Principles for Stakeholder Engagement in Observational Health Research.

This Viewpoint advocates for the inclusion of patients and other stakeholders in interpreting data for observational research studies.

Patient Perspectives on Mental Health and Pain Management Support Needed Versus Received During Opioid Deprescribing.

Prescription opioid tapering has increased significantly over the last decade. Evidence suggests that tapering too quickly or without appropriate support may unintentionally harm patients. The aim of this analysis was to understand patients' experiences with opioid tapering, including support received or not received for pain control or mental health. Patients with evidence of opioid tapering from 6 health care systems participated in semi-structured, in-depth interviews; family members of suicide decedents with evidence of opioid tapering were also interviewed. Interviews were analyzed using thematic analysis. Participants included 176 patients and 16 family members. Results showed that 24% of the participants felt their clinicians checked in with them about their taper experiences while 41% reported their clinicians did not. A majority (68%) of individuals who experienced suicide behavior during tapering reported that clinicians did check in about mood and mental health changes specifically; however, 27% of that group reported no such check-in. More individuals reported negative experiences (than positive) with pain management clinics-where patients are often referred for tapering and pain management support. Patients reporting successful tapering experiences named shared decision-making and ability to adjust taper speed or pause tapering as helpful components of care. Fifty-six percent of patients reported needing more support during tapering, including more empathy and compassion (48%) and an individualized approach to tapering (41%). Patient-centered approaches to tapering include reaching out to monitor how patients are doing, involving patients in decision-making, supporting mental health changes, and allowing for flexibility in the tapering pace. PERSPECTIVE: Patients tapering prescription opioids desire more provider-initiated communication including checking in about pain, setting expectations for withdrawal and mental health-related changes, and providing support for mental health. Patients preferred opportunities to share decisions about taper speed and to have flexibility with pausing the taper as needed.

Psychotherapy Disruption Before and After the Transition to Virtual Mental Health Care Induced by the COVID-19 Pandemic.

OBJECTIVE: This study aimed to examine population-level disruption in psychotherapy before and after the rapid shift to virtual mental health care induced by the onset of the COVID-19 pandemic in the United States. METHODS: This retrospective study used electronic health record and insurance claims data from three U.S. health systems. The sample included 110,089 patients with mental health conditions who were members of the health systems' affiliated health plans and attended at least two psychotherapy visits from June 14, 2019, through December 15, 2020. Data were subdivided into two 9-month periods (before vs. after COVID-19 onset, defined in this study as March 14, 2020). Psychotherapy visits were measured via health records and categorized as in person or virtual. Disruption was defined as a gap of >45 days between visits. RESULTS: Visits in the preonset period were almost exclusively in person (97%), whereas over half of visits in the postonset period were virtual (52%). Approximately 35% of psychotherapy visits were followed by a disruption in the preonset period, compared with 18% in the postonset period. Disruption continued to be less common (adjusted OR=0.45) during the postonset period after adjustment for visit, mental health, and sociodemographic factors. The magnitude of the difference in disruption between periods was homogeneous across sociodemographic characteristics but heterogeneous across psychiatric diagnoses. CONCLUSIONS: This study found fewer population-level disruptions in psychotherapy receipt after rapid transition to virtual mental health care following COVID-19 onset. These data support the continued availability of virtual psychotherapy.

The rapid shift to virtual mental health care: Examining psychotherapy disruption by rurality status.

