Suicide prediction among men and women with depression: A population-based study

Acetaminophen overdose: analysis of 2018 US nationwide emergency database

INTRODUCTION

Recognized risk factors for acetaminophen overdose include alcohol, opioids, and mood disorders. The aim of this study is to assess additional risk factors for acetaminophen overdose evaluated in the emergency department (ED).

METHODS

A retrospective study was performed using the 2018 US Nationwide Emergency Department Sample (NEDS). All adult ED visits for acetaminophen overdose were included in the study group and those without it were taken as control. STATA, 16.1 was used to perform multivariable logistic regression analysis and adjusted odds ratios (ORadj) were reported.

RESULTS

We identified 27,792 ED visits for acetaminophen overdose. Relative to non-acetaminophen ED visits, this group was younger (median age 32 vs 47 years; p < 0.0001), more often female (66.1% vs 57.0%; p < 0.0001), had higher ED charges ($3,506 vs $2,714; p < 0.0001), higher proportion of alcohol-related disorders (15.8% vs 3.5%; p < 0.0001), anxiety disorders (30.2% vs 8.3%; p < 0.0001), cannabis use (8.7% vs 1.4%; p < 0.0001), hematology/oncology diagnoses (13.3% vs 10.9%; p < 0.0001), mood disorders (52.4% vs 7.9%; p < 0.0001), opioid-related disorders (4.1% vs 1.0%; p < 0.0001), and suicide attempt/ideation (12.2% vs 1.1%; p < 0.0001). Multivariable analysis showed alcohol-related disorders (ORadj 2.67), anxiety disorders (ORadj 1.24), cannabis (ORadj 1.63), females (ORadj 1.45), Income Q3 (ORadj 1.09), hematology/oncology diagnoses (ORadj 1.40), mood disorders (ORadj 10.07), opioid-related disorders (ORadj 1.20), and suicide attempt/ideation (ORadj 1.68) were associated with acetaminophen overdose.

CONCLUSION

In addition to previously recognized risks, our study demonstrated that cannabis use and hematologic/oncologic comorbidities were more common among acetaminophen-overdose ED visits. These new findings are concerning because of rapid legalization of cannabis and the increasing incidence of cancer worldwide. Additional investigation into these risks should be a priority for clinicians, policymakers, and researchers.

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies

OBJECTIVE

Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.

METHOD

A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.

RESULTS

Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.

CONCLUSIONS

The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

Machine learning and the prediction of suicide in psychiatric populations: a systematic review

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.

Integrative Analyses of scRNA-seq, Bulk mRNA-seq, and DNA Methylation Profiling in Depressed Suicide Brain Tissues

BACKGROUND

Suicidal behaviors have become a serious public health concern globally due to the economic and human cost of suicidal behavior to individuals, families, communities, and society. However, the underlying etiology and biological mechanism of suicidal behavior remains poorly understood.

METHODS

We collected different single omic data, including single-cell RNA sequencing (scRNA-seq), bulk mRNA-seq, DNA methylation microarrays from the cortex of Major Depressive Disorder (MDD) in suicide subjects' studies, as well as fluoxetine-treated rats brains. We matched subject IDs that overlapped between the transcriptome dataset and the methylation dataset. The differential expression genes and differentially methylated regions were calculated with a 2-group comparison analysis. Cross-omics analysis was performed to calculate the correlation between the methylated and transcript levels of differentially methylated CpG sites and mapped transcripts. Additionally, we performed a deconvolution analysis for bulk mRNA-seq and DNA methylation profiling with scRNA-seq as the reference profiles.

RESULTS

Difference in cell type proportions among 7 cell types. Meanwhile, our analysis of single-cell sequence from the antidepressant-treated rats found that drug-specific differential expression genes were enriched into biological pathways, including ion channels and glutamatergic receptors.

CONCLUSIONS

This study identified some important dysregulated genes influenced by DNA methylation in 2 brain regions of depression and suicide patients. Interestingly, we found that oligodendrocyte precursor cells (OPCs) have the most contributors for cell-type proportions related to differential expression genes and methylated sites in suicidal behavior.

