In many mental health and some somatic disorders, efficacy of therapist-guided remote CBT and in-person CBT does not differ.

SOURCE CITATION: Zandieh S, Abdollahzadeh SM, Sadeghirad B, et al. Therapist-guided remote versus in-person cognitive behavioural therapy: a systematic review and meta-analysis of randomized controlled trials. CMAJ. 2024;196:E327-E340. 38499303.

Characteristics of Veterans Experiencing Homelessness using Telehealth for Primary Care Before and After COVID-19 Pandemic Onset.

BACKGROUND: The COVID-19 pandemic expanded telehealth use across healthcare systems, including the Veterans Health Administration (VA). Little is known about how large-scale telehealth rollout affected access to primary care for patients experiencing homelessness. OBJECTIVE: To examine the extent to which homeless-experienced veterans used telehealth services in primary care and to characterize users before and after the onset of the COVID-19 pandemic. DESIGN: Retrospective cohort study, 3/16/2019-3/15/2022. PARTICIPANTS: 394,731 veterans with homelessness diagnoses nationally using 4,068,109 primary care visits. MAIN MEASURES: The outcomes were use of 1 + telehealth visits (video, phone, secure messaging) for primary care during each year. Through multivariable regression models, we examined associations between telehealth use, patient characteristics (e.g., age, sex, race-ethnicity, comorbidity), and VA homeless services use (e.g., homeless-tailored primary care (HPACT), permanent supportive housing). KEY RESULTS: Compared to pre-pandemic, telehealth in primary care among homeless-experienced veterans increased substantially 2 years post-pandemic (video: 1.37% versus 20.56%, phone: 60.74% versus 76.58%). Secure messaging was low over time (1.57-2.63%). In adjusted models, video users were more likely to be young (65 + years: OR = 0.43, CI: 0.42-0.44), women (OR = 1.74, CI: 1.70-1.78), Black (OR = 1.14, CI: 1.12-1.16), Hispanic (OR = 1.34, CI: 1.30-1.38), and with more comorbidities (2 + on the Charlson Comorbidity Index; OR = 1.16, CI: 1.14-1.19), compared to video non-users. HPACT patients were less likely to use video (OR = 0.68, CI: 0.66-0.71) than other primary care patients. This was not observed among users of other VA homeless services. CONCLUSIONS: Despite decreased access to health information technology and low pre-pandemic telehealth use, veterans experiencing homelessness still sustained high use of telehealth in primary care post-pandemic. Women and racial-ethnic minorities had higher video uptake proportionately, suggesting that telehealth may address access disparities among these homeless-experienced patient groups. Identifying and targeting organizational characteristics (e.g., HPACT users) that predict telehealth use for improvement may be key to increasing adoption among VA primary care patients experiencing homelessness.

Telework Arrangements and Physician Burnout in the Veterans Health Administration.

This survey study of physicians in the Veterans Health Administration examines the association of burnout with various telework arrangements.

Depression care quality among patients with solid tumor cancers detected to have depression in Veterans Health Administration primary care clinics.

Patients with cancer, especially advanced cancer, experience depression at high rates. We aimed to evaluate the quality of depression care received by patients with solid tumor cancer and advanced solid tumor cancer in Veterans Affairs (VA) primary care clinics. This is a retrospective cohort study of patients seen in 82 VA primary care clinics who newly screened positive for depression on the Patient Health Questionnaire (PHQ-2). Outcomes included timely follow-up within 84 or 180 days (3+ mental health specialty, 3+ psychotherapy, or 3+ primary care visits with depression diagnosis codes) and minimum treatment within 1 year (60+ days antidepressants prescribed, 4+ mental health specialty visits, or 3+ psychotherapy visits). 608,042 individuals were seen in VA primary care clinics during this period; 49,839 patients (8.2%) had solid tumor cancer and 9,278 (1.5%) had advanced or poor-prognosis solid tumor cancer. For 686 observations of patients with cancer and new depression, rates of appropriate follow-up were 22.3% within 84 days and 38.2% within 180 days. For 73 observations of patients with advanced or poor-prognosis cancer and new depression, rates of appropriate follow-up were 21.9% within 84 days and 34.3% within 180 days. Rates of minimum treatment within 1 year were 68.4% and 64.4% for patients with cancer and patients with advanced or poor-prognosis cancer, respectively. Quality of timely depression management is low in patients with solid tumor cancers. Even in health systems with well-integrated mental health services, care gaps remain for patients with cancer and depression. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Associations Between Primary Care Providers and Staff-Reported Access Management Challenges and Patient Perceptions of Access.