BACKGROUND: Given the low usage of virtual health care prior to the COVID-19 pandemic, it was unclear whether those living in rural locations would benefit from increased availability of virtual mental health care. The rapid transition to virtual services during the COVID-19 pandemic allowed for a unique opportunity to examine how the transition to virtual mental health care impacted psychotherapy disruption (i.e., 45+ days between appointments) among individuals living in rural locations compared with those living in nonrural locations. METHODS: Electronic health record and insurance claims data were collected from three health care systems in the United States including rurality status and psychotherapy disruption. Psychotherapy disruption was measured before and after the COVID-19 pandemic onset. RESULTS: Both the nonrural and rural cohorts had significant decreases in the rates of psychotherapy disruption from pre- to post-COVID-19 onset (32.5-16.0% and 44.7-24.8%, respectively, p < 0.001). The nonrural cohort had a greater reduction of in-person visits compared with the rural cohort (96.6-45.0 vs. 98.0-66.2%, respectively, p < 0.001). Among the rural cohort, those who were younger and those with lower education had greater reductions in psychotherapy disruption rates from pre- to post-COVID-19 onset. Several mental health disorders were associated with experiencing psychotherapy disruption. CONCLUSIONS: Though the rapid transition to virtual mental health care decreased the rate of psychotherapy disruption for those living in rural locations, the reduction was less compared with nonrural locations. Other strategies are needed to improve psychotherapy disruption, especially among rural locations (i.e., telephone visits).

Impact of Opioid Dose Reductions on Patient-Reported Mental Health and Suicide-Related Behavior and Relationship to Patient Choice in Tapering Decisions.

Mental health and suicide-related harms resulting from prescription opioid tapering are poorly documented and understood. Six health systems contributed opioid prescribing data from January 2016 to April 2020. Patients 18 to 70 years old with evidence of opioid tapering participated in semi-structured interviews. Individuals who experienced suicide attempts were oversampled. Family members of suicide decedents who had experienced opioid tapering were also interviewed. Interviews were analyzed using thematic analysis. The study participants included 176 patients and 16 family members. Patients were 68% female, 80% White, and 15% Hispanic, mean age 58. All family members were female spouses of White, non-Hispanic male decedents. Among the subgroup (n = 60) who experienced a documented suicide attempt, reported experiencing suicidal ideation during tapering, or were family members of suicide decedents, 40% reported that opioid tapering exacerbated previously recognized mental health issues, and 25% reported that tapering triggered new-onset mental health concerns. Among participants with suicide behavior, 47% directly attributed it to opioid tapering. Common precipitants included increased pain, reduced life engagement, sleep problems, withdrawal, relationship dissolution, and negative consequences of opioid substitution with other substances for pain relief. Most respondents reporting suicide behavior felt that the decision to taper was made by the health care system or a clinician (67%) whereas patients not reporting suicide behavior were more likely to report it was their own decision (42%). This study describes patient-reported mental health deterioration or suicide behavior while tapering prescription opioids. Clinicians should screen for, monitor, and treat suicide behavior while assisting patients in tapering opioids. PERSPECTIVE: This work describes changes in patient-reported mental health and suicide behavior while tapering prescription opioids. Recommendations for improving care include mental health and suicide risk screening during and following opioid tapering.

Differential Effects of an Intervention to Reduce Cardiovascular Risk for Patients With Bipolar Disorder, Schizoaffective Disorder, or Schizophrenia: A Randomized Clinical Trial.

Objective: To measure the impact of a clinical decision support (CDS) tool on total modifiable cardiovascular risk at 12 months separately for outpatients with 3 subtypes of serious mental illness (SMI) identified via ICD-9 and ICD-10 codes: bipolar disorder, schizoaffective disorder, and schizophrenia.Methods: This cluster-randomized pragmatic clinical trial was active from March 2016 to September 2018; data were analyzed from April 2021 to September 2022. Clinicians and patients from 78 primary care clinics participated. All 8,922 adult patients aged 18-75 years with diagnosed SMI, at least 1 cardiovascular risk factor not at goal, and an index and follow-up visit during the study period were included. The CDS tool provided a summary of modifiable cardiovascular risk and personalized treatment recommendations.Results: Intervention patients had 4% relative reduction in total modifiable cardiovascular risk at 12 months compared to controls (relative risk ratio = 0.96; 95% CI, 0.94 to 0.98), with similar intervention benefits for all 3 SMI subtypes. At index, 10-year cardiovascular risk was higher for patients with schizophrenia (mean [SD] = 11.3% [9.2%]) than for patients with bipolar disorder (8.5% [8.9%]) or schizoaffective disorder (9.4% [8.1%]), while 30-year cardiovascular risk was highest for patients with schizoaffective disorder (44% with 2 or more major cardiovascular risk factors, compared to 40% for patients with schizophrenia and 37% for patients with bipolar disorder). Smoking was highly prevalent (47%), and mean (SD) BMI was 32.7 (7.9).Conclusions: This CDS intervention produced a clinically and statistically significant 4% relative reduction in total modifiable cardiovascular risk for intervention patients versus controls at 12 months, an effect observed across all 3 SMI subtypes and attributable to the aggregate impact of small changes in multiple cardiovascular risk factors.Trial Registration: ClinicalTrials.gov Identifier: NCT02451670.

