Illness Phase as a Key Assessment and Intervention Window for Psychosis.

The phenotype of schizophrenia, regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness stage, including temporal variation in neurobiology, can be targeted to develop phase-specific clinical assessment, biomarkers, and interventions. We review an earlier model whereby an initial glutamate signaling deficit progresses through different phases of allostatic adaptation, moving from potentially reversible functional abnormalities associated with early psychosis and working memory dysfunction, and ending with difficult-to-reverse structural changes after chronic illness. We integrate this model with evidence of dopaminergic abnormalities, including cortical D1 dysfunction, which develop during adolescence. We discuss how this model and a focus on a potential critical window of intervention in the early stages of schizophrenia impact the approach to research design and clinical care. This impact includes stage-specific considerations for symptom assessment as well as genetic, cognitive, and neurophysiological biomarkers. We examine how phase-specific biomarkers of illness phase and brain development can be incorporated into current strategies for large-scale research and clinical programs implementing coordinated specialty care. We highlight working memory and D1 dysfunction as early treatment targets that can substantially affect functional outcome.

Characteristics of Esketamine Prescribers Among Medicare Beneficiaries in the United States, 2019-2020.

This cross-sectional study examines the characteristics and geographic distribution of eskatamine prescribers among Medicare beneficiaries in the US from 2019 to 2020.

Technology and Mental Health: State of the Art for Assessment and Treatment.

Technology is ubiquitous in society and is now being extensively used in mental health applications. Both assessment and treatment strategies are being developed and deployed at a rapid pace. The authors review the current domains of technology utilization, describe standards for quality evaluation, and forecast future developments. This review examines technology-based assessments of cognition, emotion, functional capacity and everyday functioning, virtual reality approaches to assessment and treatment, ecological momentary assessment, passive measurement strategies including geolocation, movement, and physiological parameters, and technology-based cognitive and functional skills training. There are many technology-based approaches that are evidence based and are supported through the results of systematic reviews and meta-analyses. Other strategies are less well supported by high-quality evidence at present, but there are evaluation standards that are well articulated at this time. There are some clear challenges in selection of applications for specific conditions, but in several areas, including cognitive training, randomized clinical trials are available to support these interventions. Some of these technology-based interventions have been approved by the U.S. Food and Drug administration, which has clear standards for which types of applications, and which claims about them, need to be reviewed by the agency and which are exempt.

Clinical and Financial Outcomes Associated With a Workplace Mental Health Program Before and During the COVID-19 Pandemic.

Importance: Investment in workplace wellness programs is increasing despite concerns about lack of clinical benefit and return on investment (ROI). In contrast, outcomes from workplace mental health programs, which treat mental health difficulties more directly, remain mostly unknown. Objective: To determine whether participation in an employer-sponsored mental health benefit was associated with improvements in depression and anxiety, workplace productivity, and ROI as well as to examine factors associated with clinical improvement. Design, Setting, and Participants: This cohort study included participants in a US workplace mental health program implemented by 66 employers across 40 states from January 1, 2018, to January 1, 2021. Participants were employees who enrolled in the mental health benefit program and had at least moderate anxiety or depression, at least 1 appointment, and at least 2 outcome assessments. Intervention: A digital platform that screened individuals for common mental health conditions and provided access to self-guided digital content, care navigation, and video and in-person psychotherapy and/or medication management. Main Outcomes and Measures: Primary outcomes were the Patient Health Questionnaire-9 for depression (range, 0-27) score and the Generalized Anxiety Disorder 7-item scale (range, 0-21) score. The ROI was calculated by comparing the cost of treatment to salary costs for time out of the workplace due to mental health symptoms, measured with the Sheehan Disability Scale. Data were collected through 6 months of follow-up and analyzed using mixed-effects regression. Results: A total of 1132 participants (520 of 724 who reported gender [71.8%] were female; mean [SD] age, 32.9 [8.8] years) were included. Participants reported improvements from pretreatment to posttreatment in depression (b = -6.34; 95% CI, -6.76 to -5.91; Cohen d = -1.11; 95% CI, -1.18 to -1.03) and anxiety (b = -6.28; 95% CI, -6.77 to -5.91; Cohen d = -1.21; 95% CI, -1.30 to -1.13). Symptom change per log-day of treatment was similar post-COVID-19 vs pre-COVID-19 for depression (b = 0.14; 95% CI, -0.10 to 0.38) and anxiety (b = 0.08; 95% CI, -0.22 to 0.38). Workplace salary savings at 6 months at the federal median wage was US $3440 (95% CI, $2730-$4151) with positive ROI across all wage groups. Conclusions and Relevance: Results of this cohort study suggest that an employer-sponsored workplace mental health program was associated with large clinical effect sizes for employees and positive financial ROI for employers.

