The effect of Medicaid expansions on demand for care from the Veterans Health Administration

Contextual factors influencing the association between the Affordable Care Act's Medicaid expansion and Veteran VA-Medicaid dual enrollment

OBJECTIVE

To evaluate changes in dual enrollment after Affordable Care Act Medicaid expansion by VA priority group, (e.g., service connection), sex, and type of state expansion.

STUDY SETTING

Our cohort was all Veterans ages 18-64 enrolled in VA and eligible for benefits due to military service-connection or low income from 2011 to 2016; the unit of analysis was person-year.

STUDY DESIGN

Difference-in-difference and event-study analysis. The outcome was dual VA-Medicaid enrollment for at least 1 month annually. Medicaid expansion, VA priority status, whether a state expanded by a Section 1115 waiver, and sex were independent variables. We controlled for race, ethnicity, age, disease burden, distance to VA facilities, state, and year.

DATA EXTRACTION METHODS

We used data from the VA Corporate Data Warehouse (CDW) regarding age and VA Priority Group to select our cohort of VA-enrolled individuals. We then took the cohort and crossed checked it with Medicaid Analytic Extract (MAX) and T-MSIS Analytic Files (TAF) to determine Medicaid enrollment status.

PRINCIPAL FINDINGS

Service-connected Veterans experienced lower dual-enrollment increases across all sex and state-waiver groups (3.44 percentage points (95% CI: 1.83, 5.05 pp) for women, 3.93 pp (2.98, 4.98) for men, 4.06 pp (2.85, 5.27) for non-waiver states, and 3.00 pp (1.58 to 4.41) for waiver states) than Veterans who enrolled in the VA due to low income (8.19 pp (5.43, 10.95) for women, 9.80 pp (7.06, 12.54) for men, 10.21 pp (7.17, 13.25) for non-waiver states, and 7.39 pp (5.28, 9.50) for waiver states).

CONCLUSIONS

Medicaid expansion is associated with dual enrollment. Dual-enrollment changes are greatest in those enrolled in the VA due to low income, but do not differ by sex or expansion type. Results can help VA identify groups disproportionately likely to have potential care-coordination issues due to usage of multiple health care systems.

Medicaid policy data for evaluating eligibility and programmatic changes

OBJECTIVES

Medicaid and the Children's Health Insurance Program (CHIP) provide health insurance coverage to more than 90 million Americans as of early 2023. There is substantial variation in eligibility criteria, application procedures, premiums, and other programmatic characteristics across states and over time. Analyzing changes in Medicaid policies is important for state and federal agencies and other stakeholders, but such analysis requires data on historical programmatic characteristics that are often not available in a form ready for quantitative analysis. Our objective is to fill this gap by synthesizing existing qualitative policy data to create a new data resource that facilitates Medicaid policy research.

DATA DESCRIPTION

Our source data were the 50-state surveys of Medicaid and CHIP eligibility, enrollment, and cost-sharing policies, and budgets conducted near annually by KFF since 2000, which we coded through 2020. These reports are a rich source of point-in-time information but not operationalized for quantitative analysis. Through a review of the measures captured in the KFF surveys, we developed five Medicaid policy domains with 122 measures in total, each coded by state-quarter-1) eligibility (28 measures), 2) enrollment and renewal processes (39 measures), 3) premiums (16 measures), 4) cost-sharing (26 measures), and 5) managed care (13 measures).

Provider supply and access to primary care

Resource-constrained delivery systems often have access issues, causing patients to wait a long time to see a provider. We develop theoretical and empirical models of wait times and apply them to primary care delivery by the U.S. Veterans Health Administration (VHA). Using instrumental variables to handle simultaneity issues, we estimate the effect of clinician supply on new patient wait times. We find that it has a sizable impact. A 10% increase in capacity reduces wait times by 2.1%. Wait times are also associated with clinician productivity, scheduling protocols, and patient access to alternative sources of care. The VHA has adopted our models to identify underserved areas as specified by the MISSION Act of 2018.

