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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., (© 2024 The Author(s). Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Objective: The objective of this analysis was to evaluate the effect of resident program training size on clinician productivity and turnover in the Veterans Health Administration (VHA), the largest education and training platform for medical professionals in the United States., Data Sources: We retrieved administrative data on training programs and training facilities from the VA Office of Academic Affiliations and the VHA Corporate Data Warehouse. Data on primary care physician shortage areas were retrieved from the Health Resources and Services Administration., Study Design: We used a quasi-experimental instrumental variables 2SLS design and constructed an exogenous predicted training allocation treatment variable as a function of the total national training program allocation. The outcome was clinical staff productivity and turnover. Secondary analyses stratified results using Health Professional Shortage Areas data (HPSA)., Data Collection/extraction Methods: Data were obtained for a national dataset of 141 VHA medical facilities and 26 specialties that hosted training programs across 11 years from 2011 to 2021 (N = 132,177)., Principal Findings: Instrumental variables results showed that on average, an increase of one training slot in a specialty leads to a decrease of 0.039 visits per standardized clinic day (p < 0.001) and a 0.02 percentage point increase in turnover (p < 0.001). The direction of this association varied by specialty: while psychiatry and psychology specialties saw a decline in productivity, fields such as primary care and cardiology experienced an increase in productivity. HPSA stratified results indicate that negative effects on productivity and turnover are driven by areas with little to no primary care physician shortage, whereas shortage areas experienced a small increase in productivity and no effect on turnover., Conclusions: This quasi-experimental evaluation indicates that resident training program size is associated with reduced productivity and increased turnover in specialties such as psychiatry and in facilities with high baseline productivity. However, in specialties like primary care and cardiology, as well as areas with shortages of primary care, larger training programs are associated with increased productivity., (© 2024 Health Research and Educational Trust.)

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., (© 2024 Health Research and Educational Trust.)

Objective: To develop and validate a measure of provider network restrictiveness in the Medicare Advantage (MA) population., Data Sources: Prescription drug event data and beneficiary information for Part D enrollees from the Center for Medicare and Medicaid Services, along with prescriber identifiers; geographic variables from the Area Health Resources Files., Study Design: A prediction model was used to predict the unique number of primary care providers that would have been seen by MA beneficiaries absent network restrictions. The model was trained and validated on Traditional Medicare (TM) beneficiaries. A pseudo-Poisson and a random forest model were evaluated. An observed-to-expected (O/E) ratio was calculated as the number of unique providers seen by MA beneficiaries divided by the number expected based the TM prediction model. Multivariable linear models were used to assess the relationship between network restrictiveness and plan and market factors., Data Collection/extraction Methods: Prescription drug event data were obtained for a 20% random sample of beneficiaries enrolled in prescription drug coverage from 2011 to 2017., Principal Findings: Health Maintenance Organization plans were more restrictive (O/E = 55.5%; 95% CI 55.3%-55.7%) than Health Maintenance Organization-Point of Service plans (67.2%; 95% CI 66.7%-67.8%) or Preferred Provider Organization plans (74.7%; 95% CI 74.3%-75.1%), and rural areas had more restrictive networks (31.6%; 95% CI 29.0%-34.2%) than metropolitan areas (61.5%; 95% CI 61.3%-61.7%). Multivariable results confirmed these findings, and also indicated that increased provider supply was associated with less restrictive networks., Conclusions: We developed a means of estimating provider network restrictiveness in MA from claims data. Our results validate the approach, providing confidence for wider application (e.g., for other markets and specialties) and use for regulation., (© 2023 Health Research and Educational Trust.)