The Stress and Resilience Town Hall: A systems response to support the health workforce during COVID-19 and beyond.

OBJECTIVE: The COVID-19 pandemic is a traumatic stressor resulting in anxiety, depression, post-traumatic stress, and burnout among healthcare workers. We describe an intervention to support the health workforce and summarize results from its 40-week implementation in a large, tri-state health system during the COVID-19 pandemic. METHOD: We conducted 121 virtual and interactive Stress and Resilience Town Halls attended by 3555 healthcare workers. Town hall participants generated 1627 stressors and resilience strategies that we coded and analyzed using rigorous qualitative methods (Kappa = 0.85). RESULTS: We identify six types of stressors and eight types of resilience strategies reported by healthcare workers, how these changed over time, and how town halls were responsive to emerging health workforce needs. We show that town halls dedicated to groups working together yielded 84% higher mean attendance and more sharing of stressors and resilience strategies than those offered generally across the health system, and that specific stressors and strategies are reported consistently while others vary markedly over time. CONCLUSIONS: The virtual and interactive Stress and Resilience Town Hall is an accessible, scalable, and sustainable intervention to build mutual support, wellness, and resilience among healthcare workers and within hospitals and health systems responding to emerging crises, pandemics, and disasters.

Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups.

BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. METHOD: Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. RESULTS: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. DISCUSSION: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.

Decision Models and Technology Can Help Psychiatry Develop Biomarkers.

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care.

Multivariate genome-wide analysis of education, socioeconomic status and brain phenome.

Socioeconomic status (SES) and education (EDU) are phenotypically associated with psychiatric disorders and behaviours. It remains unclear how these associations influence genetic risk for psychopathology, psychosocial factors and EDU and/or SES (EDU/SES) individually. Using information from >1 million individuals, we conditioned the genetic risk for psychiatric disorders, personality traits, brain imaging phenotypes and externalizing behaviours with genome-wide data for EDU/SES. Accounting for EDU/SES significantly affected the observed heritability of psychiatric traits, ranging from 2.44% h2 decrease for bipolar disorder to 14.2% h2 decrease for Tourette syndrome. Neuroticism h2 significantly increased by 20.23% after conditioning with SES. After EDU/SES conditioning, neuronal cell types were identified for risky behaviour (excitatory), major depression (inhibitory), schizophrenia (excitatory and γ-aminobutyric acid (GABA) mediated) and bipolar disorder (excitatory). Conditioning with EDU/SES also revealed unidirectional causality between brain morphology, psychopathology and psychosocial factors. Our results indicate that genetic discoveries related to psychopathology and psychosocial factors may be limited by genetic overlap with EDU/SES.

Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study.

OBJECTIVE: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need. METHODS: Data were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment. RESULTS: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all). CONCLUSIONS: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.

Psychological resilience in U.S. military veterans: A 2-year, nationally representative prospective cohort study.