The Veterans Choice Act and Technical Efficiency of Veterans Affairs (VA) Hospitals

The Veterans Health Administration (VHA), responsible for providing 9 million veterans with quality healthcare, is not insulated from concerns about efficiency. In the aftermath of the Veterans Affairs (VA) hospital scandal in 2014, Congress passed the Veterans Choice Act of 2014, which allows eligible veterans to use non-VA hospitals instead of VA hospitals. After analyzing 118 or 119 VA hospitals each year from 2012 through 2017 in the U.S, this paper evaluates the efficiency scores of VA hospitals and examines how the 2014 Act has influenced their technical efficiency over time. Slack analysis shows that inefficient VA hospitals can improve efficiency by reallocating input resources, and regression analysis demonstrates that the overall technical efficiency of VA hospitals decreased by 0.164 after the implementation of the Act. This means that as more veterans used non-VA hospitals under the 2014 Act, the technical efficiency of VA hospitals decreased considerably. Given that a substantial portion of veterans’ demands for healthcare transferred out to non-VA hospitals, the VHA should evaluate whether the current capacity of VA hospitals is appropriate and try to reduce wasted input resources to improve efficiency.

An Alternative Method of Public Reporting of Comparative Hospital Quality and Performance Data for Transparency Initiatives

BACKGROUND

Hospital performance comparisons for transparency initiatives may be inadequate if peer comparison groups are poorly defined.

OBJECTIVE

The objective of this study was to evaluate a new approach identifying hospital peers for comparison.

DESIGN/SETTING

We used Mahalanobis distance as a new method of developing peer-specific groupings for hospitals to incorporate both external and internal complexity. We compared the overlap in groups with an existing method used by the Veterans' Health Administration's Office for Productivity, Efficiency, and Staffing (OPES).

PARTICIPANTS

One hundred twenty-two acute-care Veterans' Health Administration's Medical Facilities as defined in the OPES fiscal year 2014 report.

MEASURES

Using 15 variables in 9 categories developed from expert input, including both hospital internal measures and community-based external measures, we used principal components analysis and calculated Mahalanobis distance between each hospital pair. This method accounts for correlation between variables and allows for variables having different variances. We identified the 50 closest hospitals, then eliminated any potential peer whose score on the first component was >1 SD from the reference hospital. We compared overlap with OPES measures.

RESULTS

Of 15 variables, 12 have SDs exceeding 25% of their means. The first 2 components of our analysis explain 24.8% and 18.5% of variation among hospitals. Eight of 9 variables scaling positively on the first component measure internal complexity, aligning with OPES groups. Four of 5 variables scaling positively on the second component but not the first are factors from the policy environment; this component reflects a dimension not considered in OPES groups.

CONCLUSION

Individualized peers that incorporate external complexity generate more nuanced comparators to evaluate quality.

Possible Effects on VA Outpatient Care of Expanding Medicaid: Implications of Having Access to Overlapping Publicly Funded Health Care Services

BACKGROUND

Because veterans who use Veterans Health Administration (VA) health care retain VA eligibility while enrolling in Medicaid, increasing Medicaid eligibility may create improved health system access but also create unique challenges for the quality and coordination of health care for veterans. We analyze how pre-Affordable Care Act (ACA) state Medicaid expansions influence VA and Medicaid-funded outpatient care utilization.

MATERIALS AND METHODS

This study uses Difference-in-difference analysis to evaluate association between pre-ACA 2001 Medicaid expansions and VA utilization in a natural experiment. Veterans aged 18-64 years living in a study state during the study period were the participants. Dependent variables included participants' proportion of outpatient care received at the VA, whether a participant recorded care with both Medicaid and the VA, and total outpatient utilization. We analyzed changes between two states that expanded Medicaid in 2001 against three similar states that did not from 1999 to 2006. We adjusted for age, non-White race, gender, disease burden, and distance to VA facilities. This study was approved by the Baylor College of Medicine Institutional Review Board (IRB), protocol number H-40441.

RESULTS

In total, 346,364 VA-enrolled veterans lived in the five study states during the time of our study, 70,987 of whom were enrolled in Medicaid for at least 1 month. For low-income veterans, Medicaid expansion was associated with a 2.88 percentage-point decline in the VA proportion of outpatient services (99% CI -3.26 to -2.49), and a 2.07-point increase (1.80 to 2.35) in the percentage of patients using both VA and Medicaid services. Results also showed small increases in total (VA plus Medicaid) annual per-capita outpatient visits among low-income veterans. We estimate that this corresponds to an annual reduction of 80,338 VA visits across study states (66,155-94,521).

CONCLUSIONS

This study shows usage shifts when Medicaid expansion allows veterans to gain access to non-VA care. It highlights increased potential for care-coordination challenges among VA patients as states implement ACA Medicaid expansion and policymakers consider additional public health insurance options, as well as programs like CHOICE and the MISSION Act that increase veteran choices of traditional VA and community care providers.

Effects of State-level Medicaid Expansion on Veterans Health Administration Dual Enrollment and Utilization: Potential Implications for Future Coverage Expansions

OBJECTIVE

The objective of this study was to examine how pre-Affordable Care Act (ACA) state-level Medicaid expansions affect dual enrollment and utilization of Veterans Health Administration (VA) and Medicaid-funded care.