Predicting internalizing symptoms with machine learning: identifying individuals that need care

Objective The current project aims to identify individuals in urgent need of mental health care, using a machine learning algorithm (random forest). Comparison/contrast with conventional regression analyses is discussed. Participants: A total of 2,409 participants were recruited from an anonymous university, including undergraduate and graduate students, faculty, and staff. Methods: Answers to a COVID-19 impact survey, the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7) were collected. The total scores of PHQ-9 and GAD-7 were regressed on six composites that were created from the questionnaire items, based on their topics. A random forest was trained and validated. Results: Results indicate that the random forest model was able to make accurate, prospective predictions (R2 = .429 on average) and we review variables that were deemed predictively relevant. Conclusions: Overall, the study suggests that predictive models can be clinically useful in identifying individuals with internalizing symptoms based on daily life disruption experiences.

Prediction of suicidal ideation in children and adolescents using machine learning and deep learning algorithm: A case study in South Korea where suicide is the leading cause of death

BACKGROUND

Korea has the highest suicide rate among Organisation for Economic Co-operation and Development (OECD) countries. Consequently, central and local governments and private organizations in Korea cooperate in promoting various suicide prevention projects to actively respond to suicide problems. Machine learning has been used to predict suicidal ideation in the fields of health and medicine but not from a social science perspective.

OBJECTIVE

Since suicidal ideation is a major predictor of suicide attempts, being able to anticipate and mitigate it helps prevent suicide. Therefore, this study presents a data-based analysis method for predicting suicidal thoughts quickly and effectively and suggests countermeasures against the causes of suicidal thoughts.

PARTICIPANTS AND METHODS

To predict early signs of suicidal ideation in children and adolescents, big data collected for approximately 4 years (from 2017 to 2020) from the Korea Youth Policy Institute (NYPI) were used. To accurately predict suicidal ideation, supervised ma- chine learning classification algorithms such as logistic regression, random forest, XGBoost, multilayer perceptron (MLP), and convolutional neural network (CNN) were used.

RESULTS

Using CNN, suicidal ideation was predicted with an accuracy of approximately 90 %. The logistic regression results showed that sadness and depression increased suicidal thoughts by more than 25 times, and anxiety, loneliness, and experience of abusive language increased suicidal thoughts by more than three times.

CONCLUSIONS

Machine learning and deep learning approaches have the potential to predict and respond to suicidal thoughts in children, adolescents, and the general population, as well as help respond to the suicide crisis by preemptively identifying the cause.

Predictors of suicide attempt within 30 days of first medically documented major depression diagnosis in U.S. army soldiers with no prior suicidal ideation

BACKGROUND

Understanding mental health predictors of imminent suicide attempt (SA; within 30 days) among soldiers with depression and no prior suicide ideation (SI) can inform prevention and treatment. The current study aimed to identify sociodemographic and service-related characteristics and mental disorder predictors associated with imminent SA among U.S. Army soldiers following first documented major depression diagnosis (MDD) with no history of SI.

METHODS

In this case-control study using Army Study to Assess Risk and Resilience in Servicemembers (STARRS) administrative data, we identified 101,046 active-duty Regular Army enlisted soldiers (2010-2016) with medically-documented MDD and no prior SI (MDD/No-SI). We examined risk factors for SA within 30 days of first MDD/No-SI using logistic regression analyses, including socio-demographic/service-related characteristics and psychiatric diagnoses.

RESULTS

The 101,046 soldiers with documented MDD/No-SI were primarily male (78.0%), < 29 years old (63.9%), White (58.1%), high school-educated (74.5%), currently married (62.0%) and < 21 when first entering the Army (56.9%). Among soldiers with MDD/No-SI, 2,600 (2.6%) subsequently attempted suicide, 16.2% (n = 421) within 30 days (rate: 416.6/100,000). Our final multivariable model identified: Soldiers with less than high school education (χ²₃ = 11.21, OR = 1.5[95%CI = 1.2-1.9]); combat medics (χ²₂ = 8.95, OR = 1.5[95%CI = 1.1-2.2]); bipolar disorder (OR = 3.1[95%CI = 1.5-6.3]), traumatic stress (i.e., acute reaction to stress/not PTSD; OR = 2.6[95%CI = 1.4-4.8]), and "other" diagnosis (e.g., unspecified mental disorder: OR = 5.5[95%CI = 3.8-8.0]) diagnosed same day as MDD; and those with alcohol use disorder (OR = 1.4[95%CI = 1.0-1.8]) and somatoform/dissociative disorders (OR = 1.7[95%CI = 1.0-2.8]) diagnosed before MDD were more likely to attempt suicide within 30 days. Currently married soldiers (χ²₂ = 6.68, OR = 0.7[95%CI = 0.6-0.9]), those in service 10 + years (χ²₃ = 10.06, OR = 0.4[95%CI = 0.2-0.7]), and a sleep disorder diagnosed same day as MDD (OR = 0.3[95%CI = 0.1-0.9]) were less likely.

