Suicide and Suicide Risk in Adolescents

Suicide is the second leading cause of death for 10- to 24-year-olds in the United States and is a global public health issue, with a recent declaration of a National State of Emergency in Children's Mental Health by the American Academy of Pediatrics, American Academy of Child and Adolescent Psychiatry, and Children's Hospital Association. This clinical report is an update to the previous American Academy of Pediatrics clinical report, "Suicide and Suicide Attempts in Adolescents." Because pediatricians and pediatric health care providers are at the front line of care for adolescents amid a child and adolescent mental health crisis, and because of the chronic and severe shortage of mental health specialists, it is important that pediatric health care providers become facile with recognizing risk factors associated with suicidality and at-risk populations, screening and further assessment of suicidality as indicated, and evidence-based interventions for patients with suicidal ideation and associated behaviors. Suicide risk can be mitigated by appropriate screening, bolstering of protective factors, indicated treatment, community resources, and referrals to mental health providers when available.

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.

Emotional intelligence, alexithymia and suicidal ideation among depressive patients

BACKGROUND

Depression is more than just sentiment sad or passing a rough patch. It is a serious psychological wellbeing disorder that needs curative care and maintenance. Emotional Intelligence (EI) is a comprehensive personality construct denoting the capability to perceive and control affects within the self. Alexithymia is another personality construct that refers to the difficulty of recognizing and expressing feelings, with an outside oriented thinking style. Suicidal ideation is defined as thinking of appealing in conduct intentional to end one's life.

AIM

The current study aimed to examine the relationship between emotional intelligence, alexithymia and suicidal ideation among depressive patients.

DESIGN

A descriptive correlation design was utilized.

SETTING

This study was conducted at the Psychiatric Outpatient Clinic in El Ahrar Hospital, Alsharkia Governorate, Egypt.

SUBJECTS

A purposeful sample of 65 depressed patients was recruited for this study.

TOOLS

Data were collected by using four tools: Socio-demographic Data Sheet, Emotional Intelligence Scales, Toronto Alexithymia Scale (TAS-20) and Suicidal Ideation Scale.

RESULTS

The majority of depressed patients were having alexithymia and having low emotional intelligence. Emotional intelligence was statistically significantly negatively correlated with alexithymia and suicidal ideation. Nevertheless, alexithymia was statistically significantly positively correlated with suicidal ideation. It was statistically significantly positively predictor of suicidal ideation and alexithymia, while it was statistically significantly negatively predictor of suicidal ideation and emotional intelligence.

CONCLUSION

Lower emotional intelligence led to higher suicidal ideation in depressive patients. Increasing alexithymia in patients with depressive disorders caused an increase in their suicidal ideation. Therefore, it is recommended implementing a training program such as mindfulness and emotional intelligence skills for patients to enhance their emotional intelligence.

Risk factors for suicidal behavior in a university population in Brazil: A retrospective study

The present study aimed to evaluate whether variables related to personal and academic characteristics, past history, habits, and addictions were associated with suicidal behavior. Medical records of patients attending the Outpatient Psychiatry Department at the State University of Maringá between July 2016 and December 2017 were included in this retrospective study. The studied population was divided into two groups: 80 employees and 158 students. After performing the univariate analysis using Pearson's chi-square test or Fischer's exact test, the variables with p < 0.30 were included in a multivariate analysis using the binary logistic regression's backward stepwise method. The covariates 'childhood and adolescence maltreatment' and 'treatment phase' were highlighted. The first variable acts as a predictor of suicide attempt, increasing its likelihood by 9.86 times in employees and 5.62 times in students. On the other hand, the treatment phase represents a risk in patients in the adjustment phase and it serves as a predictor of SITBs in employees and students, increasing its likelihood by 7.68 and 8.66 times, respectively.