The scale of the problem is staggering. By 2050, the number of adults aged 65 and over is projected to nearly double, while the population aged 80 and above is expected to triple. Within this expanding demographic, combined mental and physical multimorbidity is the rule rather than the exception. Roughly one in five adults over 65 lives with a mental or neurological disorder, and World Health Organisation survey data show that up to a quarter of people with one or more chronic physical conditions also experience depression, the single most significant factor driving poorer health scores in this group. The relationship runs in both directions: physical ill-health raises the risk of developing a mental health condition, and depression in turn accelerates physical disability, frailty, and mortality.

What makes this clinical overlap so dangerous is the way healthcare systems respond to it, or fail to. Physical and mental health services across Europe are typically funded, commissioned, managed, and physically located separately, a structural divide that makes genuine integration logistically difficult. Older adults with severe mental illness die earlier than the general population, a life-expectancy gap that extends into old age and that is partly preventable through better management of chronic physical conditions and lifestyle risks. Yet there is currently no mandate anywhere in Europe requiring physical health services to be delivered in specialised mental health settings, meaning that psychiatric inpatients are often transferred to emergency departments simply to receive an in-person physical health review, disrupting continuity of care and destabilising their mental state in the process.

The joint Task and Finish Group behind the new paper draws on the combined expertise of geriatricians and old age psychiatrists, along with people with lived experience, spanning Northern, Southern, and Eastern Europe, including the United Kingdom, the Netherlands, Belgium, France, Sweden, Ireland, Spain, Portugal, Italy, Türkiye, Croatia, and Romania. The group conducted an updated literature review across MEDLINE, Scopus, CINAHL, and PsychINFO, and refined its recommendations through five consensus meetings held between February and June 2026, attended by 17 members, with patient and public involvement representatives present at two of those meetings. Notably, the group deliberately excluded dementia from its scope, because integrated dementia care has already been studied extensively, whereas older adults with serious mental illness and other functional disorders remain comparatively underserved and face distinct barriers to physical healthcare.

The clinical picture these experts describe is one of compounding vulnerability. Older people with psychiatric disorders commonly experience early onset frailty at a younger age than their peers, falls and poor mobility linked to psychotropic medications such as antipsychotics, antidepressants, and sedatives, osteoporosis and fragility fractures, and cardiovascular disease driven by lifelong risk factors including smoking, sedentary lifestyles, long-term antipsychotic exposure, and high rates of metabolic syndrome. Chronic lithium therapy can produce renal and electrolyte problems, including nephrogenic diabetes insipidus, while antidepressants can cause hyponatraemia and poor oral intake related to dysphagia and cognitive impairment adds dehydration to the list. Layered on top of all this is polypharmacy, as combined physical and mental health medication regimes multiply the risk of dangerous drug interactions.

Beyond the biology, the group catalogued a series of systemic failures that patients and carers encounter daily. Patients with psychiatric disorders frequently report stigmatisation and a feeling of not being taken seriously when they present physical symptoms. Psychiatrists often lack knowledge of somatic disorders, while physicians in general hospitals may be poorly equipped to recognise psychiatric symptoms. Patients find themselves seeing multiple teams for individual problems, coordinating a maze of outpatient appointments, and repeating the same information to different clinicians who lack access to the whole picture. There is often no central care coordinator holding the complete clinical story, social prescribing is rarely offered to this group, and advanced care planning and end-of-life care are frequently absent altogether.

The paper also grapples with the definitional question at the heart of the field: what does integrated care actually mean? The authors define it as a person-centred, coordinated, interdisciplinary approach that addresses physical, mental, social, and functional health needs through collaboration across providers, settings, and levels of care. From the older patient’s perspective, integration is closely associated with continuity, relational trust, clear communication, timely access, involvement in decision-making, and support responsive to individual needs and preferences. The International Foundation of Integrated Care’s meta-framework of nine pillars, covering shared purpose, health in context, people as partners, teamwork, coordination, governance, information and technology, finance, and learning, provides one structural template, but the authors argue that current approaches are often too limited in scope and variability to meet the needs of older adults with combined illness, and that a broader conceptualisation of integration may be required.