BACKGROUND/OBJECTIVE: Optimizing patients' access to primary care is critically important but challenging. In a national survey, we asked primary care providers and staff to rate specific care processes as access management challenges and assessed whether clinics with more of these challenges had worse access outcomes. METHODS: Study design: Cross sectional. National Primary Care Personnel Survey (NPCPS) (2018) participants included 6210 primary care providers (PCPs) and staff in 813 clinics (19% response rate) and 158,645 of their patients. We linked PCP and staff ratings of access management challenges to veterans' perceived access from 2018-2019 Survey of Healthcare Experiences of Patients-Patient Centered Medical Home (SHEP-PCMH) surveys (35.6% response rate). MAIN MEASURES: The NPCPS queried PCPs and staff about access management challenges. The mean overall access challenge score was 28.6, SD 6.0. The SHEP-PCMH access composite asked how often veterans reported always obtaining urgent appointments same/next day; routine appointments when desired and having medical questions answered during office hours. ANALYTIC APPROACH: We aggregated PCP and staff responses to clinic level, and use multi-level, multivariate logistic regressions to assess associations between clinic-level access management challenges and patient perceptions of access. We controlled for veteran-, facility-, and area-level characteristics. KEY RESULTS: Veterans at clinics with more access management challenges (> 75th percentile) had a lower likelihood of reporting always receiving timely urgent care appointments (AOR: .86, 95% CI: .78-.95); always receiving routine appointments (AOR: .74, 95% CI: .67-.82); and always reporting same- or next-day answers to telephone questions (AOR: .79, 95% CI: .70-.90) compared to veterans receiving care at clinics with fewer (< 25th percentile) challenges. DISCUSSION/CONCLUSION: Findings show a strong relationship between higher levels of access management challenges and worse patient perceptions of access. Addressing access management challenges, particularly those associated with call center communication, may be an actionable path for improved patient experience.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Primary care provider perspectives on virtual and in-person depression management during the COVID-19 pandemic.

INTRODUCTION: During the COVID-19 pandemic, primary care providers (PCPs), nurses, and integrated mental health specialists continued to collaboratively manage depression among patients using both in-person and virtual (i.e., hybrid) modalities. Few studies have characterized how hybrid services are currently delivered within interdisciplinary primary care teams. This study aimed to understand frontline PCPs' perspectives on providing hybrid virtual and in-person depression care during the pandemic. METHOD: From September to November 2020, 12 semistructured individual interviews focused on depression management were conducted with PCPs in two Veterans Health Administration (VA) clinics in Los Angeles, which resumed in-person services while balancing rising COVID-19 cases. Interviews were audio-recorded, transcribed, and coded for depression management patterns. Themes were derived using a team-based constant comparative analytic approach. RESULTS: The pandemic and subsequent expanded use of virtual care necessitated clinic adaptations to depression assessments and procedures. PCPs perceived increased depression and anxiety among patients with existing psychiatric conditions, attributed to social distancing and isolation restrictions. They expressed acceptance of virtual care modalities for patients' depression management. PCPs did not perceive a delay in mental health care delivery in the shift to virtual care but noted the possibility of patients being lost to follow-up. CONCLUSIONS: During the pandemic, there has been heightened PCP concern for patients' emotional well-being and adaptations of clinic processes to meet needs for depression care. While PCPs were optimistic about new virtual care options for depression management, virtual care transfers remained poorly defined and the extent to which patient care experiences and health outcomes have been disrupted remains unknown. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Rates of Primary Care and Integrated Mental Health Telemedicine Visits Between Rural and Urban Veterans Affairs Beneficiaries Before and After the Onset of the COVID-19 Pandemic.

