Health-Behaviors Associated With the Growing Risk of Adolescent Suicide Attempts: A Data-Driven Cross-Sectional Study

[Factors Influencing Sexual Experiences in Adolescents Using a Random Forest Model: Secondary Data Analysis of the 2019~2021 Korea Youth Risk Behavior Web-based Survey Data]

PURPOSE

The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance."

METHODS

The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix.

RESULTS

The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol.

CONCLUSION

Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care.

The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

Background

Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature.

Objective

Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified.

Methods

A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed.

Results

The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study.

Conclusion

The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.

Circumstances Contributing to Suicide Among U.S. Adolescents Aged 10-19 Years With and Without a Known Mental Health Condition: National Violent Death Reporting System, 2013-2018

PURPOSE

Suicide is the second leading cause of death for adolescents in the United States; however, suicide is preventable and a better understanding of circumstances that contribute to death can inform prevention efforts. While the association between adolescent suicide and mental health is well established, multiple circumstances contribute to suicide risk. This study examines characteristics of adolescents who died by suicide and differences in circumstances between those with and without known mental health conditions at the time of death.

METHODS

Logistic regression models were used to estimate adjusted odds ratios and 95% confidence intervals of circumstances contributing to suicide between decedents with and without known mental health conditions using data from the 2013 to 2018 National Violent Death Reporting System (analyzed in 2021).

RESULTS

Decedents with a known mental health condition were 1.2-1.8 times more likely to experience problematic alcohol misuse, substance misuse, family and other nonintimate relationship problems, and school problems; however, there were no significant differences between those with and without a known mental health condition for the preceding circumstances of arguments or conflicts, criminal or legal problems, or any crisis occurring within the two weeks prior to death.

DISCUSSION

A comprehensive suicide prevention approach can address not only mental health conditions as a risk factor but also life stressors and other crises experienced among adolescents without known mental health conditions.

Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

Positive Relationships with Adults and Resilience to Suicide Attempt among New Mexico Hispanic Adolescents

Suicide is a leading cause of adolescent death and has increased in recent years. The purpose of this study was to examine the effect that relationships with adults at home and in the community had on the probability of suicide attempts of Hispanic teenagers in New Mexico. Data from the 2019 New Mexico Youth Risk and Resiliency Survey were analyzed to identify the ways in which relationships with adults influenced suicide attempts among Hispanic adolescent students. The examined factors included: relationships with adults in the home and in the community and with same-age friends, and participation in hobbies or organizations outside of school. The resiliency factors were similar for male and female Hispanic students. As positive relationships with adults at home or in the community increased, the probability of suicide attempts decreased by 37–54%. Positive relationships with same-age friends were also associated with reduced suicide attempts. Community organization involvement and hobbies affected males and females differently. Adults at home and in the community can decrease the risk of suicide for Hispanic teenagers through supportive relationships. Hybrid programs focusing on adolescent health, positive communication, and academic support, which integrate adults from home and community environments, show promise in reducing suicidal thoughts and other risk behaviors.

A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus

The built environment affects mental health outcomes, but this relationship is less studied and understood. This article proposes a novel multi-level scenario-based predictive analytics framework (MSPAF) to explore the complex relationships between community mental health outcomes and the built environment conditions. The MSPAF combines rigorously validated interpretable machine learning algorithms and scenario-based sensitivity analysis to test various hypotheses on how the built environment impacts community mental health outcomes across the largest metropolitan areas in the US. Among other findings, our results suggest that declining socio-economic conditions of the built environment (e.g., poverty, low income, unemployment, decreased access to public health insurance) are significantly associated with increased reported mental health disorders. Similarly, physical conditions of the built environment (e.g., increased housing vacancies and increased travel costs) are significantly associated with increased reported mental health disorders. However, this positive relationship between the physical conditions of the built environment and mental health outcomes does not hold across all the metropolitan areas, suggesting a mixed effect of the built environment's physical conditions on community mental health. We conclude by highlighting future opportunities of incorporating other variables and datasets into the MSPAF framework to test additional hypotheses on how the built environment impacts community mental health.