The examples of integrated services that do exist across Europe are strikingly uneven. The Task and Finish Group identified localised models spanning liaison-type services in the Netherlands and the United Kingdom, integrated care units and joint outpatient services in Belgium and the Netherlands, and broader models in Italy and Portugal that weave together social, psychiatric, and geriatric care, sometimes in private hospital settings. One particularly novel example was an emergency physician response unit in London providing emergency department-level care within a psychiatric setting, the only service of its type identified by the group. Four of the surveyed services sat in general hospitals, three in psychiatric hospitals, two in outpatient clinics, and two in private hospitals, with only two operating across more than one setting. No examples were identified from Eastern Europe, and the group notes that services tend to be concentrated in areas of significant socioeconomic deprivation, such as Tower Hamlets in London and Leeds, where severe material strain is common, raising questions about equity of access for rural and less deprived regions alike.

From this evidence base, the group issued prioritised recommendations across four domains. Clinically, services should be proactive, person-centred, and delivered by interdisciplinary teams grounded in comprehensive geriatric assessment, with adequate outcome measures defined. In research, the central priority is identifying which integrated care models work best for which patient groups and healthcare contexts, since the evidence underpinning existing services remains thin. In training and education, the authors call for undergraduate curricula to be restructured to reflect the urgent need for skills in both the physical and mental healthcare of older people, who are the largest users of health services. At the policy level, reimbursement systems, workforce planning, and organisational structures must be aligned to support integration. Feasible near-term projects include Europe-wide surveys of how physical and mental health services are currently delivered, and of whether geriatric-psychiatric training is incorporated into postgraduate programmes.

The authors are unequivocal in their conclusion: integrated physical and mental healthcare for older adults is no longer a desirable aspiration but an urgent clinical, educational, research, and policy imperative. The continued separation of the two systems perpetuates inequity, delays diagnosis and treatment, increases avoidable hospitalisation and long-term care, and leaves some of the most vulnerable older adults without coordinated, person-centred support. Innovation exists, but it remains isolated and inconsistently implemented. The message to patients, clinicians, educators, researchers, professional societies, and policymakers is a call to action: geriatric medicine and old age psychiatry must work together to build integrated systems of care that are equitable, scalable, and responsive to the needs of an ageing population, before the demographic tide makes the current fragmentation impossible to sustain.

Subject of Research: Integrated physical and mental healthcare services for older adults in Europe

Article Title: Integrated physical and mental healthcare for older adults in Europe: from concept to implementation—a joint EuGMS and EAGP perspectives paper

Article References: Beishon, L., Nobels, A., Alves, M., Bogdanovic, N., Cowan, R., Crowther, G., Flôr-Rodrigues, B., Frisardi, V., Geoghegan, C., Miot, S., Soysal, P., Van Den Noortgate, N., Walesby, K. E., Welsh, T. J., Yilmaz, O., Voshaar, R. C. O., Benraad, C., & on behalf of the EuGMS and EAGP Task & Finish Group on Integrated Psychiatric & Geriatric Care (2026). Integrated physical and mental healthcare for older adults in Europe: from concept to implementation—a joint EuGMS and EAGP perspectives paper. European Geriatric Medicine. https://doi.org/10.1007/s41999-026-01620-w

Image Credits: AI Generated

DOI: 10.1007/s41999-026-01620-w

Keywords: integrated care, geriatric medicine, old age psychiatry, serious mental illness, multimorbidity, frailty, polypharmacy, health services research, Europe, ageing population, health policy, EuGMS

The technical foundation of the study is the Massachusetts All-Payer Claims Database, or APCD, a statewide repository that aggregates billing records from both public and private insurers. Because claims data capture nearly every reimbursed encounter across the health system, they offer something that traditional psychiatric epidemiology cannot: a view of diagnoses as they are actually recorded in routine care, rather than as they emerge from structured diagnostic interviews in research settings. The researchers analyzed person-year observations spanning 2016 through 2018, focusing on adults aged 21 to 64 who had at least one psychiatric diagnosis and were insured either through Medicaid or through commercial plans. In total, the dataset yielded 2,409,794 person-year observations representing 653,413 unique people with mental health disorders.

Defining multimorbidity in claims data required careful methodological choices. The team identified psychiatric disorders using ICD-10-CM diagnostic codes, the standardized classification system that accompanies every insurance claim, and grouped those codes into broader diagnostic categories. Psychiatric multimorbidity was then defined as the documentation of two or more distinct psychiatric diagnostic categories within a single calendar year. This calendar-year approach matters: it captures the diagnostic landscape a clinician actually faces over a sustained period, rather than a snapshot from a single visit. One important limitation shaped the analysis from the start. Substance use disorder diagnoses were redacted from the database under its privacy policies and therefore had to be excluded, a constraint the authors acknowledge makes their prevalence estimates conservative, since substance use disorders are among the most common co-occurring conditions in psychiatric populations.