Importance: Telemedicine can increase access to care, but uptake has been low among people living in rural areas. The Veterans Health Administration initially encouraged telemedicine uptake in rural areas, but telemedicine expansion efforts have broadened since the COVID-19 pandemic. Objective: To examine changes over time in rural-urban differences in telemedicine use for primary care and for mental health integration services among Veterans Affairs (VA) beneficiaries. Design, Setting, and Participants: This cohort study examined 63.5 million primary care and 3.6 million mental health integration visits across 138 VA health care systems nationally from March 16, 2019, to December 15, 2021. Statistical analysis took place from December 2021 to January 2023. Exposures: Health care systems with most clinic locations designated as rural. Main Outcomes and Measures: For every system, monthly visit counts for primary care and mental health integration specialties were aggregated from 12 months before to 21 months after pandemic onset. Visits were categorized as in person or telemedicine, including video. A difference-in-difference approach was used to examine associations in visit modality by health care system rurality and pandemic onset. Regression models also adjusted for health care system size as well as relevant patient characteristics (eg, demographic characteristics, comorbidities, broadband internet access, and tablet access). Results: The study included 63 541 577 primary care visits (6 313 349 unique patients) and 3 621 653 mental health integration visits (972 578 unique patients) (6 329 124 unique patients among the cohort; mean [SD] age, 61.4 [17.1] years; 5 730 747 men [90.5%]; 1 091 241 non-Hispanic Black patients [17.2%]; and 4 198 777 non-Hispanic White patients [66.3%]). In fully adjusted models for primary care services before the pandemic, rural VA health care systems had higher proportions of telemedicine use than urban ones (34% [95% CI, 30%-38%] vs 29% [95% CI, 27%-32%]) but lower proportions of telemedicine use than urban health care systems after pandemic onset (55% [95% CI, 50%-59%] vs 60% [95% CI, 58%-62%]), signifying a 36% reduction in the odds of telemedicine use (odds ratio [OR], 0.64; 95% CI, 0.54-0.76). The rural-urban telemedicine gap was even larger for mental health integration (OR, 0.49; 95% CI, 0.35-0.67) than for primary care services. Few video visits occurred across rural and urban health care systems (unadjusted percentages: before the pandemic, 2% vs 1%; after the pandemic, 4% vs 8%). Nonetheless, there were rural-urban divides for video visits in both primary care (OR, 0.28; 95% CI, 0.19-0.40) and mental health integration services (OR, 0.34; 95% CI, 0.21-0.56). Conclusions and Relevance: This study suggests that, despite initial telemedicine gains at rural VA health care sites, the pandemic was associated with an increase in the rural-urban telemedicine divide across the VA health care system. To ensure equitable access to care, the VA health care system's coordinated telemedicine response may benefit from addressing rural disparities in structural capacity (eg, internet bandwidth) and from tailoring technology to encourage adoption among rural users.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Electronic Population-Based Depression Detection and Management Through Universal Screening in the Veterans Health Administration.

Importance: In 2016, the US Preventive Services Task Force newly recommended universal screening for depression, with the expectation that screening would be associated with appropriate treatment. Few studies have been able to assess the population-based trajectory from screening to receipt of follow-up and treatment for individuals with depression. Objective: To examine adherence to guidelines for follow-up and treatment among primary care patients who newly screened positive for depression in the Veterans Health Administration (VA). Design, Setting, and Participants: This retrospective cohort study used VA electronic data to identify patients who newly screened positive for depression on the 2-item Patient Health Questionnaire at 82 primary care VA clinics in California, Arizona, and New Mexico between October 1, 2015, and September 30, 2019. Data analysis was performed from December 2020 to August 2021. Main Outcomes and Measures: Receipt of guideline-concordant care for screen-positive patients who were determined by clinicians as having depression was assessed. Timely follow-up (within 84 days of screening) was defined as receiving 3 or more mental health specialty visits, 3 or more psychotherapy visits, or 3 or more primary care visits with a depression diagnosis according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. Completing at least minimal treatment (within 12 months) was defined as having 60 days or more of antidepressant prescriptions filled, 4 or more mental health specialty visits, or 3 or more psychotherapy visits. Results: The final cohort included 607 730 veterans (mean [SD] age, 59.4 [18.2] years; 546 516 men [89.9%]; 339 811 non-Hispanic White [55.9%]); 8%, or 82 998 of 997 185 person-years, newly screened positive for depression. Clinicians identified fewer than half with depression (15 155 patients), of whom 32% (5034 of 15 650 person-years) met treatment guidelines for timely follow-up and 77% (12 026 of 15 650 person-years) completed at least minimal treatment. Younger age (odds ratio, 0.990; 95% CI, 0.986-0.993; P < .001), Black race (odds ratio, 1.19; 95% CI, CI 1.05-1.34; P = .01), and having comorbid psychiatric diagnoses were significantly associated with timely follow-up. Individual quality metric components (eg, medication or psychotherapy) were associated differently with overall quality results among patient groups, except for age. Conclusions and Relevance: In this cohort study, most patients met the guidelines for completing at least minimal treatment, but only a minority received timely follow-up after screening positive and being identified as having depression. More research is needed to understand whether the discrepancy between patients who screened positive and patients identified as having depression reflects a gap in recognition of needed care.

