Chuan-Fen Liu, PhD, MPH

In 2010, the Veterans Health Administration (VA) began national implementation of its patient-centered medical home (PCMH) model, called Patient Aligned Care Teams (PACTs), to improve access, coordination, and patient-centered care. We evaluated changes in reported implementation of PCMH components in all VA primary care clinics, and patients’ utilization of acute and non-acute care and total costs after 2 years.

 Longitudinal study of 2,607,902 patients from 796 VA primary care clinics.

Clinics were surveyed for their implementation of PCMH components. Patient outcomes were measured by outpatient visits for primary care, specialty care, telephone care, and emergency department (ED) care; hospitalizations for an ambulatory care—sensitive condition (ACSC); and costs of VA care in fiscal years (FYs) 2009 and 2011. Multi-level, multivariable models predicted changes in utilization and costs, adjusting for patients’ health status, clinic PCMH component scores, and a patient fixed effect.

 Clinics reported large improvements in adoption of all PCMH components from FY 2009 to FY 2011. Higher organization of practice scores was associated with fewer primary care visits (

= .012). Greater care coordination/transitions was modestly associated with more specialty care visits (

= .018), but quality/performance improvement was associated with more ED visits (

= .032). None of the PCMH components were significantly related to telephone visits, ACSC hospitalizations, or total healthcare costs.

 Improvements under organization of practice and care coordination/transitions appear to have impacted outpatient care, but reductions in acute care were largely absent.

Take-up of medical home components appeared to be high after Patient Aligned Care Teams (PACTs) began in the Veterans Health Administration. However, while improvements under organization of practice and care coordination/transitions seemed to have impacted outpatient care, reductions in acute care were largely absent.

		Identifying areas that would benefit from ongoing improvement is critical for the long-term success of PACTs.

		Better measures of PACTs implementation are also needed to evaluate the longer-term consequences of PACTs.

The spread of the patient-centered medical home (PCMH) model to numerous healthcare systems can be attributed to the promise of revitalized primary care delivery through team-based care aiming to improve continuity, coordination, quality, and access, as well as to reduce costs. Based on these principles, the Veterans Health Administration (VA) began a vast initiative in 2010 to implement the PCMH model, called Patient Aligned Care Teams (PACTs), across the entire VA healthcare system, which included more than 800 primary care clinics caring for more than 5 million patients.

Several healthcare systems have implemented pilot programs of the PCMH and found significant improvements in quality of care, reductions in acute care, and cost savings.

 The PACT model is expected to experience similar results since it focuses on improving access to primary care, increasing telephone and other non—face-to-face care, as well as reducing unnecessary emergency department (ED) visits and hospitalizations. Lower costs are expected, in part, from a reduction in (often very costly) acute care episodes.

An early PACT evaluation found that after implementation, there were significant changes: greater use of telephone care, fewer face-to-face visits, improved appointment access and continuity, and better follow-up after hospital discharge.

However, PACT models encompassed a multiplicity of PCMH features that ranged from routinely scheduling same-day appointments to tracking specialty consultations, and from coordinating post discharge care to the use of electronic decision support systems. Baseline adoption of these features was highly variable across clinics

—and clinics may have implemented new PACT features to varying degrees&mdash;so it is unknown how differences in early implementation influenced utilization and costs.

This study evaluated the changes in adoption of different PCMH components by VA clinics, and the relationship to patients’ utilization of acute and non-acute care and total costs after 2 years. We focused on a cohort of primary care patients who used VA care from 2009 to 2011, and were therefore most likely to experience the effects of PACT. This examination can guide ongoing PACT implementation, suggesting what PCMH components and aspects of care might be emphasized most productively in later stages.

We conducted a longitudinal study of a panel of primary care patients using data from before PACT implementation (fiscal year [FY] 2009) and shortly after PACT implementation began (FY 2011); VA FYs begin the October 1 prior and end September 30, of the indicated year. We obtained VA patient-level utilization and cost records and linked them to clinic-level data for FYs 2009 and 2011. Patient data included sociodemographic characteristics, VA inpatient and outpatient utilization, diagnoses, lab test results, and costs of all care; clinic data included clinic characteristics and reporting of PCMH features. The study cohort was limited to patients who had at least 2 primary care visits in FY 2009 and used any VA outpatient care in FY 2011; altogether, 544,984 patients were excluded. Patients were linked to the clinic where they had a plurality of visits. The study cohort included 2,607,902 patients from 796 clinics; our cohort was older, predominantly male, more likely to be service-connected or below the means test, and sicker based on risk scores and chronic conditions compared with excluded patients. This study received approval from the Stanford University Institutional Review Board (protocol #20124).

Non-acute care outcomes were measured for each patient by number of primary care visits, specialty care visits, and telephone visits. Acute care outcomes were measured by ED visits and potentially avoidable hospitalizations. We also measured total annual costs of care in each study year. Outpatient visit data were obtained from the VA Medical Statistical Analysis System utilization files and categorized based on the visit location for outpatient services. Potentially avoidable hospitalizations were obtained from inpatient records and identified by primary diagnosis for an ambulatory care—sensitive condition (ACSC).

