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BackgroundBeing ready for discharge is vital to successful hospital-to-home transitions. For many patients, however, the transition from psychiatric hospital care to outpatient care can be challenging. An in-depth understanding of the mental health conditions of patients at discharge is crucial and instructive for recovery research.ObjectiveThe purpose of this study was to determine the prevalence and risk factors of depression, anxiety, and poor well-being symptoms among patients who are about to be discharged from psychiatric units in Alberta, Canada. Our aim was to help determine the prevalence of anxiety, depression, and overall well-being among the general psychiatric inpatient population in Alberta before discharge and the potential factors which may influence this.MethodsThis epidemiological study used a cross-sectional quantitative survey from March 8, 2022, to November 5, 2023, to assess depression, anxiety, and well-being. Participants were invited to complete an online questionnaire that contained demographics, clinical information, and responses to the PHQ-9, GAD-7, and WHO-5 questionnaires. SPSS version 25 was used to analyze the data. Descriptive, univariate, and multivariate regression analyses were employed.ResultThe study found that the prevalence of likely depression, anxiety, and poor well-being among patients about to be discharged was 37.1%, 56.4%, and 48.3%, respectively. Based on a logistic regression model, there was a statistically significant association between anxiety, depression, and poor well-being diagnoses and multiple socio-demographic and clinical factors such as ethnicity, primary mental health diagnoses, education level, housing status, depression, anxiety, and well-being at baseline.ConclusionMental health assessment at discharge is a critical step in the recovery and transition of care. There is still a need for further research to identify the underlying causes and robust predictors of mental health symptoms in patients about to be discharged and to provide appropriate interventions and supportive resources both before and following discharge. Future research utilizing these findings may help identify key opportunities to improve outcomes for patients after discharge.

IntroductionPromising preliminary evidence suggests that EMDR may reduce suicidal ideation (SI) when used to treat Major Depressive Disorder, Posttraumatic Stress Disorder, and trauma symptoms in the context of acute mental health crises. EMDR has never been tested specifically for treating SI, and there is a lack of data regarding the safety and effectiveness of web-based, therapist-delivered EMDR in populations with known SI. The primary objective of this study was to investigate the impact of web-based, therapist-delivered EMDR, targeting experiences associated with suicidal thinking. Secondary objectives included examining the effect of EMDR treatment on symptoms of depression, anxiety, posttraumatic stress, emotional dysregulation, and dissociation, as well as safety and attrition.MethodsThis randomized control trial (ClinicalTrials.gov ID number: NCT04181047) assigned adult outpatients reporting SI to either a web-based EMDR intervention or a treatment as usual (TAU) group. TAU included primary and mental health services available within the Canadian public health system. Participants in the EMDR group received up to 12 web-based EMDR desensitization sessions, delivered twice weekly during the COVID-19 pandemic (2021-2023). The Health Research Ethics Board at the University of Alberta approved the protocol prior to initiation of data collection for this study (protocol ID number: Pro00090989).ResultsForty-two adult outpatients received either EMDR (n=20) or TAU (n=22). Participants reported a high prevalence of early onset and chronic SI, and there was a high rate of psychiatric comorbidity. In the EMDR group, median SI, depression, anxiety, and posttraumatic symptom scale scores decreased from baseline to the four month follow-up. In the TAU group, only the median SI and posttraumatic symptom scale scores decreased from baseline to four month follow up. Although sample size precludes direct comparison, there were numerically fewer adverse events and fewer dropouts in the EMDR group relative to the TAU group.ConclusionStudy results provide promising preliminary evidence that web-based EMDR may be a viable delivery approach to address SI. In this complex population, a short treatment course was associated with reductions of SI and other symptoms across multiple diagnostic categories. Further investigation is warranted to verify and extend these results.Clinical Trial Registrationhttps://clinicaltrials.gov/study/NCT04181047?id=NCT04181047&rank=1, identifier NCT04181047

Abstract Background Digital cognitive behavior therapy for insomnia (dCBT-I) is an effective treatment in alleviating insomnia. This study examined the effect of dCBT-I for improving sleep quality in patients with insomnia complaints from a clinical population in a real-world setting. Methods The study included 6,002 patients aged 18 years and above with primary complaints of dissatisfying sleep from a sleep clinic in a psychiatric hospital from November 2016 to April 2021. Patients were diagnosed with insomnia, anxiety disorders, or anxiety comorbid with insomnia or depression according to ICD-10. A mobile app was developed for self-reported assessment and delivering dCBT-I interventions and treatment prescriptions to participants. The primary outcome was change in global sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI). At 8- and 12-week follow-up, 509 patients were reassessed. Data were analyzed with non-parametric tests for repeated measures. Results Patients treated with dCBT-I monotherapy were younger, with a more frequent family history of insomnia compared to those with medication monotherapy and those with combined dCBT-I and medication therapy. Improvements of sleep quality from baseline to 8-week follow-up were significant in each treatment type. Compared to 8-week follow-up, PSQI scores at 12-week were significantly decreased in the depression group receiving combined therapy and in the anxiety group treated with dCBT-I monotherapy and with combined therapy. A time-by-treatment interaction was detected in anxiety patients indicating differential reduction in PSQI scores over time between different treatment options. Conclusion The current findings suggest dCBT-I is a practical and effective approach for lessening insomnia symptoms, especially for patients with anxiety symptoms suggesting with a more extended intervention period (i.e., 12 weeks). Trial registration Chinese Clinical Trial Registry (ChiCTR1900022699).

