Prediction of suicidal ideation in shift workers compared to non-shift workers using machine learning techniques

Association between cardiometabolic index (CMI) and suicidal ideation: A cross-sectional analysis of NHANES 2005 to 2018 data

With suicide ranking as a leading cause of death globally, identifying modifiable risk factors is crucial. Suicidal ideation (SI) is a significant precursor to suicide, and there is a growing interest in the role of cardiometabolic factors, particularly the cardiometabolic index (CMI), multiplying the triglyceride-to-high-density lipoprotein cholesterol ratio by the waist-to-height ratio, in mental health outcomes. Previous studies have shown a notable relationship among lipid abnormalities, elevated triglyceride levels, and depressive symptom severity, including SI. This research investigated the correlation between the CMI levels of adult Americans and SI, utilizing data from the National Health and Nutrition Examination Survey (NHANES) ranging from the years 2005 to 2018. After collecting data on demographics, physical examinations, and laboratory testing, multivariate logistic regression analysis was conducted to assess the relationship between CMI and SI while adjusting for relevant factors. The study, which enrolled 15,849 individuals exhibiting symptoms of SI, constituting 3.47% of the total, revealed a significant association between CMI levels and SI. A significant positive association was found between CMI and SI (adjusted OR = 1.07, 95% CI: 1.02-1.13, P = .0029). Moreover, a nonlinear relationship was identified between CMI and SI, characterized by an atypical inverted U-shaped curve with a breakpoint at approximately CMI = 2.08. Subgroup analysis revealed consistent findings across various demographic and clinical subpopulations. The findings of this study demonstrate a substantial correlation between elevated CMI levels and an increased incidence of SI within the US population. Early interventions targeted at individuals with elevated CMI levels, such as psychological support or lifestyle adjustments, may mitigate the risk of SI.

Prediction of Suicidal Thoughts and Suicide Attempts in People Who Gamble Based on Biological-Psychological-Social Variables: A Machine Learning Study

Recent research has shown that people who gamble are more likely to have suicidal thoughts and attempts compared to the general population. Despite the advancements made, no study to date has predicted suicide risk factors in people who gamble using machine learning algorithms. Therefore, current study aimed to identify the most critical predictors of suicidal ideation and suicidal attempts among people who gamble using a machine learning approach. An online survey conducted a cross-sectional analysis of 741 people who gamble (mean age: 25.9 ± 5.56). To predict the risk of suicide attempts and ideation, we employed a comprehensive set of 40 biological, psychological, social, and socio-demographic variables. The predictive models were developed using Logistic Regression, Random Forest (RF), robust eXtreme Gradient Boosting (XGBoost), and ensemble machine learning algorithms. Data analysis was performed using R-Studio software. Random Forest emerged as the top-performing algorithm for predicting suicidal ideation, with an impressive AUC of 0.934, sensitivity of 0.7514, specificity of 0.9885, PPV of 0.9473, and NPV of 0.9347. Across all models, dissociation, depression, and anxiety symptoms consistently emerged as crucial predictors of suicidal ideation. However, for suicide attempt prediction, all models exhibited weaker performance. XGBoost showed the best performance in this regard, with an AUC of 0.663, sensitivity of 0.78, specificity of 0.8990, PPV of 0.34, NPV of 0.984, and accuracy of 0.8918. Depressive symptoms and rumination severity were highlighted as the most important predictors of suicide attempts according to this model. These findings have important implications for clinical practice and public health interventions. Machine learning could help detect individuals prone to suicidal ideation and suicide attempts among people who gamble, assisting in creating tailored prevention programs to address future suicide risks more effectively.

The associations of psychosocial work exposures with suicidal ideation in the national French SUMER study

BACKGROUND

The literature remains scarce on the work-related risk factors for suicide and suicidal ideation. The objectives were to explore the associations of psychosocial work exposures with suicidal ideation in a nationally representative sample of the working population.

METHODS

The study was based on the sample of 25,977 employees (14,682 men and 11,295 women) of the national French 2016-17 SUMER survey. The outcome was suicidal ideation assessed using the PHQ-9 instrument. Psychosocial work exposures included various factors from the job strain and effort-reward imbalance models, and other concepts. Statistical analyses were performed using weighted methods, including weighted logistic regression models. Other occupational exposures and covariates were considered. Gender differences were tested.

RESULTS

The prevalence of suicidal ideation was 3.5 % without any difference between genders. Psychosocial work exposures were found to be associated with suicidal ideation. The strongest association was observed between workplace bullying and suicidal ideation. Associations were also found between job strain model factors, job insecurity, low esteem, work-family conflict, ethical conflict, teleworking, and low meaning, and suicidal ideation. The associations were in general similar for men and women.

LIMITATIONS

The study had a cross-sectional design and no causal interpretation could be done. A reporting bias and a healthy worker effect may be suspected.