The total annual costs were obtained from Decision Support System records for services provided by the VA, and from Fee Basis records for services provided by non-VA providers and paid by the VA. Because of the short study period, we did not adjust costs for inflation.

Our main independent variable, reporting of PCMH features, came from the American College of Physicians Medical Home Builder (MHB) survey administered to all VA primary care clinics in 2009 and 2011. The MHB is a self-administered practice advisor used to support practices toward recognition of National Committee for Quality Assurance (NCQA) 2008 PCMH standards.

The MHB covers 7 PCMH components: patient-centered care and communication (support for patients’ self-management and decision making and staff communication training); access and scheduling (scheduling flexibility such as same-day appointments and non—face-to-face services); care coordination and transitions in care (coordinating visits to other physicians, creating individualized treatment plans, assessing treatment barriers); organization of practice (tracking procedures, test results, medication lists, team huddles); population management (patient registries, clinical guidelines, identifying unmet needs); quality improvement and performance improvement (performance measures, satisfaction surveys); and use of technology (practice management systems, electronic health records, decision support systems). These components overlap with 6 of NCQA’s standards for receiving PCMH certification, so the MHB captures elements similar to NCQA’s measures.

The MHB was self-administered by the clinic director or other clinic leader, and the survey response rate was 100% in 2009, and 98% in 2011. The sum of PCMH features within each component was obtained for the component scores. The component scores were grouped into low and high categories based on the baseline distribution below or above the median.

In multivariable models, we included measures of time-varying patient factors in both years. We measured the Charlson Comorbidity Index (CCI) score, a measure of comorbidity burden, for each patient based on diagnoses from inpatient and outpatient records.

We also created an indicator of risk based on available lab results reported during the study years. We chose lab tests that could, if increasing or decreasing during the study period, indicate higher risk of morbidity and mortality: cholesterol tests (>100 mg/dL), hemoglobin A1C test (>8%), anemia hemoglobin (<13 g/dL), and kidney function (eGRF <45).

For descriptive purposes, we measured several patient and clinic characteristics in the baseline year that were time invariant. Clinic measures included clinic rurality, assessed by metropolitan/nonmetropolitan codes from the Area Resource File.

Clinic type was categorized as based at a VA medical center (VAMC); VA-owned community-based outpatient clinic (CBOC)—owned by the medical center and staffed by VA providers; leased CBOC&mdash;staffed by VA providers with VA governance; or contracted CBOC&mdash;staffed by contract providers without VA governance.

Patient-level measures, including age, sex, race/ethnicity, marital status, service connection, means test, and insurance, were obtained from utilization records in FY 2009. Area income and education came from US Census data on income and maximum years of educational attainment linked to patients’ zip codes. We also estimated patients’ distance to their primary care clinic.

In bivariate analyses, we compared the mean number of primary care, specialty care, telephone care, and ED visits, and ACSC hospitalizations, and total costs of care per patient in FY 2009 and FY 2011 using 1-way ANOVA with year as the independent variable to determine significant changes over time. In multivariate analyses, we estimated the longitudinal association between changes in PCMH components scores with changes in utilization and cost measures using multi-level, linear regression models for each outcome. Regression models included a fixed effect for each patient and adjusted for time-varying patient factors (risk indicator and CCI score), dummy variables for high versus low clinic PCMH component scores, and an indicator for year. The regression model for costs used the log of costs as the dependent variable since costs had a skewed distribution.

In our models, the fixed effect controls for all factors fixed over time, such as patient sociodemographic factors and clinic characteristics (eg, rurality and size). The model estimates the “within” estimator; the coefficients represent the effect of a unit change in each time-varying predictor on patient outcomes.

The coefficient for the year indicator represented the time trend in outcomes that was not explained by changes in PCMH components or health status. Additionally, regression models adjusted standard errors to account for cluster (the intra-class correlations between patients within a primary care site).

We also conducted regressions using Poisson models for utilization measures since they were counts, but standard errors could not be adjusted for clustering within clinics since we used a fixed effect. The effect sizes were similar with linear models, and these results are presented in the 

). All regressions and data analyses were conducted using Stata 11.0 (StataCorp LP, College Station, Texas).

At baseline, most of the primary care patients in the study cohort were middle-aged (45%) or elderly (46%) (

). The study cohort was predominantly male (95%), a majority were white (67%), and a majority of patients were also married (58%). The largest proportion of veterans (42%) was eligible for VA care because of a service-connected disability, or they were within 5 years of military service. Relatively few patients (19%) had a disability rating greater than 50%, and many patients (40%) reported no other health insurance. Patients traveled a mean distance of 18 miles to their primary care clinic, the mean income of their area of residence was $40,541, and less than half of patients (46%) lived in areas where more than one-fourth of the population had a college degree.

The percent of patients with high risk (as measured by lab values) for cholesterol, diabetes, anemia, or kidney function decreased slightly from 61% of the cohort in FY 2009, to 56% in FY 2011. The CCI score, which measured total comorbidity burden, increased from 0.86 (SD = 0.84) to 0.90 (SD = 0.85) during that time.