The headline numbers are striking in their simplicity. Among the insured adults with a mental health diagnosis, 68.8 percent carried only one psychiatric diagnosis in a given year. Another 17.4 percent carried two, and 13.8 percent carried three or more. Taken together, nearly a third of the population—31.2 percent—met the study’s definition of psychiatric multimorbidity. That figure aligns with a growing body of research suggesting that co-occurrence is the norm rather than the exception in mental illness. Classic epidemiological surveys, including the National Comorbidity Survey Replication, documented high rates of comorbidity decades ago, and more recent work in veterans’ health systems and specialty programs for serious mental illness has repeatedly found that multiple diagnoses cluster together. What distinguishes the new study is its setting: an entire statewide insured population spanning both Medicaid and commercial coverage, rather than a single health system, a specialty clinic, or a volunteer research cohort.

Beyond raw prevalence, the study probed who is most likely to carry multiple psychiatric diagnoses, using multivariable logistic regression to adjust for a range of patient characteristics. The results were consistent and, in places, dramatic. Women had 23 percent higher odds of psychiatric multimorbidity than men, with an adjusted odds ratio of 1.23. People with any medical comorbidity—a physical health condition such as diabetes, heart disease, or chronic lung disease—had 37 percent higher odds than those without, at an adjusted odds ratio of 1.37. Medicaid enrollees had 25 percent higher odds than people with commercial insurance. Each of these associations was estimated with tight confidence intervals, reflecting the enormous sample size, and each points toward a distinct thread of clinical and policy significance.

The most striking association involved serious mental illness. People diagnosed with conditions such as schizophrenia, bipolar disorder, or other severe psychiatric disorders had nearly nine times the odds of psychiatric multimorbidity compared with those without serious mental illness—an adjusted odds ratio of 8.92. This is not merely a statistical curiosity. It means that for the population with the most disabling psychiatric conditions, multiple concurrent diagnoses are close to a defining feature of the illness experience. It also resonates with a 2024 systematic review and meta-analysis in The Lancet Psychiatry, which found that multimorbidity, spanning both psychiatric and physical conditions, is markedly more prevalent among people with severe mental illness than in the general population. The new claims-based findings extend that picture to the diagnostic layer, showing that even within psychiatry itself, severe illness rarely presents in isolation.

The gender pattern echoes a well-documented international literature. Women consistently show higher rates of internalizing conditions such as depression and anxiety, and meta-analytic work has found that women are at greater risk of conditions including obsessive-compulsive disorder, while also being more likely to seek help from general practitioners and to report mental health symptoms. Whether the female excess in documented multimorbidity reflects true differences in disease occurrence, differences in help-seeking and diagnostic capture, or some interaction of both remains an open question in the field. Claims data cannot fully disentangle these forces, since a diagnosis must be both made and billed to appear in the record. Still, the consistency of the association across data sources and countries suggests that the higher multimorbidity burden among women is a real feature of psychiatric epidemiology that treatment systems need to account for.

The insurance-type finding carries its own weight. Medicaid enrollees, who tend to have lower incomes and higher rates of disability, showed consistently higher odds of psychiatric multimorbidity than commercially insured adults even after statistical adjustment. This aligns with prior research showing that insurance status shapes both access to mental health care and the intensity with which conditions are diagnosed and treated, and with studies documenting that Medicaid is the dominant payer for people with serious mental illness. The association may partly reflect greater clinical complexity among Medicaid populations, greater contact with the health system and therefore more opportunities for additional diagnoses to be recorded, or both. Either way, the pattern underscores that the burden of complex psychiatric presentations falls disproportionately on the publicly insured, with direct implications for how state Medicaid programs budget, design networks, and structure integrated care.

Why does all of this matter clinically? Psychiatric multimorbidity is increasingly understood as a marker of clinical complexity, associated with greater symptom burden, poorer quality of life, higher service utilization, and worse outcomes. Research in patients with schizophrenia has linked mental health multimorbidity to substantially poorer quality of life, and studies of veterans with post-traumatic stress disorder have documented extensive diagnostic overlap. Yet most mental health care remains organized around single-disorder treatment protocols, with separate pathways for depression, anxiety, PTSD, and psychosis. The authors argue that their findings point toward integrated and patient-centered treatment approaches—models that treat the person and their full diagnostic profile rather than marching through diagnoses one at a time. There is also a growing transdiagnostic movement in psychiatry, which seeks interventions that cut across diagnostic boundaries precisely because co-occurrence is so common.

