Faculty Bibliography

Racism is a social determinant of mental health which has a disproportionally negative impact on the experiences of psychiatric inpatients of color. Distinct differences in the physical space and clinical settings of two inpatient buildings at a hospital system in the tristate (New York, New Jersey, Connecticut) area of the United States led to the present investigation of racial inequities in the assignment of patients to specific buildings and units. Archival electronic medical record data were analyzed from over 18,000 unique patients over a period of six years. Hierarchical logistic regression analyses were conducted with assigned building (old vs. new building) as the binary outcome variable. Non-Hispanic White patients were set as the reference group. Black, Hispanic/Latinx, and Asian patients were significantly less likely to be assigned to better resourced units in the new building. When limiting the analysis to only general adult units, Black and Hispanic/Latinx patients were significantly less likely to be assigned to units in the new building. These results suggest ethnoracial inequities in patient assignment to buildings which differed in clinical and physical conditions. The findings serve as a call to action for hospital systems to examine the ways in which structural racism impact clinical care.

OBJECTIVE:Globally, rates of youth suicide vary considerably. Suicidal thoughts and behaviors (STB) are consistently associated with risk of death by suicide. However, international trends in STB have not yet been compared. To address this gap, an international meta-analysis of epidemiological and school-based studies that report on STB in youth was conducted. METHOD/METHODS:Systematic searches were conducted in PubMed and PsycINFO through April 2022. Eligible studies included prevalence of active suicidal ideation (SI) or suicide attempts (SA) in community youth younger than age 22. All studies were coded by 2 authors. Mixed models accounting for shared methods and including hypothesized moderators were conducted using the metafor package in R. RESULTS:There were 371 effect sizes for SI, 94 for SI with a plan, and 316 for SA, representing 149 regions. Year of data collection ranged from 1981 to 2021. Participants were 6 to 21 years old. The prevalence of SI ranged across regions from 14.3% to 22.6%; the prevalence of SA ranged from 4.6% to 15.8%. Year was not associated with increasing STB prevalence except for studies from the United States, which showed increasing rates of SI and SA since 2007. CONCLUSION/CONCLUSIONS:This is the most comprehensive meta-analysis of STB in youth, providing valuable data about how risk factors most commonly associated with suicide vary internationally and over time. International rates of STB among youth are not improving and may be getting worse in the United States, despite efforts to reduce suicide risk. Most studies did not report rates of SI or SA separately for LGBTQIA+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, and others) youth and youth of color. A better understanding of proximal risk at the individual level will be important to informing future prevention efforts, especially for high-risk groups.

Bipolar disorder (BD) can be especially challenging to treat due to the dynamic nature of its presentation; there is a critical need for a multimodal approach to adequately address patients"™ symptoms and quality of life concerns. However, most mental health professionals have not implemented a multimodal approach due to limited dissemination of evidence-based psychosocial interventions for BD and bias towards psychopharmacology-centered treatment. This is despite clear findings from numerous studies that medication alone fails to fully address most patients"™ needs and that psychosocial interventions lead to fewer relapses and a higher quality of life. This paper aims to review the evidence in support of psychosocial interventions as a key component of the treatment of BD and to highlight obstacles to the implementation of psychosocial treatment approaches. Additionally, we aim to make a case for an increase in the utilization of psychosocial interventions to improve quality of life and functioning for individuals with BD and their families, and to mitigate societal costs.