In the baseline year, the low and high categories of each PCMH component represented roughly equal distributions of clinics; the exceptions were access and scheduling, which had more scores in the low category (

). The range of the component scores overall, and for low and high categories, is also shown in Table 2. The proportion of clinics in the high-scoring groups increased over time, and the majority of clinics reported PCMH component scores in the high-scoring groups for all components in FY 2011. Almost one-fifth of primary care clinics represented in the study sample were large VAMC-based clinics, and most were community-based outpatient clinics (CBOCs), with the majority of these being leased CBOCs. Almost one-third of clinics were in nonmetropolitan areas.

Changes in Utilization and Costs Over Time

During the study period from just prior to widespread PACT implementation to 2 years after PACT implementation began, the mean number of primary care visits decreased from 4.81 to 3.99 visits per patient, which represented a 17% decrease. The rate of specialty care visits was negligibly higher, and telephone visits rose by 85% (all 

). ED visits per patient rose slightly (7%), and ACSC hospitalizations per patient also rose from 0.02 to 0.03 per patient (all

<.001). The mean total costs of VA care increased from $8469 to $9887 per patient during the study period (

Increased Medical Home Features and Utilization and Costs

Multi-level, multivariable models adjusted for both time-varying factors and fixed effects for time-invariant factors, and estimated how changes in reported PCMH components in primary care clinics explained some of these increases or decreases in use of VA care over time (

). High organization of practice scores was significantly related to 0.13 fewer mean primary care visits per patient compared with patients in low-scoring clinics (

= .012). High care coordination and transitions in care scores were significantly related to 0.06 more specialty care visits compared with the low scores (

= .010), after controlling for patients’ health status and time trend. The change in telephone visits was significantly higher over time (

<.001), but changes due to increases from low to high PCMH scores across all components were not statistically significant. High scores in care coordination and transitions in care decreased the mean number of ED visits by 0.04 visits per patient (

= .018), but high quality and performance improvement increased ED visits by 0.03 visits per patient (

= .032), relative to low scores. Admissions for an ACSC also increased over time after adjusting for worse health status (both 

<.001), and none of the PCMH components were related to higher admissions. The only significant increase in costs was explained by high risk or worse comorbidity (

<.001), and again, none of the PCMH component scores were significantly related to total costs per patient.

VA primary care clinics reported large improvements in adoption of all PCMH components from FY 2009 to FY 2011, and there were significant changes in utilization and costs for a cohort of primary care patients during this time. While various PCMH components (eg, access and scheduling) were hypothesized to increase access to and frequency of primary care, the only component that was related in adjusted models was organization of practice. Features such as team huddles and tracking lab tests were actually associated with fewer primary care visits per patient, possibly through better efficiency of primary care practice. Greater specialty care visits were modestly related to higher care coordination/transitions in care scores, so better procedures to coordinate care appeared to facilitate referrals to specialty care.

While increased care coordination/transitions of care was significantly related to fewer ED visits, higher quality and performance improvement scores were related to more ED visits. Care coordination features that reduced problems due to transitions from hospitalization or referral appeared to reduce ED care that may be unnecessary. It is unclear why greater quality/performance improvement features would be related to more use of ED care by patients. There was a significant increase in telephone visits over time, and although it was not related to any specific changes from low to high PCMH components, the push overall to increase virtual care under PACT may have led to increased telephone care. None of the PCMH components were related to potentially avoidable hospitalizations for an ACSC, although the rates in both years were very low in this cohort. Changes in healthcare costs were also not associated with increased PCMH component scores.

Our findings contribute new evidence on the specific aspects of PCMH implementation that are related to different types of healthcare utilization, and some of these results are consistent with early evidence from a PCMH demonstration in Group Health, which showed that patients had higher rates of telephone visits and specialist visits, and lower rates of ED visits in PCMH sites after 1 year.

In addition, the Group Health demonstration showed significant reductions in hospitalizations and costs per patient after 2 years, which we did not observe.

 Unlike the Group Health demonstration, which was implemented in 1 clinic, VA’s PACT initiative was rolled out to all clinics—that have considerable heterogeneity in size, staffing, governance, quality improvement orientation, and patient populations served&mdash;which may partly explain differences in evaluation results.

An earlier VA study found that higher baseline adoption of PCMH components of access/scheduling and care coordination/transitions were significantly related to lower risk of avoidable hospitalizations among primary care patients.

While we found that increased scores of these PCMH components were not related to avoidable hospitalizations, there may be a lag between implementation of features and observing effects in patients’ outcomes. Moreover, reducing ACSC hospitalizations in a stable cohort of patients may be difficult to achieve.

It is also unknown to what extent individual PCMH features (eg, using an open-access model or maintaining a disease registry) were fully functioning. The percent of clinics in the high categories of all medical home components score jumped during the study period so that most clinics reported adopting a high number of possible PCMH features. Hence, some implementation may have been rudimentary. Preliminary evidence suggests wide variation in how VA practices adopted various PCMH features, such as enhancing open access through practice redesign, or new interventions

 with successful implementation influenced by local VA leadership engagement, staffing resources, and access to information and knowledge.

Additional challenges, including defining team roles and interpersonal confl ict, have also caused implementation barriers in some sites.

 In addition to site-specific factors affecting PACT implementation, the total number of VA enrollees increased from 5.1 million veterans in 2009, to 5.5 million veterans in 2011.15 The challenges of serving a larger volume of patients may have contributed to fewer face-to-face visits and placed additional burdens on providers in some sites.