Association of Integrated Mental Health Services with Physical Health Quality Among VA Primary Care Patients.

BACKGROUND: Integrated care for comorbid depression and chronic medical disease improved physical and mental health outcomes in randomized controlled trials. The Veterans Health Administration (VA) implemented Primary Care-Mental Health Integration (PC-MHI) across all primary care clinics nationally to increase access to mental/behavioral health treatment, alongside physical health management. OBJECTIVE: To examine whether widespread, pragmatic PC-MHI implementation was associated with improved care quality for chronic medical diseases. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included 828,050 primary care patients with at least one quality metric among 396 VA clinics providing PC-MHI services between October 2013 and September 2016. MAIN MEASURE(S): For outcome measures, chart abstractors rated whether diabetes and cardiovascular quality metrics were met for patients at each clinic as part of VA's established quality reporting program. The explanatory variable was the proportion of primary care patients seen by integrated mental health specialists in each clinic annually. Multilevel logistic regression models examined associations between clinic PC-MHI proportion and patient-level quality metrics, adjusting for regional, patient, and time-level effects and clinic and patient characteristics. KEY RESULTS: Median proportion of patients seen in PC-MHI per clinic was 6.4% (IQR=4.7-8.7%). Nineteen percent of patients with diabetes had poor glycemic control (hemoglobin A1c >9%). Five percent had severely elevated blood pressure (>160/100 mmHg). Each two-fold increase in clinic PC-MHI proportion was associated with 2% lower adjusted odds of poor glycemic control (95% CI=0.96-0.99; p=0.046) in diabetes. While there was no association with quality for patients diagnosed with hypertension, patients without diagnosed hypertension had 5% (CI=0.92-0.99; p=0.046) lower adjusted odds of having elevated blood pressures. CONCLUSIONS AND RELEVANCE: Primary care clinics where integrated mental health care reached a greater proportion of patients achieved modest albeit statistically significant gains in key chronic care quality metrics, providing optimism about the expected effects of large-scale PC-MHI implementation on physical health.

Are Veterans Getting Their Preferred Depression Treatment? A National Observational Study in the Veterans Health Administration.

BACKGROUND: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes. OBJECTIVE: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment. DESIGN: Patient self-report surveys at treatment initiation linked to medical records. SETTING: Veterans Health Administration (VA) clinics nationally, 2018-2020. PARTICIPANTS: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables. KEY RESULTS: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence. CONCLUSIONS: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Mental health care integration and primary care patient experience in the Veterans Health Administration.