The study also carries a methodological lesson for the field. Structured diagnostic interviews, the gold standard of psychiatric epidemiology, capture small, carefully selected samples. Claims data capture everyone who touches the health system, at the cost of depending on the accuracy and completeness of billing diagnoses. By demonstrating that large-scale administrative data can be used to characterize psychiatric multimorbidity across an entire state, the researchers have opened a path for surveillance that could be repeated over time and replicated in other states with all-payer databases. Because substance use disorders were excluded, the true prevalence of multimorbidity is almost certainly higher than reported here. Even so, the message is difficult to escape: in the real world of American health care, mental illness most often arrives with company, and health systems designed for one diagnosis at a time are increasingly out of step with the patients they serve.

Subject of Research: Prevalence and correlates of psychiatric multimorbidity in a statewide insured population

Article Title: Measuring and Characterizing Psychiatric Multimorbidity Among Individuals with Mental Illness

Article References: Measuring and Characterizing Psychiatric Multimorbidity Among Individuals with Mental Illness. (n.d.). https://doi.org/10.1007/s10597-026-01675-w

Image Credits: AI Generated

DOI: 10.1007/s10597-026-01675-w

Keywords: psychiatric multimorbidity, mental illness, comorbidity, all-payer claims database, Medicaid, serious mental illness, ICD-10-CM, epidemiology, health services research, integrated care, substance use disorder, Massachusetts

The research team, led by Vedant Tapiavala of Dartmouth College together with collaborators at Massachusetts General Hospital, Dartmouth’s Thayer School of Engineering, Indiana University, the Geisel School of Medicine, and The Bridge in New York City, conducted a secondary analysis of three focus groups held across three supportive housing facilities in New York City. Rather than surveying attitudes with questionnaires, the investigators collected qualitative data, transcribed the discussions, and analyzed them using the RADaR technique, a rapid and rigorous qualitative data analysis method developed for applied research. The approach allows researchers to move quickly from raw transcripts to structured themes while preserving the nuance of what participants actually said, an important consideration when the subject matter touches on trust, vulnerability, and lived experience.

The findings reveal a population that is neither naively enthusiastic nor reflexively hostile toward AI. Participants expressed strong concerns about data privacy and the potential misuse of their information, worries that are amplified for people whose psychiatric histories carry heavy social stigma. At the same time, they recognized real potential in AI tools to support medication reminders, mental health maintenance, and daily functioning. This duality, hope shadowed by caution, runs through the entire study and complicates the simple narratives that often dominate public discussion of chatbots and mental health. For this community, the question is not whether AI companions should exist, but under what conditions they can be trusted.

Trust, the study found, is not a fixed trait that users either bring to or withhold from a system. Instead, it is something that emerges from design choices. Participants reported that their confidence in AI increased when tools were personalized, human-centered, and co-developed with people who understand the lived experience of serious mental illness. That last condition may be the most consequential. A chatbot designed without input from people who have navigated psychosis, mania, or severe depression may miss the signals that matter most, or worse, respond in ways that feel dismissive or unsafe. Co-design, in which end users participate as partners rather than test subjects, emerged as a key factor shaping perceived trustworthiness.

The technical implications of this are significant. Modern conversational AI systems, particularly those built on large language models, are trained on broad datasets that reflect general human communication, not the specific communication patterns, needs, and sensitivities of people with serious mental illness. Personalization in this context means more than remembering a user’s name. It involves calibrating tone, anticipating cognitive and emotional states, respecting boundaries around sensitive topics, and integrating gracefully with clinical care teams. Participants in the study effectively described a set of design requirements that developers of mental health AI would do well to treat as specifications rather than suggestions.

Yet the study also documents a darker set of worries that the participants themselves raised: the fear of losing critical thinking and becoming overreliant on AI companions. This concern is especially poignant for a population where autonomy and self-efficacy are hard-won achievements. An AI that is always available, always agreeable, and always attentive could, in principle, substitute for human connection rather than supplement it. The researchers note that it is essential these technologies lessen, rather than deepen, feelings of loneliness, a distinction that sounds subtle but carries enormous weight for the design and deployment of companion AI in mental health care.

