Association between insurance type and suicide-related behavior among US adults: The impact of the Affordable Care Act.

This study examined the association between insurance type and suicidal ideation and attempts among adults in the United States, incorporating a comparative analysis of the pre- and post-Affordable Care Act (ACA) periods. We used a nationally representative, cross-sectional, population-based survey of individuals aged 18 years and older from the 2010-2019 National Survey on Drug Use and Health. The higher rates of suicidal ideation and attempts among Medicaid and uninsured groups compared with those with private insurance. After implementation of the ACA policy, the difference-in-differences analysis showed a significantly reduced risk of suicide in the Medicare group compared with the privately insured group, with no significant differences observed in the other groups. These findings highlight the importance of improving access to mental health services, particularly for those with lower levels of insurance coverage, such as Medicaid and Medicare.

Reliability and Validity of the Korean Version of the Ask Suicide-Screening Questions (ASQ).

BACKGROUND: The Ask Suicide-Screening Questions (ASQ) tool is a simple suicide screening tool developed to screen patient suicide risk (SR). The purpose of this study was to verify the reliability and validity of the ASQ tool in hospitalized patients. METHODS: The internal consistency and test-retest reliability of the South Korean version of the ASQ tool were verified in 99 hospitalized patients admitted to a tertiary medical institution in Seoul. To verify the correlations and validity of each convergence with other scales, the Mini-International Neuropsychiatric Interview (MINI), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Satisfaction with Life Scale (SWLS) were also conducted to determine convergent and discriminant validity. Then, the receiver operating characteristic (ROC) curve diagnosis values for suicide and depression levels with the highest correlations were analyzed. RESULTS: As a result, Cronbach's alpha was 0.826, and when each item was removed sequentially, Cronbach's alpha ranged from 0.736-0.840, showing stable internal consistency. Most of the corrected item-total correlation were over 0.500; however, a relatively low correlation was shown for the fourth and fifth questions, which had values of 0.429 and 0.410, respectively. The test-retest reliability was 0.830, and the MINI and PHQ-9 showed high values of 0.872 and 0.672, respectively. The area under the curve (AUC) according to the ASQ diagnosis value was also the highest for the MINI (0.936). CONCLUSION: The validity and reliability of the South Korean version of the ASQ tool were demonstrated. Through this validation, the ASQ tool can be used for simple suicide risk screening (SRS) in hospitalized patients.

Depressive symptoms among people under COVID-19 quarantine or self-isolation in Korea: a propensity score matching analysis.

Introduction: This study aims to determine the effect of COVID-19-related hospital isolation or self-isolation on depression using the propensity score matching method. Methods: Data on 217,734 participants were divided into groups based on whether or not they underwent quarantine for their COVID-19 diagnosis. COVID-19-related anxiety, depressive symptoms, subjective health status, and perceived stress were evaluated. Results: Based on the calculated propensity score, we matched the quarantined group and non-quarantined group using 1:2 matching with nearest neighbor matching and a caliper width of 0.1. Within the quarantined group, 16.4% of participants experienced significant depressive symptoms, which was significantly higher than that of the non-quarantined group. However, there was no significant difference between the two groups in COVID-19-related anxiety, self-rated health status, and perceived stress. In our multiple logistic regression analysis with related variables corrected, the quarantined group was 1.298 times more likely to have depressive symptoms than the non-quarantined group (95% CI = 1.030-1.634). Conclusion: Our study confirmed that COVID-19 quarantine is associated with depressive symptoms. These results indicate that healthcare policymakers and healthcare professionals must consider the negative mental and physical effects of quarantine when determining quarantine measures during an infectious disease disaster such as the COVID-19 pandemic.

Association between Psychological Suffering and Suicidal Thinking in Patients with Urologic Cancer Using Real-World Data.

Cancer is a leading cause of death in Korea, and depression and suicide are major psychiatric problems in cancer patients. This study aimed to explore the correlation between anxiety, depression, social support, cancer state, and suicidality among urologic cancer patients. Sixty patients with urologic cancer were admitted to a university hospital between October 2019 and February 2020. The patients were evaluated using the Patient Health Questionnaire-9, Generalized Anxiety Disorder 7-item scale, Suicidality module of the Mini International Neuropsychiatric Interview (MINI), and the Lubben Social Network scales (LSNS). To determine which psychological or demographic factors affected suicide risk, Fisher's exact test, Wilcoxon rank-sum test, regression, and logistic regression were conducted. It was found that the greater the depressive symptoms, the higher the suicidal risk (OR = 1.32, 95% CI = 1.08-1.61). Furthermore, anxiety symptoms and the duration of cancer after diagnosis significantly increased depressive symptoms (p = 0.032). Clinicians should be able to identify the risk factors for suicide in patients with cancer, one of which is depression. To assess the risk of suicide, we must evaluate not only depressive symptoms but also the related anxiety and duration of the disease. Prevention and intervention efforts are needed to improve depressive moods and anxiety after cancer diagnosis.

