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.

Abstract 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.

Abstract 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.

Introduction: The Veterans Health Administration (VHA) supports the nation's largest primary caremental 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.

Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes. To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment. Patient self-report surveys at treatment initiation linked to medical records. Veterans Health Administration (VA) clinics nationally, 2018–2020. A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) 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. 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. 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.

Introduction Depressive disorders are more prevalent among US veterans than civilians. The Veterans Health Administration (VHA) has initiated a system of primary care–mental health integration (PC-MHI) to address this high prevalence and that of other common mental disorders by including psychologists, psychiatrists, nurses, and social workers on primary care (PC) teams to collaborate in evaluation and treatment. PC-MHI is the country’s largest implementation of a collaborative care model for treatment of common mental disorders

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.

This survey study assesses the association of Medicaid expansion in Michigan with enrollees’ employment or student status.

Medicaid expansion in Michigan, known as the Healthy Michigan Plan (HMP), emphasizes primary care and preventive services. Evaluate the impact of enrollment in HMP on access to and receipt of care, particularly primary care and preventive services. Telephone survey conducted during January–November 2016 with stratified random sampling by income and geographic region (response rate = 53.7%). Logistic regression analyses accounted for sampling and nonresponse adjustment. 4090 HMP enrollees aged 19–64 with ≥ 12 months of HMP coverage Surveys assessed demographic factors, health, access to and use of health care before and after HMP enrollment, health behaviors, receipt of counseling for health risks, and knowledge of preventive services’ copayments. Utilization of preventive services was assessed using Medicaid claims. In the 12 months prior to HMP enrollment, 33.0% of enrollees reported not getting health care they needed. Three quarters (73.8%) of enrollees reported having a regular source of care (RSOC) before enrollment; 65.1% of those reported a doctor’s office/clinic, while 16.2% reported the emergency room. After HMP enrollment, 92.2% of enrollees reported having a RSOC; 91.7% had a doctor’s office/clinic and 1.7% the emergency room. One fifth (20.6%) of enrollees reported that, before HMP enrollment, it had been over 5 years since their last primary care visit. Enrollees who reported a visit with their primary care provider after HMP enrollment (79.3%) were significantly more likely than those who did not report a visit to receive counseling about health behaviors, improved access to cancer screening, new diagnoses of chronic conditions, and nearly all preventive services. Enrollee knowledge that some services have no copayments was also associated with greater utilization of most preventive services. After enrolling in Michigan’s Medicaid expansion program, beneficiaries reported less forgone care and improved access to primary care and preventive services.

Objectives: The study objective was to assess the impact of Medicaid expansion on health and employment outcomes among enrollees with and without a behavioral health disorder (either a mental or substance use disorder). Methods: Between January and October 2016, the authors conducted a telephone survey of 4,090 enrollees in the Michigan Medicaid expansion program and identified 2,040 respondents (48.3%) with potential behavioral health diagnoses using claims-based diagnoses. Results: Enrollees with behavioral health diagnoses were less likely than enrollees without behavioral health diagnoses to be employed but significantly more likely to report improvements in health and ability to do a better job at work. In adjusted analyses, both enrollees with behavioral health diagnoses and those without behavioral health diagnoses who reported improved health were more likely than enrollees without improved health to report that Medicaid expansion coverage helped them do a better job at work and made them better able to look for a job. Among enrollees with improved health, those with a behavioral health diagnosis were as likely as those without a behavioral health diagnosis to report improved ability to work and improved job seeking after Medicaid expansion. Conclusions: Coverage interruptions for enrollees with behavioral health diagnoses should be minimized to maintain favorable health and employment outcomes.