Association Between Mental Health Staffing Level and Primary Care-Mental Health Integration Level on Provision of Depression Care in Veteran's Affairs Medical Facilities

Depression symptom outcomes and re-engagement among VA patients who discontinue care while symptomatic

OBJECTIVE

Evaluate outcomes of Veterans who discontinued treatment with at least moderate ongoing depressive symptoms.

METHOD

Veterans with elevated depression symptoms from 29 Department of Veterans Affairs facilities completed baseline surveys and follow-up assessments for one year. Analyses examined rates and predictors of treatment discontinuation, treatment re-engagement, and subsequent symptoms among patients who remained out of care.

RESULTS

A total of 242 (17.8%; n = 1359) participants discontinued treatment while symptomatic, with Black participants, participants with less severe depression, and participants receiving only psychotherapy (versus combined psychotherapy and antidepressant medications) discontinuing at higher rates. Among all participants who discontinued treatment (n = 445), 45.8% re-engaged within the following six months with participants receiving combined treatment re-engaging at higher rates. Of participants who discontinued while symptomatic within the first 6 months of the study and did not return to care (n = 112), 68.8% remained symptomatic at 12 months. Lower baseline treatment expectancy and greater depression symptom severity were associated with remaining symptomatic while untreated.

CONCLUSIONS

Black race, lower symptom severity, and treatment modality may help identify patients at higher risk for discontinuing care while symptomatic, whereas patients with lower treatment expectations may be at greater risk for remaining out of care despite continuing symptoms.

Quality of Depression Care for Veterans Affairs Primary Care Patients with Experiences of Homelessness

BACKGROUND

Persons who experience homelessness (PEH) have high rates of depression and incur challenges accessing high-quality health care. Some Veterans Affairs (VA) facilities offer homeless-tailored primary care clinics, although such tailoring is not required, within or outside VA. Whether services tailoring enhances care for depression is unstudied.

OBJECTIVE

To determine whether PEH in homeless-tailored primary care settings receive higher quality of depression care, compared to PEH in usual VA primary care.

DESIGN

Retrospective cohort study of depression treatment among a regional cohort of VA primary care patients (2016-2019).

PARTICIPANTS

PEH diagnosed or treated for a depressive disorder.

MAIN MEASURES

The quality measures were timely follow-up care (3 + completed visits with a primary care or mental health specialist provider, or 3 + psychotherapy sessions) within 84 days of a positive PHQ-2 screen result, timely follow-up care within 180 days, and minimally appropriate treatment (4 + mental health visits, 3 + psychotherapy visits, 60 + days antidepressant) within 365 days. We applied multivariable mixed-effect logistic regressions to model differences in care quality for PEH in homeless-tailored versus usual primary care settings.

KEY RESULTS

Thirteen percent of PEH with depressive disorders received homeless-tailored primary care (n = 374), compared to usual VA primary care (n = 2469). Tailored clinics served more PEH who were Black, who were non-married, and who had low income, serious mental illness, and substance use disorders. Among all PEH, 48% received timely follow-up care within 84 days of depression screening, 67% within 180 days, and 83% received minimally appropriate treatment. Quality metric attainment was higher for PEH in homeless-tailored clinics, compared to PEH in usual VA primary care: follow-up within 84 days (63% versus 46%; adjusted odds ratio [AOR] = 1.61, p = .001), follow-up within 180 days (78% versus 66%; AOR = 1.51, p = .003), and minimally appropriate treatment (89% versus 82%; AOR = 1.58, p = .004).

CONCLUSIONS

Homeless-tailored primary care approaches may improve depression care for PEH.

Veterans Health Administration Outpatient Psychiatry Staffing Model: Longitudinal Analysis on Mental Health Performance

BACKGROUND

An adequate supply of mental health (MH) professionals is necessary to provide timely access to MH services. Veterans Health Administration (VHA) continues to prioritize the expansion of the MH workforce to meet increasing demand for services.

OBJECTIVE

Validated staffing models are essential to ensure timely access to care, to plan for future demand, to ensure delivery of high-quality care, and to balance the demands of fiscal responsibility and strategic priorities.

DESIGN

Longitudinal retrospective cohort of VHA outpatient psychiatry, fiscal years 2016-2021.

PARTICIPANTS

Outpatient VHA psychiatrists.

