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Background Each year, many help seekers in need contact health helplines for mental support. It is crucial that they receive support immediately, and that waiting times are minimal. In order to minimize delay, helplines must have adequate staffing levels, especially during peak hours. This has raised the need for means to predict the call and chat volumes ahead of time accurately. Motivated by this, in this paper, we analyze real-life data to develop models for accurately forecasting call volumes, for both phone and chat conversations for online mental health support. Methods This research was conducted on real call and chat data (adequately anonymized) provided by 113 Suicide Prevention (Over ons | 113 Zelfmoordpreventie) (throughout referred to as ‘113’), the online helpline for suicide prevention in the Netherlands. Chat and phone call data were analyzed to better understand the important factors that influence the call arrival process. These factors were then used as input to several Machine Learning (ML) models to forecast the number of call and chat arrivals. Next to that, senior counselors of the helpline completed a web-based questionnaire after each shift to assess their perception of the workload. Results This study has led to several remarkable and key insights. First, the most important factors that determine the call volumes for the helpline are the trend, and weekly and daily cyclic patterns (cycles), while monthly and yearly cycles were found to be non-significant predictors for the number of phone and chat conversations. Second, media events that were included in this study only have limited—and only short-term—impact on the call volumes. Third, so-called (S)ARIMA models are shown to lead to the most accurate prediction in the case of short-term forecasting, while simple linear models work best for long-term forecasting. Fourth, questionnaires filled in by senior counselors show that the experienced workload is mainly correlated to the number of chat conversations compared to phone calls. Conclusion (S)ARIMA models can best be used to forecast the number of daily chats and phone calls with a MAPE of less than 10 in short-term forecasting. These models perform better than other models showing that the number of arrivals depends on historical data. These forecasts can be used as support for planning the number of counselors needed. Furthermore, the questionnaire data show that the workload experienced by senior counselors is more dependent on the number of chat arrivals and less on the number of available agents, showing the value of insight into the arrival process of conversations.

Background: Targeted interventions for suicide prevention rely on adequate identification of groups at elevated risk. Several risk factors for suicide are known, but little is known about the interactions between risk factors. Interactions between risk factors may aid in detecting more specific sub-populations at higher risk. Methods: Here, we use a novel machine learning heuristic to detect sub-populations at ultra high-risk for suicide based on interacting risk factors. The data-driven and hypothesis-free model is applied to investigate data covering the entire population of the Netherlands. Findings: We found three sub-populations with extremely high suicide rates (i.e. >50 suicides per 100,000 person years, compared to 12/100,000 in the general population), namely: (1) people on unfit for work benefits that were never married, (2) males on unfit for work benefits, and (3) those aged 55–69 who live alone, were never married and have a relatively low household income. Additionally, we found two sub-populations where the rate was higher than expected based on individual risk factors alone: widowed males, and people aged 25–39 with a low level of education. Interpretation: Our model is effective at finding ultra-high risk groups which can be targeted using sub-population level interventions. Additionally, it is effective at identifying high-risk groups that would not be considered risk groups based on conventional risk factor analysis.

Background While frank discussion of suicidal thoughts in patients with depression is important for the prevention of suicide, suicide exploration of General Practitioners (GPs) is suboptimal. This study aimed to assess whether an intervention that prompts pop-up screens nudges GPs to more frequently explore suicidal thoughts over the course of two years. Methods From January 2017 to December 2018, the intervention was incorporated in the information system of the Dutch general practice sentinel network. New registration of an episode of depression triggered a pop-up screen referring to a questionnaire about GPs’ behaviour with regard to exploring suicidal thoughts. In two years, 625 questionnaires were completed by GPs and analysed using multilevel logistic regression analyses. Results Compared to the first year, GPs were 50% more likely to explore suicidal thoughts among patients in the second year (OR 1.48; 95%CI 1.01–2.16). When adjusting for patients’ gender and age we found that the effect of the pop-up screens disappeared (OR 1.33; 95% CI 0.90–1.97). Suicide exploration occurred less frequently in women than in men (OR 0.64; 95% CI 0.43–0.98) and in older compared to younger patients (OR 0.97; 95% CI 0.96–0.98 per year older). In addition, 26% of variation in suicide exploration was because of differences in general practice. There was no evidence that general practices developed differently over time. Conclusions Although low cost and easy to administer, the pop-up system was not effective in nudging GPs to explore suicidality more frequently. We encourage studies to test whether implementing these nudges as part of a multifaceted approach will lead to a stronger effect. Moreover, we recommend researchers to include more variables, such as work experience or previous mental health training, to better understand the effects of the intervention on GPs’ behaviour.

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Background: Predicted increases in suicide were not generally observed in the early months of the COVID-19 pandemic. However, the picture may be changing and patterns might vary across demographic groups. We aimed to provide a timely, granular picture of the pandemic's impact on suicides globally. Methods: We identified suicide data from official public-sector sources for countries/areas-within-countries, searching websites and academic literature and contacting data custodians and authors as necessary. We sent our first data request on 22nd June 2021 and stopped collecting data on 31st October 2021. We used interrupted time series (ITS) analyses to model the association between the pandemic's emergence and total suicides and suicides by sex-, age- and sex-by-age in each country/area-within-country. We compared the observed and expected numbers of suicides in the pandemic's first nine and first 10-15 months and used meta-regression to explore sources of variation. Findings: We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-within-the-country, four with both). There was no evidence of greater-than-expected numbers of suicides in the majority of countries/areas-within-countries in any analysis; more commonly, there was evidence of lower-than-expected numbers. Certain sex, age and sex-by-age groups stood out as potentially concerning, but these were not consistent across countries/areas-within-countries. In the meta-regression, different patterns were not explained by countries’ COVID-19 mortality rate, stringency of public health response, economic support level, or presence of a national suicide prevention strategy. Nor were they explained by countries’ income level, although the meta-regression only included data from high-income and upper-middle-income countries, and there were suggestions from the ITS analyses that lower-middle-income countries fared less well. Interpretation: Although there are some countries/areas-within-countries where overall suicide numbers and numbers for certain sex- and age-based groups are greater-than-expected, these countries/areas-within-countries are in the minority. Any upward movement in suicide numbers in any place or group is concerning, and we need to remain alert to and respond to changes as the pandemic and its mental health and economic consequences continue. Funding: None.