These concerns do not arise in a vacuum. The study’s reference list situates the work within a rapidly growing literature on the mental health risks of large language model chatbots, including recent scoping reviews on chatbot-related harms, analyses of AI-associated delusions and mechanisms of delusion co-creation, and debates in leading psychiatry journals about whether generative AI chatbots might increase psychosis risk. For people with schizophrenia spectrum disorders in particular, the possibility that an AI companion could reinforce distorted thinking is a clinical question of the first order. The study’s participants, by voicing their own concerns about overreliance, demonstrated a sophisticated awareness of these risks that contradicts stereotypes about people with serious mental illness being unable to engage critically with technology.

At the same time, the broader research landscape suggests why AI companions hold such appeal. Loneliness and social isolation are widespread among adults in the United States and are especially pronounced among people with serious mental illness, who face stigma, social withdrawal, and reduced lifespans compared with the general population. Some studies have even found that third-party evaluators perceive AI responses as more compassionate than those of expert humans, and systematic reviews of chatbots versus human healthcare professionals have examined empathy in patient care with mixed but intriguing results. Against that backdrop, a companion that offers unconditional availability and a consistent, nonjudgmental presence has an obvious emotional pull, particularly during the nights and weekends when human support is scarce.

The study’s methodology also deserves attention for what it models. By recruiting participants through supportive housing facilities and partnering with community organizations, the research team reached a population that digital health studies frequently overlook. People with serious mental illness have historically been excluded from technology design processes, sometimes on the assumption that they cannot use or evaluate complex tools. Earlier qualitative work on how patients with schizophrenia use the internet, and international consensus efforts on digital mental health for severe mental illness, have pushed back against that exclusion. This study extends that tradition by treating participants as experts in their own lives, capable of articulating precisely what would make an AI companion feel safe, useful, and trustworthy.

The path forward, the authors suggest, lies in using these lived-experience insights to guide the development of AI tools for people with serious mental illnesses. That means building privacy protections that address the specific fears participants raised, designing personalization that respects clinical complexity, involving peer specialists and people with lived experience throughout the development cycle, and building in safeguards against overreliance. It also means honest acknowledgment of the tensions involved: the same system that tells a lonely person someone cares can become a substitute for the human relationships that recovery ultimately depends on. As AI reshapes healthcare through clinical decision support, communication tools, and patient monitoring, this study offers a reminder that the people most affected by these technologies have clear views about how they should work, and that listening to them is not just ethical but essential to building systems that actually help.

Subject of Research: Perspectives of people with serious mental illness on artificial intelligence-based companions in mental health care

Article Title: Perspectives of People with Serious Mental Illness on Artificial Intelligence-Based Companions: “If no one told you I love you today, you have someone that cares for you.”

Article References: Tapiavala, V., Chilakapati, S. S., Moore, D., Morales, S., Kumaran, A., Heller, R., Werlin, J., & Fortuna, K. L. (2026). Perspectives of People with Serious Mental Illness on Artificial Intelligence-Based Companions: “If no one told you I love you today, you have someone that cares for you.”. Community Mental Health Journal. https://doi.org/10.1007/s10597-026-01732-4

Image Credits: AI Generated

DOI: 10.1007/s10597-026-01732-4

Keywords: artificial intelligence, serious mental illness, AI companions, loneliness, schizophrenia, bipolar disorder, major depressive disorder, co-design, data privacy, digital mental health, qualitative research, supportive housing

The study, led by Tanya B. Horwitz and Allison G. Harvey of the University of California, Berkeley, is a secondary analysis of a randomized controlled trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction, known as TSC. The researchers examined data from 489 patients treated in Community Mental Health Centers across ten California counties. All participants had serious mental illness, defined as at least one DSM-5 disorder causing substantial interference with major life activities for at least a year, along with significant sleep or circadian problems. Crucially, the sample was clinically diverse: patients reported an average of 2.29 psychiatric disorders, and 76 percent reported at least two.

TSC itself is a modular treatment designed to target the sleep and circadian mechanisms that cut across diagnostic boundaries. Rather than treating insomnia as a symptom of depression or mania, TSC addresses the underlying sleep health directly. The Standard version consists of eight 50-minute weekly sessions, including four cross-cutting modules such as motivation enhancement, four core modules covering the basic building blocks of sleep health like regular sleep-wake timing, and seven optional modules tailored to individual presentations, such as reducing sleep-related worry. An Adapted version, developed specifically for the under-resourced community mental health context, condenses this into four 20-minute weekly sessions with a single optional module.

