A Community-Engaged Process for Adapting a Cardiovascular Health Intervention for Persons with Serious Mental Illness.

Introduction: People with serious mental illness experience grave disparities in cardiovascular disease risk factors. To promote scale-up of effective cardiovascular disease risk reduction interventions from clinical trials, it is important to involve end-users in adapting interventions to fit the needs of community-based settings. Objective: We describe a novel, theory-informed process of garnering community input to adapt IDEAL Goals, an evidence-based intervention for improving cardiovascular disease risk factors in persons with serious mental illness. Setting: Outpatient community mental health programs in Maryland and Michigan implementing behavioral health homes, which provide enhanced support to people living with both physical and mental illnesses. Participants: Clinicians, frontline staff, and administrators from community mental health organizations and persons with serious mental illness. Methods: Our approach to community engagement is based on the Replicating Effective Programs (REP) framework. During the REP preimplementation phase, we used 2 community engagement activities: (1) a "needs assessment" to identify anticipated implementation barriers and facilitators, and (2) "community working groups" to collaboratively engage with end-users in adapting the intervention and implementation strategies. Main Findings: We used the Stakeholder Engagement Reporting Questionnaire to describe our processes for conducting a needs assessment, involving site-level surveys (N=26) and individual interviews (N=94), and convening a series of community working groups with clinicians and staff (mean, 24 per meeting) and persons with serious mental illness (mean, 8 per meeting). Conclusions: By specifying the nature and extent of our community engagement activities, we aim to contribute to the evidence base of how to better integrate and measure community-engaged processes in the adaptation of evidence-based interventions.

Technologic and Nontechnologic Barriers to Implementing Behavioral Health Homes in Community Mental Health Settings During the COVID-19 Pandemic.

Background: Persons with serious mental illnesses (SMIs) experience disparities in health care and are more likely to die from physical health conditions than the general population. Behavioral health homes are used in public sector mental health programs to deploy collaborative care to improve physical health for those with SMIs. During the COVID-19 pandemic, these programs faced new challenges in delivering care to this vulnerable group. Purpose: To describe barriers to implementing or sustaining behavioral health homes, experienced by community mental health workers during the COVID-19 pandemic, and the strategies used to address these challenges. Methods: In-depth qualitative interviews were conducted among the behavioral health workforce in Maryland and Michigan community mental health programs. Interview questions were derived from the Consolidated Framework for Implementation Research (CFIR), and responses related to implementing and sustaining health homes during the pandemic were coded and themes were analyzed by using an inductive approach. Results: Overall, 72 staff members across 21 sites in Maryland and Michigan were interviewed. Implementation barriers/strategies identified occurred across multiple CFIR domains (client, mental health system, physical health system). Interviewees discussed technologic and nontechnologic challenges as well as strategies to address technology issues. Strategies were more frequently discussed by providers when the barrier was viewed at the client level (eg, low technology literacy) versus the broader system (eg, canceled primary care visits). Conclusions: Community mental health staff described barriers beyond technology in caring for individuals with SMIs and physical health conditions. Further research should examine how implementation strategies address both technologic and nontechnologic barriers to collaborative care.

Collaborative chronic care models for mental health conditions: cumulative meta-analysis and metaregression to guide future research and implementation.

OBJECTIVE: Prior meta-analysis indicates that collaborative chronic care models (CCMs) improve mental and physical health outcomes for individuals with mental disorders. This study aimed to investigate the stability of evidence over time and identify patient and intervention factors associated with CCM effects to facilitate implementation and sustainability of CCMs in clinical practice. METHODS: We reviewed 53 CCM trials that analyzed depression, mental quality of life (QOL), or physical QOL outcomes. Cumulative meta-analysis and metaregression were supplemented by descriptive investigations across and within trials. RESULTS: Most trials targeted depression in the primary care setting, and cumulative meta-analysis indicated that effect sizes favoring CCM quickly achieved significance for depression outcomes, and more recently achieved significance for mental and physical QOL. Four of 6 CCM elements (patient self-management support, clinical information systems, system redesign, and provider decision support) were common among reviewed trials, whereas 2 elements (health care organization support and linkages to community resources) were rare. No single CCM element was statistically associated with the success of the model. Similarly, metaregression did not identify specific factors associated with CCM effectiveness. Nonetheless, results within individual trials suggest that increased illness severity predicts CCM outcomes. CONCLUSIONS: Significant CCM trials have been derived primarily from 4 original CCM elements. Nonetheless, implementing and sustaining this established model will require health care organization support. Although CCMs have typically been tested as population-based interventions, evidence supports stepped care application to more severely ill individuals. Future priorities include developing implementation strategies to support adoption and sustainability of the model in clinical settings while maximizing fit of this multicomponent framework to local contextual factors.

Comparative effectiveness of collaborative chronic care models for mental health conditions across primary, specialty, and behavioral health care settings: systematic review and meta-analysis.

