Symptom characteristics of health care workers seeking outpatient psychiatric care during the COVID-19 pandemic.

BACKGROUND: Though there is a growing body of research establishing a broad negative psychological impact of COVID-19 among healthcare workers (HCWs), there are comparably fewer studies evaluating symptom presentation and clinical diagnoses among treatment-seeking HCWs. The present report seeks to fill this gap in the literature by establishing the prevalence of anxiety, depression, post-traumatic stress, alcohol misuse, and well-being among treatment-seeking HCWs. METHOD: Data were collected from 421 treatment-seeking HCWs in an outpatient hospital-based mental health setting. Both self-report measures and semi-structured interviews were utilized to assess symptom severity and render psychiatric diagnosis at intake. RESULTS: Adjustment disorders were the most prevalent diagnosis at 44.2%. Of the 347 who completed self-report measures, over 47% endorsed moderate-to-severe depressive symptoms, with 13% endorsing suicidal ideation (SI). Fifty-eight percent scored in the moderate-to-severe range for anxiety, and 19% screened positive for COVID-related post-traumatic stress disorder. Further analyses revealed that those in medical support roles endorsed significantly greater depression symptoms relative to other groups and also reported SI at greater frequency. Medical trainees also endorsed SI at higher frequencies. CONCLUSIONS: These findings are consistent with previous research on the adverse impact of COVID-19 stressors on HCWs' mental health. We further identified vulnerable groups that are underrepresented in the literature. These findings highlight the need for targeted outreach and intervention among overlooked HCWs populations.

Longitudinal Mental Health Outcomes of Third-year Medical Students Rotating Through the Wards During COVID-19.

This study investigated third year medical students' psychological well-being during clinical rotations at Mount Sinai hospitals in New York City during the COVID-19 pandemic. All students (n = 147) starting rotations (psychiatry, surgery, obstetrics-gynecology, neurology, pediatrics, and medicine) could participate in quarterly, online, anonymous surveys comprised of validated screeners for: psychological symptoms, risk, coping, and protective factors, demographics, COVID-19 worries, and stressful clerkship-related events. Associations between variables were examined with Chi-squared, Fisher's exact, t-, Wilcoxon Rank Sum, one-way ANOVA, and McNemar tests. Significant univariate predictors of psychological distress were included in stepwise multivariable linear regression models. The baseline survey was completed by 110 (74.8%) students; ninety-two (62.6%) completed at least one other survey. During the year, 68 (73.9%) students screened positive for depression, anxiety, or PTSD. The prevalence of psychiatric symptoms peaked in June 2020 without significant changes in average scores over time. COVID-19 worries decreased over time but did not influence psychological symptoms at year-end. Eighty-three students (90.2%) experienced stressful clerkship-related events, which were traumatic and/or COVID-19-related for 26 (28.3%) and 22 students (24.0%), respectively. Baseline psychological distress, childhood emotional abuse, and resilience predicted depression, anxiety, and/or PTSD by year-end. This study highlights the importance of recognizing psychological distress and implementing interventions to support students' well-being.

Neuropsychiatric Consequences of COVID-19 Pandemic: A Synthetic Review from a Global Perspective.

Some research suggests that distress, secondary to isolation and fear following COVID-19 infection, can negatively affect the long-term more than the COVID-19 infection itself. This narrative review aims to provide a global view on the neuropsychiatric consequences of COVID-19 that can be ascribed to several factors, ranging from the direct effect of infection, to the body's responses against the infection, or to the psychological sequelae of social isolation, unemployment, and fear for one's health and livelihood. Current findings show that the more severe the respiratory infection, the more likely are central nervous system (CNS) complications regarding the infection itself. The immune reactions to the infection may result in symptoms similar to chronic fatigue as well as neurocognitive deficits, which last long after the infection is gone. An increase in symptoms of depression, anxiety, and trauma-related stress may also follow upon economic fears and isolation from friends and family. The consequences of the pandemic are not limited to adults; children learning remotely and away from classmates and routine activities may develop adjustment disorders, acute stress disorder, and a variety of manifestations of grief. A summary of case reports suggests that COVID-19-related stress, economic recession, and political unrest increase the risk of suicidal behaviors and acts of violence. However, it is unknown whether manifestations of mental disorders result from social causes or whether CNS complications may be responsible.

A prospective cohort study of the psychological consequences of the COVID-19 pandemic on frontline healthcare workers in New York City.

OBJECTIVES: We sought to describe the course and correlates of psychological distress in frontline healthcare workers (FHCWs) during the COVID-19 pandemic in New York City (NYC). METHODS: A prospective cohort study of FHCWs at the Mount Sinai Hospital was conducted during the initial 2020 surge (T1) and 7 months later (T2). Psychological distress [i.e., positive screen for pandemic-related post-traumatic stress disorder (PTSD), major depressive disorder (MDD), and/or generalized anxiety disorder (GAD)], occupational and personal exposures to COVID-19, coping strategies, and psychosocial characteristics were assessed. Four courses of psychological distress response were identified: no/minimal, remitted, persistent, and new-onset. Multinomial logistic regression and relative importance analyses were conducted to identify factors associated with courses of distress. RESULTS: Of 786 FHCWs, 126 (16.0%) FHCWs had persistent distress; 150 (19.1%) remitted distress; 35 (4.5%) new-onset distress; and 475 (60.4%) no/minimal distress. Relative to FHCWs with no/minimal distress, those with persistent distress reported greater relationship worries [19.8% relative variance explained (RVE)], pre-pandemic burnout (18.7% RVE), lower dispositional optimism (9.8% RVE), less emotional support (8.6% RVE), and feeling less valued by hospital leadership (8.4% RVE). Relative to FHCWs with remitted symptoms, those with persistent distress reported less emotional support (29.7% RVE), fewer years in practice (28.3% RVE), and psychiatric history (23.6% RVE). CONCLUSIONS: One-fifth of FHCWs in our study experienced psychological distress 7 months following the COVID-19 surge in NYC. Pandemic-related worries, pre-pandemic burnout, emotional support, and feeling valued by leaders were linked to persistent distress. Implications for prevention, treatment, and organizational efforts to mitigate distress in FHCWs are discussed.