Development and Initial Validation of a Measure to Assess Self-Efficacy to Reduce Sedentary Behavior.

INTRODUCTION: The purpose of this study was to assess the reliability and construct validity of a measure designed to evaluate self-efficacy to reduce sedentary behavior (SB). METHODS: Initial instrument development was based on semistructured interviews and in-depth review of existing measures of self-efficacy for physical activity (PA). Items were drafted by the study authors and reviewed by SB experts. Participants recruited via Amazon Mechanical Turk completed the pool of items and the Exercise Confidence Survey and provided self-reported PA, SB, and demographic information. The sample was divided into two random halves, and exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed on each half, respectively. Internal consistency reliability (Cronbach's alpha) was calculated for the final scale. Initial criterion validity was examined in relation to self-reported SB and PA. Analyses were conducted in SAS 9.4 and Mplus 8.3. RESULTS: Data were collected from N = 818 adults (47.6% women, mean (standard deviation) age, 37.8 (10.6)). EFA results strongly supported a one-factor scale. Items with low factor loadings (<0.65) were dropped from the scale; 10 items were retained. CFA demonstrated that the 10-item measure fit the data well, but one item had a low factor loading. A final scale with nine items was retained, with good fit to the data ( χ2 (27) = 90.79, P < 0.0001, comparative fit statistic = 0.97, root mean square error of approximation = 0.08 [90% confidence interval, 0.06-0.09], standardized root mean square residual = 0.03), and all items had high factor loadings (>0.70). Internal consistency reliability was high ( α = 0.91). Self-efficacy to reduce SB was significantly and positively correlated with exercise confidence ( r = 0.32-0.38, P < 0.0001). CONCLUSIONS: We developed a nine-item measure of self-efficacy to reduce SB that demonstrates strong initial psychometric properties. Although related to exercise self-efficacy, self-efficacy to reduce SB represents a distinct construct.

A randomized controlled trial of an intervention to reduce stigma toward people with opioid use disorder among primary care clinicians.

BACKGROUND: Many primary care clinicians (PCCs) hold stigma toward people with opioid use disorder (OUD), which may be a barrier to care. Few interventions exist to address PCC stigma toward people with OUD. This study examined whether an online training incorporating patient narratives reduced PCCs' stigma toward people with OUD (primary) and increased intentions to treat people with OUD compared to an attention-control training (secondary). METHODS: PCCs from 15 primary care clinics were invited to complete a 30 min online training for an electronic health record-embedded clinical decision support (CDS) tool that alerts PCCs to screen, diagnose, and treat people with OUD. PCCs were randomized to receive a stigma-reduction version of the training with patient narrative videos or a control training without patient narratives and were blinded to group assignment. Immediately after the training, PCCs completed surveys of stigma towards people with OUD and intentions and willingness to treat OUD. CDS tool use was monitored for 6 months. Analyses included independent samples t-tests, Pearson correlations, and logistic regression. RESULTS: A total of 162 PCCs were randomized; 88 PCCs (58% female; 68% white) completed the training (Stigma = 48; Control = 40) and were included in analyses. There was no significant difference between intervention and control groups for stigma (t = - 0.48, p = .64, Cohen's d = - 0.11), intention to get waivered (t = 1.11, p = .27, d = 0.26), or intention to prescribe buprenorphine if a waiver were no longer required (t = 0.90, p = 0.37, d = 0.21). PCCs who reported greater stigma reported lower intentions both to get waivered (r = - 0.25, p = 0.03) and to prescribe buprenorphine with no waiver (r = - 0.25, p = 0.03). Intervention group and self-reported stigma were not significantly related to CDS tool use. CONCLUSIONS: Stigma toward people with OUD may require more robust intervention than this brief training was able to accomplish. However, stigma was related to lower intentions to treat people with OUD, suggesting stigma acts as a barrier to care. Future work should identify effective interventions to reduce stigma among PCCs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04867382. Registered 30 April 2021-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04867382.