RESEARCH DESIGN

We employed difference-in-difference analysis to determine the association between pre-ACA Medicaid expansions in New York and Arizona in 2001 and VA utilization. Participants' dual enrollment in Medicaid and VA, the distribution of their annual hospital admissions and emergency department (ED) visits between VA and Medicaid were dependent variables. We controlled for age, race, sex, disease burden, distance to VA facilities and income-based eligibility for VA services.

MEASURES

Secondary data collected from 1999 to 2006 in 2 states expanding Medicaid and 3 demographically similar nonexpansion states. We obtained residency, enrollment and utilization data from VA's Corporate Data Warehouse and Medicaid Analytic Extract files.

RESULTS

For low-income Veterans, Medicaid expansion was associated with increased dual enrollment of 4.87 percentage points (99% confidence interval: 4.48-5.25), a 4.63-point decline in VA proportion of admissions (-5.87 to -3.38), and a 11.70-point decrease in the VA proportion of ED visits (-13.06 to -10.34). Results also showed increases in the number of total (VA plus Medicaid) annual per-capita hospitalizations and ED visits among the group of VA enrollees most likely to be eligible for expansion.

CONCLUSIONS

This study shows slight usage shifts when Veterans gain access to non-VA care. It highlights the need to overcome care-coordination challenges among VA patients as states implement ACA Medicaid expansion and policymakers consider additional expansions of public health insurance programs such as Medicare-for-All.

The Impact of Medicaid Enrollment on Veterans Health Administration Enrollees' Behavioral Health Services Use

OBJECTIVE

To examine Veterans Health Administration (VA) enrollees' use of VA services for treatment of behavioral health conditions (BHCs) after gaining Medicaid, and if VA reliance varies by complexity of BHCs.

DATA SOURCES/STUDY SETTING

VA and Medicaid Analytic eXtract utilization data from 31 states, 2006-2010.

STUDY DESIGN

A retrospective, longitudinal study of Veterans enrolled in VA care in the year before and year after enrollment in Medicaid among 7,249 nonelderly Veterans with serious mental illness (SMI), substance use disorder (SUD), posttraumatic stress disorder (PTSD), depression, or other BHCs.

DATA COLLECTION/EXTRACTION METHODS

Utilization and VA reliance (proportion of care received at VA) for BH outpatient and inpatient services in unadjusted and adjusted analyses.

PRINCIPAL FINDINGS

In adjusted analyses, we found that overall Veterans did not significantly change their use of VA outpatient BH services after Medicaid enrollment. In beta-binomial models predicting VA BH outpatient reliance, veterans with SMI (IRR = 1.38, p < .05), PTSD (IRR = 1.62, p < .01), and depression (IRR = 1.36, p < .05) had higher reliance than veterans with other BHCs after Medicaid enrollment.

CONCLUSIONS

While veterans did not change the amount of VA outpatient BH services they used after enrolling in Medicaid, the proportion of care they received through VA or Medicaid varied by BHC.

External Determinants of Veterans' Utilization of VA Health Care

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.

Use of Veterans Affairs and Medicaid Services for Dually Enrolled Veterans

OBJECTIVES

To examine how dual coverage for nonelderly, low-income veterans by Veterans Affairs (VA) and Medicaid affects their demand for care.

DATA SOURCES

Veterans Affairs utilization data and Medicaid Analytic Extract Files.

STUDY DESIGN

A retrospective, longitudinal study of VA users prior to and following enrollment in Medicaid 2006-2010.

DATA COLLECTION/EXTRACTION METHODS

Veterans Affairs reliance, or proportion of care provided by VA, was estimated with beta-binomial models, adjusting for patient and state Medicaid program factors.

PRINCIPAL FINDINGS

In a cohort of 19,890 nonelderly veterans, VA utilization levels were similar before and after enrolling in Medicaid. VA outpatient reliance was 0.65, and VA inpatient reliance was 0.53 after Medicaid enrollment. Factors significantly associated with greater VA reliance included sociodemographic factors, having a service-connected disability, comorbidity, and higher state Medicaid reimbursement. Factors significantly associated with less VA reliance included months enrolled in Medicaid, managed care enrollment, Medicaid eligibility type, longer drive time to VA care, greater Medicaid eligibility generosity, and better Medicaid quality.