The trial assigned patients either to receive TSC immediately alongside their usual care, or to a comparison group receiving usual care first, followed by delayed treatment. Sites were cluster-randomized by county to deliver either the Standard or Adapted version. The researchers then asked a deceptively simple question: did the number of psychiatric disorders a patient reported predict how impaired they were at baseline, and did it predict how much they improved?

The first answer was a clear yes. Patients with more diagnoses arrived at treatment in worse shape across nearly every measure. Each additional disorder was associated with significantly higher scores on the DSM-5 Level 1 Cross-Cutting Symptom Measure, a 23-item questionnaire spanning 13 psychiatric symptom domains. More diagnoses also predicted significantly greater sleep-related daytime impairment, worse overall sleep health on a six-dimension composite covering regularity, timing, efficiency, duration, satisfaction, and alertness, and greater functional impairment on the Sheehan Disability Scale. The relationship with sleep disturbance itself was positive but only marginally significant after conservative outlier removal. These results echo the National Comorbidity Survey Replication, which found that nearly half of respondents with three or more psychiatric disorders qualified as serious cases, compared with fewer than ten percent of those with a single disorder.

The second answer, however, was a resounding no, and this is where the study becomes genuinely striking. The number of psychiatric disorders did not predict treatment outcome at all. Patients with many comorbidities improved just as much as those with fewer. Nor did diagnostic count determine whether patients fared better with the Standard or the Adapted version of TSC. All interaction tests examining whether comorbidity moderated treatment benefit came back non-significant. In other words, carrying multiple diagnoses, which might seem like an obvious reason to expect poorer response, made no measurable difference to how much patients gained from sleep-focused treatment.

The researchers then went a step further, analyzing the five most common broad diagnostic groups independently: unipolar mood features, bipolar mood features, anxiety disorders, psychosis, and trauma and stressor-related disorders. The results were remarkably consistent. Every one of the five groups showed significantly more improvement in the immediate TSC condition than in the usual care comparison group on both sleep disturbance and sleep-related impairment, even after applying statistical corrections for multiple testing. Patients with unipolar mood features and anxiety disorders, the two largest groups, improved significantly across all five outcomes, including psychiatric symptoms, overall sleep health, and functional impairment. The psychosis group showed significant gains on sleep measures, sleep health, and daily functioning, with psychiatric symptom improvement reaching the nominal threshold. The trauma and stressor-related group improved significantly on all outcomes except the cross-cutting symptom measure.

These findings matter because sleep is increasingly understood as a mechanistic driver of psychiatric illness rather than a mere byproduct. Sleep and circadian disruption has been implicated in the worsening of delusions and hallucinations, is a common prodrome of mania in bipolar disorder, and predicts the development of post-traumatic stress disorder after traumatic events. Poor sleep and psychiatric symptoms can feed each other in destructive loops: a patient may use substances to fall asleep and stimulants to fight daytime fatigue, deepening both the sleep problem and the risk of addiction. By targeting sleep directly, TSC appears to interrupt these loops at a shared point of vulnerability, which may explain why a single treatment produces benefits across such clinically different populations.

The study also carries a quieter but important message about measurement. Because provider-reported diagnostic data were missing for many patients, the researchers relied primarily on patients’ own reports of their diagnoses. Far from being a fatal weakness, this choice was supported by prior evidence: self-reported diagnostic accuracy has been shown to reach 70 percent or higher for most psychiatric disorders, and polygenic risk scores for schizophrenia have been found to be essentially equivalent whether diagnoses come from self-report, research interviews, or medical records. The finding that self-reported diagnostic counts meaningfully predicted baseline impairment suggests that patients’ knowledge of their own psychiatric histories is a usable and resource-efficient clinical signal, one that aligns with person-centered approaches to care.

Limitations remain. The analyses of treatment moderation may have been underpowered, since detecting three-way statistical interactions requires substantially larger samples. Post-treatment assessments had considerable missing data, session counts varied between patients, and the clinical diversity that makes transdiagnostic research possible also makes interpretation harder. Still, the bottom line is hopeful and practical: patients with more psychiatric disorders, despite starting treatment with more severe symptoms and greater impairment, can be strong candidates for sleep-focused transdiagnostic care. A high diagnostic count is not an insurmountable barrier to recovery. For community mental health centers serving low-income populations with limited resources, that is a message worth acting on, because it means the patients who are often considered the most complicated may be exactly the ones this treatment can reach.