Abstract Background Specialized mental health services for the treatment of Child Sexual Abuse (CSA) are generally expensive and labour intensive. They require a trauma-informed approach that may involve multiple services and therapeutic modalities, provided over the course of several months. That said, given the broad-ranging, long term negative sequelae of CSA, an evaluation of the cost-benefit analysis of treatment is clearly justified. Methods We performed a Social Return on Investment (SROI) analysis of data gathered as part of the treatment program at the Be Brave Ranch in Edmonton, Canada to determine the value-for-money of the services provided. We endeavoured to take a conservative, medium-term (5 year) perspective; this is in contrast to short term (1–2 year) effects, which may rapidly dissipate, or long term (15–20 year) effects, which are likely diffuse and difficult to measure. As such, our analysis was based on an average annual intake of 100 children/adolescents (60:40 split) and their families, followed over a five-year timeframe. Financial proxies were assigned to benefits not easily monetized, and six potential domains of cost savings were identified. Results Our analyses suggest that each dollar spent in treatment results in an average cost savings of $11.60 (sensitivity analysis suggests range of 9.20–12.80). The largest value-for-money was identified as the domain of crisis prevention, via the avoidance of rare but costly events associated with the long term impacts of CSA. Somewhat surprisingly, savings related to the area of criminal justice were minimal, compared to other social domains analysed. Implications are discussed. Conclusions Our results support the cost effectiveness of the investment associated with specialized, evidence-based early interventions for CSA. These approaches alleviate severe, negative outcomes associated with CSA, resulting in both economic savings and social benefits. These findings rest upon a number of assumptions, and generalizability of these results is therefore limited to similar programs located in comparable areas. However, the SROI ratio achieved in this analysis, in excess of $11:1, supports the idea that, while costly, these services more than pay for themselves over time.

Abstract Background Alcohol dependence is a mental disorder with a high relapse rate. However, specific neuroimaging biomarkers have not been determined for alcohol dependence and its relapse. We conducted data-driven research to investigate resting-state functional magnetic resonance imaging (rs-fMRI) during early abstinence from alcohol dependence and its potential ability to predict relapse. Methods Participants included 68 alcohol-dependent patients and 68 healthy controls (HCs). The regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF) were compared between the alcohol dependence group and the HCs and between the relapse group and the nonrelapse group. The brain regions that presented significantly different ReHo and/or fALFF between the alcohol-dependent patients and HCs and/or between the relapsed and nonrelapsed patients were selected as the seeds to calculate the functional connectivities (FCs). Results During a 6-month follow-up period, 52.24% of alcohol-dependent patients relapsed. A regression model for differentiating alcohol-dependent patients and HCs showed that reductions in ReHo in the left postcentral region, fALFF in the right fusiform region, and FC in the right fusiform region to the right middle cingulum were independently associated with alcohol dependence, with an area under the receiver operating characteristic curve (AUC) of 0.841. The baseline FC of the left precentral to the left cerebellum of the relapse group was significantly lower than that of the nonrelapse group. The AUC of this FC to predict relapse was 0.774. Conclusions Our findings contribute to advancing research on the neurobiological etiology and predictive biomarkers for relapse associated with alcohol dependence.

Background: This cross-sectional study explored variation of the prevalence of perceived stress, depression and anxiety among different self-identified gender identity groups in the Canadian population during the early stages of the COVID-19 pandemic. Methods: Anxiety, depression, and stress were assessed using the Generalized Anxiety Disorder 7-item (GAD-7) scale, Patient Health Questionnaire-9 (PHQ-9), and Perceived Stress Scale (PSS) respectively. Data were analyzed using one-way analysis of variance. Results: There were 8267 respondents to the online survey; 982 (12.0%) were male-identified, 7120 (86.9%) female-identified, and 92 (1.1%) identified as a diverse gender group. Prevalence rates for clinically meaningful anxiety (333 (41.7%), 2882 (47.6%), 47 (61.0%)), depression (330 (40.2%), 2736 (44.3%), 46 (59.7%)), and stress (702 (79.6%), 5711 (86.4%), 74 (90.2%)) were highest among respondents who self-identified as “other gender” followed by female-identified and then male-identified, respectively. There were statistically significant differences between gender groups for mean scores on GAD-7 (F (2, 6929) = 18.02, p < 0.001), PHQ-9 (F (2, 191.4) = 11.17, p < 0.001), and PSS (F (2, 204.6) = 21.13, p < 0.001). Conclusions: Gender identity differences exist in terms of the prevalence and severity of anxiety, depressive, and stress symptoms during the COVID-19 pandemic. This finding highlights the importance of incorporating self-identified gender identity in medical research, clinical practice, and policy.

