Trends in Adolescent Suicide by Method in the US, 1999-2020

Ormiston, Cameron K.; Lawrence, Wayne R.; Sulley, Saanie; Shiels, Meredith S.; Haozous, Emily A.; Pichardo, Catherine M.; Stephens, Erica S.; Thomas, Aleah L.; Adzrago, David; Williams, David R.; Williams, Faustine

doi:10.1001/jamanetworkopen.2024.4427

Cited by 10 publications

(2 citation statements)

References 67 publications

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“…The prevalence of suicide is exceptionally high among adolescents and young adults, specifically those aged 15 to 44, it is not a universal phenomenon [ 9 ]. Research indicates that, in some countries the lower risk of suicide among older individuals may be due to their enhanced resilience and greater capacity to cope with adversity, potentially reducing the likelihood of suicidal behavior [ 10 , 11 ]. The other common factor can be gender.…”

Section: Introductionmentioning

confidence: 99%

“…Research by Walsh, Ribeiro, and Franklin (2017) demonstrated the superior performance of ML over conventional methods in accurately identifying suicide attempts [ 9 ]. ML methods have gained prominence due to their ability to extract valuable insights from diverse datasets and organize data efficiently [ 10 , 11 ]. While ML shows promise in predicting suicide events, it is vital to consider the varied outcomes produced by different ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Ehtemam,

Sadeghi Esfahlani,

Sanaei

et al. 2024

BMC Med Inform Decis Mak

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ehtemam,

Sadeghi Esfahlani,

Sanaei

et al. 2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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Suicidal Thoughts and Behaviors in American Indian and Alaska Native Adolescents

Price,

Khubchandani

2024

J Community Health

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American Indians and Alaska Natives (AI/AN) have consistently exhibited suicide rates that surpass all other racial and ethnic groups in the United States. However, not much has been published regarding the epidemiology of AI/AN youth suicides. The objectives of this study on AI/AN adolescents were to assess the prevalence of suicidal thoughts and behaviors by age and gender, assess the number of years of life lost to suicide before the age of 80, and assess the most common methods used to commit suicide by AI/AN adolescents. Data utilized for this study were from the national Youth Risk Behavior Surveys and the Web-Based Injury Statistics Query and Reporting System. We conducted a cross-sectional descriptive analysis of the suicide-related data from years 2015, 2017, 2019, and 2021. We found AI/AN adolescents averaged one in four contemplated suicides, one in five planned suicides, and one in six attempted suicides. A total of 257 adolescents committed suicide during the study period where the majority (62.5%) were observed in males and suicides were more common in older adolescents. AI/AN adolescents had the highest crude suicide death rate of any racial or ethnic group and the most common methods used to commit suicide were suffocation (57.5%) and firearms (35.5%). AI/ AN adolescents lost almost 16,000 years of potential life during the four years of the study and the majority were lost by males. Professionals and policymakers desiring to reduce suicidal thoughts and behaviors among AI/AN adolescents need to focus more of their efforts on providing youths with resilience factors to establish sufficient ego strength in them to deal with all types of stressors. Concurrently, federal, state, and tribal leaders need to work together to improve the social and economic circumstances faced by many AI/AN families and children.

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