CONCLUSIONS

SA risk within 30 days following first MDD is more likely among soldiers with less education, combat medics, and bipolar disorder, traumatic stress, and "other" disorder the same day as MDD, and alcohol use disorder and somatoform/dissociative disorders before MDD. These factors identify imminent SA risk and can be indicators for early intervention.

Vested in support: Applying vested interest theory to increase support for close others with depression

Guided by vested interest theory, we assessed whether a lack of stake explains the discrepancy between people having positive attitudes toward their loved one's recovery from depression and the provision of support. We further investigated whether increasing the perceived personal consequences of providing support (i.e. stake) increased willingness to provide support. A stake-boosting message had no direct, but significant indirect effects on willingness to provide support when compared to a control and comparison condition. In summary, increasing stake in a loved one's recovery indirectly increases intentions to provide support.

Deliberate Self-Poisoning: Real-Time Characterization of Suicidal Habits and Toxidromes in the Food and Drug Administration Adverse Event Reporting System

INTRODUCTION

Deliberate self-poisoning (DSP) using drugs is the preferred method of suicide at a global level. Its investigation is hampered by limited sample sizes and data reliability. We investigate the role of the US FDA Adverse Event Reporting System (FAERS), a consolidated pharmacovigilance database, in outlining DSP habits and toxidromes.

METHODS

We retrieved cases of 'intentional overdose' and 'poisoning deliberate' from the FAERS (January 2004-December 2021). Using descriptive and disproportionality analyses, we estimated temporal trends, potential risk factors, toxidromes, case-fatality rates and lethal doses (LDs) for the most frequently reported drugs.

RESULTS

We retrieved 42,103 DSP cases (17% fatal). Most cases were submitted in winter. Reports of DSP involved younger people, psychiatric conditions, and alcohol use, compared with non-DSP, and fatality was higher in men and older patients. Suspected drugs were mainly antidepressants, analgesics, and antipsychotics. Multiple drug intake was recorded in more than 50% of the reports, especially analgesics, psychotropics, and cardiovascular agents. The most frequently reported drugs were paracetamol, promethazine, amlodipine, quetiapine, and metformin. We estimated LD25 for paracetamol (150 g).

CONCLUSION

Worldwide coverage of the FAERS complements existing knowledge about DSP and may drive tailored prevention measures to timely address the DSP phenomenon and prevent intentional suicides.

Structural abnormalities in adolescents with conduct disorder and high versus low callous unemotional traits

There may be distinct conduct disorder (CD) etiologies and neural morphologies in adolescents with high callous unemotional (CU) traits versus low CU traits. Here, we employed surface-based morphometry methods to investigate morphological differences in adolescents diagnosed with CD [42 with high CU traits (CD-HCU) and 40 with low CU traits (CD-LCU)] and healthy controls (HCs, N = 115) in China. Whole-brain analyses revealed significantly increased cortical surface area (SA) in the left inferior temporal cortex and the right precuneus, but decreased SA in the left superior temporal cortex in the CD-LCU group, compared with the HC group. There were no significant cortical SA differences between the CD-HCU and the HC groups. Compared to the CD-HCU group, the CD-LCU group had a greater cortical thickness (CT) in the left rostral middle frontal cortex. Region-of-interest analyses revealed significant group differences in the right hippocampus, with CD-HCU group having lower right hippocampal volumes than HCs. We did not detect significant group differences in the amygdalar volume, however, the right amygdalar volume was found to be a significant moderator of the correlation between CU traits and the proactive aggression in CD patients. The present results suggested that the manifestations of CD differ between those with high CU traits versus low CU traits, and underscore the importance of sample characteristics in understanding the neural substrates of CD.