The Medical Home Builder survey was self-reported, so we were not able to validate the adoption of reported PCMH features; because of ongoing PACT evaluation efforts, clinics may have been motivated to provide more positive responses as a result. While the statistical methods used here accounted for measurable and unmeasurable fixed factors over time, it did not account for any time-varying omitted factors, so if unmeasured time-varying factors such as some other technological change were related to reported PCMH features and patients’ utilization and costs, the effects of PCMH features would be biased.

We did not have reports of non-VA utilization, so if patients shifted to using more non-VA care such as ED visits, we could not measure this change. Finally, we focused on regular primary care users who continued to use VA outpatient care during the study period, so our results may not be generalizable to less frequent users of primary care or new users who were younger and had different demands for VA care.

These results document short-term changes during the early implementation of PACT that can provide feedback as PACT continues to be implemented and developed. While take-up of many PCMH features appeared to be high after the initiative began, certain improvements in care under organization of practice and care coordination/transitions appear to have affected only certain types of non-acute outpatient care; reductions in acute care ED visits and potentially avoidable hospitalizations largely remain to be seen. Identifying areas for ongoing improvement is critical to ensuring long-term success of care transformation under PACT. Refining PACT models in later stages may require more than adding new interventions; it may involve evaluation of staffing and other resource needs, better understanding of cost-effective care, ensuring support of clinical leadership, and widespread dissemination of successful practices. Better measures of PACT implementation are also needed to evaluate the longer-term consequences of PACT.

The authors wish to acknowledge Jack Needleman and Ciaran Phibbs for valuable comments on earlier drafts.

Health Economics Resource Center (JY, JL), and Center for Innovation to Implementation (JY), VA Palo Alto Health Care System, Menlo Park, CA; Center for Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound (C-FL), Seattle, WA; Department of Health Services, University of Washington (C-FL), Seattle, WA; Patient Care Services, Veterans Health Administration (GS, RS), Washington, DC; Center of Innovation for the Study of Healthcare Innovation, Implementation, & Policy, VA Greater Los Angeles (LVR, EMY), Sepulveda, CA; RAND Corp (LVR), Santa Monica, CA; Department of Health Policy and Management, Fielding School of Public Health (LVR, EMY), and School of Medicine (LVR), University of California, Los Angeles, CA.

This work was supported by the Department of Veterans Affairs, Veterans Health Administration, Patient Care Services (XVA 65-018). Dr Yano’s effort was covered by a VA HSR&D Senior Research Career Scientist Award (Project #RCS 05-195). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States federal government.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. VA Patient Care Services designed and administered the survey, and Drs. Stark and Schectman from VA Patient Care Services contributed to the interpretation of results.

 Concept and design (JY, LVR, EMY, CL); acquisition of data (EMY); analysis and interpretation of data (JY, CL, JL); drafting of the manuscript (JY, CL); critical revision of the manuscript for important intellectual content (LVR, EMY, RS, GS); statistical analysis (JY, JL); provision of patients or study materials (RS, GS); obtaining funding (LVR, EMY); administrative, technical, or logistic support (JL); supervision (JY, LVR, EMY).

Jean Yoon, PhD, MHS, 795 Willow Rd (152 MPD), Menlo Park, CA 94025. E-mail: 

1. Reid RJ, Coleman K, Johnson EA, et al. The Group Health medical home at year two: cost savings, higher patient satisfaction, and less burnout for providers. 

2. Reid RJ, Fishman PA, Yu O, et al. Patient-centered medical home demonstration: a prospective, quasi-experimental, before and after evaluation. 

Articles

Significant changes were found in patients' utilization of healthcare related to early implementation of medical home components in VA primary care clinics.

Early Changes in VA Medical Home Components and Utilization

	Veterans Health Administration (VA) operates the largest integrated health system in the nation. The Affordable Care Act (ACA) does not require any changes to VA, but the individual mandate and expanded health insurance options may change veterans’ preferences for coverage. We examined the impact of healthcare reform in Massachusetts, which also included these policy changes, on veterans’ enrollment in VA, private insurance, and Medicaid.

	Massachusetts’ healthcare reform in June 2006 served as a natural experiment. Using data from the 2004-2013 Current Population Surveys, we examined enrollment in VA, private insurance, and Medicaid, comparing veterans residing in Massachusetts with veterans residing in neighboring New England states that did not undergo health reform.

	We estimated the probability of being enrolled in VA, private insurance, and Medicaid before and after healthcare reform, using multivariate probit models while adjusting for individual characteristics. Using a difference-in-difference approach, we compared pre-post changes in enrollment probability among Massachusetts and non-Massachusetts veterans, respectively.

	Compared with other New England veterans, Massachusetts veterans decreased their enrollment in VA and private insurance by 0.2 (

 = .666) percentage points, respectively, following health reform. In contrast, Medicaid enrollment increased by 2.5 percentage points (

	Healthcare reform in Massachusetts was associated with greater Medicaid enrollment, but was not significantly associated with VA and private insurance enrollment. Our results are significant for informing VA fiscal planning in the post ACA era.

	Prior studies examining healthcare reform in Massachusetts have not measured the impact on enrollment in the Veterans Health Administration (VA), the largest integrated health system in the United States.

