Suicidal Behavior in the First Wave of the COVID-19 Pandemic.

PURPOSE OF REVIEW: Since the declaration of the COVID-19 pandemic, there have been numerous social changes to try to ​​contain the spread of the disease. These sudden changes in daily life have also changed the way we relate to others, in addition to creating a climate of uncertainty and fear. Therefore, the objective of this review is to compile published data of the consequences of suicidal behavior in the first months from the onset of the pandemic. RECENT FINDINGS: The analysis reflects a concern about issues related to suicide since the beginning of the pandemic. A large number of online surveys have been released and have provided data on relatively large populations. The percentage of the population with suicidal ideation in that period seems to be approximately 5-15%. Many studies associate suicidal ideation with being young, female, and presence of sleep problems. Surveys of healthcare workers do not seem to indicate a higher prevalence of suicidal ideation compared to the general population. The incidence of suicide attempts seen in emergency departments did not seem to change, while the number of visits for other issues, unrelated to suicide, did decrease. The few studies on completed suicide do not indicate an increase in incidence in these first 6 months since March 2020, when the WHO declared the start of the pandemic. It does not seem that there have been major changes in the figures related to suicidal behavior in the studies from the first wave of the COVID-19 pandemic, although it is still too early to know the consequences it will have long term. The social and economic damages resulting from the pandemic will certainly take a long time to recover.

Impulsivity in fatal suicide behaviour: A systematic review and meta-analysis of psychological autopsy studies.

Our aim is to review and perform a meta-analysis on the role of impulsivity in fatal suicide behaviour. We included papers who used psychological autopsy methodology, assessed adult death by suicide, and included assessment of impulsivity. We excluded papers about assisted suicide, terrorist suicide, or other cause of death other than suicide or postmortem diagnosis made only from medical records or database. 97 articles were identified. 33 were included in the systematic review and nine in the meta-analysis. We found that people who die by suicide with high impulsivity are associated with younger age, substance abuse, and low intention to die, whereas those with low impulsivity were associated with older age, depression, schizophrenia, high intention to die and low social support. In the meta-analysis, suicide cases had higher impulsivity scores than living controls (Hedges' g = 0.59, 95 % CI [0.28, 0.89], p=.002). However, studies showed heterogeneity (Q = 90.86, p<.001, I2=89.0 %). Impulsivity-aggressiveness interaction was assessed through meta-regression (ß=0.447, p=.045). Individuals with high impulsivity would be exposed to a higher risk of fatal suicide behaviour, aggressiveness would play a mediating role. People who die by suicide with high and low impulsivity display distinct characteristics, which may reflect different endophenotypes leading to suicide by different pathways.

Low Level of Agreement Between Self-Report and Clinical Assessment of Passive Suicidal Ideation.

OBJECTIVE: Discrepancies between patient reports during clinical evaluations and self-reported suicide ideation are of vital importance. We study the agreement in passive suicidal ideation between reports made by clinicians and patients' self-reports. METHOD: Wish of death in 648 outpatients was assessed by attending clinicians. Within 24 h after clinical evaluation, patients completed a self-report questionnaire in which they were asked whether they had no desire to live. We used cluster analysis to determine the clinical profile of a population of patients according to the concordance between reports made by clinicians and self-reported information. RESULTS: A low level of agreement (kappa = 0.072) was found between clinicians and patients, as 56.4% (n = 366) of clinician reports classified as containing no death-related ideas although on self-report the patient did state that they had no desire to live. In this group containing discrepancies between the two reports, two clusters were found to have shared characteristics: female sex, middle age, cohabitation, active employment, no history of suicidal behavior, and diagnosis of neurotic, stress-related, and somatoform disorders. In a third, more severe cluster, patients self-reported sleep disturbances, less appetite, poor treatment adherence, and aggressiveness. CONCLUSIONS: We found low agreement between self-reports and clinician assessments regarding the death wish. Self-reporting may be useful in assessing suicide risk. HIGHLIGHTSLow agreement was found between self-reports and clinician assessments regarding passive suicidal ideation.Most patients in whom the clinician underestimated the risk of suicide were women.Our results suggest that clinicians require adequate documentation of suicidal risk assessment to identify the high-risk population.

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly sampled data sequences. In our method, we model each of them with a recurrent neural network, and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-distributed stochastic neighbor embedding (t-SNE) representation of the latent space. Furthermore, the most relevant input features are identified and interpreted medically.