Objective

Innovative approaches are needed for assessing treatment preferences of individuals with schizophrenia. Conjoint analysis methods may help to identify preferences, but the usability and validity of these methods for individuals with schizophrenia remain unclear. This study examined computerized conjoint analysis for persons with schizophrenia and whether preferences for weight management programs predict service use.

Methods

A computerized, patient-facing conjoint analysis system was developed through iterative consultation with 35 individuals with schizophrenia enrolled at a community mental health clinic. An additional 35 overweight participants with schizophrenia then used the system to choose among psychosocial weight management programs varying in four attributes: location (community or clinic), delivery mode (Internet or in person), leader (clinician or layperson), and training mode (individual or group). A multilevel logit model with partial preference data determined contributions of each attribute to groupwide preferences. Associations were studied between preferences and use of a psychosocial weight management group.

Results

Conjoint analysis system usability was rated highly. Groupwide preferences were significantly influenced by location (p<0.001; clinic was preferred), leader (p=0.02; clinician was preferred), and training mode (p<0.001; group was preferred) but not delivery mode (p=0.68). Preferences did not correlate with age, gender, body mass index, illness severity, or subsequent program use. Participants described barriers to program attendance, including transportation, scheduling, privacy, psychiatric illness, and lack of motivation.

Conclusions

Computerized conjoint analysis can produce valid assessments of treatment preferences of persons with schizophrenia and inform treatment development and implementation. Although preferences may affect treatment use, they are one of multiple factors.

Objective

Innovative approaches are needed for assessing treatment preferences of individuals with schizophrenia. Conjoint analysis methods may help to identify preferences, but the usability and validity of these methods for individuals with schizophrenia remain unclear. This study examined computerized conjoint analysis for persons with schizophrenia and whether preferences for weight management programs predict service use.

Methods

A computerized, patient-facing conjoint analysis system was developed through iterative consultation with 35 individuals with schizophrenia enrolled at a community mental health clinic. An additional 35 overweight participants with schizophrenia then used the system to choose among psychosocial weight management programs varying in four attributes: location (community or clinic), delivery mode (Internet or in person), leader (clinician or layperson), and training mode (individual or group). A multilevel logit model with partial preference data determined contributions of each attribute to groupwide preferences. Associations were studied between preferences and use of a psychosocial weight management group.

Results

Conjoint analysis system usability was rated highly. Groupwide preferences were significantly influenced by location (p<0.001; clinic was preferred), leader (p=0.02; clinician was preferred), and training mode (p<0.001; group was preferred) but not delivery mode (p=0.68). Preferences did not correlate with age, gender, body mass index, illness severity, or subsequent program use. Participants described barriers to program attendance, including transportation, scheduling, privacy, psychiatric illness, and lack of motivation.

Conclusions

Computerized conjoint analysis can produce valid assessments of treatment preferences of persons with schizophrenia and inform treatment development and implementation. Although preferences may affect treatment use, they are one of multiple factors.

Understanding client perceptions of need for underlying social determinant support may improve services for depression care. This secondary analysis examines perceptions of "social needs" related to housing and employment, financial, and legal (EFL) concerns among individuals with depression. Data were analyzed from Community Partners in Care, a randomized comparative effectiveness trial of multi-sector collaborative care for depression among a sample of people who were predominantly racial/ethnic minorities and low-income. Adults with depression (n = 980) in both interventions were surveyed at 36-month follow-up for (1) being asked about and (2) having social needs for housing or EFL concerns. In multivariate models, life difficulty and mental health visits in non-healthcare sectors predicted being asked about housing and EFL. Lower social determinants of health-related life satisfaction increased the odds of having unmet housing and EFL needs. These findings underscore the role of non-healthcare organizations as community resources for depression care and in screening and addressing social needs.

A state-academic-community partnership formed in response to the mental health needs fueled by the COVID-19 pandemic and the disproportionate effects on marginalized communities. Taking a community-partnered approach and using a health equity lens, the partnership developed a website to guide users through digital mental health resources, prioritizing accessibility, engagement, and community needs.

PURPOSE OF REVIEW:We review recent community interventions to promote mental health and social equity. We define community interventions as those that involve multi-sector partnerships, emphasize community members as integral to the intervention, and/or deliver services in community settings. We examine literature in seven topic areas: collaborative care, early psychosis, school-based interventions, homelessness, criminal justice, global mental health, and mental health promotion/prevention. We adapt the social-ecological model for health promotion and provide a framework for understanding the actions of community interventions. RECENT FINDINGS:There are recent examples of effective interventions in each topic area. The majority of interventions focus on individual, family/interpersonal, and program/institutional social-ecological levels, with few intervening on whole communities or involving multiple non-healthcare sectors. Findings from many studies reinforce the interplay among mental health, interpersonal relationships, and social determinants of health. There is evidence for the effectiveness of community interventions for improving mental health and some social outcomes across social-ecological levels. Studies indicate the importance of ongoing resources and training to maintain long-term outcomes, explicit attention to ethics and processes to foster equitable partnerships, and policy reform to support sustainable healthcare-community collaborations.

The coronavirus pandemic of 2019 (COVID-19) has created unprecedented changes to everyday life for millions of Americans due to job loss, school closures, stay-at-home orders and health and mortality consequences. In turn, physicians, academics, and policymakers have turned their attention to the public mental health toll of COVID-19. This commentary reporting from the field integrates perceptions of academic, community, health system, and policy leaders from state, county, and local levels in commenting on community mental health needs in the COVID-19 pandemic. Stakeholders noted the broad public health scope of mental health challenges while expressing concern about exacerbation of existing disparities in access and adverse social determinants, including for communities with high COVID-19 infection rates, such as African Americans and Latinos. They noted rapid changes toward telehealth and remote care, and the importance of understanding impacts of changes, including who may benefit or have limited access, with implications for future services delivery. Needs for expanded workforce and training in mental health were noted, as well as potential public health value of expanding digital resources tailored to local populations for enhancing resilience to stressors. The COVID-19 pandemic has led to changes in delivery of health care services across populations and systems. Concerns over the mental health impact of COVID-19 has enhanced interest in remote mental care delivery and preventive services, while being mindful of potential for enhanced disparities and needs to address social determinants of health. Ongoing quality improvement across systems can integrate lessons learned to enhance a public mental well-being.