MAIN MEASURES

Quarterly outpatient staff-to-patient ratios (SPRs), defined as the number of full-time equivalent clinically assigned providers per 1000 veterans receiving outpatient MH care, were calculated. Longitudinal recursive partitioning models were created to identify optimal cut-offs for the outpatient psychiatry SPR associated with success on VHA's measures of quality, access, and satisfaction.

KEY RESULTS

Among outpatient psychiatry staff, the root node identified an outpatient SPR of 1.09 for overall performance (p < 0.001). For metrics associated with Population Coverage, a root node identified an SPR of 1.36 (p < 0.001). Metrics associated with continuity of care and satisfaction were associated with a root node of 1.10 and 1.07 (p < 0.001), respectively. In all analyses, the lowest SPRs were associated with the lowest group performance on VHA MH metrics of interest.

CONCLUSIONS

Establishing validated staffing models associated with high-quality MH care is critical given the national psychiatry shortage and increasing demand for services. Analyses support VHA's current recommended minimum outpatient psychiatry-specific SPR of 1.22 as a reasonable target to provide high-quality care, access, and satisfaction.

Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach

Background

Healthcare systems monitor and improve mental health treatment quality, access, continuity and satisfaction through use of population-based and efficiency-based staffing models, the former focused on staffing ratios and the latter, staff productivity. Preliminary evidence suggests that both high staffing ratios and moderate-to-high staff productivity are important for ensuring a full continuum of mental health services to indicated populations.

Methods & findings

With an information-theoretic approach, we conducted a longitudinal investigation of mental health staffing, productivity and treatment at the largest integrated healthcare system in American, the Veterans Health Administration (VHA). VHA facilities (N = 140) served as the unit of measure, with mental health treatment quality, access, continuity and satisfaction predicted by facility staffing and productivity in longitudinal mixed models. An information-theoretic approach: (a) entails the development of a comprehensive set of plausible models that are fit, ranked and weighted to quantitatively assess the relative support for each, and (b) accounts for model uncertainty while identifying best-fit model(s) that include important and exclude unimportant explanatory variables. In best-fit models, higher staffing was the strongest and most consistent predictor of better treatment quality, access, continuity and satisfaction. Higher staff productivity was often, but not always associated with better treatment quality, access, continuity and satisfaction. Results were further nuanced by differential prediction of treatment by between- and within-facility predictor effects and variable interactions.

Conclusions

A population-based mental health staffing ratio and an efficiency-based productivity value are important longitudinal predictors of mental health treatment quality, access, continuity and satisfaction. Our longitudinal design and use of mixed regression models and an information-theoretic approach addresses multiple limitations of prior studies and strengthen our results. Results are discussed in terms of the provision of mental health treatment by healthcare systems, and analytical modeling of treatment quality, access, continuity and satisfaction.

Factors Affecting Adoption of Coordinated Anxiety Learning and Management (CALM) in Veterans' Affairs Community-Based Outpatient Clinics

PURPOSE

Many US military veterans experience anxiety, depression, and trauma-related disorders. A major goal of the Veterans Health Administration (VHA) has been to increase access to evidence-based psychotherapies (EBPs) such as cognitive-behavioral therapy to address veterans' substantial health burden. However, despite widespread implementation of EBPs throughout the VHA, smaller clinics that often serve rural veterans face barriers to delivering these interventions. The Veterans Affairs Coordinated Anxiety Learning and Management (VA CALM) program aims to empower providers in rural areas with varying levels of training and experience in delivering EBPs to provide high-quality cognitive-behavioral therapy for anxiety, depression, and trauma-related disorders. The goal of this study was to better understand, through qualitative interviews, VHA community-based outpatient clinic providers' perspectives on implementing VA CALM.

METHODS

Qualitative interviews with providers (N = 22) were conducted to understand implementation of VA CALM. Template analysis was used to organize and summarize responses.

FINDINGS

Providers noted several facilitators for implementing VA CALM in rural community clinics, including its perceived effectiveness, broad applicability, and structure. Barriers to implementation included scheduling problems and patient-related barriers.

CONCLUSIONS

Incorporating providers' perspectives on factors that affect implementing cognitive-behavioral therapy in this setting may inform future efforts to disseminate-implement EBPs in smaller, more remote VHA clinics.