Design of a pragmatic clinical trial to improve screening and treatment for opioid use disorder in primary care.

BACKGROUND: Opioid-related deaths continue to rise in the U.S. A shared decision-making (SDM) system to help primary care clinicians (PCCs) identify and treat patients with opioid use disorder (OUD) could help address this crisis. METHODS: In this cluster-randomized trial, primary care clinics in three healthcare systems were randomized to receive or not receive access to an OUD-SDM system. The OUD-SDM system alerts PCCs and patients to elevated risk of OUD and supports OUD screening and treatment. It includes guidance on OUD screening and diagnosis, treatment selection, starting and maintaining patients on buprenorphine for waivered clinicians, and screening for common comorbid conditions. The primary study outcome is, of patients at high risk for OUD, the percentage receiving an OUD diagnosis within 30 days of index visit. Additional outcomes are, of patients at high risk for or with a diagnosis of OUD, (a) the percentage receiving a naloxone prescription, or (b) the percentage receiving a medication for OUD (MOUD) prescription or referral to specialty care within 30 days of an index visit, and (c) total days covered by a MOUD prescription within 90 days of an index visit. RESULTS: The intervention started in April 2021 and continues through December 2023. PCCs and patients in 90 clinics are included; study results are expected in 2024. CONCLUSION: This protocol paper describes the design of a multi-site trial to help PCCs recognize and treat OUD. If effective, this OUD-SDM intervention could improve screening of at-risk patients and rates of OUD treatment for people with OUD.

30-year Cardiovascular Disease Risk for Young Adults with Serious Mental Illness.

Objective: To estimate 30-year CVD risk and modifiable risk factors in young adults with serious mental illness (SMI) versus those without, and assess variations in CVD risk by race, ethnicity, and sex. Method: In this cross-sectional study, we estimated and compared the Framingham 30-year CVD risk score and individual modifiable CVD risk factors in young adult (20-39 years) primary care patients with and without SMI at two US healthcare systems (January 2016-Septemeber 2018). Interaction terms assessed whether the SMI-risk association differed across demographic groups. Results: Covariate-adjusted 30-year CVD risk was significantly higher for those with (n=4228) versus those without (n=155,363) SMI (RR 1.28, 95% CI [1.26, 1.30]). Patients with SMI had higher rates of hypertension (OR 2.02 [1.7, 2.39]), diabetes (OR 3.14 [2.59, 3.82]), obesity (OR 1.93 [1.8, 2.07]), and smoking (OR 4.94 [4.6, 5.36]). The increased 30-year CVD risk associated with SMI varied significantly by race and sex: there was an 8% higher risk in Black compared to White patients (RR 1.08, [1.04, 1.12]) and a 9% lower risk in men compared to women (RR 0.91 [0.88, 0.94]). Conclusions: Young adults with SMI are at increased 30-year risk of CVD, and further disparities exist for Black individuals and women.

Clinical implementation of suicide risk prediction models in healthcare: a qualitative study.

BACKGROUND: Suicide risk prediction models derived from electronic health records (EHR) are a novel innovation in suicide prevention but there is little evidence to guide their implementation. METHODS: In this qualitative study, 30 clinicians and 10 health care administrators were interviewed from one health system anticipating implementation of an automated EHR-derived suicide risk prediction model and two health systems piloting different implementation approaches. Site-tailored interview guides focused on respondents' expectations for and experiences with suicide risk prediction models in clinical practice, and suggestions for improving implementation. Interview prompts and content analysis were guided by Consolidated Framework for Implementation Research (CFIR) constructs. RESULTS: Administrators and clinicians found use of the suicide risk prediction model and the two implementation approaches acceptable. Clinicians desired opportunities for early buy-in, implementation decision-making, and feedback. They wanted to better understand how this manner of risk identification enhanced existing suicide prevention efforts. They also wanted additional training to understand how the model determined risk, particularly after patients they expected to see identified by the model were not flagged at-risk and patients they did not expect to see identified were. Clinicians were concerned about having enough suicide prevention resources for potentially increased demand and about their personal liability; they wanted clear procedures for situations when they could not reach patients or when patients remained at-risk over a sustained period. Suggestions for making risk model workflows more efficient and less burdensome included consolidating suicide risk information in a dedicated module in the EHR and populating risk assessment scores and text in clinical notes. CONCLUSION: Health systems considering suicide risk model implementation should engage clinicians early in the process to ensure they understand how risk models estimate risk and add value to existing workflows, clarify clinician role expectations, and summarize risk information in a convenient place in the EHR to support high-quality patient care.

Patient Perspectives on Opioid Risk Discussions in Primary Care.

Purpose: Both patients and clinicians have described discussions of potential opioid risks as challenging. This study's goal was to understand patient perspectives on discussing opioid risks with primary care clinicians (PCCs). Methods: Patients identified to be at elevated risk for problems with opioids (ie, opioid use disorder [OUD] diagnosis, taking a medication for OUD, or having ≥3 opioid prescriptions in the last year) were recruited from an integrated, Upper Midwest health system to participate in semi-structured qualitative interviews. Interview questions aimed to better understand patient views on conversations about opioid risks with PCCs and perceptions of OUD screening and treatment in primary care. Interviews were analyzed using an inductive thematic analysis approach. Results: A total of 20 patients participated (mean age: 53.5 years; 65% male). Six themes emerged: 1) archetypes of patient relationships with opioids (long-term opioid use, acute opioid use, OUD in treatment, OUD no treatment) require different approaches in discussing opioid risks; 2) patients may develop their own archetypes about PCCs and opioids; 3) these archetypes may help guide how conversations about opioids are conducted (eg, PCC demeanor, terminology); 4) most patients believe that primary care is an appropriate setting for opioid risk discussions; 5) patients may have limited awareness of the availability and value of overdose rescue medications; and 6) handouts are more acceptable if perceived to come from the PCC's assessment instead of a computer. Conclusions: Results suggest that patients generally perceive discussing opioid risks with PCCs acceptable. PCCs should tailor opioid risk conversations to patients' specific situations and needs.

Impact of the COVID-19 Pandemic on Type 2 Diabetes Care Delivery.

The coronavirus disease 2019 (COVID-19) pandemic instigated major changes in care delivery, but our understanding of how the rapid transition from in-person to telehealth encounters affected the care of patients with chronic conditions such as type 2 diabetes remains incomplete. This study examined changes in primary care encounters, A1C testing rates, and the likelihood of meeting A1C guidelines before and during the first 9 months of the COVID-19 pandemic in a large health care system. It found significant decreases in utilization and testing rates and the likelihood of meeting A1C guidelines, primarily driven by missing A1C tests. Patients who had all telehealth encounters or no encounters, who identified as racial or ethnic minorities, or had Medicaid or no insurance were significantly more likely to miss A1C tests.

Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice.