Objective: To investigate the relationship between community care (CC) treatment and a postoperative surgical complication in elective hernia surgery among Veterans using multiple approaches to control for potential selection bias., Data Sources and Study Setting: Veterans Health Administration (VHA) data sources included Corporate Data Warehouse (VHA encounters and patient data), the Program Integrity Tool and Fee tables (CC encounters), the Planning Systems Support Group (geographic information), and the Paid file (VHA primary care providers)., Study Design: Prior works suggest patient outcomes are better in VHA than in CC settings; however, these studies may not have appropriately accounted for the selection of higher-risk cases into CC. We estimated (1) a naïve logistic regression model to calculate the effect of CC setting on the probability of a complication, controlling for facility fixed effects and patient and procedure characteristics, and (2) a 2-stage model using the hernia patient's primary care provider's 1-year prior CC referral rate as the instrument., Data Collection: We identified patients residing ≤40 miles from a VHA surgical facility with elective VHA or CC hernia surgery from 2018 to 2019., Principal Findings: Of 7991 hernia surgeries, 772 (9.7%) were in CC. The overall complication rate was 4.2%; 286/7219 (4.0%) among VHA surgeries versus 51/5772 (6.6%, p < 0.05) in CC. We observed a 2.8 percentage point increase in the probability of postoperative complication given CC surgery (95% confidence interval: 0.7, 4.8) in the naïve model. After accounting for the VHA provider's historical rate of CC referral, we no longer observed a relationship between surgery setting and risk of postoperative complication., Conclusions: After accounting for the selection of higher-risk patients to CC settings, we found no difference in hernia surgery postoperative complications between CC and VHA. Future VHA and non-VHA comparisons should account for unobserved as well as observed differences in patients seen in each setting., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA.)

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., (© 2022 Health Research and Educational Trust.)

Objective: To estimate the effect of wait times on patients' choice of provider and simulate changes in choice of provider due to compliance with VA MISSION Act wait time targets., Data Sources: We use nationwide administrative data (2014-2017) on Veterans who are enrolled in Medicare and the Veterans Health Administration (VHA), the Survey of VHA Enrollees, Area Health Resource Files, and other data provided by the Centers for Medicare & Medicaid Services., Study Design: We use an instrumental variables approach to identify the effect of VHA wait times on the proportion of total (Medicare and VHA) services that are paid for by the VHA ("reliance"). We exploit shocks to VHA provider supply to isolate supply-driven changes in wait times and estimate the effect on VHA reliance. We control for market and time fixed effects and local demand factors., Data Collection/extraction Methods: We use monthly aggregated data on 140 markets (groups of counties). VHA reliance is computed among patients aged 65 years or older who are dually enrolled in VHA and Medicare. VHA wait times and reliance are calculated for multiple specialties: cardiology, gastroenterology, orthopedics, urology, dermatology, and ophthalmology/optometry., Principal Findings: A 10% increase in the mean wait time (+2.8 days) reduces VHA reliance by 2.3 percentage points (95% CI: 2.3, 2.7), or 7.9% of the sample mean. This implies that meeting the MISSION Act wait time targets may have multi-billion-dollar budgetary impacts. Effects vary across specialties. For example, a 10% increase in the mean wait time for cardiology services (+2.0 days) reduces reliance by 1.8 percentage points (95% CI: 1.6, 2.1), or 6.3% of the sample mean for cardiology services., Conclusions: Meeting statutory wait time targets may have substantial unforeseen impacts on federal health care spending as patients sort to providers who have lower wait times., (© 2022 Health Research and Educational Trust.)

Objective: To characterize the rate of guideline-concordant initiation of oral anticoagulation (OAC) among elderly Veterans with atrial fibrillation (AF) and high stroke risk., Data Sources/study Setting: Veterans Health Administration (VHA) Corporate Data Warehouse (CDW) linked with Medicare claims 2011-2015., Study Design: We identified 6619 elderly, high stroke-risk patients with a new episode of AF initially diagnosed in the VHA during fiscal years 2012-2015. We used logistic regression to estimate marginal effects of associations between patient characteristics and OAC initiation within 90 days of the first AF episode., Data Extraction Methods: We identified OACs using generic drug names. We calculated comorbidities and risk scores using diagnosis codes from 1 year of baseline data., Principal Findings: Overall, 66.5% of Medicare-eligible Veterans with AF at high risk of stroke initiated an OAC within 90 days. We found lower initiation rates for patients enrolled in Medicare Part D and those ineligible for drug co-payment subsidies. OAC initiation rates increased during the study among VHA-reliant patients but not among dual VHA-Part D enrollees., Conclusions: One-third of elderly Veterans at risk of stroke are not receiving recommended therapy. Increased coordination between Medicare and VHA providers may lead to improvements in anticoagulation quality and stroke prevention., (© Health Research and Educational Trust.)

Objective: Veterans' utilization of Veterans Affairs (VA) health care is likely influenced by community factors external to the VA, including Medicaid eligibility and unemployment, although such factors are rarely considered in models predicting such utilization. We measured the sensitivity of VA utilization to changes in such community factors (hereafter, "external determinants"), including the 2014 Medicaid expansion following the Affordable Care Act., Data Sources/study Setting: We merged VA health care enrollment and utilization data with area-level data on Medicaid policy, unemployment, employer-sponsored insurance, housing prices, and non-VA physician availability (2008-2014)., Study Design: For veterans aged 18-64 and ≥65, we estimated the sensitivity of annual individual VA health care utilization, measured by the cost ($) of care received, to changes in external determinants using longitudinal regression models controlling for individual fixed effects., Principal Findings: All external determinants were associated with small but significant changes in VA health care utilization. In states that expanded Medicaid in 2014, this expansion was associated with 9.1 percent ($826 million) reduction in VA utilization among those aged 18-64; sizable changes occurred in all services used (inpatient, outpatient, and prescription drugs)., Conclusions: Changes in alternative insurance coverage and other external determinants may affect VA health care spending. Policy makers should consider these factors in allocating VA resources to meet local demand., (Published 2018. This article is a U.S. Government work and is in the public domain in the USA.)

Objective: Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers., Data Sources: Veterans Health Administration's Corporate Data Warehouse., Study Design: Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE., Principal Findings: Of 1,730 FY2012-2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c-statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4-0.5 predicted probability of an AE, PPV was 38 percent., Conclusions: The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE., (Published 2018. This article is a U.S. Government work and is in the public domain in the USA.)

Objective: To describe a new Veterans Health Administration (VHA) program to foster the learning health system paradigm by rigorously evaluating health care initiatives and to report key lessons learned in designing those evaluations., Principal Findings: The VHA's Quality Enhancement Research Initiative and its Health Services Research and Development Service are cooperating on several large, randomized program evaluations aimed at improving the care veterans receive and the efficiency with which it is delivered. The evaluations we describe involve collaborative design, outcomes assessment, and implementation science through partnerships between VHA operations and researchers. We review key factors to assess before committing to an evaluation. In addition to traditional design issues (such as ensuring adequate power and availability of data), these include others that are easily overlooked: the stability of intervention financing, means of controlling and commitment to adhering to randomized roll-out, degree of buy-in from key implementation staff, and feasibility of managing multiple veto points for interventions that span several programs, among others., Conclusions: Successful program implementation and rigorous evaluation require resources, specialized expertise, and careful planning. If the learning health system model is to be sustained, organizations will need dedicated programs to prioritize resources and continuously adapt evaluation designs., (© Health Research and Educational Trust.)

Objective: To examine factors associated with 0- to 7-day admission after outpatient surgery in high-volume specialties: general surgery, orthopedics, urology, ear/nose/throat, and podiatry., Study Design: We calculated rates and assessed diagnosis codes for 0- to 7-day admission after outpatient surgery for Centers for Medicare and Medicaid Services (CMS) and Veterans Health Administration (VA) dually enrolled patients age 65 and older. We also estimated separate multilevel logistic regression models to compare patient, procedure, and facility characteristics associated with postoperative admission., Data Collection: 2011-2013 surgical encounter data from the VA Corporate Data Warehouse; geographic data from the Area Health Resources File; CMS enrollment and hospital admission data., Principal Findings: Among 63,585 outpatient surgeries in 124 facilities, 0- to 7-day admission rates ranged from 5 percent (podiatry) to 28 percent (urology); nearly 66 percent of the admissions occurred on the day of surgery. Only 97 admissions were detected in the CMS data (1 percent). Surgical complications were diagnosed in 4 percent of admissions. Procedure complexity, measured by relative value units or anesthesia risk score, was associated with admission across all specialties., Conclusion: As many as 20 percent of VA outpatient surgeries result in an admission. Complex procedures are more likely to be followed by admission, but more evidence is required to determine how many of these reflect potential safety or quality problems., (Published 2018. This article is a U.S. Government work and is in the public domain in the USA.)

