study populations and study efficacy, respectively.Hedges’ g will be calculated as an effect size measure.A multilevel logistic regression analysis will be used to assess the efficacy of the intervention on dichotomous variables (suicide attempt and death by suicide) post-treatment. RRs and the corresponding 95% confidence intervals will be calculated using the Kenward-Roger variance estimator.Again, a random effects model will be used.We will investigate the moderating effects of patient and intervention characteristics by adding the selected moderators as well as the moderator*intervention interaction terms to the multilevel (logistic) regression analyses, separating within- and across-study interaction effects to avoid ecological bias by centering the patient-level moderator about its mean.Each potential moderator will be added separately to the model.The moderators that show a statistically significant effect will then be tested simultaneously in one model to investigate which moderators have an independent effect on treatment outcome. The above analyses will additionally be performed in subgroups of psychiatric disorder and type of psychotherapy. For types of psychotherapy, subgroup analyses will likely be performed for CBT, DBT, problem-solving therapy (PST), CT, mindfulness-based therapy, and collaborative assessment and management of suicidality (CAMS), as these are the mostly used psychotherapies for suicidality. Regarding psychiatric patient groups, the following subgroups will likely be analyzed: (borderline) personality disorder, depression, substance abuse, post-traumatic stress disorder, psychosis, and sleep disorder. Subgroup analyses will also be performed for different suicidality scales and comparison groups to investigate whether effect sizes differ substantially when using a different scale or control condition. Other subgroup analyses may be performed depending on which factors reach statistical significance in the moderator analyses.

Background:\ud The COVID-19 pandemic is having profound mental health consequences for many people. Concerns have been expressed that, at their most extreme, these consequences could manifest as increased suicide rates. We aimed to assess the early effect of the COVID-19 pandemic on suicide rates around the world.\ud \ud Methods:\ud We sourced real-time suicide data from countries or areas within countries through a systematic internet search and recourse to our networks and the published literature. Between Sept 1 and Nov 1, 2020, we searched the official websites of these countries’ ministries of health, police agencies, and government-run statistics agencies or equivalents, using the translated search terms “suicide” and “cause of death”, before broadening the search in an attempt to identify data through other public sources. Data were included from a given country or area if they came from an official government source and were available at a monthly level from at least Jan 1, 2019, to July 31, 2020. Our internet searches were restricted to countries with more than 3 million residents for pragmatic reasons, but we relaxed this rule for countries identified through the literature and our networks. Areas within countries could also be included with populations of less than 3 million. We used an interrupted time-series analysis to model the trend in monthly suicides before COVID-19 (from at least Jan 1, 2019, to March 31, 2020) in each country or area within a country, comparing the expected number of suicides derived from the model with the observed number of suicides in the early months of the pandemic (from April 1 to July 31, 2020, in the primary analysis).\ud \ud Findings:\ud We sourced data from 21 countries (16 high-income and five upper-middle-income countries), including whole-country data in ten countries and data for various areas in 11 countries). Rate ratios (RRs) and 95% CIs based on the observed versus expected numbers of suicides showed no evidence of a significant increase in risk of suicide since the pandemic began in any country or area. There was statistical evidence of a decrease in suicide compared with the expected number in 12 countries or areas: New South Wales, Australia (RR 0·81 [95% CI 0·72–0·91]); Alberta, Canada (0·80 [0·68–0·93]); British Columbia, Canada (0·76 [0·66–0·87]); Chile (0·85 [0·78–0·94]); Leipzig, Germany (0·49 [0·32–0·74]); Japan (0·94 [0·91–0·96]); New Zealand (0·79 [0·68–0·91]); South Korea (0·94 [0·92–0·97]); California, USA (0·90 [0·85–0·95]); Illinois (Cook County), USA (0·79 [0·67–0·93]); Texas (four counties), USA (0·82 [0·68–0·98]); and Ecuador (0·74 [0·67–0·82]).\ud \ud Interpretation:\ud This is the first study to examine suicides occurring in the context of the COVID-19 pandemic in multiple countries. In high-income and upper-middle-income countries, suicide numbers have remained largely unchanged or declined in the early months of the pandemic compared with the expected levels based on the pre-pandemic period. We need to remain vigilant and be poised to respond if the situation changes as the longer-term mental health and economic effects of the pandemic unfold.