Caring for patients with personality disorders can be challenging due to risks associated with suicidal ideation, homicidal threats, splitting, and acting out with problematic behavior in psychiatric inpatient units. Limited resources on inpatient units further add to the stress and burden on staff. This case summarizes how trauma-informed care was implemented in an inpatient setting to produce marked improvement in a patient’s treatment outcomes as well as better staff engagement and satisfaction. This culture change in the approach to care was not an easy process, as effortful planning and resources were required for key elements such as ongoing coaching, education, and regular staff debriefings. This case report signals the need for service providers to enable health systems to examine rules and exceptions from a cultural perspective of considering equity, diversity, and inclusion (EDI)—to allow openness to rational exceptions, even if they are unconventional.

Self- and value-based reward processing were investigated from an individual difference perspective. Participants learnt to associate geometric shapes with three identities (self, friend, stranger) on one occasion and three values (e.g., high, medium, low monetary value) on another occasion. Participants then carried out a perceptual matching task of judging whether shape-label pairings matched (e.g., triangle-self or circle-£16). This personal matching task was followed by a self-report measure concerning personal distance from others. Both self-identification and high value-associations led to better task performance (faster responses with higher perceptual sensitivity) in the matching tasks. Correlations between self- and high value-based reward biases varied as a function of participant ratings of personal distance between themselves and others. For individuals who rated with a large personal distance, there were no correlations between the self- and reward-biases. In contrast, self- and reward-biases did correlate for individuals who rated a close personal distance between themselves and others. These conclusions were supported by cluster analyses, which showed either distinct or common similarity structures for matching, based on personal and value relevance, corresponding to individuals’ self-rating of a large or close distance to others. The data suggest an intertwining model of self-value at the individual level. This model has significant implications for understanding emotion regulation in relation to self and reward interactions and may be relevant for advancing our understanding self in relation to normal and psychopathological processes.

Abstract A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key role for a third area, the temporal pole (TP) for childhood anxiety in this context. This finding is based on differential fMRI responses to emotional faces (angry versus fearful faces) in children with one or more of generalized anxiety, separation anxiety, and social phobia (n = 22) compared with matched controls (n = 23). In our machine learning (Adaptive Boosting) model, the right TP distinguished anxious from control children (accuracy = 81%). Involvement of the TP as significant for neurocognitive aspects of pediatric anxiety is a novel finding worthy of further investigation.

BackgroundMental illness is not uncommon among males. It is estimated that males are more likely to die by suicide, become dependent on alcohol, report frequent drug use, and be dissatisfied with their life, compared to women. In this study, we assessed the potential to offer support to this population using Text4Hope, a texting mental health service.MethodsThe study was a naturalistic randomized controlled trial comparing two populations of Text4Hope male subscribers; an intervention group (IG, Text4Hope subscribers who received once-daily supportive text messages for 6 weeks) and a control group (CG, Text4Hope subscribers who joined the program in the same time frame but were yet to receive text messages). Inferential statistics were used to compare the severity and the prevalence of the likely stress, anxiety, and depression, between the two groups, using the Perceived Stress Scale (PSS-10), the Generalized Anxiety Disorder 7-item (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9), and defined the Composite Mental Health (CMH) score as the sum of these three. T-test, Chi-squared association, and binary logistic regression analyses were applied.ResultsThere were 286 male subscribers to Text4Hope. The majority were above 40 years, white, employed, had postsecondary education, were in a relationship, and owned a home. Mean scores of PSS-10, GAD-7, and PHQ-9 scales and the CMH were significantly higher for the CG compared to the IG, 11.4, 28.8, 25.8, and 18.7%, respectively. Similarly, a statistically significantly lower prevalence in IG, compared to the CG, on likely MDD (58.15 vs. 37.4%) and likely GAD (50 vs. 30.8%), with a small effect size. The IG was a significant predictor for lower odds of both likely MDD and likely GAD while controlling for sociodemographic characteristics.ConclusionsThe Text4Hope service is an effective tool for mental health support for male subscribers during the COVID-19 pandemic. Compared to the males who didn't receive the service, those who received it were in better mental health conditions. Further effort is still needed to encourage males to participate in such online services that can help them receive adequate support, particularly during crisis times.

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model—both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.

Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.