CONCLUSION

Psychosocial work exposures played a major role in suicidal ideation. More research may be needed to confirm our results, as suicidal ideation is an important warning signal for suicide prevention. More primary prevention towards the psychosocial work environment may be useful to reduce suicidal ideation at the workplace.

Prediction of suicidal ideation among preadolescent children with machine learning models: A longitudinal study

BACKGROUND

Machine learning (ML) has been widely used to predict suicidal ideation (SI) in adolescents and adults. Nevertheless, studies of accurate and efficient models of SI prediction with preadolescent children are still needed because SI is surprisingly prevalent during the transition into adolescence. This study aimed to explore the potential of ML models to predict SI among preadolescent children.

METHODS

A total of 4691 Chinese children (54.89 % boys, Mage = 10.92 at baseline) and their parents completed relevant measures at baseline and the children provided 6-month follow-up data for SI. The current study compared four ML models: Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), to predict SI and to identify variables with predictive value based on the best-performing model among Chinese preadolescent children.

RESULTS

The RF model achieved the highest discriminant performance with an AUC of 0.92, accuracy of 0.93 (balanced accuracy = 0.88). The factors of internalizing problems, externalizing problems, neuroticism, childhood maltreatment, and subjective well-being in school demonstrated the highest values in predicting SI.

CONCLUSION

The findings of this study suggested that ML models based on the observation and assessment of children's general characteristics and experiences in everyday life can serve as convenient screening and evaluation tools for suicide risk assessment among Chinese preadolescent children. The findings also provide insights for early intervention.

Evidence summary on the non-pharmacological management of sleep disorders in shift workers

PURPOSE

This study aimed to evaluate, and integrate the relevant evidence on the non-pharmacological management of sleep disorders in shift workers to provide a reference for improving sleep of shift workers.

METHODS

According to the "6S" pyramid model of evidence, a comprehensive search was conducted in evidence-based databases, including BMJ-Best Practice, UpToDate, DynaMed, Cochrane Library, and Joanna Briggs Institute (JBI); clinical practice guideline websites, such as the Guidelines International Network; professional association websites, such as the World Sleep Society; and literature databases, including PubMed, Embase, CINAHL, China National Knowledge Infrastructure (CNKI), Wanfang Database, and Chinese Biology Medicine disc (CBM) from inception to November 30, 2022. Two researchers independently evaluated the literature in accordance with the evaluation standards; conducted the extraction, classification, and synthesis of the evidence; and evaluated its grade and recommendation grade.

RESULTS

A total of 18 studies were included, including 2 clinical decisions, 2 guidelines, 3 expert consensuses, and 11 systematic reviews. In total, 25 pieces of evidence were summarized from 6 aspects: sleep assessment, sleep scheduling, sleep hygiene, light therapy, workplace intervention, and other managements.

CONCLUSION

This study summarized the best evidence for the non-pharmacological management of sleep disorders in shift workers. Shift workers should reasonably arrange their sleep time and develop good sleep hygiene. Additionally, work organizations should jointly promote sleep to improve the sleep conditions of shift workers and promote their physical and mental health.

Examine the relationships between health-related quality of life, achievement motivation and job performance: the case of Taiwan hospitality industry

Background

Employees are considered as one of the most important assets in many organizations, and their health well-being is critical to help achieve a sustainable and motivated workforce that is committed to delivering quality hospitality services through enhanced performance and productivity. Given the extent of the challenges and impact presented by the COVID-19 pandemic to the hospitality industry, it is timely to gain further insights on employees’ health well-being. The key purpose of this study is to examine the relationships between health-related quality of life, achievement motivation and job performance in the Taiwan hospitality industry, to acquire a better understanding of their relationships through the job performance pathway models.

Methods

This study has used a purposeful sampling technique to select the 10 highest-earning hospitality companies in Taiwan. A total of 292 questionnaires were collected from the employees of these hospitality companies. Based on the multi-dimensional concept of health-related quality of life (HRQoL), the relationships between the five key dimensions (i.e. psychological health, physical health, social health, achievement motivation, and job performance) were examined. To measure these dimensions, the survey questions were adapted from previous research such as the World Health Organization’s WHOQOL-BREF scale, Minnesota Satisfaction Questionnaire. Partial least squares - Structural Equation Modeling method was used to explore these dimensions, and two job performance pathway models (for manager and staff) were subsequently developed.

Results and conclusions

Findings showed that psychological health directly affected the manager’s job performance and physical health had a similar effect through social health. While psychological health had not affected the staff’s job performance, but it could affect achievement motivation through both direct and indirect effects of social health. The pathway models that were developed indicated that the manager’s job performance was mainly affected by psychological health and social health, whereas the key dimension that had affected the staff’s job performance was achievement motivation.