Subject of Research: Transdiagnostic sleep and circadian treatment for serious mental illness and psychiatric comorbidity in community mental health settings

Article Title: The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) in Community Mental Health: Evaluating Self-Reported Psychiatric Disorders as a Predictor of Symptoms and Treatment Outcome

Article References: B. Horwitz, T., D. Sarfan, L., E. Milner, A., Varghese, J., A. Callaway, C., & G. Harvey, A. (2026). The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) in Community Mental Health: Evaluating Self-Reported Psychiatric Disorders as a Predictor of Symptoms and Treatment Outcome. Community Mental Health Journal. https://doi.org/10.1007/s10597-026-01719-1

Image Credits: AI Generated

DOI: 10.1007/s10597-026-01719-1

Keywords: sleep disorders, circadian rhythms, serious mental illness, psychiatric comorbidity, transdiagnostic treatment, community mental health, randomized controlled trial, insomnia, self-reported diagnoses, TSC intervention, treatment outcomes, psychosis

The study, led by Sandra Japuntich of Hennepin Healthcare Research Institute and the University of Minnesota Medical School, was designed as a pilot type 2 hybrid effectiveness-implementation trial, a research design that gives equal weight to testing whether an intervention can help patients and whether the organizational machinery needed to deliver it can actually function in a real-world clinic. The setting was a Certified Community Behavioral Health Clinic, or CCBHC, in rural Minnesota, an organization with five campuses serving roughly 10,000 clients each year across case management, crisis services, chemical dependence treatment, outpatient mental health, and family therapy. CCBHCs are a value-based model of community mental health care developed by the Substance Abuse and Mental Health Services Administration and the Centers for Medicare and Medicaid Services, and they now serve more than three million Americans with mental health and substance use disorders annually.

The intervention rested on two complementary strategies grounded in clinical practice guidelines. The first was the 5As framework, long recommended by the US Public Health Service for every healthcare setting: ask about tobacco use, advise quitting, assess interest in quitting, assist with cessation, and arrange follow-up. Mental health providers were instructed to deliver the 5As at intake and at treatment planning visits every six months. The second strategy was proactive outreach, a chronic disease management approach in which a registry of clients who smoke is generated from health records and dedicated staff telephone clients regardless of their stated readiness to quit, offering connections to cessation counseling and medication. Three clinic staff members, two peer support specialists and a case manager, were trained as tobacco treatment specialists and made three outreach contacts per participant over nine months, following a person-centered manual designed to build motivation, strengthen self-efficacy, and encourage treatment use.

Crucially, the researchers recognized that simply telling clinicians what to do rarely changes practice. Barriers such as competing clinical priorities, fear that addressing smoking could destabilize clients, and lack of training consistently undermine tobacco treatment in mental health settings. To overcome these, the team adapted the Science to Service Laboratory, a multicomponent implementation strategy previously used by the Addiction Technology Transfer Center Network. The approach bundled a two-hour virtual didactic training for all clinical staff, monthly performance feedback reports drawn from patient surveys and chart documentation, and external coaching in which a tobacco treatment expert attended monthly staff meetings at each of the three clinic sites for case consultation and brief topical discussions, while also meeting monthly with the clinic’s Chief Clinical Officer. Experimental evidence has shown that didactic training combined with ongoing feedback and coaching outperforms self-study or single-component training approaches.

On the feasibility front, the study largely succeeded. Forty-nine client participants were recruited over three months, nearly matching the recruitment target of fifty. The first two outreach calls achieved connection rates of 90 and 94 percent respectively, with the third call still reaching 67 percent of participants, figures consistent with or exceeding previous proactive outreach studies that reported response rates between 62 and 71 percent. Study assessment completion ranged from 85 to 98 percent, and 84 percent of client participants completed the primary outcome assessment at twelve months. On the implementation side, 77 of 125 employed providers attended the training sessions, coaching was delivered at 27 of 29 scheduled staff meetings, and all three clinic sites received monthly performance feedback. Client satisfaction, measured with the eight-item Client Satisfaction Questionnaire, averaged 26.32 out of 32 at twelve months, indicating high acceptability of the intervention.