BACKGROUND:Digital technology has the potential to transform psychiatry, but its adoption has been limited. The proliferation of telepsychiatry during the COVID-19 pandemic has increased the urgency of optimizing technology for clinical practice. Understanding clinician attitudes and preferences is crucial to effective implementation and patient benefit. OBJECTIVE:Our objective was to elicit clinician perspectives on emerging digital technology. METHODS:Clinicians in a large psychiatry department (inpatient and outpatient) were invited to complete a web-based survey about their attitudes toward digital technology in practice, focusing on implementation, clinical benefits, and expectations about patients' attitudes. The survey consisted of 23 questions that could be answered on either a 3-point or 5-point Likert scale. We report the frequencies and percentages of responses. RESULTS:In total, 139 clinicians completed the survey-they represent a variety of years of experience, credentials, and diagnostic subspecialties (response rate 69.5%). Overall, 83.4% (n=116) of them stated that digital data could improve their practice, and 23.0% (n=32) of responders reported that they had viewed patients' profiles on social media. Among anticipated benefits, clinicians rated symptom self-tracking (n=101, 72.7%) as well as clinical intervention support (n=90, 64.7%) as most promising. Among anticipated challenges, clinicians mostly expressed concerns over greater time demand (n=123, 88.5%) and whether digital data would be actionable (n=107, 77%). Furthermore, 95.0% (n=132) of clinicians expected their patients to share digital data. CONCLUSIONS:Overall, clinicians reported a positive attitude toward the use of digital data to not only improve patient outcomes but also highlight significant barriers that implementation would need to overcome. Although clinicians' self-reported attitudes about digital technology may not necessarily translate into behavior, our results suggest that technologies that reduce clinician burden and are easily interpretable have the greatest likelihood of uptake.

OBJECTIVE/UNASSIGNED:Intensive residential treatment (IRT) for obsessive-compulsive disorder (OCD) includes frequent meetings with a cognitive-behavioral therapist. We examined whether this therapeutic working alliance relates to IRT outcomes. METHOD/UNASSIGNED:= 124) who received IRT at a specialty OCD clinic. Patients completed measures of OCD severity and well-being at admission and discharge. Both the patient and treating psychologist completed the Working Alliance Inventory-Short Form (WAI-SF). Alliance ratings were tested as predictors in models predicting outcomes (discharge scores adjusting for baseline and treatment duration) as well as logistic regression predicting treatment response (≥35% symptom reduction in OCD symptoms). RESULTS/UNASSIGNED:Patient and clinician ratings of the quality of the alliance were weakly yet significantly correlated. Patient ratings of the alliance predicted outcomes, while therapist ratings did not. Moreover, greater discrepancy between patient and client ratings predicted worse outcomes. Patient ratings of the task dimension of the alliance uniquely related to responder status. CONCLUSIONS/UNASSIGNED:Patient perceptions of the working alliance, particularly as pertaining to agreement on therapeutic tasks, related to success with IRT for OCD. Further study is needed test interventions to improve task alliance as a strategy to enhance treatment.

Previous research has examined how mood affects individuals"™ judgment. Our study aims to extend this research to evaluate the influence of mood on judgment in vivo. Using a smartphone-based design, we prompted participants (N = 103) three times a day to rate their mood and perform one of three judgment tasks three times a day for one week: (1) evaluate their self-efficacy on a word unscrambling task, (2) identify face emotions, or (3) judge risk by pumping virtual balloons. Our results showed that, contrary to our hypotheses, mood did not have a significant effect on the judgments individuals made. Prior task performance and task trial were significant predictors of self-efficacy; sex and task trial were associated with face emotion recognition; and balloons popped the day prior, and task trial influenced how individuals evaluated risk. The in vivo design of this study is a novel and more ecologically valid than some earlier work, but it is not without limitations, including the self-reported nature of mood, and potential for unmeasured third variable effects. This research raises questions about the validity of lab-based studies of the relation between mood and judgment, and shows the capacity of in vivo research and technology to challenge and enhance our understanding of how mood influences behavior.

BACKGROUND:Previous studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored. OBJECTIVE:The aim of this study is to understand clinician perspectives regarding computational mental health insights from patients' social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations. METHODS:We developed a prototype that can analyze consented patients' Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient's social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis. RESULTS:Clinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients' verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients' social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings. CONCLUSIONS:Our findings support the touted potential of computational mental health insights from patients' social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology.

BACKGROUND:Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. OBJECTIVE:We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health-related content. METHODS:Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. RESULTS:A majority (27/43, 63%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44% (19/43) of participants. Queries related to traumatic experiences were present among 33% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). CONCLUSIONS:Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients.