Effect of mental health staffing inputs on initiation of care among recently separated Veterans.

OBJECTIVE: To estimate a causal relationship between mental health staffing and time to initiation of mental health care for new patients. DATA SOURCES AND STUDY SETTING: As the largest integrated health care delivery system in the United States, the Veterans Health Administration (VHA) provides a unique setting for isolating the effects of staffing on initiation of mental health care where demand is high and out-of-pocket costs are not a relevant confounder. We use data from the Department of Defense and VHA to obtain patient and facility characteristics and health care use. STUDY DESIGN: To isolate exogenous variation in mental health staffing, we used an instrumental variables approach-two-stage residual inclusion with a discrete time hazard model. Our outcome is time to initiation of mental health care after separation from active duty (first appointment) and our exposure is mental health staffing (standardized clinic time per 1000 VHA enrollees per pay period). DATA COLLECTION/EXTRACTION METHODS: Our cohort consists of all Veterans separating from active duty between July 2014 and September 2017, who were enrolled in the VHA, and had at least one diagnosis of post-traumatic stress disorder, major depressive disorder, and/or substance use disorder in the year prior to separation from active duty (N = 54,209). PRINCIPAL FINDINGS: An increase of 1 standard deviation in mental health staffing results in a higher likelihood of initiating mental health care (adjusted hazard ratio: 3.17, 95% confidence interval: 2.62, 3.84, p < 0.001). Models stratified by tertile of mental health staffing exhibit decreasing returns to scale. CONCLUSIONS: Increases in mental health staffing led to faster initiation of care and are especially beneficial in facilities where staffing is lower, although initiation of care appears capacity-limited everywhere.

The effects of the Veterans Health Administration's Referral Coordination Initiative on referral patterns and waiting times for specialty care.

OBJECTIVE: To investigate whether the Veterans Health Administration's (VA) 2019 Referral Coordination Initiative (RCI) was associated with changes in the proportion of VA specialty referrals completed by community-based care (CC) providers and mean appointment waiting times for VA and CC providers. DATA SOURCES/STUDY SETTINGS: Monthly facility level VA data for 3,097,366 specialty care referrals for eight high-volume specialties (cardiology, dermatology, gastroenterology, neurology, ophthalmology, orthopedics, physical therapy, and podiatry) from October 1, 2019 to May 30, 2022. STUDY DESIGN: We employed a staggered difference-in-differences approach to evaluate RCI's effects on referral patterns and wait times. Our unit of analysis was facility-month. We dichotomized facilities into high and low RCI use based on the proportion of total referrals for a specialty. We stratified our analysis by specialty and the staffing model that high RCI users adopted: centralized, decentralized, and hybrid. DATA COLLECTION/EXTRACTION METHODS: Administrative data on referrals and waiting times were extracted from the VA's corporate data warehouse. Data on staffing models were provided by the VA's Office of Integrated Veteran Care. PRINCIPAL FINDINGS: We did not reject the null hypotheses that high RCI use do not change CC referral rates or waiting times in any of the care settings for most specialties. For example, high RCI use for physical therapy-the highest volume specialty studied-was associated with -0.054 (95% confidence interval [CI]: -0.114 to 0.006) and 2.0 days (95% CI: -4.8 to 8.8) change in CC referral rate and waiting time at CC providers, respectively, among centralized staffing model adopters. CONCLUSIONS: In the initial years of the RCI program, RCI does not have a measurable effect on waiting times or CC referral rates. Our findings do not support concerns that RCI might be impeding Veterans' access to CC providers. Future evaluations should examine whether RCI facilitates Veterans' ability to receive care in their preferred setting.

Effect of mental health staffing inputs on suicide-related events.

OBJECTIVE: To estimate the effects of changes in Veterans Health Administration (VHA) mental health services staffing levels on suicide-related events among a cohort of Veterans. DATA SOURCES: Data were obtained from the VHA Corporate Data Warehouse, the Department of Defense and Veterans Administration Infrastructure for Clinical Intelligence, the VHA survey of enrollees, and customized VHA databases tracking suicide-related events. Geographic variables were obtained from the Area Health Resources Files and the Centers for Medicare and Medicaid Services. STUDY DESIGN: We used an instrumental variables (IV) design with a Heckman correction for non-random partial observability of the use of mental health services. The principal predictor was a measure of provider staffing per 10,000 enrollees. The outcome was the probability of a suicide-related event. DATA COLLECTION/EXTRACTION METHODS: Data were obtained for a cohort of Veterans who recently separated from active service. PRINCIPAL FINDINGS: From 2014 to 2018, the per-pay period probability of a suicide-related event among our cohort was 0.05%. We found that a 1% increase in mental health staffing led to a 1.6 percentage point reduction in suicide-related events. This was driven by the first tertile of staffing, suggesting diminishing returns to scale for mental health staffing. CONCLUSIONS: VHA facilities appear to be staffing-constrained when providing mental health care. Targeted increases in mental health staffing would be likely to reduce suicidality.