OBJECTIVE: Collaborative chronic care models (CCMs) improve outcome in chronic medical illnesses and depression treated in primary care settings. The effect of such models across other treatment settings and mental health conditions has not been comprehensively assessed. The authors performed a systematic review and meta-analysis to assess the comparative effectiveness of CCMs for mental health conditions across disorders and treatment settings. METHOD: Randomized controlled trials comparing CCMs with other care conditions, published or in press by August 15, 2011, were identified in a literature search and through contact with investigators. CCMs were defined a priori as interventions with at least three of the six components of the Improving Chronic Illness Care initiative (patient self-management support, clinical information systems, delivery system redesign, decision support, organizational support, and community resource linkages). Articles were included if the CCM effect on mental health symptoms or mental quality of life was reported. Data extraction included analyses of these outcomes plus social role function, physical and overall quality of life, and costs. Meta-analyses included comparisons using unadjusted continuous measures. RESULTS: Seventy-eight articles yielded 161 analyses from 57 trials (depression, N=40; bipolar disorder, N=4; anxiety disorders, N=3; multiple/other disorders, N=10). The meta-analysis indicated significant effects across disorders and care settings for depression as well as for mental and physical quality of life and social role function (Cohen's d values, 0.20-0.33). Total health care costs did not differ between CCMs and comparison models. A systematic review largely confirmed and extended these findings across conditions and outcome domains. CONCLUSIONS: CCMs can improve mental and physical outcomes for individuals with mental disorders across a wide variety of care settings, and they provide a robust clinical and policy framework for care integration.

Risk of smoking and receipt of cessation services among veterans with mental disorders.

OBJECTIVE: The purpose of this study was to determine rates of smoking and receipt of provider recommendations to quit smoking among patients with mental disorders treated in U.S. Department of Veterans Affairs (VA) treatment settings. METHODS: The authors conducted a secondary analysis of the yearly, cross-sectional 2007 Veterans Health Administration Outpatient Survey of Healthcare Experiences of Patients (N=224,193). Logistic regression was used to determine the independent association of mental health diagnosis and the dependent variables of smoking and receipt of provider recommendations to quit smoking. RESULTS: Patients with mental disorders had greater odds of smoking, compared with those without mental disorders (p<.05). Those with various mental disorders reported similar rates of receiving services (more than 60% to 80% reported receiving selected services), compared with those without these disorders, except that those with schizophrenia had more than 30% lower odds of receiving advice to quit smoking from their physicians (p<.05). Moreover, those who had co-occurring posttraumatic stress disorder or substance use disorders had significantly greater odds of reporting that they received advice to quit, recommendations for medications, and physician discussions of quitting methods, compared with those without these disorders (p<.05). Older patients, male patients, members of ethnic minority groups, those who were unmarried, those who were disabled or unemployed, and those living in rural areas had lower odds of receiving selected services (p<.05). CONCLUSIONS: The majority of patients with mental disorders served by the VA reported receiving cessation services, yet their smoking rates remained high, and selected groups were at risk for receiving fewer cessation services, suggesting the continued need to disseminate cessation services.

Trial of an electronic decision support system to facilitate shared decision making in community mental health.

OBJECTIVES: Involvement of community mental health consumers in mental health decision making has been consistently associated with improvements in health outcomes. Electronic decision support systems (EDSSs) that support both consumer and provider decision making may be a sustainable way to improve dyadic communication in a field with approximately 50% workforce turnover per year. This study examined the feasibility of such a system and investigated proximal outcomes of the system's performance. METHODS: A cluster randomized design was used to evaluate an EDSS at three urban community mental health sites. Case managers (N=20) were randomly assigned to the EDSS-supported planning group or to the usual care planning group. Consumers (N=80) were assigned to the same group as their case managers. User satisfaction with the care planning process was assessed for consumers and case managers (possible scores range from 1 to 5, with higher summary scores indicating more satisfaction). Recall of the care plan was assessed for consumers. Linear regression with adjustment for grouping by worker was used to assess satisfaction scores. A Wilcoxon rank-sum test was used to examine knowledge of the care plan. RESULTS: Compared with case managers in the control group, those in the intervention group were significantly more satisfied with the care planning process (mean ± SD score=4.0 ± .5 versus 3.3 ± .5; adjusted p=.01). Compared with consumers in the control group, those in the intervention group had significantly greater recall of their care plans three days after the planning session (mean proportion of plan goals recalled=75% ± 28% versus 57% ± 32%; p=.02). There were no differences between the clients in the intervention and control groups regarding satisfaction. CONCLUSIONS: This study demonstrated that clients can build their own care plans and negotiate and revise them with their case managers using an EDSS.

Using a pharmacy-based intervention to improve antipsychotic adherence among patients with serious mental illness.

