The coronavirus disease 2019 (COVID-19) pandemic has disrupted our everyday life. Along with the fear of getting infected or of having loved ones infected, the lifestyle changes and the socioeconomic consequences of the pandemic have profound impact on mental health of the general population. While numerous studies on immediate psychological responses to COVID-19 are being published, there is a lack of discussion on its possible long-term sequelae. In this study, we systematically reviewed and meta-analyzed longitudinal studies that examined mental health of the general population prior to and during the pandemic. Furthermore, we explored the long-term psychiatric implications of the pandemic with data from South Korea. Our analysis showed that the number of suicidal deaths during the pandemic was lower than the previous years in many countries, which is in contrast with the increased depression, anxiety, and psychological distress in the general population in South Korea as well as in other countries. To explain this phenomenon, we propose a possibility of delayed impacts. The post-traumatic stress, long-term consequences of social restrictions, and maladaptive response to the “new normal” are discussed in the paper. COVID-19 being an unprecedented global crisis, more research and international collaboration are needed to understand, to treat, and to prevent its long-term effects on our mental health.
Purpose: Attitudes toward suicide are essential in suicide prevention, as suicide is socio-culturally nuanced. Although the relationship between individual attitudes and suicidal behavior has been extensively studied, the role of community attitudes—aggregated by region—on suicide mortality remains ambiguous. This study explored the association between community attitudes and real-world suicide mortality. Methods: Data on attitudes toward suicide from the 2018 Korea National Suicide Survey (N=1500) and individual mortality data from the MicroData Integrated System were obtained. Confirmatory factor analysis supported a factor structure with three factors: "Permissiveness," "Unjustified behavior," and "Readiness to help/Preventability." Thirty regional units in South Korea aggregated the data for ecological analysis. We used negative binomial models to examine the association at the regional level, and stratified analysis by gender and age group was conducted. Results: "Permissiveness" was associated with reduced suicide rates in a univariate model (P<0.001). Adjusting for gender, age, and additional sociodemographics did not alter the association. Additionally, this relationship was observed in males and individuals under 60 years of age after stratification. However, "Unjustified Behavior" and "Readiness to help/Preventability " exhibited no significant association with suicide in any model or stratum. Conclusion: The observed inverse association between permissive community attitudes and suicide contradicts the findings of previous research that links permissive individual attitudes to increased suicidal behavior. Instead, our findings indicate that attitudes may behave differently at the individual and group levels, and this could potentially guide future research and novel approaches to suicide prevention.
BACKGROUND Assessing a patient’s suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. OBJECTIVE The purpose of this study is to investigate cross-sectional and longitudinal approaches to the assessment of suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. METHODS We collected 348 voice recordings during clinical interviews from 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed with the Beck’s Scale for Suicidal Ideation (SSI), and suicidal behavior with the Columbia Suicide Severity Rating Scale (C-SSRS). Acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross-sectionally to detect individuals at high risk for suicide and a within-person classification model that detects significant worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4-months. RESULTS A combined set of 12 acoustic features and three demographic variables (age, gender, and past suicide attempts) were included in the deep neural network algorithm for the between-person classification model. Thirteen acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy, whereas the within-person model was able to predict worsening suicidality over two months with 79% accuracy The second model showed 62% accuracy in predicting increased suicidality in the external sets CONCLUSIONS Within-person analysis using changes in acoustic features within an individual is a promising approach for detecting increased suicidality. Automated analysis of voice can be used to support real-time assessment of suicide risk in primary care or telemedicine.
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