Background: Treatment-resistant depression (TRD) is considered one of the major clinical challenges in the field of psychiatry. An estimated 44% of patients with major depressive disorder (MDD) do not respond to two consecutive antidepressant therapies, and 33% do not respond to up to four antidepressants. Over 15% of all patients with MDD remain refractory to any treatment intervention. rTMS is considered a treatment option for patients with TRD. Likewise, iCBT is evidence-based, symptom-focused psychotherapy recommended for the treatment of TRD. Objective: This study aimed to evaluate the initial comparative clinical effectiveness of rTMS treatment with and without iCBT as an innovative intervention for the treatment of participants diagnosed with TRD. Methods: This study is a prospective two-arm randomized controlled trial. Overall, 78 participants diagnosed with TRD were randomized to one of two treatment interventions: rTMS sessions alone and rTMS sessions plus iCBT. Participants in each group were made to complete evaluation measures at baseline, and 6 weeks (discharge) from treatment. The primary outcome measure was baseline to six weeks change in mean score for the 17-item Hamilton depression rating scale (HAMD-17). Secondary outcomes included mean baseline to six-week changes in the Columbia suicide severity rating scale (CSSRS) for the rate of suicidal ideations, the QIDS-SR16 for subjective depression, and the EQ-5D-5L to assess the quality of health in participants. Results: A majority of the participants were females 50 (64.1%), aged ≥ 40 39 (50.0%), and had college/university education 54 (73.0%). After adjusting for the baseline scores, the study failed to find a significant difference in the changes in mean scores for participants from baseline to six weeks between the two interventions under study on the HAMD-17 scale: F (1, 53) = 0.15, p = 0.70, partial eta squared = 0.003, CSSRS; F (1, 56) = 0.04 p = 0.85, partial eta squared = 0.001, QIDS-SR16 scale; F (1, 53) = 0.04 p = 0.61, partial eta squared = 0.005, and EQ-5D-VAS; F (1, 51) = 0.46 p = 0.50, and partial eta squared = 0.009. However, there was a significant reduction in means scores at week six compared to baseline scores for the combined study population on the HAMD-17 scale (42%), CSSRS (41%), QIDS-SR16 scale (35%), and EQ-VAS scale (62%). Conclusion: This study did not find that combined treatment of TRD with rTMS + iCBT (unguided) was superior to treatment with rTMS alone. Our findings do not support the use of combined treatment of rTMS + iCBT for the management of TRD disorders.

The 2016 Alberta wildfire, the largest insured natural disaster in Canada, led to a mass evacuation of residents of Fort McMurray, a small city in northern Alberta. The wildfire resulted in significant damages to housing and community infrastructure. The entire community was displaced for several weeks. Post-disaster, community members experienced individual and collective trauma, and other negative mental health impacts in response to the significant losses and grief they endured. Spirituality has been found to be a major protective factor in facilitating resiliency and recovery following the experience of disaster. Nonetheless, little focus has been directed toward how spirituality can strengthen and empower community capacity and growth during post-disaster recovery. Our study explored various meanings and concerns, along with tools and strategies that helped to nurture spiritual resilience and well-being among residents of Fort McMurray following the Alberta wildfire. Data were collected through interviews and focus group discussions with community influencers working to support long-term recovery efforts in the city. Participants identified a number of spiritual resources such as a strong sense of belonging, a shared positive outlook, faith and hope, compassion, and sense of gratitude, which contributed to increased resilience and positive health and well-being and helped them to support families and communities in the post disaster recovery period. Our findings indicate that spiritual values and beliefs can play a significant role in building resilience and promoting individual and communal healing and recovery post-disaster. These findings have important implications for post-disaster recovery strategies, as they highlight the need to ensure supports for interventions and initiatives that strengthen a collective sense of identity and social cohesion, informed by communal norms and beliefs, including programs and resources which support opportunities for reflexivity to foster shared healing and ongoing recovery processes.

Abstract Background In order to examine the impact of disasters on adolescent mental health, this study compared population mental health survey data from two communities in Alberta, Canada: Fort McMurray, which experienced a major natural disaster, and Red Deer, which did not. Methods Data from 3070 grade 7–12 students from Fort McMurray, Alberta, Canada (collected in 2017, 18 months after the 2016 wildfire) was compared with data from 2796 grade 7–12 students from Red Deer, Alberta, Canada (collected in 2014). The same measurement scales were used for both surveys. Both of these cities have populations of approximately 100,000, and both cities are located in Alberta, Canada. For this reason, Red Deer is an appropriate non-disaster impacted community to compare to the disaster impacted community of Fort McMurray. Results The results of this comparison demonstrate that mental health symptoms were statistically significantly elevated in the Fort McMurray population when compared to the control population in Red Deer. This occurred for scores consistent with a diagnosis of depression (31% vs. 17%), moderately severe depression (17% vs. 9%), suicidal thinking (16% vs. 4%), and tobacco use (13% vs. 10%). Consistent with there being major mental health impacts from the 2016 Fort McMurray wildfire, self-esteem scores and quality of life scores were also statistically significantly lower in Fort McMurray. While the rates of anxiety disorder were similar (15% vs. 16%), the mean scores on the anxiety scale were slightly higher, with this difference reaching statistical significance. There were no statistical differences in the rates or scores for alcohol or substance use. Conclusions Our results are consistent with previous findings showing a significant negative impact of disasters on many aspects of adolescent mental, with a particular increase in symptoms related to depression and suicidal thinking. These findings highlight first, the need to identify adolescents most at risk of developing psychiatric symptoms after experiencing the trauma of disaster and second, the importance and necessity of implementing short and long term mental health intervention programs specifically aimed at adolescents, in order to help mitigate the negative effects of disasters on their mental health.