		Overall, healthcare reform was not significantly associated with VA enrollment; this relationship, however, was contingent on the state of the economy.

		Veterans who might otherwise have enrolled in VA or private insurance opted for Medicaid instead.

		Understanding the impact of healthcare reform in Massachusetts is important to inform policy makers about veterans’ likely VA enrollment in the post Affordable Care Act era.

The Veterans Health Administration (VA) is the largest integrated health system in the United States. In fiscal year 2012, 8.8 million of the nation’s 21.2 million veterans were enrolled in VA.

 The minimum requirement for VA enrollment is veteran status, defined as discharge from active military, naval, or air service for any reason other than dishonorable. Veterans must actively apply for VA health benefits and are enrolled based on service-related disability, financial means, or special circumstances, such as prisoner of war status.2 Once enrolled, veterans have full access to all VA health services. Veterans who are sufficiently disabled due to their military service or have income below a means-tested threshold may qualify for VA care exempt from co-payments. All other veterans are required to make co-payments when obtaining VA services, including $15 and $50 co-payments for primary and specialty care visits, respectively.

Veterans who enroll in VA can concurrently enroll in and obtain care through other health insurance programs including Medicare, Medicaid, and private insurance. Survey data indicate that 77% of VA enrollees have at least 1 other source of health coverage.

 Dual enrollment is particularly common among VA enrollees who are eligible for Medicare.

 According to studies in Medicare-eligible veterans’ preference for care prior to Part D and the Affordable Care Act (ACA), veterans’ choice of VA care was driven, in part, by other available provider options.

To date, no studies have examined the potential impact of the ACA on veterans’ enrollment in VA and use of VA health services. Veterans eligible for VA health benefits may continue to use VA as they did before ACA, but specific aspects of healthcare reform may change the way veterans interact with VA. Taken together, these elements of healthcare reform may cause total VA enrollment and enrollees’ use of VA services to increase or decrease markedly. VA enrollment may decrease for at least 2 reasons, since ACA increases the number of health plan options available through health in- surance exchanges and the expansion of Medicaid. First, uninsured veterans who might otherwise rely on VA may take advantage of these new options. Second, these new non-VA options may also be appealing to low-income veterans already enrolled in VA if they are eligible for health insurance subsidies or are able to take advantage of expanded Medicaid coverage.

At least 2 aspects of healthcare reform may result in greater VA enrollment. First, veterans with commercial insurance may seek VA care if small businesses decide to stop offering coverage as a result of the employer mandate. A prior study found employees of small firms faced higher insurance premiums and greater out-of-pocket costs after healthcare reform in Massachusetts.

 Second, if the options in health insurance exchanges or the Medicaid expansion are not appealing, uninsured veterans who did not previously use VA may seek health coverage through VA in order to satisfy the requirements of the individual mandate under ACA.

In this study, we sought to examine the potential impact of ACA on veterans’ enrollment in VA, private insurance, and Medicaid. We used the Massachusetts Health Care Reform Act (MHCRA), implemented in June 2006, as a proxy for ACA. MHCRA includes many of the components currently in ACA, so examining the impact of MHCRA on veterans’ enrollment in VA, private insurance, and Medicaid may inform policy makers about veterans’ likely VA enrollment from other status in the post ACA era.

Data for this study were drawn from the Current Population Survey (CPS) over the period 2004-2013.

 CPS is a publicly available database jointly sponsored by the US Census Bureau and the Bureau of Labor Statistics. CPS is traditionally known for labor market statistics; however, individual-level sociodemographic, behavioral, and health measures such as health insurance coverage are also included. Sample households in CPS responded to a core set of questions on a monthly basis for 4 months, then were not interviewed for 8 months, and finally were re-interviewed for 4 additional months. Supplemental questions, including those related to health plan coverage, were asked annually in the CPS March Supplement.

Implementation of the MHCRA began in June 2006, which served as a natural experiment because the MHCRA introduced an individual mandate as well as greater health insurance options and an expansion of Medicaid. Since MHCRA was a natural experiment, any enrollment changes among Massachusetts veterans after 2006 are likely to be attributable to MHCRA and not confounded by other factors. To consider the effect of healthcare reform in Massachusetts, we examined enrollment in VA, private insurance, and Medicaid, comparing veterans residing in Massachusetts with veterans residing in neighboring states in New England that did not undergo health reform. Using CPS data, we identified 131,330 individuals self-identified as veterans who completed the March Supplement during the period 2004-2013. We defined the treatment group as the set of veterans residing in Massachusetts. Correspondingly, the control group consisted of all other New England veterans residing in Connecticut, Maine, New Hampshire, Rhode Island, or Vermont. We defined the pre-MHCRA period as the interval from March 2004 to March 2006 and the post MHCRA period as March 2007 to March 2013. Of the sample of New England veterans, 4581 were from the pre-MHCRA period (630 Massachusetts and 3951 non-Massachusetts) and 9535 were from the post MHCRA period (1053 Massachusetts and 8482 non-Massachusetts). The unit of analysis was individual-level observations.

