Faculty Bibliography

The impact of the COVID-19 pandemic on adolescents is significant. Educational progress and mental health, in particular, have been negatively affected. Among youth from vulnerable communities, pre-existing academic and health disparities have been exacerbated. Youth outcomes are often attributed to individual resilience - or lack thereof; in this paper, we describe how failure to adapt and effectively cope at the system level (ie, lack of system resilience) is implicated in the current dual educational and mental crisis. We describe opportunities to make our systems more nimble and better-equipped to support youth moving forward.

Background and Aims: Diabetes technology (DT) use is associated with lower HbA1c in type 1 diabetes (T1D). Non- Hispanic Black and Hispanic populations are more likely to have lower DT use and higher HbA1c compared to non-Hispanic White populations. We examined the extent to which differential DT use explains outcome disparities at an outpatient pediatric diabetes center in NYC.
Method(s): Patients identifying as non-White, Hispanic, or non-English language preference were grouped (minority race/ language; MRL) and compared to non-Hispanic White, Englishpreferred patients. HbA1c >9% was categorized as high. T-test and chi-square statistics compared patient characteristics by HbA1c category. Binomial regression with generalized estimating equations estimated associations (risk ratios, RR; 95% confidence intervals, CI) between MRL and high HbA1c. First, models were adjusted for insurance type and Child Opportunity Index (COI), then additionally for CGM and pump use.
Result(s): Patients (n = 331) aged 2-25 years with T1D >= 3 months attended 709 visits (mean 2.2, SD 1.2) from 2020-2021; 32% identified as MRL. At the most recent visit, 16% had HbA1c>9% (MRL 29%, non-MRL 10%), 87% used CGMs (MRL 77%, non-MRL 92%), and 78% used pumps (MRL 72%, non-MRL 81%). MRL youth were 2.5 (95% CI 1.6-4.0) times more likely to have HbA1c>9% as compared to non-MRL youth, adjusted for insurance and COI. After adjusting for DT use, MRL youth remained twice as likely to have HbA1c>9% (RR 2.0, 95% CI 1.2-3.3).
Conclusion(s): While the disparity in HbA1c between MRL and non-MRL youth can be partially attributed to DT use, disparity persists even after accounting for DT use

Identifying substance use disorders (SUDs) early and accurately improves case formulation and treatment. Previous studies have investigated validity and reliability of the Child and Adolescent Symptom Inventory (CASI) for anxiety, mood, and behavior problems. The present study's aim was to test if the embedded CASI Substance Use (SU) subscale can discriminate adolescents and young adults (AYA) with and without a SUD diagnosis accurately enough to justify clinical application within an evidence-based assessment framework. N = 479 outpatient AYA (age 14-21) and their caregivers completed K-SADS-PLW semistructured diagnostic interviews; caregivers completed the CASI and adolescents completed a parallel version, the Youth (self-report) Inventory (YI). K-SADS-PLW indicated that 33 youth met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for SUDs. Receiver Operating Characteristic (ROC) analyses found that both CASI and YI Substance Use subscale scores significantly identified K-SADS-diagnosed SUDs in AYA: Caregiver area under curve (AUC) = .91, p < .0005; YI(AUC) = .90, p < .0005. There was no significant difference in diagnostic accuracy between informants. Both subscales showed diagnostic and clinical utility in identifying AYA SUDs in outpatient mental health settings. Findings suggest that the CASI-4R subscale could be a helpful screening instrument for AYA SUDs. A case vignette illustrates the clinical application of study findings. Future research should examine rapport as a moderator of reporting accuracy, and replicate use of these measures under varying clinical scenarios. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Women are particularly susceptible to mental health challenges during the perinatal period. With the onset of the COVID-19 pandemic in 2020, much concern was raised about the impact that the associated isolation, uncertainty, grief, loss and economic upheaval would have on mental health. Women experienced a disproportionate amount of environmental strain during this time, including economic stress and challenges associated with being essential workers; stressors were perhaps most prevalent in communities of color and immigrant groups. For women who were pregnant during the height of the pandemic, it is clear that stress, anxiety, and depression were increased due to changes in medical care and decreases in social support. Increased mental health challenges in the perinatal period have been shown to impact social-emotional, cognitive and behavioral health in infants and children, so the potential consequences of the COVID-19 era are great. This paper discusses these potential impacts and describes important pathways for future research.