BACKGROUND: Mental health specialists and care managers facilitate comprehensive care provision within medical homes. Despite implementation challenges, mental health integration is thought to improve patient-centered primary care. OBJECTIVES: To examine the relationship between primary care patient experience and mental health integration. RESEARCH DESIGN: Cross-sectional surveys from 168 primary care clinicians (PCPs) (n = 226) matched with assigned patients' surveys (n = 1734) in one Veterans Health Administration (VA) region, fiscal years 2012-2013. Multilevel regression models examined patient experience and mental health integration, adjusting for patient and PCP characteristics. MEASURES: Patient experience outcomes were (1) experience with PCP and (2) receipt of comprehensive care, such as talked about "stress". Independent variables represented mental health integration- (1) PCP-rated communication with mental health and (2) proportion of clinic patients who saw integrated specialists. RESULTS: 50% and 43% of patients rated their PCPs 10/10 and reported receiving comprehensive care, respectively. Neither patient experience or receipt of comprehensive care was significantly associated with PCP's ratings of communication with mental health, nor with proportion of clinic patients who saw integrated specialists. Among a subsample of patients who rated their mental health as poor/fair, however, we detected an association between proportion of clinic patients who saw integrated specialists and patient experience (odds ratio = 1.05, 95% confidence interval = 1.01-1.09, p = .01). CONCLUSIONS: No association was observed between mental health integration and primary care patients' reported care experiences, but a significant association existed among patients who reported poor/fair mental health. More research is needed to understand patient experiences with regard to care model implementation.

Integrating Mental Health in Safety-net Primary Care: A Five-year Observational Study on Visits in a County Health System.

BACKGROUND: Beginning in 2010, Los Angeles County Departments of Health Services and Mental Health collaborated to increase access to effective mental health care. The Mental Health Integration Program (MHIP) embedded behavioral health specialists in primary care clinics to deliver brief, problem-focused treatments, and psychiatric consultation support for primary care-prescribed psychotropic medications. OBJECTIVE: The aim was to compare primary care visits associated with psychiatric diagnoses before and after MHIP implementation. METHODS: This retrospective cohort study (2009-2014) examined 62,945 patients from 8 safety-net clinics that implemented MHIP in a staggered manner in Los Angeles. Patients' primary care visits (n=695,354) were either associated or not with a previously identified or "new" (defined as having no diagnosis within the prior year) psychiatric diagnosis. Multilevel regression models used MHIP implementation to predict odds of visits being associated with psychiatric diagnoses, controlling for time, clinic, and patient characteristics. RESULTS: 9.4% of visits were associated with psychiatric diagnoses (6.4% depression, 3.1% anxiety, <1% alcohol, and substance use disorders). Odds of visits being associated with psychiatric diagnoses were 9% higher [95% confidence interval (CI)=1.05-1.13; P<0.0001], and 10% higher for diagnoses that were new (CI=1.04-1.16; P=0.002), after MHIP implementation than before. This appeared to be fueled by increased visits for depression post-MHIP (odds ratio=1.11; CI=1.06-1.15; P<0.0001). CONCLUSIONS: MHIP implementation was associated with more psychiatric diagnoses coded in safety-net primary care visits. Scaling up this effort will require greater attention to the notable differences across patient populations and languages, as well as the markedly low coding of alcohol and substance use services in primary care.

Comorbid mental disorders, depression symptom severity, and role impairment among Veterans initiating depression treatment through the Veterans Health Administration.

BACKGROUND: Psychiatric comorbidities may complicate depression treatment by being associated with increased role impairments. However, depression symptom severity might account for these associations. Understanding the independent associations of depression severity and comorbidity with impairments could help in treatment planning. This is especially true for depressed Veterans, who have high psychiatric comorbidity rates. METHODS: 2,610 Veterans beginning major depression treatment at the Veterans Health Administration (VHA) were administered a baseline self-report survey that screened for diverse psychiatric comorbidities and assessed depression severity and role impairments. Logistic and generalized linear regression models estimated univariable and multivariable associations of depression severity and comorbidities with impairments. Population attributable risk proportions (PARPs) estimated the relative importance of depression severity and comorbidities in accounting for role impairments. RESULTS: Nearly all patients (97.8%) screened positive for at least one comorbidity and half (49.8%) for 4+ comorbidities. The most common positive screens were for generalized anxiety disorder (80.2%), posttraumatic stress disorder (77.9%), and panic/phobia (77.4%). Depression severity and comorbidities were significantly and additively associated with impairments in multivariable models. Associations were attenuated much less for depression severity than for comorbidities in multivariable versus univariable models. PARPs indicated that 15-60% of role impairments were attributable to depression severity and 5-32% to comorbidities. LIMITATIONS: The screening scales could have over-estimated comorbidity prevalence. The cross-sectional observational design cannot determine either temporal or causal priorities. CONCLUSIONS: Although positive screens for psychiatric comorbidity are pervasive among depressed VHA patients, depression severity accounts for most of the associations of these comorbidities with role impairments.