Although many cross-sectional studies have examined the correlates of psychological resilience in U.S. military veterans, few longitudinal studies have identified long-term predictors of resilience in this population. The current prospective cohort study utilized data from a nationally representative sample of 2157 U.S. military veterans who completed web-based surveys in two waves (2011 and 2013) as part of the National Health and Resilience in Veterans Study (NHRVS). Cluster analysis of cumulative lifetime exposure to potentially traumatic events and Wave 2 measures of current symptoms of posttraumatic stress, major depressive, and generalized anxiety disorders was performed to characterize different profiles of current trauma-related psychological symptoms. Different profiles were compared with respect to sociodemographic, clinical, and psychosocial characteristics. A three-group cluster analysis revealed a Control group with low lifetime trauma exposure and low current psychological distress (59.5%), a Resilient group with high lifetime trauma and low current distress (27.4%), and a Distressed group with both high trauma exposure and current distress symptoms (13.1%). These results suggest that the majority of trauma-exposed veterans (67.7%) are psychologically resilient. Compared with the Distressed group, the Resilient group was younger, more likely to be Caucasian, and scored lower on measures of physical health difficulties, past psychiatric history, and substance abuse. Higher levels of emotional stability, extraversion, dispositional gratitude, purpose in life, and altruism, and lower levels of openness to experiences predicted resilient status. Prevention and treatment efforts designed to enhance modifiable factors such as gratitude, sense of purpose, and altruism may help promote resilience in highly trauma-exposed veterans.

The burden of alcohol use disorders in US military veterans: results from the National Health and Resilience in Veterans Study.

AIMS: To analyze data from a large, contemporary, nationally representative sample of US veterans to evaluate: (1) the prevalence of life-time alcohol use disorder (AUD) and past-year AUD; (2) common psychiatric comorbidities associated with life-time AUD; and (3) correlates of life-time and past-year probable AUD. DESIGN: Data were analyzed from the National Health and Resilience in Veterans Study (NHRVS), a web-based survey of a random probability sample of a contemporary, nationally representative sample of US military veterans. SETTING: United States. PARTICIPANTS: Nationally representative sample of 3157 US veterans aged 21 years and older. MEASUREMENTS: Life-time alcohol abuse and dependence were assessed according to DSM-IV diagnostic criteria using the Mini International Neuropsychiatric Interview, and combined into a single variable: AUD. Past-year probable AUD was assessed using the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C). Correlates of AUD, including psychiatric comorbidities, suicidality and demographic characteristics, were also assessed. FINDINGS: The prevalence of life-time AUD and past-year probable AUD was 42.2% [95% confidence interval (CI) = 40.5-43.9%)] and 14.8% (95% CI = 13.6-16.0%), respectively. Compared with veterans without AUD, those with life-time AUD had substantially elevated rates of life-time and current mood and anxiety disorders [odds ratios (ORs) = 2.6-4.1], drug use disorder (OR = 10.7), life-time suicide attempt (OR = 4.1) and current suicidal ideation (OR = 2.1). Younger age, male sex, lower education, lower annual household income and greater number of life-time traumatic events were associated independently with life-time AUD. Younger age, male sex, unpartnered marital status and a life-time diagnosis of major depressive disorder were associated independently with past-year probable AUD. CONCLUSIONS: More than 40% of US military veterans have a life-time history of alcohol use disorder. Veterans with a life-time history of alcohol use disorder have substantial comorbid psychiatric burden, including elevated rates of suicidal ideation and attempts. Certain socio-demographic (e.g. younger age, male sex, lower education) and clinical (e.g. trauma burden, history of depression) characteristics are associated with increased risk of AUD.

Cost and cost-effectiveness in a randomized trial of long-acting risperidone for schizophrenia.

OBJECTIVE: The effect of long-acting injectable (LAI) risperidone on health care costs was determined in a multisite clinical trial. METHOD: Veterans Health Administration patients with unstable schizophrenia or schizoaffective disorder established by the Structured Clinical Interview for DSM-IV (N = 369) were randomized between 2006 and 2009 to long-acting risperidone or physician's choice of oral antipsychotic. Health care utilization and cost were tracked in administrative data. Medication administered by the trial was recorded on case report forms. Medication cost was based on unit costs to the US Medicaid program. Economic outcomes were assessed with the Quality of Well-Being instrument. RESULTS: Participants randomized to LAI risperidone (n = 187) incurred $14,916 per quarter in total health care costs, which was not significantly different from the $13,980 cost incurred by the control group (P = .732) (n = 182). The LAI group incurred $3,028 per quarter in medication cost, significantly more than the $1,913 incurred by the control group (P = .003). Hospitalization costs were $7,088 in the experimental group and $6,891 in the control group (P = .943); outpatient costs were $11,888 in the experimental group and $12,067 in the control group (P = .639). LAI risperidone did not result in better outcomes as evaluated by a measure of schizophrenia symptoms or an assessment of health related quality of life and incurred more adverse events. CONCLUSIONS: Patients with unstable schizophrenia were randomized in a practical trial of LAI risperidone. This antipsychotic significantly increased medication costs but did not reduce hospital or total health care cost or improve outcomes and was thus not cost-effective. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00132314.