BACKGROUND: Suicide risk prediction models derived from electronic health records (EHR) and insurance claims are a novel innovation in suicide prevention but patient perspectives on their use have been understudied. METHODS: In this qualitative study, between March and November 2020, 62 patients were interviewed from three health systems: one anticipating implementation of an EHR-derived suicide risk prediction model and two others piloting different implementation approaches. Site-tailored interview guides focused on patients' perceptions of this technology, concerns, and preferences for and experiences with suicide risk prediction model implementation in clinical practice. A constant comparative analytic approach was used to derive themes. RESULTS: Interview participants were generally supportive of suicide risk prediction models derived from EHR data. Concerns included apprehension about inducing anxiety and suicidal thoughts, or triggering coercive treatment, particularly among those who reported prior negative experiences seeking mental health care. Participants who were engaged in mental health care or case management expected to be asked about their suicide risk and largely appreciated suicide risk conversations, particularly by clinicians comfortable discussing suicidality. CONCLUSION: Most patients approved of suicide risk models that use EHR data to identify patients at-risk for suicide. As health systems proceed to implement such models, patient-centered care would involve dialogue initiated by clinicians experienced with assessing suicide risk during virtual or in person care encounters. Health systems should proactively monitor for negative consequences that result from risk model implementation to protect patient trust.

What is success in treatment for opioid use disorder? Perspectives of physicians and patients in primary care settings.

INTRODUCTION: Clinicians and researchers have traditionally relied on treatment retention and opioid abstinence as the primary measures of treatment success for people with opioid use disorder (OUD). However, these measures may not capture the range of clinically important treatment outcomes. The study sought to identify indicators of success in primary care-based medication for OUD (MOUD) treatment from the perspectives of patients with OUD and the physicians who treat them. METHODS: The study recruited patients (N = 18; M age = 38.1 years, SD = 11.5; 44% female) and physicians (N = 14; M age = 34.6, SD = 6.8 years; 57% female) from two academic family medicine residency clinics in the upper Midwest to participate in semi-structured qualitative interviews. Participants reflected on signs of progress and success in primary care MOUD treatment. Interviews were recorded, transcribed, and analyzed using an inductive thematic analysis approach. RESULTS: Seven themes of success emerged: (1) staying sober; (2) tapering off buprenorphine; (3) taking steps to improve physical and mental health; (4) improved psychological well-being; (5) improved relationships; (6) improved role functioning; and (7) decreased stigma and shame. Interviews with both patients and physicians supported five of the seven themes, with patients also describing themes of tapering off buprenorphine and reduced stigma and shame. CONCLUSIONS: Themes suggest that a wider view of success, in addition to maintaining sobriety, is needed when considering outcomes for MOUD programs delivered in primary care settings. Future work should identify appropriate outcome measures and potential adjunctive treatments.

A New Pandemic of Loneliness.

Loneliness is the subjective feeling people experience when they feel less socially connected to others than they desire. Beyond the impact to mental health and well-being, loneliness is linked to detrimental health outcomes. During the COVID-19 pandemic, social distancing and isolation requirements likely exacerbated the prevalence of loneliness, which was reported by 1 in 5 American adults before the pandemic. Whether it be through in-person or virtual visits, primary care clinicians have tools and expertise to screen patients for loneliness, provide them supportive consultations, and refer persons with loneliness to helpful resources. As the societal changes from the pandemic continue to evolve, we recommend that primary care providers include loneliness screens as part of their standard workflow and consult with patients about effective interventions to reduce loneliness.

Depression and cardiovascular risk in primary care patients.