In Fort McMurray, Alberta, Canada, the wildfire of May 2016 forced the population of 88,000 to rapidly evacuate in a traumatic and chaotic manner. Ten percentage of the homes in the city were destroyed, and many more structures were damaged. Since youth are particularly vulnerable to negative effects of natural disasters, we examined possible long-term psychological impacts. To assess this, we partnered with Fort McMurray Public and Catholic Schools, who surveyed Grade 7–12 students (aged 11–19) in November 2017, 2018, and 2019—i.e., at 1.5, 2.5, and 3.5 years after the wildfire. The survey included validated measurement scales for post-traumatic stress disorder (PTSD), depression, anxiety, drug use, alcohol use, tobacco use, quality of life, self-esteem, and resilience. Data analysis was done on large-scale anonymous surveys including 3,070 samples in 2017; 3,265 samples in 2018; and 3,041 samples in 2019. The results were unexpected and showed that all mental health symptoms increased from 2017 to 2019, with the exception of tobacco use. Consistent with this pattern, self-esteem and quality of life scores decreased. Resilience scores did not change significantly. Thus, mental health measures worsened, in contrast to our initial hypothesis that they would improve over time. Of note, we observed higher levels of mental health distress among older students, in females compared to male students, and in individuals with a minority gender identity, including transgender and gender-non-conforming individuals. These findings demonstrate that deleterious mental health effects can persist in youth for years following a wildfire disaster. This highlights the need for multi-year mental health support programs for youth in post-disaster situations. The indication that multi-year, post-disaster support is warranted is relatively novel, although not unknown. There is a need to systematically investigate factors associated with youth recovery following a wildfire disaster, as well as efficacy of psychosocial strategies during later phases of disaster recovery relative to early post-disaster interventions.

Objective: Where traditional approaches fall short, widely accessible and accepted, yet under leveraged, digital technologies such as text messaging present novel opportunities to solve a range of health care solutions. The following provides a preliminary analysis of the Text4Support program, a text-messaging intervention using the principles of cognitive behavioral therapy, which seeks to support the health and well-being of individuals seeking support for addiction or mental health concerns. The goal of this study was to assess whether the Text4Support program improved the perceived overall mental well-being of participants.Methods: The evaluation analyzes survey responses of individuals who were enrolled in the Text4Support program beginning in July 2019, who had completed the 6-months program by May 2020. Participants were asked to provide responses to three surveys during their time in the program—at baseline, 12-weeks and 6-months, which included questions documenting demographic information, general satisfaction with the program, and a participants' level of “global distress” through use of the Clinical Outcomes Routine Evaluation System (CORE-10)—a validated brief 10-item assessment and outcome measurement tool used to assess conditions including anxiety, depression, physical problems, and risk to self.Results and Conclusions: This data set did not include a large enough sample of participants to reach statistical significance. Nevertheless, the study provides some preliminary analysis, and identifies opportunities for the future analysis and research.

Introduction: With the sudden onset and global dispersal of the SARS-CoV-2 virus, many nations including Canada attempted to reduce spread of the resultant COVID-19 syndrome with self-isolation and quarantine, while seeking a cure or vaccine for this disease. Understanding impacts of self-isolation and self-quarantine on stress, anxiety, and depression will help us to mitigate these issues through appropriate development of mental health services.Methods: The sample was drawn from individuals who self-subscribed to Text4Hope, a service that delivers text messages based on a cognitive behavioral therapy framework. Text4Hope was developed to support Albertans during the COVID-19 pandemic. Subscribers were asked for demographic information and if they had to self-isolate or self-quarantine during the pandemic via a survey link. Mental health was assessed using the validated instruments: Perceived Stress Scale (PSS), Generalized Anxiety Disorder-7 item scale (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9). Descriptive statistics and Chi-Square test results were derived using Statistical Package for Social Sciences (SPSS) version-26.Results: 6,041 of 32,805 Text4Hope subscribers (18.4%) completed the survey. Of these respondents, 19.2% had self-isolated or self-quarantined in Alberta as of March 31, 2020 during the COVID-19 pandemic. Post-hoc analysis using adjusted residuals suggested that individuals aged 60 years of age or older, and retirees had a higher likelihood of self-isolation or self-quarantine, compared to respondents with other age or employment characteristics. One-week prevalence rates for self-reported measures of moderate to high stress, likely Generalized Anxiety Disorder (GAD), and likely Major Depressive Disorder (MDD) were 84.9, 46.7, and 41.4%, respectively. Respondents who had to self-isolate or self-quarantine during the COVID-19 pandemic were significantly more likely to present with moderate to high stress, significant anxiety symptomatology, and significant depressive symptomatology.Conclusions: Older age and employment status were significantly associated with the likelihood of self-isolation or self-quarantine. We found elevated self-reported levels of anxiety and depression associated with self-reported COVID-19 pandemic-related self-isolation and self-quarantine activity. These findings have mental health implications both during and after the pandemic and demonstrate the need for greater focus on psychological complications of self-isolation and self-quarantine, and development of optimal ways to manage these pandemic consequences.

