Behavioral Health Provider Burnout and Mental Health Care in the Veterans Health Administration.

BACKGROUND: Although many studies assess predictors of provider burnout, few analyses provide high-quality, consistent evidence on the impact of provider burnout on patient outcomes exist, particularly among behavioral health providers (BHPs). OBJECTIVE: To assess the impact of burnout among psychiatrists, psychologists, and social workers on access-related quality measures in the Veterans Health Administration (VHA). DESIGN: This study used burnout in VA All Employee Survey (AES) and Mental Health Provider Survey (MHPS) data to predict metrics assessed by the Strategic Analytics for Improvement and Learning Value, Mental Health Domain (MH-SAIL), VHA's quality monitoring system. The study used prior year (2014-2018) facility-level burnout proportion among BHPs to predict subsequent year (2015-2019) facility-level MH-SAIL domain scores. Analyses used multiple regression models, adjusting for facility characteristics, including BHP staffing and productivity. PARTICIPANTS: Psychologists, psychiatrists, and social workers who responded to the AES and MHPS at 127 VHA facilities. MAIN MEASURES: Four compositive outcomes included two objective measures (population coverage, continuity of care), one subjective measure (experience of care), and one composite measure of the former three measures (mental health domain quality). KEY RESULTS: Adjusted analyses showed prior year burnout generally had no impact on population coverage, continuity of care, and patient experiences of care but had a negative impact on provider experiences of care consistently across 5 years (p < 0.001). Pooled across years, a 5% higher facility-level burnout in AES and MHPS had a 0.05 and 0.09 standard deviation worse facility experiences of care from the prior year, respectively. CONCLUSIONS: Burnout had a significant negative impact on provider-reported experiential outcome measures. This analysis showed that burnout had a negative effect on subjective but not on objective quality measures of Veteran access to care, which could inform future policies and interventions regarding provider burnout.

Relationships between work-environment characteristics and behavioral health provider burnout in the Veterans Health Administration.

OBJECTIVE: To identify work-environment characteristics associated with Veterans Health Administration (VHA) behavioral health provider (BHP) burnout among psychiatrists, psychologists, and social workers. DATA SOURCES: The 2015-2018 data from Annual All Employee Survey (AES); Mental Health Provider Survey (MHPS); N = 57,397 respondents; facility-level Mental Health Onboard Clinical (MHOC) staffing and productivity data, N = 140 facilities. STUDY DESIGN: For AES and MHPS separately, we used mixed-effects logistic regression to predict BHP burnout using surveys from year pairs (2015-2016, 2016-2017, 2017-2018; six models). Within each year-pair, we used the earlier year of data to train models and tested the model in the later year, with burnout (emotional exhaustion and/or depersonalization) as the outcome for each survey. We used potentially modifiable work-environment characteristics as predictors, controlling for employee demographic characteristics as covariates, and employment facility as random intercepts. DATA COLLECTION/EXTRACTION METHODS: We included work-environment predictors that appeared in all 4 years (11 in AES; 17 in MHPS). PRINCIPAL FINDINGS: In 2015-2018, 31.0%-38.0% of BHPs reported burnout in AES or MHPS. Work characteristics consistently associated with significantly lower burnout were included for AES: reasonable workload; having appropriate resources to perform a job well; supervisors address concerns; given an opportunity to improve skills. For MHPS, characteristics included: reasonable workload; work improves veterans' lives; mental health care provided is well-coordinated; and three reverse-coded items: staffing vacancies; daily work that clerical/support staff could complete; and collateral duties reduce availability for patient care. Facility-level staffing ratios and productivity did not significantly predict individual-level burnout. Workload represented the strongest predictor of burnout in both surveys. CONCLUSIONS: This study demonstrated substantial, ongoing impacts that having appropriate resources including staff, workload, and supervisor support had on VHA BHP burnout. VHA may consider investing in approaches to mitigate the impact of BHP burnout on employees and their patients through providing staff supports, managing workload, and goal setting.

Mental health treatment and the role of tele-mental health at the veterans health administration during the COVID-19 pandemic.

To quantify overall trends in patients treated for mental health disorders and adverse events, including via tele-mental health (TMH) and psychopharmacology during pandemic-related health care transformation. Longitudinal observational study including veterans receiving mental health treatment at a Veterans Health Administration (VHA) facility from January 1, 2017 to June 16, 2020. Observed and expected patient care for on-going and new treatment of depression, posttraumatic stress, substance use disorder, severe mental illness diagnoses, overdose, and suicide attempts, and psychotropic prescriptions for antidepressant, antipsychotic, benzodiazepine, opioid, and mood stabilizing medications are depicted. Percent change between actual and expected counts in the early months of the COVID pandemic (March 18-May 5, 2020) are computed. Decreases in counts of patients receiving mental health treatment early in the pandemic ranged from 7% to 20% for on-going treatment, and 28% to 37% for new treatment. TMH rapidly expanded across VHA, becoming the primary means by which encounters were delivered. Counts of patients receiving on-going care for suicide attempts were stable, and for overdoses, decreased by 17%. Counts of patients initiating care for suicide attempts and overdoses decreased by 30% and 38%, respectively. Weekly prescriptions and medication on-hand for psychotropics ranged from a 2% decrease to a 4% increase. New patient prescribing decreased 21%-50%. VHA and other large healthcare systems will need to expand outreach and continue to develop TMH services to maintain care continuity and initiate care for existing and new patients during COVID-19 and future large-scale outbreaks, epidemics, and disasters. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Emotion Differentiation in Current and Remitted Major Depressive Disorder.

