The study used a quasi-experimental design, with the intervention implemented at one community mental health clinic and prospectively compared to usual care at the remaining community mental health clinics within a large urban public behavioural health system. Criteria for admission to the behavioural health system clinics include a SMI diagnosis; co-occurring substance use disorder, co-morbid medical issue, or cognitive impairment; frequent psychiatric acute care service utilization and/or legal system involvement; and circumstances substantiating need for intensive case management (e.g., homelessness, assaultive behaviour, adherence difficulties). The intervention clinic was chosen based on strong pre-existing relationships with the research group, an investment in improving primary care from clinic leadership, and a high proportion of patients with complex psychosocial and medical needs. The intervention clinic provides outpatient care, case management, and support services to approximately 600 publicly insured adults with SMI annually.
The usual care group continued to receive regular care in their mental health clinics and could also receive care from primary care providers in other clinics, without explicit integration of services. While providers from different specialties could access common electronic health records, patients in usual care seeking non-psychiatric medical care had to go to separate clinics and laboratories for screening without additional support.
Participants were adults (18 and older) diagnosed with SMI and continuously enrolled in the public behavioural health system from January 2014 to December 2015. Exclusion criteria included being in jail and/or in a locked short-term care facility during the study period. This study received IRB approval from the University of California, San Francisco. The IRB approved a waiver of consent for clinic patients as the recruitment procedures involved routine review of medical records, did not adversely affect the rights and welfare of participants, and posed minimal risk to subjects and their privacy.
Development of CRANIUM has been previously described.25 Briefly, study researchers used the Behavioural Change Wheel model, an implementation science framework that identifies targets for behavioural intervention and has proven to be effective in patients with SMI.27 Feedback from focus groups of psychiatrists and patients informed the design and implementation of the intervention,25, 27 which consisted of the following four components:
Patient-centred team-based care: CRANIUM utilized pre-existing resources in specialty mental health clinics (the psychiatrist and case manager) and added a primary care consultant at 0.1 FTE. Rather than co-locating primary care providers at a Federally Qualified Health Centre as has been attempted previously,17 the primary care provider was integrated as an electronic consultant (eConsultant), available to answer questions (e.g., medication initiation, connecting to primary care services). A peer navigator was also integrated into the team and prepared lab slips, accompanied selected patients to lab facilities, and entered results into the EHR.
Panel management with patient registries: The CRANIUM registry included test results from three separate EHRs operating across the health system: mental health EHR (AVATAR), primary care EHR (Invision), and laboratory results (LabCorp Beacon). Each month, research staff extracted data from these EHRs on patients who had treatment plans due and compiled information into a single separate electronic database. The information was distributed to psychiatrists and case managers via a personalized spreadsheet. Laboratory slips were pre-completed for all identified patients and provided to the psychiatrist. The staff and research team met quarterly to conduct panel management and problem solve for those requiring screening or treatment.
Trainings and protocols for psychiatrists on metabolic screening and HIV testing: Psychiatrists were trained to order annual screening for hypertension, A1c, total cholesterol, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Given that people with SMI are also at greater risk for HIV, but have low testing rates,12, 28 we included annual HIV testing. Laboratory slips were pre-completed for all identified patients and provided to the psychiatrist. To promote consistent screening, psychiatrists received a monthly registry-based personalized list of patients missing labs or vital signs. For those patients who were missing labs, based upon individual need and preference, a peer navigator was available to accompany patients with missing labs to appropriate lab facilities, and an on-site nurse was available to draw labs.
Training and protocols for treatment of diabetes, hypertension, and dyslipidaemia: To mitigate concerns previously reported by psychiatrists in prescribing non-psychotropic medications,29 the primary care consultant provided a one-time training on guideline-recommended pharmacological management of common metabolic abnormalities to all psychiatrists. This training is now available via SMI Adviser.30 In addition, easy-to-use, evidence-based medication algorithms were available in all treatment rooms and online. Patients with cardiovascular risk factors were highlighted to facilitate treatment discussions during panel management meetings.
The CRANIUM intervention was delivered in 2015 over twelve months at modest cost: $74 annually per patient. 31
This report focuses on screening outcomes only. The main outcomes of interest were individual-level screening for diabetes (A1c), fasting lipids, and HIV testing at least once in the year before and after the intervention. We did not include annual blood pressure measurement as an outcome due to the lack of reliable data (across arms, 72% missing or unmeasured outcomes in 2015). Inconsistent information about previous participant diagnoses of diabetes, hypercholesterolemia, and HIV precluded our ability to exclude people with these pre-existing conditions from the analysis. Demographics (age, sex, race/ethnicity) were also collected.
Chi-square tests and t-tests were used to compare baseline demographics between the study arms. Mixed effects logistic models with nested random effects for clinic and participant and including interaction term for intervention condition and time were used to estimate between-arm differences in the change from pre- to post-intervention in A1c and lipid screening and HIV testing rates, first without adjustment, then adjusting for age, sex, race, and ethnicity. A two-sided P value < .05 was considered statistically significant. The planned sample of 5,000 was estimated to provide 80% power in 2-sided tests to detect 5–6 percentage point differences between arms in the change in screening and testing rates. Estimates were obtained by approximating the covariance matrix of the coefficients estimated by the random effects logistic model used for the analysis and implemented in R (R Foundation for Statistical Computing, Vienna, Austria, 2020). Analyses were performed using Stata MP Version 15.1 (College Station, Texas).