Background: A prolonged COVID-19 pandemic has the potential to trigger a global mental health crisis increasing demand for mental health emergency services. We undertook a rapid review of the impact of pandemics and epidemics on emergency department utilization for mental health (MH) and substance use (SU).Objective: To rapidly synthesize available data on emergency department utilization for psychiatric concerns during COVID-19.Methods: An information specialist searched Medline, Embase, Psycinfo, CINAHL, and Scopus on June 16, 2020 and updated the search on July 24, 2020. Our search identified 803 abstracts, 7 of which were included in the review. Six articles reported on the COVID-19 pandemic and one on the SARS epidemic.Results: All studies reported a decrease in overall and MH related ED utilization during the early pandemic/epidemic. Two studies found an increase in SU related visits during the same period. No data were available for mid and late stage pandemics and the definitions for MH and SU related visits were inconsistent across studies.Conclusions: Our results suggest that COVID-19 has resulted in an initial decrease in ED visits for MH and an increase in visits for SU. Given the relative paucity of data on the subject and inconsistent analytic methods used in existing studies, there is an urgent need for investigation of pandemic-related changes in ED case-mix to inform system-level change as the pandemic continues.

Background: Alcohol dependence (AD) is a chronic recurrent brain disease that causes a heavy disease burden worldwide, partly due to high relapse rates after detoxification. Verified biomarkers are not available for AD and its relapse, although the nucleus accumbens (NAc) and medial prefrontal cortex (mPFC) may play important roles in the mechanism of addiction. This study investigated AD- and relapse-associated functional connectivity (FC) of the NAc and mPFC with other brain regions during early abstinence.Methods: Sixty-eight hospitalized early-abstinence AD male patients and 68 age- and education-matched healthy controls (HCs) underwent resting-functional magnetic resonance imaging (r-fMRI). Using the NAc and mPFC as seeds, we calculated changes in FC between the seeds and other brain regions. Over a follow-up period of 6 months, patients were measured with the Alcohol Use Disorder Identification Test (AUDIT) scale to identify relapse outcomes (AUDIT ≥ 8).Results: Thirty-five (52.24%) of the AD patients relapsed during the follow-up period. AD displayed lower FC of the left fusiform, bilateral temporal superior and right postcentral regions with the NAc and lower FC of the right temporal inferior, bilateral temporal superior, and left cingulate anterior regions with the mPFC compared to controls. Among these FC changes, lower FC between the NAc and left fusiform, lower FC between the mPFC and left cingulate anterior cortex, and smoking status were independently associated with AD. Subjects in relapse exhibited lower FC of the right cingulate anterior cortex with NAc and of the left calcarine sulcus with mPFC compared to non-relapsed subjects; both of these reductions in FC independently predicted relapse. Additionally, FC between the mPFC and right frontal superior gyrus, as well as years of education, independently predicted relapse severity.Conclusion: This study found that values of FC between selected seeds (i.e., the NAc and the mPFC) and some other reward- and/or impulse-control-related brain regions were associated with AD and relapse; these FC values could be potential biomarkers of AD or for prediction of relapse. These findings may help to guide further research on the neurobiology of AD and other addictive disorders.

ObjectivesTo assess the likely prevalence rates of Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD) and Post-Traumatic Stress Disorder (PTSD) in staff of Fort McMurray School Districts eighteen months after a May 2016 wildfire, and to determine possible predictors.MethodsA quantitative cross-sectional survey was used to collect data through self-administered online questionnaires to determine likely MDD, GAD and PTSD using well validated self-report questionnaires.ResultsOf 1,446 staff who were sent the online survey link in an e-mail, 197 completed the survey, of which there were 168 females (85%) and 29 males (15%). The one-month prevalence rates for likely MDD, GAD and PTSD among the school staff were 18.3, 15.7 and 10.2% respectively. There were statistically significant associations between multiple socio-demographic and clinical variables likely MDD, GAD and PTSD among respondents.ConclusionKnowledge of key factors for MDD, GAD and PTSD may be helpful for policy makers when formulating population level social and clinical programs, to mitigate the mental health effects of future natural disasters.

Background: Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder. Methods: The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups. Results: Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables. Conclusions: Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

This study explores differences in stress, anxiety, and depression experienced by different ethnic groups during the COVID-19 pandemic. This was a cross-sectional online survey of subscribers of the COVID-19 Text4Hope text messaging program in Alberta. Stress, anxiety, and depression were measured among Caucasian, Indigenous, Asian, and other ethnic groups using the Perceived Stress Scale (PSS)-10, Generalized Anxiety Disorder (GAD)-7, and Patient Health Questionnaire (PHQ)-9 scales, respectively. The burden of depression and stress were significantly higher in Indigenous populations than in both Caucasian and Asian ethnic groups. The mean difference between Indigenous and Caucasian for PHQ-9 scores was 1.79, 95% CI of 0.74 to 2.84, p < 0.01 and for PSS-10 it was 1.92, 95% CI of 0.86 to 2.98, p < 0.01). The mean difference between Indigenous and Asian for PHQ-9 scores was 1.76, 95% CI of 0.34 to 3.19, p = 0.01 and for PSS-10 it was 2.02, 95% CI of 0.63 to 3.41, p < 0.01. However, Indigenous participant burden of anxiety was only significantly higher than Asian participants’ (mean difference for GAD-7 was 1.91, 95% CI of 0.65 to 3.18, p < 0.01). Indigenous people in Alberta have higher burden of mental illnesses during the COVID-19 pandemic. These findings are helpful for service planning and delivery.