Challenges in the design and conduct of controlled clinical effectiveness trials in schizophrenia.

BACKGROUND: The introduction of antipsychotic medication has been a major advance in the treatment of schizophrenia and allows millions of people to live outside of institutions. It is generally believed that long-acting intramuscular antipsychotic medication is the most effective approach to increasing medication adherence and thereby reduce relapse in high-risk patients with schizophrenia, but the data are scant. PURPOSE: To report the design of a study to assess the effect of long-acting injectable risperidone in unstable patients and under more realistic conditions than previously studied and to evaluate the effect of this medication on psychiatric inpatient hospitalization, schizophrenia symptoms, quality of life, medication adherence, side effects, and health care costs. METHODS: The trial was an open randomized clinical comparative effectiveness trial in patients with schizophrenia or schizo-affective disorders in which parenteral risperidone was compared to an oral antipsychotic regimen selected by each control patient's psychiatrist. Participants had unstable psychiatric disease defined by recent hospitalization or exhibition of unusual need for psychiatric services. The primary endpoint was hospitalization for psychiatric indications; the secondary endpoint was psychiatric symptoms. RESULTS: Overall, 382 patients were randomized. Determination of a persons' competency to understand the elements of informed consent was addressed. The use of a closed-circuit TV interview for psychosocial measures provided an economical, high quality, reliable means of collecting data. A unique method for insuring that usual care was optimal was incorporated in the follow-up of all subjects. LIMITATIONS: Patients with schizophrenia or schizo-affective disorders and with the common co-morbid illnesses seen in the VA are a challenging group of subjects to study in long-term trials. Some techniques unique in the VA and found useful may not be generalizable or applicable in other research or treatment settings. CONCLUSIONS: The trial tested a new antipsychotic medication early in its adoption in the Veterans Health Administration. The VA has a unique electronic medical record and database which can be used to identify the endpoint, that is, first hospitalization due to a psychiatric problem, with complete ascertainment. Several methodologic solutions addressed competency to understand elements of consent, the costs and reliability of collecting interview data gathering, and insuring usual care.

Practitioner review: adolescent alcohol use disorders: assessment and treatment issues.

BACKGROUND: Alcohol use disorders in adolescents are associated with significant morbidity and mortality. Over the past decade, there has been a burgeoning of research on adolescent alcohol use disorders. METHODS: A summary of the alcohol assessment tools is provided, and randomized studies reviewed and synthesized to provide an overview of state of the art knowledge of treatment of adolescent alcohol use disorders. Animal models of addiction are also briefly reviewed, and the value of translational research approaches, using findings from basic studies to guide the design of clinical investigations, is also highlighted. RESULTS: Comorbidity is the rule, not the exception in adolescent alcohol use disorders. Comprehensive assessment of psychiatric and other substance use disorders, trauma experiences, and suicidality is indicated in this population to optimize selection of appropriate clinical interventions. In terms of available investigated treatments for adolescents with alcohol use disorders, Multidimensional Family Therapy and group administered Cognitive Behavioral Therapies have received the most empirical support to date. There is a paucity of research on pharmacological interventions in this patient population, and no firm treatment recommendations can be made in this area. CONCLUSIONS: Given the high rate of relapse after treatment, evaluation of combined psychosocial and pharmacological interventions, and the development of novel intervention strategies are indicated.

Identifying cognitive mechanisms targeted for treatment development in schizophrenia: an overview of the first meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia Initiative.

This overview describes the generation and development of the ideas that led to the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative. It also describes the organization, process, and products of the first meeting. The CNTRICS initiative involves a series of three conferences that will systematically address barriers to translating paradigms developed in the basic animal and human cognitive neuroscience fields for use in translational research aimed at developing novel treatments for cognitive impairments in schizophrenia. The articles in this special section report on the results of the first conference, which used a criterion-based consensus-building process to develop a set of cognitive constructs to be targeted for translation efforts.