BACKGROUND: Similar to patients with other chronic disorders, patients with serious mental illness (SMI) are often poorly adherent with prescribed medications. OBJECTIVE: We conducted a randomized controlled trial examining the effectiveness of a pharmacy-based intervention (Meds-Help) in increasing antipsychotic medication adherence among Department of Veterans Affairs (VA) patients with SMI. We also examined the impact of Meds-Help on psychiatric symptoms, quality of life, and satisfaction with care. METHODS: We enrolled 118 patients from 4 VA facilities with schizophrenia, schizoaffective, or bipolar disorder who were on long-term antipsychotics but had antipsychotic medication possession ratios (MPRs) <0.8 in the prior year. Patients were randomized to usual care (UC; n = 60) or the pharmacy-based intervention (Meds-Help; n = 58). We reassessed adherence at 6 and 12 months, at which time patients completed Positive and Negative Symptom Scales (PANSS), Quality of Well-being Scales (QWB), and Client Satisfaction Questionnaires (CSQ-8). RESULTS: Prior to enrollment, Meds-Help and UC patients had mean antipsychotic MPRs of 0.54 and 0.55, respectively. At 6 months, mean MPRs were 0.91 for Meds-Help and 0.64 for UC patients; at 12 months, they were 0.86 for Meds-Help and 0.62 for UC patients. In multivariate analyses adjusting for patient factors, Meds-Help patients had significantly higher MPRs at 6 and 12 months (P < .0001). There were no significant differences between groups in PANSS, QWB, or CSQ-8 scores, but power to detect small effects was limited. CONCLUSIONS: Congruent with prior studies of patients with other disorders, a practical pharmacy-based intervention increased antipsychotic adherence among patients with SMI. However, SMI patients may require additional care management components to improve outcomes.

Comprehensive electronic decision support systems.

This Open Forum describes a vision of comprehensive electronic decision support systems that could provide information and supports to both clients and clinicians in order to facilitate shared decision making and encourage collaborative management of illness and wellness. The authors argue that these systems have the potential to improve mental health care by enhancing and connecting the three elements of evidence-based medicine: client preferences, research evidence, and clinician skills.

Shared decision making in public mental health care: perspectives from consumers living with severe mental illness.

OBJECTIVE: Most theoretical and empirical work regarding decision making in mental health suggests that mental health consumers have better outcomes when their preferences are integrated into quality of life decisions. A wealth of research, however, indicates that providers have difficulty predicting what their clients' priorities are. This study investigates consumer decision-making preferences and understanding of construction of decisions in community mental health. METHODS: People living with severe mental illness being treated in the public mental health care system (N=16) participated in qualitative interviews regarding case management decision making as a part of a larger study investigating a decision support system to facilitate shared decision making. Interviews were transcribed, coded, and cross-case thematic analyses were conducted. RESULTS: Mental health consumers generally endorse a "shared" style of decision making. When asked what "shared" means, however, consumers describe a two-step process which first prioritizes autonomy, and if that is not possible, defers to case managers' judgment. Consumers also primarily focused on the relationship and affective components of decision making, rather than information-gathering or deliberating on options. Finally, when disagreements arose, consumers primarily indicated they handled them. CONCLUSIONS: Mental health consumers may have a different view of decision making than the literature on shared decision making suggests. Mental health consumers may consciously decide to at least verbally defer to their case managers, and remain silent about their preferences or wishes.

The role of staff turnover in the implementation of evidence-based practices in mental health care.

OBJECTIVES: This study examined turnover rates of teams implementing psychosocial evidence-based practices in public-sector mental health settings. It also explored the relationship between turnover and implementation outcomes in an effort to understand whether practitioner perspectives on turnover are related to implementation outcomes. METHODS: Team turnover was measured for 42 implementing teams participating in a national demonstration project examining implementation of five evidence-based practices between 2002 and 2005. Regression techniques were used to analyze the effects of team turnover on penetration and fidelity. Qualitative data collected throughout the project were blended with the quantitative data to examine the significance of team turnover to those attempting to implement the practices. RESULTS: High team turnover was common (M+/-SD=81%+/-46%) and did not vary by practice. The 24-month turnover rate was inversely related to fidelity scores at 24 months (N=40, beta=-.005, p=.01). A negative trend was observed for penetration. Further analysis indicated that 71% of teams noted that turnover was a relevant factor in implementation. CONCLUSIONS: The behavioral health workforce remains in flux. High turnover most often had a negative impact on implementation, although some teams were able to use strategies to improve implementation through turnover. Implementation models must consider turbulent behavioral health workforce conditions.

Using pharmacy data on partial adherence to inform clinical care of patients with serious mental illness.

OBJECTIVE: Low adherence to antipsychotic medications is a risk factor for poor outcomes for people with serious mental illness. Pharmacy data might be used by health systems to identify partially adherent patients for interventions. This study assessed whether using pharmacy data is an accurate screening method for identifying at-risk patients. METHODS: Administrative data were used to identify 1,712 veterans as having schizophrenia or a schizoaffective or bipolar disorder and who had 12-month antipsychotic medication possession ratios (MPRs) of less than .80. Patients' charts were reviewed for alternative explanations for low rates of filling prescriptions for antipsychotic medication. RESULTS: Of 1,712 patients whose pharmacy data indicated partial adherence (MPRs less than .80), 17% (N=297) may have been adherent. Patients with bipolar disorder had higher odds of receiving a false-positive designation (adjusted odds ratio of 1.8, 95% confidence interval of 1.31-2.39). CONCLUSIONS: MPRs constructed from pharmacy data can be a useful first screen for identifying patients who need assistance with medication adherence.