Treatment Differences in Primary and Specialty Settings in Veterans with Major Depression.

INTRODUCTION: The Veterans Health Administration (VHA) supports the nation's largest primary care-mental health integration (PC-MHI) collaborative care model to increase treatment of mild to moderate common mental disorders in primary care (PC) and refer more severe-complex cases to specialty mental health (SMH) settings. It is unclear how this treatment assignment works in practice. METHODS: Patients (n = 2610) who sought incident episode VHA treatment for depression completed a baseline self-report questionnaire about depression severity-complexity. Administrative data were used to determine settings and types of treatment during the next 30 days. RESULTS: Thirty-four percent (34.2%) of depressed patients received treatment in PC settings, 65.8% in SMH settings. PC patients had less severe and fewer comorbid depressive episodes. Patients with lowest severity and/or complexity were most likely to receive PC antidepressant medication treatment; those with highest severity and/or complexity were most likely to receive combined treatment in SMH settings. Assignment of patients across settings and types of treatment was stronger than found in previous civilian studies but less pronounced than expected (cross-validated AUC = 0.50-0.68). DISCUSSION: By expanding access to evidence-based treatments, VHA's PC-MHI increases consistency of treatment assignment. Reasons for assignment being less pronounced than expected and implications for treatment response will require continued study.

Association of Veterans Affairs Primary Care Mental Health Integration With Care Access Among Men and Women Veterans.

Importance: Women veterans increasingly seek care yet continue to face barriers in the Veterans Health Administration (VA), which predominantly cares for men. Evidence-based collaborative care models can improve patient access to treatment of depression, which is experienced at higher rates by women. While the VA has implemented these care models nationally, it is not known whether access improvements occur equitably across genders in primary care. Objective: To examine whether the VA's national Primary Care-Mental Health Integration (PC-MHI) initiative (beginning 2007) expanded realized access to mental health care similarly for men and women. Design, Setting, and Participants: This cohort study included 5 377 093 million primary care patients assigned to 396 VA clinics that provided integrated mental health services nationally between October 2013 and September 2016. Data analysis occurred between May 2017 and July 2020. Exposures: Clinic PC-MHI penetration, calculated as the proportion of clinic patients who saw an integrated specialist per fiscal year. Main Outcomes and Measures: Estimates of mean VA health care utilization (mental health, primary care, other specialty care, telephone, hospitalizations) and median total costs for men and women. Multilevel models adjusted for year, clinic, patient characteristics, and interactions between patient-defined gender and clinic PC-MHI penetration. Results: This study examined 5 377 093 veterans (448 455 [8.3%] women; 3 744 140 [69.6%] White) with a mean (SD) baseline age 62.0 (16.6) years. Each percentage-point increase in the proportion of clinic patients who saw an integrated specialist was associated with 38% fewer mental health visits per year for women (incidence rate ratio [IRR], 0.62; 95% CI, 0.60-0.65), but 39% more visits for men (IRR, 1.39; 95% CI, 1.34-1.44; P < .001). Both men and women had more primary care visits (men: IRR, 1.40; 95% CI, 1.36-1.45; women: IRR, 1.22; 95% CI, 1.17-1.28; P < .001) and total costs (men: ß [SE], 2.23 [0.10]; women: ß [SE], 1.24 [0.15]; P = .06), but women had 74% fewer hospitalizations than men related to clinics with mental health integration (IRR, 0.26; 95% CI, 0.19-0.36 vs IRR, 1.02; 95% CI, 0.83-1.24; P < .001). Conclusions and Relevance: While greater outpatient service use for men was observed in this study, PC-MHI was associated with a decrease in mental health specialty visits (and hospitalizations) for women veterans, potentially signifying a shift of services to primary care. With increasing patient choice for where veterans receive care, the VA must tailor medical care to the needs of rising numbers of women patients. Differences in health care utilization by gender highlight the importance of anticipating policy impacts on and tailoring services for patients in the numerical minority in the VA and other health systems.