The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review

Research has posited that machine learning could improve suicide risk prediction models, which have traditionally performed poorly. This systematic review and meta-analysis evaluated the performance of machine learning models in predicting longitudinal outcomes of suicide-related outcomes of ideation, attempt, and death and examines outcome, data, and model types as potential covariates of model performance. Studies were extracted from PubMed, Web of Science, Embase, and PsycINFO. A bivariate mixed effects meta-analysis and meta-regression analyses were performed for studies using machine learning to predict future events of suicidal ideation, attempts, and/or deaths. Risk of bias was assessed for each study using an adaptation of the Prediction model Risk Of Bias Assessment Tool. Narrative review included 56 studies, and analyses examined 54 models from 35 studies. The models achieved a very good pooled AUC of 0.86, sensitivity of 0.66 (95% CI [0.60, 0.72)], and specificity of 0.87 (95% CI [0.84, 0.90]). Pooled AUCs for ideation, attempt, and death were similar at 0.88, 0.87, and 0.84 respectively. Model performance was highly varied; however, meta-regressions did not provide evidence that performance varied by outcome, data, or model types. Findings suggest that machine learning has the potential to improve suicide risk detection, with pooled estimates of machine learning performance comparing favourably to performance of traditional suicide prediction models. However, more studies with lower risk of bias are necessary to improve the application of machine learning in suicidology.

Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study

Background

Suicide remains the leading cause of premature death in South Korea. This study aims to develop machine learning algorithms for screening Korean adults at risk for suicidal ideation and suicide planning or attempt.

Methods

Two sets of balanced data for Korean adults aged 19–64 years were drawn from the 2012–2019 waves of the Korea Welfare Panel Study using the random down-sampling method (N = 3292 for the prediction of suicidal ideation, N = 488 for the prediction of suicide planning or attempt). Demographic, socioeconomic, and psychosocial characteristics were used to predict suicidal ideation and suicide planning or attempt. Four machine-learning classifiers (logistic regression, random forest, support vector machine, and extreme gradient boosting) were tuned and cross-validated.

Results

All four algorithms demonstrated satisfactory classification performance in predicting suicidal ideation (sensitivity 0.808–0.853, accuracy 0.843–0.863) and suicide planning or attempt (sensitivity 0.814–0.861, accuracy 0.864–0.884). Extreme gradient boosting was the best-performing algorithm for predicting both suicidal outcomes. The most important predictors were depressive symptoms, self-esteem, income, consumption, and life satisfaction. The algorithms trained with the top two predictors, depressive symptoms and self-esteem, showed comparable classification performance in predicting suicidal ideation (sensitivity 0.801–0.839, accuracy 0.841–0.846) and suicide planning or attempt (sensitivity 0.814–0.837, accuracy 0.874–0.884).

Limitations

Suicidal ideation and behaviors may be under-reported due to social desirability bias. Causality is not established.

Discussion

More than 80% of individuals at risk for suicidal ideation and suicide planning or attempt could be predicted by a number of mental and socioeconomic characteristics of respondents. This finding suggests the potential of developing a quick screening tool based on the known risk factors and applying it to primary care or community settings for early intervention.

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This study develops machine learning models to predict suicidal ideation and behaviors.

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Logistic regression, random forest, support vector machine, and extreme gradient boosting are used.

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The algorithms correctly identifyed 80–90% of suicidal cases.

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The algorithms with the top two predictors (depressive symptoms and self-esteem) could achieve comparable accuracy.

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Our findings can be used to design a quick screening tool for use in primary care or community settings.

Human-Computer Interaction in Digital Mental Health

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.

The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review

Suicidal men feel the need to be self-reliant and that they cannot find another way out of relationship or socioeconomic issues. Suicide prevention is of crucial importance worldwide. The much higher rate of suicide in men engenders action. The prelude is a subjective experience that can be very isolating and severely distressing. Men may not realize a change in their thinking and behaviors, which makes it more difficult to seek and get help, thereby interrupting a "downward spiral". Stoicism often prevents men from admitting to their personal struggle. The lack of "quality" connections and "non-tailored" therapies has led to a high number of men "walking out" on traditional clinical approaches. But there are complicated relationships in motivations and formative behaviors of suicide with regards to emotional state, psychiatric disorders, interpersonal life events and suicidal behavior method selection. Middle-aged and older men have alternated as the most at-risk of suicide. There is no one solution that applies to all men, but digital tools may be of assistance (e.g., video conferences, social networks, telephone calls, and emails). Digital interventions require higher levels of effectiveness for distress and suicidality but self-guided approaches may be the most suitable for men especially where linked with an integrated online suicide prevention platform (e.g., quick response with online chats, phone calls, and emails). Furthermore, technology-enabled models of care offer promise to advance appropriate linking to mental health services through better and faster understanding of the specific needs of individuals (e.g., socio-cultural) and the type and level of suicidality experienced. Long-term evidence for suicidality and its evaluation may benefit from progressing human computer-interaction and providing impetus for an eminent integrated digital platform.