Abstract Background Depression is projected to be the primary cause of disability worldwide by 2030. In a recent survey, the most commonly cited unmet need among 42.4% of depressed Albertans was the lack of sufficient, accessible, and affordable counselling. Our aim was to test the efficacy of a supportive text messaging mobile health intervention in improving treatment outcomes in depressed patients. Methods We performed a single-rater-blinded randomized trial involving 73 patients with Major Depressive Disorder. Patients in the intervention group (n = 35) received twice-daily supportive text messages for 3 months while those in the control group (n = 38) received a single text message every fortnight thanking them for participating in the study. The primary outcome of this study was: “Mean changes in the BDI scores from baseline“. Results After adjusting for baseline BDI scores, a significant difference remained in the 3 month mean BDI scores between the intervention and control groups: (20.8 (SD = 11.7) vs. 24.9 (SD = 11.5), F (1, 60) = 4.83, p = 0.03, ηp2 = 0.07). The mean difference in the BDI scores change was significant with an effect size (Cohen’s d) of 0.67. Furthermore, after adjusting for baseline scores, a significant difference remained in the 3 month mean self-rated VAS scores (EQ-5D-5 L scale) between the intervention and control groups, 65.7 (SD = 15.3) vs. 57.4 (SD = 22.9), F (1, 60) =4.16, p = 0.05, ηp2 = 0.065. The mean difference in change mean self-rated VAS scores was also statistically significant with an effect size (Cohen’s d) of 0.51. Conclusions Our findings suggest that supportive text messages are a potentially useful psychological intervention for depression, especially in underserved populations. Further studies are needed to explore the implications of our findings in larger clinical samples. Trial registration ClinicalTrials.gov NCT02327858 . Registered 24 December 2014.

Neuroimaging: Brain connectivity pattern predicts symptom severity Brain network analyses from functional magnetic resonance imaging (fMRI) data may help diagnose schizophrenia and predict symptom severity. Detecting neuroimaging patterns requires large-scale analysis across multiple data sets. Mina Gheiratmand and colleagues from the University of Alberta, along with researchers at the IBM T.J. Watson Research Center analyzed brain imaging data from the Function Biomedical Informatics Research Network, a study designed to test the reproducibility of brain scan results taken on different fMRI machines from people with schizophrenia and schizoaffective disorders, as well as healthy controls. They studied brain networks at different levels of resolution from data gathered while study participants conducted a common auditory test. The researchers showed that they could discriminate between patients with schizophrenia and controls with 74% accuracy across multiple neuroimaging sites using the strength of connection in a functional network. They observed the most robust and discriminative connectivity differences between the thalamus and primary motor and sensory cortices as well as between the precuneus and other brain regions. Moreover, they could determine symptom severity based on the connectivity changes involving these areas. This new approach towards finding objective, reliable neuroimaging biomarkers for schizophrenia and its severity could be used for diagnosis and to assess disease progression and therapeutic efficacy.

Objectives: To assess prevalence of likely posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and generalized anxiety disorder (GAD) in patients attending the only out-of-hours primary care clinic in Fort McMurray some 18 months following a major fire.Methods: A quantitative cross-sectional survey was used to collect data through self-administered paper-based questionnaires to determine likely PTSD, MDD, and GAD using the PTSD Checklists for Diagnostic and Statistical Manual (DSM) 5, Patient Health Questionnaire (PHQ) 9, and GAD-7, respectively, from residents of Fort McMurray who were impacted by the wildfires. This was carried out eighteen (18) months after a major wildfire, which required the rapid evacuation of the entire city population (approximately 90,000 individuals).Results: We achieved a response rate of 48% and results from the 290 respondents showed the 1 month prevalence rate for likely PTSD was 13.6%, likely MDD was 24.8%, and likely GAD was 18.0%. Compared to self-reported prevalence rates before the wildfire (0%, 15.2%, and 14.5% respectively), these were increased for all diagnoses. After controlling for other factors in a logistic regression model, there were statistically significant associations between individuals who had likely PTSD, MDD, and GAD diagnoses and multiple socio-demographic, clinical, and exposure-related variables as follows: PTSD: History of anxiety disorder and received counselling had odds ratios (ORs) of 5.80 and 7.14, respectively. MDD: Age, witnessed the burning of homes, history of depressive disorder, and receiving low level support from friends and family had ORs of 2.08, 2.29, 4.63, and 2.5, respectively. GAD: Fearful for their lives or the lives of friends/family, history of depressive disorder, and history of anxiety disorder had ORs of 3.52, 3.04, and 2.68, respectively. There were also associations between individuals with a likely psychiatric diagnosis and those who also had likely alcohol or drug abuse/dependence.Conclusion: Our study suggests there are high prevalence rates for mental health and addiction conditions in patients attending the out-of-hours clinic 18 months after the wildfires, with significant associations between multiple variables and likely PTSD, MDD, and GAD. Further studies are needed to explore the impact of population-based mental health interventions on the long-term mental health effects of the wildfires.

