Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide.

Our sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups.

High-risk (n = 452) patients tended to be younger, White, unmarried, homeless, and have more mental health diagnoses compared to moderate- (n = 2149) and low-risk (n = 2209) patients. Moderate- and low-risk patients tended to be older, married, Black, and Native American or Pacific Islander, and have more physical health diagnoses compared to high-risk patients. Low-risk patients had more missing data than higher-risk patients.

Study expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.

While past research suggested that living arrangements are associated with suicide death, no study has examined the impact of sustained living arrangements and the change in living arrangements. Also, previous survival analysis studies only reported a single hazard ratio (HR), whereas the actual HR may change over time. We aimed to address these limitations using causal inference approaches.

Multi-point data from a general Japanese population sample were used. Participants reported their living arrangements twice within a 5-year time interval. After that, suicide death, non-suicide death and all-cause mortality were evaluated over 14 years. We used inverse probability weighted pooled logistic regression and cumulative incidence curve, evaluating the association of time-varying living arrangements with suicide death. We also studied non-suicide death and all-cause mortality to contextualize the association. Missing data for covariates were handled using random forest imputation.

A total of 86,749 participants were analysed, with a mean age (standard deviation) of 51.7 (7.90) at baseline. Of these, 306 died by suicide during the 14-year follow-up. Persistently living alone was associated with an increased risk of suicide death (risk difference [RD]: 1.1%, 95% confidence interval [CI]: 0.3–2.5%; risk ratio [RR]: 4.00, 95% CI: 1.83–7.41), non-suicide death (RD: 7.8%, 95% CI: 5.2–10.5%; RR: 1.56, 95% CI: 1.38–1.74) and all-cause mortality (RD: 8.7%, 95% CI: 6.2–11.3%; RR: 1.60, 95% CI: 1.42–1.79) at the end of the follow-up. The cumulative incidence curve showed that these associations were consistent throughout the follow-up. Across all types of mortality, the increased risk was smaller for those who started to live with someone and those who transitioned to living alone. The results remained robust in sensitivity analyses.

Individuals who persistently live alone have an increased risk of suicide death as well as non-suicide death and all-cause mortality, whereas this impact is weaker for those who change their living arrangements.

Little is known about when youth may be at greatest risk for attempting suicide, which is critically important information for the parents, caregivers, and professionals who care for youth at risk. This study used adolescent and parent reports, and a case-crossover, within-subject design to identify 24-hour warning signs (WS) for suicide attempts.

Adolescents (N = 1094, ages 13 to 18) with one or more suicide risk factors were enrolled and invited to complete bi-weekly, 8–10 item text message surveys for 18 months. Adolescents who reported a suicide attempt (survey item) were invited to participate in an interview regarding their thoughts, feelings/emotions, and behaviors/events during the 24-hours prior to their attempt (case period) and a prior 24-hour period (control period). Their parents participated in an interview regarding the adolescents’ behaviors/events during these same periods. Adolescent or adolescent and parent interviews were completed for 105 adolescents (81.9% female; 66.7% White, 19.0% Black, 14.3% other).

Both parent and adolescent reports of suicidal communications and withdrawal from social and other activities differentiated case and control periods. Adolescent reports also identified feelings (self-hate, emotional pain, rush of feelings, lower levels of rage toward others), cognitions (suicidal rumination, perceived burdensomeness, anger/hostility), and serious conflict with parents as WS in multi-variable models.

This study identified 24-hour WS in the domains of cognitions, feelings, and behaviors/events, providing an evidence base for the dissemination of information about signs of proximal risk for adolescent suicide attempts.

Suicidal behaviors are prevalent among college students; however, students remain reluctant to seek support. We developed a predictive algorithm to identify students at risk of suicidal behavior and used telehealth to reduce subsequent risk.

Data come from several waves of a prospective cohort study (2016–2022) of college students (n = 5454). All first-year students were invited to participate as volunteers. (Response rates range: 16.00–19.93%). A stepped-care approach was implemented: (i) all students received a comprehensive list of services; (ii) those reporting past 12-month suicidal ideation were directed to a safety planning application; (iii) those identified as high risk of suicidal behavior by the algorithm or reporting 12-month suicide attempt were contacted via telephone within 24-h of survey completion. Intervention focused on support/safety-planning, and referral to services for this high-risk group.

5454 students ranging in age from 17–36 (s.d. = 5.346) participated; 65% female. The algorithm identified 77% of students reporting subsequent suicidal behavior in the top 15% of predicted probabilities (Sensitivity = 26.26 [95% CI 17.93–36.07]; Specificity = 97.46 [95% CI 96.21–98.38], PPV = 53.06 [95% CI 40.16–65.56]; AUC range: 0.895 [95% CIs 0.872–0.917] to 0.966 [95% CIs 0.939–0.994]). High-risk students in the Intervention Cohort showed a 41.7% reduction in probability of suicidal behavior at 12-month follow-up compared to high-risk students in the Control Cohort.

Predictive risk algorithms embedded into universal screening, coupled with telehealth intervention, offer significant potential as a suicide prevention approach for students.

In 2015, the Canadian Supreme Court declared that an absolute Criminal Code prohibition on assisted suicide and euthanasia was unconstitutional. In response, the Canadian parliament enacted Bill C-14 in 2016 permitting assisted suicide and euthanasia for the end-of-life context, which it termed “Medical Assistance in Dying” (MAiD). In 2021, Bill C-7 expanded eligibility for MAiD to those with disabilities not approaching their natural death. By 2021, MAiD accounted for 3.3% of all deaths in Canada with some areas of Canada presently reporting MAiD death rates upward of 7%. In 2021, Canada had 10,064 deaths by MAiD, surpassing all jurisdictions for yearly reported assisted deaths.

To examine the impact of the Canadian MAiD program and analyze its safeguards.

A working group of physicians from diverse practice backgrounds and a legal expert, several with bioethics expertise, reviewed Canadian MAiD data and case reports. Grey literature was also considered, including fact-checked and reliable Canadian mainstream newspapers and parliamentary committee hearings considering the expansion of MAiD.

Several scientific studies and reviews, provincial and correctional system authorities have identified issues with MAiD practice. As well, there is a growing accumulation of narrative accounts detailing people getting MAiD due to suffering associated with a lack of access to medical, disability, and social support.

The Canadian MAiD regime is lacking the safeguards, data collection, and oversight necessary to protect Canadians against premature death. The authors have identified these policy gaps and used MAiD cases to illustrate these findings.

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.