Enrollment in VA, Medicaid, and Private Health

The CPS March Supplement contains a series of yes/no questions to ascertain individuals’ sources of health coverage. Our primary outcome, VA enrollment, was a dichotomous variable equal to 1 if an individual reported being covered by VA at any time during the prior calendar year. We defined analogous measures for private insurance and Medicaid as secondary outcomes. We considered individuals to have private health insurance if coverage was obtained through an employer, purchased on the private market, or obtained through another individual’s plan. Enrollment outcomes (VA, private insurance, and Medicaid) are not mutually exclusive since veterans are able to concurrently enroll in VA and other health plans.

  The impact of MHCRA on veterans’ enrollment in VA, private insurance, and Medicaid was examined using a difference-in-difference approach (DID).10 Specifically, we calculated the change in the adjusted probability of being enrolled in VA from the pre-period prior to MHCRA to the postperiod after MHCRA for veterans in Massachusetts and outside of Massachusetts, respectively. We then subtracted the pre-post change among Massachusetts veterans by the pre-post change among the non-Massachusetts veterans to produce the change in VA enrollment probability attributable to MHCRA. This DID estimate accounts for trends in VA enrollment that were common to all veterans and would otherwise confound estimated treatment effects. Analogous DID estimates were calculated for private insurance and Medicaid enrollment. Because outcome variables measure enrollment over a calendar year, DID estimates reflect the change in average annual enrollment.

We calculated the adjusted probability of being enrolled in VA, private insurance, and Medicaid jointly using multivariate probit models. We estimated a 3-equation model allowing for correlation in the error terms across equations. The multivariate probit model allows for veterans to be enrolled in 1 or more sources. All models include 3 explanatory variables to generate probabilities for the DID approach: indicator for Massachusetts veteran, indicator for post MHCRA observation, and the interaction between these 2 variables. Regression models also adjusted for demographics (age, gender, marital status, race, education, income, residence in urban area, household size), employment status, self-reported health, and disability.

All analyses were weighted to the average annual veteran population in New England over the 10-year sample period. Standard errors for regression coefficients were calculated using a bootstrap procedure.11 The delta method was then used to estimate DID standard errors. A nominal P value of .10 was used to test statistical significance. Statistical models were fit using the STATA Software (Version 12.0, College Station, Texas).

Veterans Before and After Healthcare Reform

presents descriptive statistics for veterans in the pre- and post MHCRA periods, stratified by state of residence (Massachusetts or non-Massachusetts) and weighted to the average annual population of New England veterans. Prior to MHCRA, veterans in Massachusetts were older (P = .009), less likely to be married (P =.001), and more likely to have postbaccalaureate education (P <.001), have a disability preventing work (P =.032), and reside in an urban area (P <.001), compared with non-Massachusetts veterans. After implementation of MHCRA, veterans in Massachusetts remained older (P <.001), more likely to have postbaccalaureate education (P <.001), and more likely to reside in urban areas (P <.001) compared with non-Massachusetts veterans. Massachusetts veterans were also less likely to be employed full-time (P = .005) or part-time (P = .007) following MHCRA.

Unadjusted Enrollment in VA, Private Insurance,

presents unadjusted rates of enrollment in VA, private insurance, and Medicaid in Massachusetts and other neighboring New England states before and after MHCRA. VA enrollment increased 2.1 percentage points from the pre-MHCRA period to the post MHCRA period for Massachusetts veterans (pre-MHCRA = 5.3%, post MHCRA = 7.4%) and 3.2 percentage points for non- Massachusetts veterans (pre-MHCRA = 6.2%, post MHCRA = 9.4%). Private insurance enrollment decreased 0.5 percentage points for Massachusetts veterans, from 72.2% pre-MHCRA to 71.7% post MHCRA, but decreased 2.3 percentage points from pre-MHCRA to post MHCRA (72.9% to 70.6%) for non-Massachusetts veterans. Medicaid enrollment increased 2.2 percentage points from the pre-MHCRA period to the post MHCRA period for Massachusetts veterans (pre-MHCRA = 5.9%, post MHCRA = 8.1%) and 1.0 percentage point for non-Massachusetts veterans (pre-MHCRA = 5.8%, post MHCRA = 6.8%). 

 presents unadjusted trends in VA enrollment for Massachusetts and non-Massachusetts veterans, respectively. Trends in VA enrollment were similar across groups in the pre-MHCRA (2004-2006) period. In the 2 years following MHCRA (2007-2008), VA enrollment decreased for both groups; however, the decline was more substantial among Massachusetts veterans. In 2009, VA enrollment increased sharply in the Massachusetts group compared with a more modest increase in the non-Massachusetts group. Between 2011 and 2013, VA enrollment decreased modestly among Massachusetts veterans, but increased among non-Massachusetts veterans. 

Private insurance enrollment was higher among non-Massachusetts veterans over the period 2004-2010; however, trends were parallel over this period. In 2011, enrollment increased sharply among Massachusetts veterans, then decreased sharply over the period 2012-2013. In contrast, enrollment among non-Massachusetts veterans decreased in 2011 and remained stable through 2013.

Trends in Medicaid enrollment were parallel over the period 2004-2007, diverged in 2008, and returned to similar levels in 2009. Among Massachusetts vet- erans, Medicaid enrollment was sharply higher in 2010 and 2011, but decreased substantially in 2012. In contrast, Medicaid enrollment remained constant between 2009 and 2011, but increased markedly in 2012 among non-Massachusetts veterans.

