Fatal and Non-fatal Self-Injury in the USA: Critical Review of Current Trends and Innovations in Prevention

Evaluating the ability of artificial intelligence to predict suicide: A systematic review of reviews

INTRODUCTION

Suicide remains a critical global public health issue, with approximately 800,000 deaths annually. Despite various prevention efforts, suicide rates are rising, highlighting the need for more effective strategies. Traditional suicide risk assessment methods often fall short in accuracy and predictive capability. This has driven interest in artificial intelligence (AI), particularly machine learning (ML), as a potential solution. This paper reviews systematic evaluations of AI's effectiveness in predicting suicide risk, aiming to explore AI's potential while addressing its challenges and limitations.

METHODOLOGY

A meta-research approach was used to review existing systematic reviews on AI's role in suicide risk prediction. Following PRISMA guidelines, a comprehensive search was conducted in PubMed and Web of Science for publications from 2004 to 2024. Relevant studies were selected based on specific inclusion criteria, and data were extracted on review characteristics, AI techniques, outcomes, and methodological quality. The review focuses on AI/ML models predicting suicidal ideation (SI), suicide attempts (SA), and suicide deaths (SD) separately, excluding non-suicidal self-injury.

RESULTS

Out of 96 initial articles, 23 met the inclusion criteria for full-text review. Most studies focused on developing ML models to identify suicide risk, showing promising results in enhancing accuracy and effectiveness. These models utilize various data sources and analytical techniques. However, challenges remain, including high bias risk and issues with interpretability, which necessitate further validation and refinement of AI-driven methods.

CONCLUSION

The review underscores the significant potential of AI, especially ML, in predicting suicide risk and attempts. Although ML models show promise, challenges like data limitations, bias, and interpretability issues need addressing. Continued research and ethical scrutiny are crucial to fully realize AI's potential in suicide prevention.

Occupation-specific risk estimates for suicide and non-fatal self-harm from a Swedish cohort of male construction workers followed 1987-2018

OBJECTIVES

While suicidal behaviour has become less prevalent in non-manual workers in recent decades, rates have increased in manual workers. We aimed to identify occupations within the construction industry with excess risk of suicide and non-fatal self-harm.

METHODS

This cohort of Swedish construction workers comprises 389 132 individuals examined 1971-1993 and followed 1987-2018 using national hospital and cause of death registers. More than 200 job titles were merged into 22 occupational groups. For 296 891 men alive in 1987 and active in the construction sector, survival was calculated from baseline to first event of non-fatal self-harm or suicide and censored for emigration, long-term unemployment, disability pension, retirement, death from other causes or end of follow-up. HRs with 95% CIs were obtained from multiple Cox proportional hazard regression.

RESULTS

Overall, 1618 cases of suicide and 4774 events of non-fatal self-harm were registered. Self-harm before baseline was the single largest risk factor for suicide, HR 9.3 (95% CI 7.5 to 11.6). Compared with the overall mean, labourers and rock workers had excess risk for suicide, HR 1.4 (95% CI 1.1 to 1.7) and 1.5 (95% CI 1.0 to 2.3), respectively, while electricians, clerks and foremen had reduced risk. Labourers, concrete workers, sheet metal workers, painters, glaziers and the group 'other construction workers' were at increased risk for non-fatal self-harm. Almost all categories of manual workers were at increased risk for suicidal behaviour relative to clerks and foremen.

CONCLUSIONS

Specific occupations within the construction sector were associated with excess risk for suicidal behaviour. Future studies should identify underlying risk factors to inform tailored interventions.