Fewer potentially avoidable health care events in rural veterans with self-directed care versus other personal care services.

BACKGROUND: Rural residents face more barriers to healthcare access, including challenges in receiving home- and community-based long-term services, compared to urban residents. Self-directed services provide flexibility and choice in care options and may be particularly well suited to help older adults with multiple chronic conditions and functional limitations who reside in rural areas remain independent and live in the community. METHODS: We conducted a retrospective observational study to understand whether differences in health outcomes between Veteran-Directed Care (VDC), a self-directed Veterans Health Administration (VHA)-paid care program, and other VHA-paid home- and community-based personal care services vary in rural/urban location. The sample included 37,395 veterans receiving VHA-paid home- and community-based long-term care services in FY17. Our primary outcomes were changes in monthly incidence of VHA or VHA-paid community acute care admissions, nursing home admissions, and emergency department (ED) visits. We used fixed effects logistic regression models on unmatched and coarsened exact matched cohorts, stratified by rural/urban location. RESULTS: Both urban and rural VDC recipients were significantly less likely to be admitted to VHA-paid nursing homes, compared to those receiving other VHA-paid personal care services (rural: incremental effect = -0.22, [-0.30, -0.14]; urban: incremental effect = -0.14, [-0.20, -0.07]). Rural, but not urban, VDC enrollees had significantly fewer VHA-paid acute care admissions and ED visits, relative to recipients of other VHA-paid personal care services (acute care, rural: incremental effect = -0.07, 95% CI = [-0.14, -0.01], urban: incremental effect = -0.01, [-0.06, 0.03]; ED, rural: incremental effect = -0.08, [-0.14, -0.02], urban: incremental effect = 0.01, [-0.03, 0.05]). CONCLUSIONS: VDC recipients had fewer incidents of potentially avoidable VHA-paid health care use, compared to similar veterans receiving other VHA-paid personal care services. These differences were more pronounced among rural VDC recipients than urban VDC recipients.

The Relationship Between Follow-up Appointments and Access to Primary Care.

BACKGROUND: Health care operations managers need to balance scheduling frequent follow-ups for patients with chronic conditions and fitting in patients requiring care for new complaints. OBJECTIVE: We quantify how frequency of follow-up visits corresponds with access to care for patients receiving care from the Department of Veterans Affairs (VA). DESIGN: We use patient data collected between October 2013 and June 2016 by the Survey of Healthcare Experiences of Patients (SHEP). Our sample is comprised of 94,496 patients. We estimate logistic models with 1-month lagged facility-level predictors. MAIN MEASURES: We calculate monthly measures characterizing facility-level service provision, including the average time between successive primary care visits, the average primary care visit length, the percentage of primary care appointments that are overbooked, the percent of visits that are unscheduled (i.e., walk-ins), and the ratio of patients to providers. We control for economic factors that are associated with health care supply and demand, including median household income, veteran priority status, the Zillow Housing Price Index, and veteran unemployment rates. We also control for patient demographics. PATIENTS: We restrict the data to patients with at least one in-person primary care visit who have provided information on their ability to access urgent and routine care. KEY RESULTS: We find that shorter average follow-up times are associated with better access for patients needing urgent or routine care. A 1-month increase in the average time between successive primary care visits is associated with 10% (p < 0.001) lower odds of reporting being able to access urgent care within 1 day and 13% (p < 0.001) lower odds of reporting usually or always being able to access routine care when needed. CONCLUSION: Facilities with higher average follow-up times are more likely to have patients report that they are unable to quickly access urgent or routine primary care.

Making the grade: a first attempt at a health and human rights report card.

We propose a methodology to evaluate fulfillment of the human right to health, using eight health indicators as proxies. Each health indicator was plotted against purchasing power parity US dollars gross domestic product (GDP)/capita to control for wealth. Generalized linear regression was used to derive a "best fit" curve. An "expected" value for each variable was calculated based on the GDP/capita of each country. The observed (reported) value was then divided by the "expected" value to give a score for that variable. Scores for each variable were averaged to give an overall health-related human rights score for each country. We believe that this report card is an initial step in the development of an effective means of monitoring health and human rights and can become a useful tool to quantify the fulfillment of the right to health. We invite comment on the approach.