Association Between Breakfast Consumption and Suicidal Attempts in Adolescents.

Purpose: It has been previously reported that skipping breakfast is positively associated with increased depression, anxiety, stress, and psychological distress. This study examined the effects of breakfast consumption on suicide attempts among adolescents. Patients and Methods: The Korean Youth Risk Behavior Web-based Survey (KYRBWS) is an anonymous self-report survey conducted with middle- and high-school students to understand the health behavior of Korean adolescents. Variables related to suicide risk such as suicide attempts, breakfast frequency, depression, anxiety, and violence were analyzed using multivariate logistic regression analysis. The class selected as the sample class was surveyed for all students and the number of youths selected as the sample in 2020 was 57,925 students from 800 schools (400 middle schools and 400 high schools) in 17 cities and provinces nationwide. Among them, the dataset for this study was 54,948 completed adolescent health behavior surveys. Results: Adolescents who attempted suicide often skipped breakfast and had high stress levels. In the regression analysis, those who ate breakfast less than once a week had a stronger association with suicide attempts than the group of six or seven times a week (OR = 2.186; 95% CI = 1.873-2.552). In the group of feeling sadness or hopeless for more than two weeks in the past year, those who ate breakfast zero or once a week (OR = 1.269; 95% CI = 1.044-1.542), or two or three times (OR = 1.300; 95% CI = 1.043-1.619), showed a stronger correlation with suicide attempts than the group of six or seven times a week. Conclusion: Breakfast can affect adolescents' emotions. Helping adolescents regularly eat breakfasts might be a suicide prevention strategy.

Using Boosted Machine Learning to Predict Suicidal Ideation by Socioeconomic Status among Adolescents.

(1) Background: This study aimed to use machine learning techniques to identify risk factors for suicidal ideation among adolescents and understand the association between these risk factors and socioeconomic status (SES); (2) Methods: Data from 54,948 participants were analyzed. Risk factors were identified by dividing groups by suicidal ideation and 3 SES levels. The influence of risk factors was confirmed using the synthetic minority over-sampling technique and XGBoost; (3) Results: Adolescents with suicidal thoughts experienced more sadness, higher stress levels, less happiness, and higher anxiety than those without. In the high SES group, academic achievement was a major risk factor for suicidal ideation; in the low SES group, only emotional factors such as stress and anxiety significantly contributed to suicidal ideation; (4) Conclusions: SES plays an important role in the mental health of adolescents. Improvements in SES in adolescence may resolve their negative emotions and reduce the risk of suicide.

A Machine Learning Approach for Predicting Wage Workers' Suicidal Ideation.

(1) Background: Workers spend most of their days working. One's working environment can be a risk factor for suicide. In this study, we examined whether suicidal ideation can be predicted using individual characteristics, emotional states, and working environments. (2) Methods: Nine years of data from the Korean National Health and Nutrition Survey were used. A total of 12,816 data points were analyzed, and 23 variables were selected. The random forest technique was used to predict suicidal thoughts. (3) Results: When suicidal ideation cases were predicted using all of the independent variables, 98.9% of cases were predicted, and 97.4% could be predicted using only work-related conditions. (4) Conclusions: It was confirmed that suicide risk could be predicted efficiently when machine learning techniques were applied using variables such as working environments.

A Network Analysis of Depressive Symptoms in the Elderly with Subjective Memory Complaints.