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.

Supplemental material, sj-pdf-1-qhr-10.1177_10497323211022316 for Additional Value of Peer Informants in Psychological Autopsy Studies of Youth Suicides by Milou Looijmans, Diana van Bergen, Renske Gilissen, Arne Popma, Elias Balt, Daan Creemers, Lieke van Domburgh, Wico Mulder, Sanne Rasing and Saskia Mérelle in Qualitative Health Research

Supplemental material, sj-pdf-1-qhr-10.1177_10497323211022316 for Additional Value of Peer Informants in Psychological Autopsy Studies of Youth Suicides by Milou Looijmans, Diana van Bergen, Renske Gilissen, Arne Popma, Elias Balt, Daan Creemers, Lieke van Domburgh, Wico Mulder, Sanne Rasing and Saskia Mérelle in Qualitative Health Research

Additional file 1: Questionnaire round 1 of the Delphi study, to achieve convergence of opinion. Quality indicators were rated by the participants on two selection criteria: relevance and action orientation

Questionnaire Gatekeeper training. Data identifies people at risk of suicide, referral to GP, referral to 113 Suicide Prevention, knowledge and confidence. Personal data of participants and ordinal data on a 5 point scale. (DOCX 15 kb)

Mensen die lesbisch, homo, bi en/of transgender (LHBT) zijn of die gevoelens hebben, staan voor extra uitdagingen in vergelijking met anderen. Negatieve reacties, pesten en geweld komen veel voor. Dit maakt LHBT’ers kwetsbaar voor onder meer depressie en zelfmoord. In dit factsheet zet 113 Zelfmoordpreventie in samenwerking met Movisie en de Rijksuniversiteit Groningen de meest recente cijfers op een rij.

In this thesis, chapter 2 presents the protocol of the SUPRANET Care implementation study. The SUPRANET Care study (N=13) uses an uncontrolled longitudinal prospective design, collecting data on on the following three study outcomes by using a 2-level structure (institutional level and team level): 1] reduced suicide rates, 2] improved registration of suicide attempts, and 3] improved professionals’ knowledge and adherence towards the suicide prevention guideline. In chapter 3, a Delphi study is described. An expert panel of 90 participants scored 11 quality indicators (QIs) on two criteria (relevance and action orientation). Data analysts (n=6) from the participating institutions rated the QIs on a third criterion: feasibility. We found that 5 QIs were scored as relevant and action oriented: 1] screening for suicidal thoughts and suicidal behavior, 2] safety plans, 3] early follow-up on discharge, 4] continuity of care, and 5] family- or caretaker involvement. Only one indicator reached consensus on feasibility: early follow-up on discharge. In chapter 4 of this thesis, a total of 400 mental health care professionals were included, working in 22 teams (crisis teams and ambulatory care teams) across 12 institutions. Although the majority of mental health professionals (N=400) scored relatively high on professionals’ knowledge, and attitude during baseline, most of them were not aware to what extent the guideline was used by their colleagues (i.e. adherence towards the guideline). After the data was analyzed on team level, we found that crisis teams scored significantly better on professionals’ knowledge (B(95%-CI)=2.00(1.32-2,67); p

Background Predicted increases in suicide were not generally observed in the early months of the COVID-19 pandemic. However, the picture may be changing and patterns might vary across demographic groups. We aimed to provide a timely, granular picture of the pandemic's impact on suicides globally. Methods We identified suicide data from official public-sector sources for countries/areas-within-countries, searching websites and academic literature and contacting data custodians and authors as necessary. We sent our first data request on 22nd June 2021 and stopped collecting data on 31st October 2021. We used interrupted time series (ITS) analyses to model the association between the pandemic's emergence and total suicides and suicides by sex-, age-and sex-by-age in each country/area-within-country. We compared the observed and expected numbers of suicides in the pandemic's first nine and first 10-15 months and used meta-regression to explore sources of variation. Findings We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-within-the-country, four with both). There was no evidence of greater-than-expected numbers of suicides in the majority of countries/areas-within-countries in any analysis; more commonly, there was evidence of lower-than-expected numbers. Certain sex, age and sex-by-age groups stood out as potentially concerning, but these were not consistent across countries/areas-within-countries. In the meta-regression, different patterns were not explained by countries' COVID-19 mortality rate, stringency of public health response, economic support level, or presence of a national suicide prevention strategy. Nor were they explained by countries' income level, although the meta-regression only included data from high-income and upper-middle-income countries, and there were suggestions from the ITS analyses that lower-middle-income countries fared less well. Interpretation Although there are some countries/areas-within-countries where overall suicide numbers and numbers for certain sex- and age-based groups are greater-than-expected, these countries/areas-within-countries are in the minority. Any upward movement in suicide numbers in any place or group is concerning, and we need to remain alert to and respond to changes as the pandemic and its mental health and economic consequences continue. Copyright (C) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)