Despite considerable progress in pharmacotherapy over the past seven decades, many mental disorders remain insufficiently treated. This situation is in part due to the limited knowledge of the pathophysiology of these disorders and the lack of biological markers to stratify and individualize patient selection, but also to a still restricted number of mechanisms of action being targeted in monotherapy or combination/augmentation treatment, as well as to a variety of challenges threatening the successful development and testing of new drugs. In this paper, we first provide an overview of the most promising drugs with innovative mechanisms of action that are undergoing phase 2 or 3 testing for schizophrenia, bipolar disorder, major depressive disorder, anxiety and trauma-related disorders, substance use disorders, and dementia. Promising repurposing of established medications for new psychiatric indications, as well as variations in the modulation of dopamine, noradrenaline and serotonin receptor functioning, are also considered. We then critically discuss the clinical trial parameters that need to be considered in depth when developing and testing new pharmacological agents for the treatment of mental disorders. Hurdles and perils threatening success of new drug development and testing include inadequacy and imprecision of inclusion/exclusion criteria and ratings, sub-optimally suited clinical trial participants, multiple factors contributing to a large/increasing placebo effect, and problems with statistical analyses. This information should be considered in order to de-risk trial programmes of novel agents or known agents for novel psychiatric indications, increasing their chances of success.

In this Clinical Perspective, we argue that, at least for some patients, formulation, rather than diagnosis, should be the cornerstone in clinical practice in child and adolescent psychiatry. As opposed to a rigid, tick-the-box approach to formulation, we advocate for a conceptualization of formulation that moves the practice of (child and adolescent) psychiatry into the realm of storytelling and construction of narratives. We suggest that the use of role playing and narrative art forms, such as novels or films, during the training may contribute to develop the skills in telling a story (ie, the formulation) about/to a patient.

INTRODUCTION/BACKGROUND:Emotional dysregulation and irritability are common in individuals with autism spectrum disorder (ASD). We conducted the first meta-analysis assessing the efficacy of a broad range of pharmacological interventions for emotional dysregulation and irritability in ASD and predictors of response. METHOD/METHODS:Following a pre-registered protocol (PROSPERO: CRD42021235779), we systematically searched multiple databases until 01/01/2021. We included placebo-controlled randomized controlled trials (RCTs) and evaluated the efficacy of pharmacological interventions and predictors of response for emotional dysregulation and irritability. We assessed heterogeneity using Q statistics and publication bias. We conducted sub-analyses and meta-regressions to identify predictors of response. The primary effect size was the Standardized Mean Difference. Quality of studies was assessed using the "Cochrane Risk of Bias Tool" (RoB2). RESULTS:2,856 individuals with ASD in 45 studies were included, of which 26.7% of RCTs were at high risk of bias. Compared to placebo, antipsychotics (1.028, 0.824 to 1.232) and medications used to treat ADHD (0.471, 0.061 to 0.881) were significantly better than placebo in improving emotional dysregulation and irritability, while evidence of efficacy was not found for other drug classes (p>0.05). Within individual medications, evidence of efficacy was found for aripiprazole (1.179, 0.838 to 1.520) and risperidone (1.074, 0.818 to 1.331). Increased rates of comorbid epilepsy (β=-0.049, p=0.026) were associated with a lower efficacy. CONCLUSION/CONCLUSIONS:Some pharmacological interventions (particularly risperidone and aripiprazole) have proved efficacy for short-term treatment of emotional dysregulation and irritability in ASD and should be considered within a multimodal treatment plan, taking into account also tolerability profile and families' preferences.