Objectives: To demonstrate how falsification tests can be used to evaluate instrumental variables methods applicable to a wide variety of comparative effectiveness research questions., Study Design: Brief conceptual review of instrumental variables and falsification testing principles and techniques accompanied by an empirical application. Sample STATA code related to the empirical application is provided in the Appendix., Empirical Application: Comparative long-term risks of sulfonylureas and thiazolidinediones for management of type 2 diabetes. Outcomes include mortality and hospitalization for an ambulatory care-sensitive condition. Prescribing pattern variations are used as instrumental variables., Conclusions: Falsification testing is an easily computed and powerful way to evaluate the validity of the key assumption underlying instrumental variables analysis. If falsification tests are used, instrumental variables techniques can help answer a multitude of important clinical questions., (© Health Research and Educational Trust.)

Objective: To investigate how integration between Medicare Advantage plans and health care providers is related to plan premiums and quality ratings., Data Source: We used public data from the Centers for Medicare and Medicaid Services (CMS) and the Area Resource File and private data from one large insurer. Premiums and quality ratings are from 2009 CMS administrative files and some control variables are historical., Study Design: We estimated ordinary least-squares models for premiums and plan quality ratings, with state fixed effects and firm random effects. The key independent variable was an indicator of plan-provider integration., Data Collection: With the exception of Medigap premium data, all data were publicly available. We ascertained plan-provider integration through examination of plans' websites and governance documents., Principal Findings: We found that integrated plan-providers charge higher premiums, controlling for quality. Such plans also have higher quality ratings. We found no evidence that integration is associated with more generous benefits., Conclusions: Current policy encourages plan-provider integration, although potential effects on health insurance products and markets are uncertain. Policy makers and regulators may want to closely monitor changes in premiums and quality after integration and consider whether quality improvement (if any) justifies premium increases (if they occur)., (© Health Research and Educational Trust.)

Objective: The aim of this study was to measure the relationship between days spent waiting for primary care and health outcomes among patients diagnosed with diabetes, especially among the elderly population., Data Source: Secondary data from VA administrative databases and Medicare claims., Study Design: This is a retrospective observational study. Outcome variables include primary care utilization, mortality, heart attack, stroke, and ambulatory-care sensitive condition (ACSC) hospitalization. The main explanatory variable of interest is VA primary care wait time. Negative binomial models predict utilization and stacked logistic regression models predict the probability of experiencing each health outcome. Models are stratified by the presence of a selected health condition and age., Principal Findings: Longer wait times were predicted to decrease utilization between 2 and 4 percent. There was no significant relationship between wait times and health outcomes for the overall sample. In stratified analyses, longer waits were associated with undesirable outcomes for those over age 70 with one of the selected health conditions or in certain narrower 5-year age groups, but the overall pattern of results does not indicate a systematic and significant effect., Conclusions: There was a modest effect of long wait times on primary care utilization but no robust effect of longer wait times on health outcomes. Waiting for care did not significantly compromise long-term health outcomes for veterans with diabetes., (© Health Research and Educational Trust.)

Objective: To measure the relationship between time spent waiting for health care services and patients' mortality., Data Source: Data on the number of days until the next available appointment at 89 Veterans Affairs (VA) medical centers in 2001 were extracted from a VA administrative database. These facility-level data were merged with individual-level data for a sample of veterans who visited a VA geriatric outpatient clinic in 2001. The merged dataset includes facility-level data on waiting times and individual-level data on demographics, health status (e.g., diagnoses), and mortality., Study Design: This was a retrospective observational study using secondary data from administrative sources. The dependent variable was mortality within a 6-month follow-up period. The main explanatory variable of interest was VA facility-level wait times for outpatient visits measured in number of days. Random effects logistic regression models were risk adjusted for prior individual health status and facility-level differences in case mix., Principal Findings: Veterans who visited a VA medical center with facility-level wait times of 31 days or more had significantly higher odds of mortality (odds ratio=1.21, p=0.027) compared with veterans who visited a VA medical center with facility-level wait times of <31 days., Conclusions: Our findings support the largely assumed association between long wait times for outpatient health care and negative health outcomes, such as mortality. Future research should focus on the causes of long waits for health care (e.g., physician reimbursement levels), the consequences of long waits in other populations, and effective policies to decrease long waits for health care services.