Impact of Healthcare Reform on VA, Private Insurance,

), the adjusted probability of being enrolled in VA increased 2.4 percentage points between the pre- and post MHCRA periods (from 5.3% to 7.7%). For veterans in neighboring New England states, the probability of VA enrollment increased even more (2.6 percentage points), from 6.3% in the pre- MHCRA period to 8.9% in the post MHCRA period. Subtracting the change for the Massachusetts group from the change for the non-Massachusetts group produced a nonsignificant 0.2 percentage point decrease (P = .857) in VA enrollment attributable to MHCRA.

The adjusted probability of being enrolled in private insurance decreased 1.8 percentage points among Massachusetts veterans, from 72.9% in the pre-MHCRA period to 71.1% in the post MHCRA period. For non- Massachusetts veterans, the probability of being enrolled in private insurance decreased by 0.9 percentage points (72.0% pre-MHCRA vs 71.1% post MHCRA) across periods. Combining first differences across groups yielded a nonsignificant 0.9 percentage point decrease (P = .666) in private insurance enrollment attributable to MHCRA. The probability of being enrolled in Medicaid increased from 5.3% to 8.3% between the pre- and post MHCRA periods among Massachusetts veterans, translating into a 3.0 percentage point increase. Medicaid enrollment increased by 0.5 percentage points (6.2% pre-MHCRA, 6.7% post MHCRA) among non-Massachusetts veterans. Combining first differences yielded a significant 2.5 percentage point increase (P = .038) in Medicaid enrollment attributable to MHCRA.

After excluding observations from 2009 and 2010, we identified a significant 2.4 percentage point decrease (P = .081) in VA enrollment attributable to MHCRA. MHCRA was also associated with a significant 2.4 percentage point increase (P = .060) in Medicaid enrollment, but was not significantly associated with private insurance enrollment.

  These analyses compared changes in VA, private insurance, and Medicaid enrollment after healthcare reform among Massachusetts and non-Massachusetts veterans, which parallels prior work examining the impact of MHCRA on nonveterans.

 In the pre-MHCRA period, trends in VA enrollment among Massachusetts and non- Massachusetts veterans were largely similar. In the post MHCRA period, VA enrollment was substantially lower among Massachusetts veterans than among veterans in the other 5 New England states in all post MHCRA years except 2009 and 2010. In DID analyses, we identified a small and nonsignificant decrease in VA enrollment attributable to MHCRA. 

Overall, our results suggest that the impact of MHCRA was contingent on the state of the economy. VA enrollment rates were sharply higher for Massachusetts veter- ans in 2009 and 2010, years coincident with the Great Recession. This sharp increase in 2009-2010 VA enrollment is consistent with prior studies finding greater use of VA care during periods of higher unemployment.

After MHCRA, the individual mandate prevented Massachusetts veterans from completely forgoing health coverage. It appears that these veterans were responding to the loss of employment and associated health coverage during the Great Recession by enrolling in VA, in part, to satisfy the individual mandate. The decline in VA annual enrollment attributable to MHCRA was even greater (2.4 percentage points) when the years of the Great Recession (2009-2010) were excluded. Both DID estimates for VA enrollment contrast recent Congressional testimony in which VA forecast a modest 66,000 net increase in enrollment following ACA.20

We also found a nonsignificant decrease in private insurance enrollment that was attributable to MHCRA. Our findings differ from previous studies of non-VA populations that found an uptake in private insurance coverage among Massachusetts residents after MHCRA.

For example, Long and colleagues found a 3.1 percentage point increase in the rate of employer-sponsored insurance coverage, which was primarily concentrated among lower-income adults. Our results may differ from prior studies because unlike the general population, veterans also have the option of enrolling in VA to meet the individual mandate.

Finally, we found Medicaid annual enrollment increased by up to 2.5 percentage points following MHCRA. Our estimates are in contrast to the 10 percentage point increase in Medicaid enrollment among eligible Massachusetts nonveterans without private coverage that was identified by Sommers and colleagues.

 A recent study by Sonier and colleagues found a 19.4 percentage point increase in Medicaid participation attributable to MHCRA among low-income parents.

 The change in Medicaid enrollment identified in this study was smaller in part because we did not limit our sample to uninsured veterans or low-income veterans. It appears that veterans increasingly took advantage of the expanded Medicaid options that were part of the MHCRA. Our results also suggest that veterans who might otherwise have enrolled in VA or private insurance opted for Medicaid instead.

Understanding veterans’ choice of enrollment into VA and other health plans following health reform is significant for a number of reasons. First, predicting changes in VA enrollment in the post ACA era is important because enrollment projections inform annual budget appropriations from Congress.

 Veterans’ choice of enrolling in VA may also indirectly affect coverage projections for non-VA health plans. Under ACA, VA enrollment precludes eligibility for subsidies to purchase health insurance through the open marketplace, which may affect veterans’ preferences for health coverage.23 Finally, changes in health coverage following reform may undermine continuity of care and result in poorer health outcomes.