People with current major depressive disorder (MDD) experience diminished emotion differentiation. We tested the hypothesis that this emotional disturbance is chronic and also characterizes those whose MDD has remitted. As our main aim, we examined emotion differentiation in conjunction with elevated negative and diminished positive emotional intensity, which are both cardinal symptoms of MDD. As an exploratory aim, we examined the predominant theoretical conceptualization that people low in emotion differentiation use more general state terms (e.g., bad) and fewer emotion terms (e.g., anger) to describe their emotional experience. Participants (assessed via diagnostic interview) included individuals who had current MDD (current depressed; n = 48), individuals whose MDD was in full remission (remitted depressed; n = 80), and healthy controls (n = 87). Participants also completed two self-report measures of depressive symptoms and reported momentary emotion repeatedly for 14 days via experience sampling, from which we computed emotion differentiation (i.e., intraclass correlation coefficient) and emotional intensity (i.e., average of the mean emotion ratings across surveys). Finally, participants described a momentary emotional experience via an open-response format, which was coded for the use of general state and emotion terms. Compared to the healthy control group, the current and remitted depressed groups showed similarly low levels of negative and positive emotion differentiation. These findings suggest that diminished emotion differentiation may be a stable characteristic of depressive disorders and a possible target for future prevention efforts. Diminished negative emotion differentiation was significantly associated with higher depressive symptoms as assessed by only one of the depression measures, though this finding did not hold after adjusting for negative emotional intensity. Finally, participants' emotion differentiation was not associated with use of general state and emotion terms, and groups did not use general state and emotion terms in ways that were consistent with the predominant theoretical conceptualization of emotion differentiation, suggesting the need for clarification in this research domain.

Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach.

BACKGROUND: Healthcare systems monitor and improve mental health treatment quality, access, continuity and satisfaction through use of population-based and efficiency-based staffing models, the former focused on staffing ratios and the latter, staff productivity. Preliminary evidence suggests that both high staffing ratios and moderate-to-high staff productivity are important for ensuring a full continuum of mental health services to indicated populations. METHODS & FINDINGS: With an information-theoretic approach, we conducted a longitudinal investigation of mental health staffing, productivity and treatment at the largest integrated healthcare system in American, the Veterans Health Administration (VHA). VHA facilities (N = 140) served as the unit of measure, with mental health treatment quality, access, continuity and satisfaction predicted by facility staffing and productivity in longitudinal mixed models. An information-theoretic approach: (a) entails the development of a comprehensive set of plausible models that are fit, ranked and weighted to quantitatively assess the relative support for each, and (b) accounts for model uncertainty while identifying best-fit model(s) that include important and exclude unimportant explanatory variables. In best-fit models, higher staffing was the strongest and most consistent predictor of better treatment quality, access, continuity and satisfaction. Higher staff productivity was often, but not always associated with better treatment quality, access, continuity and satisfaction. Results were further nuanced by differential prediction of treatment by between- and within-facility predictor effects and variable interactions. CONCLUSIONS: A population-based mental health staffing ratio and an efficiency-based productivity value are important longitudinal predictors of mental health treatment quality, access, continuity and satisfaction. Our longitudinal design and use of mixed regression models and an information-theoretic approach addresses multiple limitations of prior studies and strengthen our results. Results are discussed in terms of the provision of mental health treatment by healthcare systems, and analytical modeling of treatment quality, access, continuity and satisfaction.

Mental disorder prevalence among populations impacted by coronavirus pandemics: A multilevel meta-analytic study of COVID-19, MERS & SARS.

OBJECTIVE: Through a systematic review and meta-analysis of research on COVID-19, severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS) pandemics, we investigated whether mental disorder prevalence: (a) was elevated among populations impacted by coronavirus pandemics (relative to unselected populations reported in the literature), and (b) varied by disorder (undifferentiated psychiatric morbidity, anxiety, depressive, posttraumatic stress disorders [PTSD]) and impacted population (community, infected/recovered, healthcare provider, quarantined). METHOD: From 68 publications (N = 87,586 participants), 808 estimates were included in a series of multilevel meta-analyses/regressions including random effects to account for estimates nested within studies. RESULTS: Median summary point prevalence estimates varied by disorder and population. Psychiatric morbidity (20-56%), PTSD (10-26%) and depression (9-27%) were most prevalent in most populations. The highest prevalence of each disorder was found among infected/recovered adults (18-56%), followed by healthcare providers (11-28%) and community adults (11-20%). Prevalence estimates were often notably higher than reported for unselected samples. Sensitivity analyses demonstrated that overall prevalence estimates moderately varied by pandemic, study location, and mental disorder measure type. CONCLUSION: Coronavirus pandemics are associated with multiple mental disorders in several impacted populations. Needed are investigations of causal links between specific pandemic-related stressors, threats, and traumas and mental disorders.

Addressing the mental health impact of COVID-19 through population health.

The COVID-19 pandemic has and will continue to result in negative mental health outcomes such as depression, anxiety and traumatic stress in people and populations throughout the world. A population mental health perspective informed by clinical psychology, psychiatry and dissemination and implementation science is ideally suited to address the broad, multi-faceted and long-lasting mental health impact of the pandemic. Informed by a systematic review of the burgeoning empirical research on the COVID-19 pandemic and research on prior coronavirus pandemics, we link pandemic risk factors, negative mental health outcomes and appropriate intervention strategies. We describe how social risk factors and pandemic stressors will contribute to negative mental health outcomes, especially among vulnerable populations. We evaluate the scalability of primary, secondary and tertiary interventions according to mental health target, population, modality, intensity and provider type to provide a unified strategy for meeting population mental health needs. Traditional models, in which evidence-based therapies delivered are delivered in-person, by a trained expert, at a specialty care location have proved difficult to scale. The use of non-traditional models, tailoring preventive interventions to populations based on their needs, and ongoing coordinated evaluation of intervention implementation and effectiveness will be critical to refining our efforts to increase reach.