Background: The May 2016 wildfire in Fort McMurray, Alberta, Canada forced evacuation of the population of 88,000 individuals and destroyed 10% of the homes. Youth are particularly impacted by disaster.Methods: Eighteen months after the wildfire, Fort McMurray Public and Catholic Schools surveyed 3,252 of the 4,407 students in Grades 7–12 to determine possible long-term psychological impacts. The survey included validated measurement scales for post-traumatic stress disorder (PTSD), depression, anxiety, use of drugs, alcohol, and tobacco, quality of life, self-esteem, and resilience. Data analysis was possible for only 3,070 students, i.e., 70% of the total student population. Anonymized data were analyzed to compare students who directly experienced lesser or greater impact from the wildfire, with greater impact defined as personally seeing the fire or having one’s home destroyed. Also, students with greater or lesser scores on the Child and Youth Resilience Measure (CYRM-12) were compared.Results: Of the 3,070 students, 37% met criteria for probable PTSD; 31% met criteria for probable depression, and 17% for probable depression of at least moderate severity; 27% of students met criteria for probable anxiety, and 15% for probable alcohol or substance use disorder; 46% of all students met criteria for one or more probable diagnosis of PTSD, depression, anxiety, or alcohol/substance abuse, and this included students who were both present and not present in Fort McMurray at the time of the wildfire. Students with greater impact from the wildfire exhibited significantly higher scores on measures of PTSD, depression, anxiety, and alcohol/substance use. They also had lower self-esteem and quality of life scores. Students with lower resilience scores exhibited a similar pattern.Conclusions: These findings highlight first the negative impact of disasters on youth mental health, particularly for those who directly experience wildfire, and second the role of resilience on youth mental health, with lower resilience associated with substantially lower mental health outcomes. These results emphasize the need for long-term mental health supports for youth post-disaster, with specific focus on increasing youth resilience, which may serve as a protective factor against effects of disaster on mental health.

Background: Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers. Methods: Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14–19), severe depression (HRSD 20–23), and very severe depression (HRSD ≥24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls. Results: The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups. Conclusions: Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

The Fort McMurray wildfire was the costliest disaster in Canadian history, with far-reaching impacts. The purpose of this paper is to examine the prevalence and risk factors of elevated generalized anxiety disorder (GAD) symptomatology in residents of Fort McMurray 6 months after the wildfire. Data were collected via random selection procedures from 486 participants. Generalized anxiety disorder symptoms were measured via the GAD-7. The 1-month prevalence rate for GAD symptomatology 6 months after the disaster was 19.8% overall, regression analyses revealed six variables with significant unique contributions to prediction of GAD symptomatology. Significant predictors were: pre-existing anxiety disorder, witnessing of homes being destroyed by the wildfire, living in a different home after the wildfire, receiving limited governmental support or limited family support, and receiving counseling after the wildfire. Participants with these risk factors were between two to nearly seven times more likely to present with GAD symptomatology. In addition, participants who presented with elevated symptomatology were more likely to increase use or problematically use substances post-disaster. This study extends the literature on mental health conditions and risk factors following disasters, specifically in the area of generalized anxiety. Findings and implications are discussed.

BackgroundBetel quid (BQ) is a common psychoactive substance worldwide with particularly high usage in many Asian countries. This study aimed to explore the effect of BQ use on functional connectivity by comparing global functional brain networks and their subset between BQ chewers and healthy controls (HCs).MethodsResting-state functional magnetic resonance imaging (fMRI) was obtained from 24 betel quid-dependent (BQD) male chewers and 27 healthy male individuals on a 3.0T scanner. We used independent component analysis (ICA) to determine components that represent the brain’s functional networks and their spatial aspects of functional connectivity. Two sample t-tests were used to identify the functional connectivity differences in each network between these two groups.ResultsSeventeen networks were identified by ICA. Nine of them showed connectivity differences between BQD and HCs (two sample t-tests, p

BackgroundDelusional thinking is one of the hallmark symptoms of schizophrenia. However, the underlying neural substrate for delusions in schizophrenia remains unknown. In an attempt to further our understanding of the neural basis of delusions, we explored gray matter deficits and their clinical associations in first-episode schizophrenia patients with and without delusions.MethodsTwenty-four first-episode schizophrenia patients with delusions and 18 without delusions as well as 26 healthy controls (HC) underwent clinical assessment and whole-brain structural imaging which were acquired a 3.0 T scanner. Voxel-based morphometry was used to explore inter-group differences in gray matter volume using analysis of covariance, and Spearman correlation coefficients (rho) between the Scale for the Assessment of Positive Symptoms (SAPS)-delusion scores and mean regional brain volumes was obtained.ResultsPatients with delusions showed decreased brain gray matter volumes in the left putamen, thalamus, and caudate regions compared with HC. Patients with delusions also showed decreased regional volume in the left putamen and thalamus compared with patients without delusions. SAPS-delusion scores were negatively correlated with the gray matter volumes of the left putamen and thalamus.DiscussionLeft putamen and thalamus volume loss may be biological correlates of delusions in schizophrenia.