Suicide and non-fatal self-injury-related emergency department visits among individuals with autism spectrum disorder

LAY ABSTRACT

This study used data for 14.4 million individuals with 43.5 million emergency department visits from all hospitals in the state of New York to explore the association between suicide and non-fatal self-injury-related (self-injury) emergency department visits and autism spectrum disorder. Overall, we found that individuals with autism spectrum disorder had more emergency department visits and admissions through the emergency department, more years of emergency department utilization, and higher prevalence of mental health-related comorbidities. Individuals with autism spectrum disorder were also significantly more likely to have at least one self-injury-related emergency department visit compared to those without autism spectrum disorder. These results emphasize the need to raise awareness across both family caregivers and healthcare providers on the increased suicide and self-injury risks that individuals with autism spectrum disorder face and to improve care delivery practices. In addition, effort to promote and increase timely access to mental health care is an urgent priority for individuals with autism spectrum disorder.

Predictors of nonfatal suicide attempts within 30 days of discharge from psychiatric hospitalization: Sex-specific models developed using population-based registries

BACKGROUND

Risk for nonfatal suicide attempts is heightened in the month after psychiatric hospitalization discharge. Investigations of factors associated with such attempts are limited.

METHODS

We conducted a case-subcohort study using data from Danish medical, administrative, and social registries to develop sex-specific risk models using two machine learning methods: classification trees and random forests. Cases included individuals who received a diagnostic code for a nonfatal suicide attempt within 30 days of discharge following a psychiatric hospitalization between January 1, 1995 and December 31, 2015 (n = 3166, 56.5% female). The comparison subcohort consisted of a 5% random sample of individuals living in Denmark (n = 24,559, 51.3% female) on January 1, 1995 who had a psychiatric hospitalization during the study period.

RESULTS

Histories of self-poisoning, substance-related disorders, and eating disorders were important predictors of nonfatal suicide attempt among women, with notable interactions observed between age, self-poisoning history, and other characteristics (e.g., medication use). Self-poisoning, substance-related disorders, and severe stress reactions were among the most important variables for men, with key interactions noted between self-poisoning history, age, major depressive disorder diagnosis, and prescription classes.

LIMITATIONS

Findings are based on Danish administrative data, which may be subject to inaccuracies, missingness, etc. It is unclear whether results would generalize to other populations.

CONCLUSIONS

Markers of behavioral dysregulation were important predictors of nonfatal suicide attempts in the 30 days after psychiatric hospitalization discharge for both sexes. Examining risk markers for nonfatal suicide attempt following discharge is important to enhance support for this vulnerable population.

Evaluation of an Intensive Suicide- Reattempt–Prevention Programme based on Problem-Solving Therapy in a Catchment Area of 430,000 people in Madrid, Spain

Suicide prevention is a primary goal of mental health care, and a past history of suicide attempts is considered a high-risk factor for subsequent attempts. This study aims to evaluate the effectiveness of an intensive suicide-reattempt-prevention program (ISRPP) in a health catchment area of 430.000 inhabitants.

National trends in non-fatal suicidal behaviors among adults in the USA from 2009 to 2017

BACKGROUND

The age-adjusted rate of suicide death in the USA has increased significantly since 2000 and little is known about national trends in non-fatal suicidal behaviors (ideation, plan, and attempt) among adults and their associated sociodemographic and clinical characteristics. This study examined trends in non-fatal suicidal behaviors among adults in the USA.

METHODS

Data were obtained from adults 18-65 years of age who participated in the National Survey on Drug Use and Health (NSDUH), including mental health assessment, from 2009 to 2017 (n = 335 359). Examinations of data involved trend analysis methods with the use of logistic regressions and interaction terms.

RESULTS

Suicidal ideation showed fluctuation from 2009 to 2017, whereas suicide plan and attempt showed significantly positive linear trends with the odds increasing by an average of 3% and 4%, respectively. Suicide plan increased the most for females and adults ages 18-34, and attempt increased the most for adults with drug dependence. Both plan and attempt increased the most among adults who either had mental illness but were not in treatment or had no mental illness.

CONCLUSIONS

Given attempted suicide is the strongest known risk factor for suicide death, reducing non-fatal suicidal behaviors including attempt are important public health and clinical goals. The interactional findings of age, sex, mental health status, and drug dependence point toward the importance of tailoring prevention efforts to various sociodemographic and clinical factors.

Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults

Background:

Artificial Intelligence is making a significant transformation in human lives. Its application in the medical and healthcare field has also been observed to make an impact and improve overall outcomes. There has been a quest for similar processes in mental health due to the lack of observable changes in the areas of suicide prevention. In the last five years, there has been an emerging body of empirical research applying the technology of artificial intelligence (AI) and machine learning (ML) in mental health.

Objective:

To review the clinical applicability of the AI/ML-based tools in suicide prevention.

Methods:

The compelling question of predicting suicidality has been the focus of this research. We performed a broad literature search and then identified 36 articles relevant to meet the objectives of this review. We review the available evidence and provide a brief overview of the advances in this field.

Conclusion:

In the last five years, there has been more evidence supporting the implementation of these algorithms in clinical practice. Its current clinical utility is limited to using electronic health records and could be highly effective in conjunction with existing tools for suicide prevention. Other potential sources of relevant data include smart devices and social network sites. There are some serious questions about data privacy and ethics which need more attention while developing these new modalities in suicide research.

Declining Life Expectancy in the United States: Missing the Trees for the Forest

In recent years, life expectancy in the United States has stagnated, followed by three consecutive years of decline. The decline is small in absolute terms but is unprecedented and has generated considerable research interest and theorizing about potential causes. Recent trends show that the decline has affected nearly all race/ethnic and gender groups, and the proximate causes of the decline are increases in opioid overdose deaths, suicide, homicide, and Alzheimer's disease. A slowdown in the long-term decline in mortality from cardiovascular diseases has also prevented life expectancy from improving further. Although a popular explanation for the decline is the cumulative decline in living standards across generations, recent trends suggest that distinct mechanisms for specific causes of death are more plausible explanations. Interventions to stem the increase in overdose deaths, reduce access to mechanisms that contribute to violent deaths, and decrease cardiovascular risk over the life course are urgently needed to improve mortality in the United States.

Suicide and the agent-host-environment triad: leveraging surveillance sources to inform prevention

Suicide in the US has increased in the last decade, across virtually every age and demographic group. Parallel increases have occurred in non-fatal self-harm as well. Research on suicide across the world has consistently demonstrated that suicide shares many properties with a communicable disease, including person-to-person transmission and point-source outbreaks. This essay illustrates the communicable nature of suicide through analogy to basic infectious disease principles, including evidence for transmission and vulnerability through the agent-host-environment triad. We describe how mathematical modeling, a suite of epidemiological methods, which the COVID-19 pandemic has brought into renewed focus, can and should be applied to suicide in order to understand the dynamics of transmission and to forecast emerging risk areas. We describe how new and innovative sources of data, including social media and search engine data, can be used to augment traditional suicide surveillance, as well as the opportunities and challenges for modeling suicide as a communicable disease process in an effort to guide clinical and public health suicide prevention efforts.

Why Are Suicide Rates Increasing in the United States? Towards a Multilevel Reimagination of Suicide Prevention

Suicide, a major public health concern, takes around 800,000 lives globally every year and is the second leading cause of death among adolescents and young adults. Despite substantial prevention efforts, between 1999 and 2017, suicide and nonfatal self-injury rates have experienced unprecedented increases across the United States - as well as in many other countries in the world. This chapter reviews the existing evidence on the causes behind increased suicide rates and critically evaluates the impact of a range of innovative approaches to suicide prevention. First, we briefly describe current trends in suicide and suicidal behaviors and relate them to recent time trends in relevant suicide risk markers. Then, we review the existing evidence in suicide prevention at the individual and the population levels, including new approaches that are currently under development. Finally, we advocate for a new generation of suicide research that examines causal factors beyond the proximal and clinical and fosters a socially conscious reimagining of suicidal prevention. To this end, we emphasize the need for the conceptualization of suicide and suicidal behaviors as complex phenomena with causes at several levels of organization. Future interdisciplinary research and interventions should be developed within a multilevel causal framework that can better capture the social, economic, and political settings where suicide, as a process, unfolds across the life course.