(1) Background: Subjective memory complaints (SMCs) are common among the elderly and are important because they can indicate early cognitive impairment. The factor with the greatest correlation with SMCs is depression. The purpose of this study is to examine depressive symptoms among elderly individuals with SMCs through a network analysis that can analyze disease models between symptoms; (2) Methods: A total of 3489 data collected from elderly individuals in the community were analyzed. The Subjective Memory Complaints Questionnaire and Patient Health Questionnaire-9 were evaluated. For statistical analysis, we investigated the features of the depressive symptoms network, including centrality and clustering; (3) Results: Network analysis of the SMC group showed strong associations in the order of Q1-Q2 (r = 0.499), Q7-Q8 (r = 0.330), and Q1-Q6 (r = 0.239). In terms of centrality index, Q2 was highest in strength and expected influence, followed by Q1 in all of betweenness, strength, and expected influence; (4) Conclusions: The network analysis confirmed that the most important factors in the subjective cognitive decline group were depressed mood and anhedonia, which also had a strong correlation in the network pattern.

The Relationships between Abnormal Serum Lipid Levels, Depression, and Suicidal Ideation According to Sex.

(1) Background: Serum lipid levels affect not only nutritional status but also emotional state. The purpose of this study was to examine the effects of various socio-demographic characteristics, abnormal cholesterol levels, and BMI indicators on depressive symptoms and suicidal ideation in the Korean population. (2) Methods: A total of 23,692 people were surveyed using data from the Korea National Health and Nutrition Examination Survey (KNHNES) 2014, 2016, and 2018. Data from 11,653 patients were analyzed. Age, sex, chronic disease, smoking, alcohol consumption, total cholesterol (HDL, triglycerides), BMI, depression, and suicidal ideation were measured. (3) Results: According to sex, low HDL, high triglycerides, and suicidal ideation were significant, along with low education level, smoking, binge drinking, and high BMI. High triglyceride level was shown to significantly increase the risk of depression in males (OR = 1.535, 95% CI = 1.098-2.147). Factors affecting suicidal ideation in males were age, binge drinking, and depression, while blood lipid factors were not significant. (4) Conclusions: Of the types of serum lipid factors affecting depression and suicidal ideation, high triglycerides were found to be a risk factor for depression in men. Serum lipids can be used as biomarkers to reflect depressive symptoms in men depending on cholesterol level.

The Effectiveness of Predicting Suicidal Ideation through Depressive Symptoms and Social Isolation Using Machine Learning Techniques.

(1) Background: Social isolation is a major risk factor for suicidal ideation. In this study, we investigated whether the evaluation of both depression and social isolation in combination could effectively predict suicidal ideation; (2) Methods: A total of 7994 data collected from community residents were analyzed. Statistical analysis was performed using age, the Patient Health Questionnaire-9, and the Lubben Social Network Scale as predictors as the dependent variables for suicidal ideation; machine learning (ML) methods K-Nearest Neighbors, Random Forest, and Neural Network Classification were used; (3) Results: The prediction of suicidal ideation using depression and social isolation showed high area under the curve (0.643-0.836) and specificity (0.959-0.987) in all ML techniques. In the predictor model (model 2) that additionally evaluated social isolation, the validation accuracy consistently increased compared to the depression-only model (model 1); (4) Conclusions: It is confirmed that the machine learning technique using depression and social isolation can be an effective method when predicting suicidal ideation.

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

BACKGROUND: Shift work can affect sleep and increase the risk of suicide. This study attempted to predict suicidal ideation according to shift work by using machine learning techniques. METHODS: We analyzed a total of 43,095 data conducted by using the 10-year Korean National Health and Nutrition Examination Survey (KHANES). Shift workers and daytime workers were categorized and analyzed using random forest (RF) and decision tree (DT) techniques of machine learning techniques. RESULTS: Shift workers were more than twice as likely to have suicidal ideation as daytime workers. The RF model showed an accuracy of 91.6% for shift workers and 98% for daytime workers. In the DT technique, the rate of suicidal ideation was the highest among shift workers (82.7%) when they were depressed and had an EuroQol-5 Dimension (EQ-5D) score of less than 0.71. LIMITATIONS: Shift work type was evaluated questionnaire and based on screening data, it was not possible to reflect recent changes in the work type and we evaluated for only suicidal ideation for suicide risk factors. CONCLUSION: The variables influencing the suicide risk of shift workers and daytime workers differ. In the case of shift workers, negative factors such as depression and low quality of life are risk factors for suicide. Efforts are needed to reduce risk factors through administrative and policy interventions to manage workers' health by early screening.

The Relationship between Living Arrangements and Sleep Quality in Older Adults: Gender Differences.