The Psychiatric Burden on Medical Students in New York City Entering Clinical Clerkships During the COVID-19 Pandemic.

For medical students first entering the clinical space in July 2020, the unique challenges related to the coronavirus pandemic threatened to amplify the psychological distress associated with clerkship rotations. This study aimed to characterize the mental health of third-year medical students starting clinical clerkships in the midst of a pandemic by assessing symptoms of major depressive disorder (MDD), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD) as well as risk, coping, and protective factors associated with psychological outcomes. Of 147 third-year medical students at the Icahn School of Medicine at Mount Sinai in New York City, 110 (75%) participated in this prospective survey-based study with 108 included in the final analysis. 43 (39.8%) respondents screened positive for symptoms of either MDD, GAD, or PTSD. Multiple regression analyses revealed that greater overall symptom severity was associated with more avoidant coping, more traumatic events witnessed, poorer student and leisure functioning, lower trait emotional stability, and lower social support. Worries related to COVID-19 did not significantly influence outcome variables. To better understand the role of the pandemic on psychological outcomes in third-year medical students, additional research should focus on the trajectory of these outcomes over the year during the coronavirus pandemic.

COVID-19 Pandemic Support Programs for Healthcare Workers and Implications for Occupational Mental Health: A Narrative Review.

This narrative review aims to summarize initiatives developed during the COVID-19 pandemic to support healthcare workers' emotional well-being within the context of a pre-existing framework of occupational mental health guidelines. This occupational mental health framework integrates principles from multiple disciplines to optimize prevention and management of mental health issues among employees. We conducted an online search on Medline/PubMed, Cochrane Library, and Embase for studies that reported on design or execution of medical institution-based interventions, aiming to support healthcare worker mental health during the COVID-19 pandemic. Inclusion criteria was intentionally broad in order to incorporate as many types of interventions at varying stages of development or evaluation. We included 31 studies in our review that reported on newly designed psychological support interventions for healthcare workers (HCW) during the COVID-19 pandemic. We found that most programs commonly supported HCW mental health through offering one or more of the following initiatives: expanded basic need resources/services, additional workplace training programs that bolstered professional preparedness while also indirectly boosting HCW emotional health, and/or expanded psychological support programs, such as peer support programs, psychoeducational or counseling services. Most programs, however, did not consider methods to ensure program longevity or sustainability. The COVID-19 pandemic has underscored the acuity of HCW mental health issues and is likely to leave long lasting mental health strains among HCW. This pandemic is a critical point in time to catalyze much needed progress in reducing stigma and expanding HCW mental health care access.

A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation.

BACKGROUND: The COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with HCWs representing a particularly vulnerable group. The scope of the COVID-19 pandemic necessitates the development of a scalable mental health platform to provide services to large numbers of at-risk or affected individuals. The Mount Sinai Health System in New York City was at the epicenter of the pandemic in the United States. OBJECTIVE: The Center for Stress, Resilience, and Personal Growth (CSRPG) was created to address the current and anticipated psychological impact of the pandemic on the HCWs in the health system. The mission of the Center is to support the resilience and mental health of employees through educational offerings, outreach, and clinical care. Our aim was to build a mobile app to support the newly founded Center in its mission. METHODS: We built the app as a standalone digital platform that hosts a suite of tools that users can interact with on a daily basis. With consideration for the Center's aims, we determined the overall vision, initiatives, and goals for the Wellness Hub app, followed by specific milestone tasks and deliverables for development. We defined the app's primary features based on the mental health assessment and needs of HCWs. Feature definition was informed by the results of a resilience survey widely distributed to Mount Sinai HCWs and by the resources offered at CSRPG, including workshop content. RESULTS: We launched our app over the course of two phases, the first phase being a "soft" launch and the second being a broader launch to all of Mount Sinai. Of the 231 HCWs who downloaded the app, 173 (74.9%) completed our baseline assessment of all mental health screeners in the app. Results from the baseline assessment show that more than half of the users demonstrate a need for support in at least one psychological area. As of 3 months after the Phase 2 launch, approximately 55% of users re-entered the app after their first opening to explore additional features, with an average of 4 app openings per person. CONCLUSIONS: To address the mental health needs of HCWs during the COVID-19 pandemic, the Wellness Hub app was built and deployed throughout the Mount Sinai Health System. To our knowledge, this is the first resilience app of its kind. The Wellness Hub app is a promising proof of concept, with room to grow, for those who wish to build a secure mobile health app to support their employees, communities, or others in managing and improving mental and physical well-being. It is a novel tool offering mental health support broadly.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.