Empirical evidence indicates a significant bidirectional association between mental disorders and physical diseases, but the prospective impact of men-tal disorders on clinical outcomes of physical diseases has not been comprehensively outlined. In this PRISMA- and COSMOS-E-compliant umbrella review, we searched PubMed, PsycINFO, Embase, and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, up to March 15, 2022, to identify systematic reviews with meta-analysis that examined the prospective association between any mental disorder and clinical outcomes of physical diseases. Primary outcomes were disease-specific mortality and all-cause mortality. Secondary outcomes were disease-specific incidence, functioning and/or disability, symptom severity, quality of life, recurrence or progression, major cardiac events, and treatment-related outcomes. Additional inclusion criteria were further applied to primary studies. Random effect models were employed, along with I² statistic, 95% prediction intervals, small-study effects test, excess significance bias test, and risk of bias (ROBIS) assessment. Associations were classified into five credibility classes of evidence (I to IV and non-significant) according to established criteria, complemented by sensitivity and subgroup analyses to examine the robustness of the main analysis. Statistical analysis was performed using a new package for conducting umbrella reviews (https://metaumbrella.org). Population attributable fraction (PAF) and generalized impact fraction (GIF) were then calculated for class I-III associations. Forty-seven systematic reviews with meta-analysis, encompassing 251 non-overlapping primary studies and reporting 74 associations, were included (68% were at low risk of bias at the ROBIS assessment). Altogether, 43 primary outcomes (disease-specific mortality: n=17; all-cause mortality: n=26) and 31 secondary outcomes were investigated. Although 72% of associations were statistically significant (p<0.05), only two showed convincing (class I) evidence: that between depressive disorders and all-cause mortality in patients with heart failure (hazard ratio, HR=1.44, 95% CI: 1.26-1.65), and that between schizophrenia and cardiovascular mortality in patients with cardiovascular diseases (risk ratio, RR=1.54, 95% CI: 1.36-1.75). Six associations showed highly suggestive (class II) evidence: those between depressive disorders and all-cause mortality in patients with diabetes mellitus (HR=2.84, 95% CI: 2.00-4.03) and with kidney failure (HR=1.41, 95% CI: 1.31-1.51); that between depressive disorders and major cardiac events in patients with myocardial infarction (odds ratio, OR=1.52, 95% CI: 1.36-1.70); that between depressive disorders and dementia in patients with diabetes mellitus (HR=2.11, 95% CI: 1.77-2.52); that between alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C (RR=3.15, 95% CI: 2.87-3.46); and that between schizophrenia and cancer mortality in patients with cancer (standardized mean ratio, SMR=1.74, 95% CI: 1.41-2.15). Sensitivity/subgroup analyses confirmed these results. The largest PAFs were 30.56% (95% CI: 27.67-33.49) for alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C, 26.81% (95% CI: 16.61-37.67) for depressive disorders and all-cause mortality in patients with diabetes mellitus, 13.68% (95% CI: 9.87-17.58) for depressive disorders and major cardiac events in patients with myocardial infarction, 11.99% (95% CI: 8.29-15.84) for schizophrenia and cardiovascular mortality in patients with cardiovascular diseases, and 11.59% (95% CI: 9.09-14.14) for depressive disorders and all-cause mortality in patients with kidney failure. The GIFs confirmed the preventive capacity of these associations. This umbrella review demonstrates that mental disorders increase the risk of a poor clinical outcome in several physical diseases. Prevention targeting mental disorders - particularly alcohol use disorders, depressive disorders, and schizophrenia - can reduce the incidence of adverse clinical outcomes in people with physical diseases. These findings can inform clinical practice and trans-speciality preventive approaches cutting across psychiatric and somatic medicine.

The amygdala has long held the center seat in the neural basis of threat conditioning. However, a rapidly growing literature has elucidated extra-amygdala circuits in this process, highlighting the sensory cortex for its critical role in the mnemonic aspect of the process. While this literature is largely focused on the auditory system, substantial human and rodent findings on the olfactory system have emerged. The unique nature of the olfactory neuroanatomy and its intimate association with emotion compels a review of this recent literature to illuminate its special contribution to threat memory. Here, integrating recent evidence in humans and animal models, we posit that the olfactory (piriform) cortex is a primary and necessary component of the distributed threat memory network, supporting mnemonic ensemble coding of acquired threat. We further highlight the basic circuit architecture of the piriform cortex characterized by distributed, auto-associative connections, which is prime for highly efficient content-addressable memory computing to support threat memory. Given the primordial role of the piriform cortex in cortical evolution and its simple, well-defined circuits, we propose that olfaction can be a model system for understanding (transmodal) sensory cortical mechanisms underlying threat memory.

BACKGROUND:Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. METHODS:We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. RESULTS:The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. CONCLUSION:This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.