(1) Background: This study examined the effects of living arrangements on the quality of sleep among older adults and analyzed related gender differences; (2) Methods: A total of 4756 older adults in Seoul were included. After adjusting for socio-demographic factors, older adults living alone showed a poorer sleep quality compared with those living with others; (3) Results: When we analyzed the effects of living arrangements on sleep quality by gender, there was no difference in the risk of poor sleep quality between male older adults living alone and those living with others (OR = 1.089, 95% CI = 0.729-1.628), whereas the risk of poor sleep quality was higher for female older adults living alone than those living with others (OR = 1.359, 95% CI = 1.088-1.696); (4) Conclusions: In this study, we have confirmed that older women living alone had poor sleep quality compared to older men. Hence, gender-based approaches will be helpful when providing social support resources to older adults living alone.

Effect of noise sensitivity on psychophysiological response through monoscopic 360 video and stereoscopic sound environment experience: a randomized control trial.

Noise sensitivity is a crucial factor affecting subjective psychophysiological responses to the acoustic environment of various indoor and outdoor spaces. This study examines how noise sensitivity or hyperacusis affects the recovery of emotional and autonomic nervous system (ANS) responses when experiencing various monoscopic 360 video and stereoscopic sound environments (urban and natural) that represent the actual environment. A total of 60 general participants with mild depression, stress, and anxiety were examined using a survey to investigate individual characteristics, including noise sensitivity, and K-means clustering was used to classify them into sensitivity groups. Emotional and physiological responses were measured using the Korean edition of Profile of Mood States and by assessing heart rate variability, respectively. Overall, the emotional recovery effect was greater in the natural than the urban environment, and the homeostatic mechanism of the ANS was better maintained, thereby increasing stress resistance. Noise sensitivity did not have considerable effect on psychophysiological recovery in the natural environment, but had a significant effect on emotional response in the urban environment. This can be used as basic data in seeking customized emotional recovery for individuals using monoscopic 360 video and stereoscopic sound technology in the future.

The Difference of Suicidal Ideation Between Shift Workers and Day Workers by Gender.

Shift work does not synchronize with the 24-hour human circadian rhythm, so shift workers experience various problems related to this disruption. This study investigated the relationship between suicidal risk and emotional difficulties such as depression and anxiety in shift workers according to gender. We analyzed data collected from 79,009 health checkup recipients. We checked psychosocial factors for different genders using the Chi-square test and t-tests. To examine the relationship between shift work and suicidal ideation, we estimated an odds ratio after adjustment for age, depression, and insomnia. There was no difference in terms of the risk of suicidal ideation between shift workers and day workers among women (adjusted odds ratio [AOR], 0.957; 95% confidence interval [CI], 0.826-1.109), whereas the risk of suicidal ideation was significantly higher for shift workers than for day workers among men (AOR, 1.157; 95% CI, 1.021-1.310). This study confirmed that associated factors of shift work-related suicidal ideation could act differently by gender. Shift work for women is generally associated with emotional difficulties such as depression and anxiety, whereas maladaptation to shift work may be related to suicidal ideation among men. A gender-specific suicide prevention approaches will be needed for shift workers.

Suicidal ideation among the elderly living in the community: Correlation with living arrangement, subjective memory complaints, and depression.

BACKGROUND: This study aimed to investigate the correlations of living arrangement, subjective memory complaints, and depression on suicidal ideation in elderly men and women. METHODS: There were 1,412 participants (women, 63.5%) from a rural community in South Korea, aged ≥60 years (mean±SD, 73.2 ± 8.0 years). We measured suicidal ideation, subjective memory complaints, and depression using the Columbia-Suicide Severity Rating Scale, Subjective Memory Complaints Questionnaire, and the short form of the Geriatric Depression Scale, respectively. We then estimated correlations of living arrangement, subjective memory complaints, and depression with recent suicidal ideation using multivariate logistic regression analysis. RESULTS: With all participants as a group, living alone, subjective memory complaints (SMC+) and high risk of depression (HRD) were found to be significant independent predictors (p = 0.021 for living alone; p = 0.006 for SMC+; p < 0.001 for HRD, respectively) of suicidal ideation (SI+). When men and women were analyzed separately, HRD remained significant both in men and women (OR = 6.01, p < 0.005 for men; OR = 7.23, p < 0.001 for women), while living alone and SMC+ were significant only in men (OR = 3.36, p = 0.013 for living alone; OR = 3.30, p = 0.016 for SMC+). LIMITATIONS: The results may not be generalizable to the urban setting as this study included only elderly persons living in the rural community. CONCLUSIONS: Living alone and subjective memory complaints were significant predictors of recent suicidal ideation only in men while depression was a significant predictor both in men and women.