Future studies should measure the magnitude of changes in VA healthcare use and costs as well as examine the role of healthcare reform on specific categories of VA utilization. Understanding changes in veterans’ intensity of VA use is relevant because the majority of VA enrolled veterans dually use non-VA sources of care.

 VA also serves a number of special populations, including a disproportionately large number of veterans with mental illness or who are homeless. Overall estimates measuring the impact of healthcare reform may mask heterogeneous effects among these vulnerable populations served by VA, and future research should examine these potential differences. Finally, ACA seeks to improve access to care, so future studies should examine whether healthcare reform improves access to care for veterans.        

This study has a number of limitations. Veterans can remain continuously enrolled in VA regardless of the level of future utilization, so it is likely that veterans responding to CPS equate VA coverage to recent use of VA services. Study findings should be interpreted accordingly. As highlighted in previous studies, VA serves a population that has higher health risk and a greater burden of chronic illness relative to the general population.

 Although we adjusted for self-reported health, no objective measures of health risk are available in CPS data. Changes in unobserved health status may confound estimated MHCRA effects, but only if the changes disproportionately affected Massachusetts veterans in the MHCRA period. Third, our analyses did not consider the extent to which veterans utilized VA care. Prior studies have indicated that veterans enrolled in VA who are dually covered by a non-VA source selectively choose where they obtain health services. MHCRA may have differentially impacted the utilization of healthcare sources for veterans with access to multiple health plans. Fourth, the impact of MHCRA identified in this study may not fully generalize to the ACA because of state differences in implementing ACA measures such as Medicaid expansion. Finally, all CPS data were obtained through survey and may be subject to recall bias.

In summary, we found that healthcare reform in Massachusetts was associated with greater Medicaid enrollment, but was not significantly associated with VA and private insurance enrollment. For VA enrollment, the impact of healthcare reform differed based on the state of economy. Because a number of MHCRA components are comparable to ACA provisions, our results provide important insights about the potential impact of ACA on veterans in the US and VA. Results from this study are applicable for fiscal planning in VA and other public and private health insurance programs.

Northwest Center for Outcomes Research in Older Adults, VA Puget Sound Health Care System, Seattle, WA (ESW, PLH, CLB, C-FL); Department of Health Services, University of Washington, Seattle, WA (ESW, PLH, C-FL); Center for Health Services Research in Primary Care, Durham VA Medical Center, Durham, NC, and Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC (MLM); and Department of Medicine, University of Washington, Seattle, WA (CLB).

Dr Wong is supported by a VA Health Services Research and Development Career Development Award (CDA 13-024). Dr Maciejewski is a VA Research Career Scientist (RCS 10-391). The views expressed are those of the authors and do not necessarily reflect the views of the Department of Veterans Affairs, the University of Washington, and Duke University.

The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (ESW, PLH, MLM, C-FL); acquisition of data (ESW); analysis and interpretation of data (ESW, MLM, PLH, CB, C-FL); drafting of the manuscript (ESW); critical revision of the manuscript for important intellectual content (PLH, MLM, C-FL); statistical analysis (ESW, PLH, C-FL); obtaining funding (ESW); administrative, technical, or logistic support (ESW); and supervision (ESW).

Edwin S. Wong, PhD, Center of Innovation for Veteran-Centered and Value-Driven Care, 1100 Olive Way, Suite 1400, Seattle, WA 98101. E-mail: edwin.wong@va.gov.

	1. National Center for Veterans Analysis and Statistics. Number of veteran patients by healthcare priority group: FY2000 to FY2012. http://www.va.gov/vetdata/Utilization.asp. Published June 2013. Accessed September 16, 2013.

	2. Department of Veterans Affairs. Department of Veterans Affairs health care benefits overview. http://www.va.gov/healthbenefits/resources/publications/1B10-185_healthcare_benefits_overview_2012png.pdf. Published January 2012. Accessed September 16, 2013.

	3. Department of Veterans Affairs, Veterans Health Administration, Office of the Assistant Deputy Under Secretary for Health for Policy and Planning. 2011 Survey of Veteran Enrollees’ Health and Reliance Upon VA, with selected comparison to the 1999 - 2010 survey. http://www.va.gov/healthpolicyplanning/soe2011/soe2011_report.pdf. Published March 2012. Accessed September 16, 2013.

	4. Liu CF, Manning WG, Burgess JF Jr, et al. Reliance on Veterans Affairs outpatient care by Medicare-eligible veterans. Med Care. 2011;49(10):911-917.

	5. Liu CF, Bryson CL, Burgess JF Jr, et al. Use of outpatient care in VA and Medicare among disability-eligible and age-eligible veteran patients. BMC Health Serv Res. 2012;12:51.

	6. Hynes DM, Koelling K, Stroupe K, et al. Veterans’ access to and use of Medicare and Veterans Affairs health care. Med Care. 2007;45(3):214-223.

	7. Long SK, Stockley K. Massachusetts health reform: employer coverage from employees’ perspective. Health Aff (Millwood). 2009;28(6):w1079-w1087.

	8. Kizer KW. Veterans and the Affordable Care Act. JAMA. 2012;307(8):789-790.

Health reform increased Medicaid enrollment, but was not associated with Veterans Health Administration and private insurance enrollment among Massachusetts veterans.

Chuan-Fen Liu, PhD, MPH

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