CONCLUSION

Veterans Affairs utilization following new Medicaid enrollment remained relatively unchanged, and the VA continued to provide the large majority of care for dually enrolled veterans. There was variation among patients as Medicaid eligibility and other program factors influenced their use of Medicaid services.

Longer wait times affect future use of VHA primary care

BACKGROUND

Improving access to the Veterans Health Administration (VHA) is a high priority, particularly given statutory mandates of the Veterans Access, Choice and Accountability Act. This study examined whether patient-reported wait times for VHA appointments were associated with future reliance on VHA primary care services.

METHODS

This observational study examined 13,595 VHA patients dually enrolled in fee-for-service Medicare. Data sources included VHA administrative data, Medicare claims and the Survey of Healthcare Experiences of Patients (SHEP). Primary care use was defined as the number of face-to-face visits from VHA and Medicare in the 12 months following SHEP completion. VHA reliance was defined as the number of VHA visits divided by total visits (VHA+Medicare). Wait times were derived from SHEP responses measuring the usual number of days to a VHA appointment with patients' primary care provider for those seeking immediate care. We defined appointment wait times categorically: 0 days, 1day, 2-3 days, 4-7 days and >7 days. We used fractional logistic regression to examine the relationship between wait times and reliance.

RESULTS

Mean VHA reliance was 88.1% (95% CI = 86.7% to 89.5%) for patients reporting 0day waits. Compared with these patients, reliance over the subsequent year was 1.4 (p = 0.041), 2.8 (p = 0.001) and 1.6 (p = 0.014) percentage points lower for patients waiting 2-3 days, 4-7 days and >7 days, respectively.

CONCLUSIONS

Patients reporting longer usual wait times for immediate VHA care exhibited lower future reliance on VHA primary care.

IMPLICATIONS

Longer wait times may reduce care continuity and impact cost shifting across two federal health programs.

Did Massachusetts Health Reform Affect Veterans Affairs Primary Care Use?

Massachusetts Health Reform (MHR), implemented in 2006, introduced new health insurance options that may have prompted some veterans already enrolled in the Veterans Affairs Healthcare System (VA) to reduce their reliance on VA health services. This study examined whether MHR was associated with changes in VA primary care (PC) use. Using VA administrative data, we identified 147,836 veterans residing in Massachusetts and neighboring New England (NE) states from October 2004 to September 2008. We applied difference-in-difference methods to compare pre–post changes in PC use among Massachusetts and other NE veterans. Among veterans not enrolled in Medicare, VA PC use was not significantly different following MHR for Massachusetts veterans relative to other NE veterans. Among VA–Medicare dual enrollees, MHR was associated with an increase of 24.5 PC visits per 1,000 veterans per quarter ( p = .048). Despite new non-VA health options through MHR, VA enrollees continued to rely on VA PC.

VA and Medicare Utilization Among Dually Enrolled Veterans with Type 2 Diabetes: A Latent Class Analysis

BACKGROUND

Many Veterans treated within the VA Healthcare System (VA) are also enrolled in fee-for-service (FFS) Medicare and receive treatment outside the VA. Prior research has not accounted for the multiple ways that Veterans receive services across healthcare systems.

OBJECTIVE

We aimed to establish a typology of VA and Medicare utilization among dually enrolled Veterans with type 2 diabetes.

DESIGN

This was a retrospective cohort.

PARTICIPANTS

316,775 community-dwelling Veterans age ≥ 65 years with type 2 diabetes who were dually enrolled in the VA and FFS Medicare in 2008-2009.

METHODS

Using latent class analysis, we identified classes of Veterans based upon their probability of using VA and Medicare diabetes care services, including patient visits, laboratory tests, glucose test strips, and medications. We compared the amount of healthcare use between classes and identified factors associated with class membership using multinomial regression.

KEY RESULTS

We identified four distinct latent classes: class 1 (53.9%) had high probabilities of VA use and low probabilities of Medicare use; classes 2 (17.2%), 3 (21.8%), and 4 (7.0%) had high probabilities of VA and Medicare use, but differed in their Medicare services used. For example, Veterans in class 3 received test strips exclusively through Medicare, while Veterans in class 4 were reliant on Medicare for medications. Living ≥ 40 miles from a VA predicted membership in classes 3 (OR 1.1, CI 1.06-1.15) and 4 (OR 1.11, CI 1.04-1.18), while Medicaid eligibility predicted membership in class 4 (OR 4.30, CI 4.10-4.51).

CONCLUSIONS

Veterans with diabetes can be grouped into four distinct classes of dual health system use, representing a novel way to characterize how patients use multiple services across healthcare systems. This classification has applications for identifying patients facing differential risk from care fragmentation.