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods.

PURPOSE: Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices. PATIENTS AND METHODS: We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words. RESULTS: Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented. CONCLUSION: Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.

Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques.

(1) Background: The MMPI-2-RF is the most widely used and most researched test among the tools for assessing psychopathology, and previous studies have established its validity. Mood disorders are the most common mental disorders worldwide; they present difficulties in early detection, go undiagnosed in many cases, and have a poor prognosis. (2) Methods: We analyzed a total of 8645 participants. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. (3) Results: Through the machine learning technique, depressive symptoms were predicted with an AUC of 0.634-0.767, and the corresponding value range for hypomanic symptoms was 0.770-0.840. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant classification. When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. (4) Conclusions: Using machine learning analysis, we defined that participants' mood symptoms could be classified and predicted better than when using the Restructured Clinical scales.

Detecting suicidal risk using MMPI-2 based on machine learning algorithm.

Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of adaptation for a large group in clinical settings, schools, and corporations. This study aims to evaluate the utility of MMPI-2 in assessing suicidal risk using the results of MMPI-2 and suicidal risk evaluation. A total of 7,824 datasets collected from college students were analyzed. The MMPI-2-Resturcutred Clinical Scales (MMPI-2-RF) and the response results for each question of the Mini International Neuropsychiatric Interview (MINI) suicidality module were used. For statistical analysis, random forest and K-Nearest Neighbors (KNN) techniques were used with suicidal ideation and suicide attempt as dependent variables and 50 MMPI-2 scale scores as predictors. On applying the random forest method to suicidal ideation and suicidal attempts, the accuracy was 92.9% and 95%, respectively, and the Area Under the Curves (AUCs) were 0.844 and 0.851, respectively. When the KNN method was applied, the accuracy was 91.6% and 94.7%, respectively, and the AUCs were 0.722 and 0.639, respectively. The study confirmed that machine learning using MMPI-2 for a large group provides reliable accuracy in classifying and predicting the subject's suicidal ideation and past suicidal attempts.

Can the MMPI Predict Adult ADHD? An Approach Using Machine Learning Methods.

(1) Background: Adult attention-deficit/hyperactivity disorder (ADHD) symptoms cause various social difficulties due to attention deficit and impulsivity. In addition, in contrast to ADHD in childhood, ADHD in adulthood is difficult to diagnose due to mixed psychopathologies. This study aimed to determine whether it is possible to predict ADHD symptoms in adults using the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) with machine learning (ML) techniques; (2) Methods: Data collected from 5726 college students were analyzed. The MMPI-2-Restructured Form (MMPI-2-RF) was used, and ADHD symptoms in adults were evaluated using the Attention-Deficit/Hyperactivity Disorder Self-Report Scale (ASRS). For statistical analysis, three ML algorithms were used, i.e., K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest, with the ASRS evaluation result as the dependent variable and the 50 MMPI-2-RF scales as predictors; (3) Results: When the KNN, LDA, and random forest techniques were applied, the accuracy was 93.1%, 91.2%, and 93.6%, respectively, and the area under the curve (AUC) was 0.722, 0.806, and 0.790, respectively. The AUC of the LDA method was the largest, with an excellent level of diagnostic accuracy; (4) Conclusions: ML using the MMPI-2 in a large group could provide reliable accuracy in screening for adult ADHD.

Is personality linked to season of birth?

The environment is a very significant factor in early childhood development. Season of birth (SOB) is a proxy viable for the environment to which the babies are exposed, thus also significant in early development. This study investigates the association between SOB and personality. A total 2,962 college students were included as study participants. The participants were classified into four seasonal groups based on their birth month and underwent a personality assessment using the Temperament and Character Inventory (TCI). Statistical analysis was performed using one-way analysis of variance (ANOVA) and multinomial logistic regression analysis. The male participants born in autumn scored high on the Disorderliness (NS4) subscale (ß = 0.055, P = 0.042) and the male participants born in summer and winter scored high on the Extravagance (NS3) subscale (summer: ß = 0.072, P = 0.01, winter: ß = 0.078, P = 0.003). The difference observed indicates a relationship between the SOB and temperament, especially NS. Our findings suggest that environmental factors may affect temperament in early development, although further research is likely needed to clarify the causality between them.