OBJECTIVE: This study assessed the relationship of both depression diagnosis and clinically significant depressive symptoms with individual cardiovascular risk factors and estimated total cardiovascular risk in primary care patients. METHODS: This study used a cross-sectional and retrospective design. Patients who had a primary care encounter between January 2016 and September 2018 and completed depression screening (PHQ-9) during the year prior to their appointment (N = 70,980) were included in this study. Data examining estimated total cardiovascular risk, specific cardiovascular risk factors, and relevant clinical diagnoses (including depression diagnosis) were extracted from the electronic health record. Patients were categorized into three groups: no depression (PHQ-9 < 10 and no depression diagnosis), controlled depression (PHQ-9 < 10 with previous depression diagnosis), and current depression (PHQ-9 ≥ 10). Groups were compared on estimated total risk and specific cardiovascular risk factors (e.g., body mass index [BMI], smoking status, lipids, blood pressure, and glucose). RESULTS: In adjusted analyses, patients with current depression (n = 18,267) demonstrated significantly higher 10-year and 30-year cardiovascular risk compared to patients with controlled depression (n = 33,383; 10-year: b = 0.59 [95% CI = 0.44,0.74]; 30-year: OR = 1.32 [95% CI = 1.26,1.39]) and patients without depression (n = 19,330; 10-year: b = 0.55 [95% CI = 0.37,0.73]; 30-year: OR = 1.56 [95% CI = 1.48,1.65]). Except for low-density lipoprotein (LDL), patients with current depression had the greatest cardiovascular risk across specific risk factors. CONCLUSIONS: Individuals who had a depression diagnosis and clinically significant depressive symptoms had the greatest cardiovascular risk. Pathways to prevent cardiovascular disease in those with depression might focus on treating depressive symptoms as well as specific uncontrolled cardiovascular risk factors.

Cardiovascular Risk for Patients With and Without Schizophrenia, Schizoaffective Disorder, or Bipolar Disorder.

Background To compare estimated 10-year and 30-year cardiovascular risk in primary care patients with and without serious mental illness (SMI; bipolar disorder, schizophrenia, or schizoaffective disorder). Methods and Results All patients aged 18 to 75 years with a primary care visit in January 2016 to September 2018 were included and were grouped into those with and without SMI using diagnosis codes. Ten-year cardiovascular risk was estimated using atherosclerotic cardiovascular disease scores for patients aged 40 to 75 years without cardiovascular disease; 30-year cardiovascular risk was estimated using Framingham risk scores for patients aged 18 to 59 years without cardiovascular disease. Demographic, vital sign, medication, diagnosis, and health insurance data were collected from the electronic health record by a clinical decision support system. Descriptive statistics examined unadjusted differences, while general linear models examined differences for continuous variables and logistic regression models for categorical variables. Models were then adjusted for age, sex, race, ethnicity, and insurance type. A total of 11 333 patients with SMI and 579 924 patients without SMI were included. After covariate adjustment, 10-year cardiovascular risk was significantly higher in patients with SMI (mean, 9.44%; 95% CI, 9.29%-9.60%) compared with patients without SMI (mean, 7.99%; 95% CI, 7.97-8.02). Similarly, 30-year cardiovascular risk was significantly higher in those with SMI (25% of patients with SMI in the highest-risk group compared with 11% of patients without SMI; P<0.001). The individual cardiovascular risk factors contributing most to increased risk for those with SMI were elevated body mass index and smoking. Among SMI subtypes, patients with bipolar disorder had the highest 10-year cardiovascular risk, while patients with schizoaffective disorder had the highest 30-year cardiovascular risk. Conclusions The significantly increased cardiovascular risk associated with SMI is evident even in young adults. This suggests the importance of addressing uncontrolled major cardiovascular risk factors in those with SMI at as early an age as possible. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02451670.

Effect of Clinical Decision Support on Cardiovascular Risk Among Adults With Bipolar Disorder, Schizoaffective Disorder, or Schizophrenia: A Cluster Randomized Clinical Trial.