The participant population underscored why this work matters. Clients were 64.6 percent female and predominantly White, and 80 percent met criteria for serious mental illness based on the Kessler-6 measure of psychological distress. They smoked an average of 15.35 cigarettes per day, showed moderate nicotine dependence on the Fagerström Test for Cigarette Dependence, and reported moderate motivation and confidence to quit. Most had previously tried cessation medications, most commonly nicotine replacement therapy, but few had ever tried cessation counseling. Qualitative interviews, analyzed using the Practical Robust Implementation and Sustainability Model, revealed that clients expected to be asked about smoking in healthcare settings and appreciated the accountability of multiple touchpoints, while providers viewed tobacco treatment as a good fit with the CCBHC mission of integrating behavioral and physical health care, a notable shift from the reluctance documented in earlier research.

Implementation data showed genuine movement in provider behavior. Provider surveys revealed statistically significant improvements in asking about tobacco use, advising cessation, and assisting with treatment between baseline and the nine-month follow-up. Once a revised tobacco clinical reminder embedded all relevant documentation fields on a single form, rates of documented asking, assessing, and assisting all reached 100 percent. On any given outreach call, between 27 and 43 percent of clients requested counseling, and nearly all who requested it, 95 to 100 percent, received it. Across the study, 38.8 percent of participants reported using cessation counseling at some point, 49.0 percent reported using cessation medication, and 28.6 percent used both, suggesting the intervention succeeded in connecting clients to evidence-based care.

The harder truth lies in the cessation outcomes. Self-reported seven-day point-prevalence abstinence was 10.4 percent at three months, 8.3 percent at six months, and 12.5 percent at twelve months. Of the six participants reporting abstinence at twelve months, five completed a carbon monoxide breath test and four showed values confirming abstinence, yielding a biochemically verified twelve-month abstinence rate of 8.3 percent. Participants did reduce their smoking by an average of 3.42 cigarettes per day from baseline to twelve months. These numbers align with a broader pattern: clinic-level implementation strategies alone have not yet produced statistically significant increases in client-level cessation in community mental health settings. A trial across 13 community mental health clinics improved provider skills and medication use but achieved only 4.2 percent self-reported abstinence at one year, and a cluster-randomized trial in drug and alcohol treatment centers changed medication use without changing abstinence. In contrast, proactive interventions delivered regardless of readiness to quit have produced significant population-level gains, including a 50 percent relative increase in abstinence from a single outreach call to veterans using mental health clinics and a 2.4-fold increase in quit rates from a community health worker intervention for people with serious mental illness.

The intervention also proved safe. Two serious adverse events, one hospitalization and one death, were reported, neither judged related to the study, and global distress scores showed no significant change from baseline across any follow-up timepoint. The study’s limitations are clear: it was small, non-randomized, and conducted at a single rural clinic with a population that was nearly 90 percent White, so it cannot isolate the contribution of each treatment component or generalize to all populations. High staff turnover, with 28 percent of surveyed providers leaving within a year, threatens sustainability, and provider survey completion declined from 72 to 54 percent over time. Providers themselves offered practical fixes, including refresher trainings, embedded electronic health record reminders, champions, and turnover-resistant plans, and noted that the 5As may be more sustainable than standalone counseling because it can be billed within regular visits. The research team’s next step is a fully powered, multi-site randomized clinical trial. If proactive outreach and clinic-wide implementation supports can demonstrate effectiveness at scale, CCBHCs, which already track tobacco screening and treatment as organizational performance metrics, may become the frontline battleground where the deadliest health disparity facing people with serious mental illness finally begins to shrink.

Subject of Research: A pilot type 2 hybrid effectiveness-implementation study of proactive smoking cessation interventions delivered in a Certified Community Behavioral Health Clinic

Article Title: Implementing Proactive Smoking Cessation Induction Interventions in a Certified Community Behavioral Health Clinic: A Pilot Type 2 Hybrid Effectiveness-Implementation Study

Article References: Japuntich, S., Adkins-Hempel, M., Sacasa, N. G., Lundtvedt, C., Becker, S., Helseth, S., Pratt, R., Tidey, J., Dunsiger, S., Evins, A. E., & Fu, S. (2026). Implementing Proactive Smoking Cessation Induction Interventions in a Certified Community Behavioral Health Clinic: A Pilot Type 2 Hybrid Effectiveness-Implementation Study. Community Mental Health Journal. https://doi.org/10.1007/s10597-026-01727-1

Image Credits: AI Generated

DOI: 10.1007/s10597-026-01727-1

Keywords: smoking cessation, tobacco use disorder, mental health, Certified Community Behavioral Health Clinic, proactive outreach, implementation science, serious mental illness, 5As, nicotine dependence, behavioral health, hybrid effectiveness-implementation trial, community mental health