IMPORTANCE: Adults with schizophrenia, schizoaffective disorder, or bipolar disorder, collectively termed serious mental illness (SMI), have shortened life spans compared with people without SMI. The leading cause of death is cardiovascular (CV) disease. OBJECTIVE: To assess whether a clinical decision support (CDS) system aimed at primary care clinicians improves CV health for adult primary care patients with SMI. DESIGN, SETTING, AND PARTICIPANTS: In this cluster randomized clinical trial conducted from March 2, 2016, to September 19, 2018, restricted randomization assigned 76 primary care clinics in 3 Midwestern health care systems to receive or not receive a CDS system aimed at improving CV health among patients with SMI. Eligible clinics had at least 20 patients with SMI; clinicians and their adult patients with SMI with at least 1 modifiable CV risk factor not at the goal set by the American College of Cardiology/American Heart Association guidelines were included. Statistical analysis was conducted on an intention-to-treat basis from January 10, 2019, to December 29, 2021. INTERVENTION: The CDS system assessed modifiable CV risk factors and provided personalized treatment recommendations to clinicians and patients. MAIN OUTCOMES AND MEASURES: Patient-level change in total modifiable CV risk over 12 months, summed from individual modifiable risk factors (smoking, body mass index, low-density lipoprotein cholesterol level, systolic blood pressure, and hemoglobin A1c level). RESULTS: A total of 80 clinics were randomized; 4 clinics were excluded for having fewer than 20 eligible patients, leaving 42 intervention clinics and 34 control clinics. A total of 8937 patients with SMI (4922 women [55.1%]; mean [SD] age, 48.4 [13.5] years) were enrolled. There was a 4% lower rate of increase in total modifiable CV risk among intervention patients relative to control patients (relative rate ratio [RR], 0.96; 95% CI, 0.94-0.98). The intervention favored patients who were 18 to 29 years of age (RR, 0.89; 95% CI, 0.81-0.98) or 50 to 59 years of age (RR, 0.93; 95% CI, 0.90-0.96), Black (RR, 0.93; 95% CI, 0.88-0.98), or White (RR, 0.96; 95% CI, 0.94-0.98). Men (RR, 0.96; 95% CI, 0.94-0.99) and women (RR, 0.95; 95% CI, 0.92-0.97), as well as patients with any SMI subtype (bipolar disorder: RR, 0.96; 95% CI, 0.94-0.99; schizoaffective disorder: RR, 0.94; 95% CI, 0.90-0.98; schizophrenia: RR, 0.92; 95% CI, 0.85-0.99) also benefited from the intervention. Despite treatment effects favoring the intervention, there were no significant differences in individual modifiable risk factors. CONCLUSIONS AND RELEVANCE: This CDS intervention resulted in a rate of change in total modifiable CV risk that was 4% lower among intervention patients compared with control patients. Results were driven by the cumulative effects of incremental and mostly nonsignificant changes in individual modifiable risk factors. These findings emphasize the value of using CDS to prompt early primary care intervention for adults with SMI. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02451670.

Change that Matters: A Health Behavior Change and Behavioral Health Curriculum for Primary Care.

Although primary care is an ideal setting in which to address behavioral influences on health, clinicians spend little time discussing preventive care, including lifestyle counseling. There is a dearth of comprehensive training and evidence-based resources to educate clinicians in how to effectively engage with patients about these topics. This study describes and evaluates the acceptability of Change that Matters: Promoting Healthy Behaviors, a ten-module curriculum to train clinicians in brief, evidence-based interventions. Each module includes three parts: interactive patient handouts, didactic training, and electronic health record templates to guide the discussion and after visit summary. A two-part, mixed-methods pilot study was used to evaluate the acceptability of the curriculum in a family medicine residency clinic. In Study 1, external family medicine faculty experts (N = 11) provided written feedback on the patient handouts. In Study 2, 20 residents and 20 patients completed qualitative interviews regarding their experience with curricular materials. Content analysis was used to extract qualitative themes. Experts rated the patient handouts as highly understandable and actionable. Resident themes indicated that the curriculum provided concrete tools to address health behavior change, helped structure patient discussions, and increased confidence. Patients felt empowered to make behavior changes. This new curriculum addresses a gap in existing resources, and is available for free download online which can facilitate dissemination ( https://changethatmatters.umn.edu/ ). Research has found the curriculum to be acceptable to experts, residents, and patients. Future studies need to explore its impact on the behavior of both clinicians and patients.