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The American Journal of Managed Care January 2017
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The Effect of Massachusetts Health Reform on Access to Care for Medicaid Beneficiaries
Laura G. Burke, MD, MPH; Thomas C. Tsai, MD, MPH; Jie Zheng, PhD; E. John Orav, PhD; and Ashish K. Jha, MD, MPH
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The Effect of Massachusetts Health Reform on Access to Care for Medicaid Beneficiaries

Laura G. Burke, MD, MPH; Thomas C. Tsai, MD, MPH; Jie Zheng, PhD; E. John Orav, PhD; and Ashish K. Jha, MD, MPH
Although concerns remain that expanding insurance coverage may have a “crowding-out” effect, we saw no evidence of this for Medicaid beneficiaries in Massachusetts following statewide health reform.

Objectives: To address concerns that expanding insurance coverage without expanding provider supply can lead to worse access for the previously insured, we examined whether previously insured Medicaid beneficiaries faced greater difficulties accessing primary care after statewide insurance expansion in Massachusetts.

Study Design: We used the Medicaid Analytic eXtract databases for Massachusetts and 3 New England control states for 2006 and 2009. We calculated rates of overall, acute, and chronic preventable admissions (or Prevention Quality Indicators [PQIs]) and a composite of control conditions for adults aged 21 to 64 years.

Methods: We used multivariate Poisson regression models, adjusting for age, race, gender, reason for Medicaid eligibility, and state-level physician supply, as well as a difference-in-differences (DID) approach to compare the change in the rate of PQIs and control admissions in Massachusetts versus control states before and after health reform.

Results: Massachusetts and control states had increases in unadjusted rates of overall, acute, and chronic PQIs. When adjusting for age, race, gender, reason for eligibility, and physician supply, this increase persisted for overall and chronic PQIs in control states, with no significant difference in the relative increase between the 2 groups for any of the PQI measures. For the within-Massachusetts analysis, low-uptake counties had a significant increase in admission for chronic PQIs that was greater than that observed for high-uptake counties (+148.0 vs +36.0; P = .045 for DID). There was no significant DID for acute or overall PQIs between the 2 groups.

Conclusions: We found no evidence that insurance expansion in Massachusetts, compared with control states, reduced access to primary care for vulnerable Medicaid beneficiaries.

Am J Manag Care. 2017;23(1):e24-e30
Takeaway Points

We found no evidence that insurance expansion in Massachusetts, compared with control states, reduced access to primary care for vulnerable Medicaid beneficiaries. Our findings indicate that: 
  • Massachusetts saw no increase in preventable admissions relative to control states following health insurance expansion, and no greater increase in Prevention Quality Indicators (PQIs) within Massachusetts among those counties with high insurance uptake compared with counties with low insurance uptake. 
  • In looking at PQI rates for African American beneficiaries—who typically have had greater barriers to accessing care—we saw no differential change between Massachusetts and control states. 
  • The lack of a negative spillover effect in this study of vulnerable Medicaid beneficiaries should be reassuring to policy makers.
The Affordable Care Act (ACA) remains controversial 6 years after its passage.1 Although the law has increased the number of insured Americans,2 its impact on the broader healthcare system remains unclear. One important concern regarding insurance expansions is how such efforts might impact those Americans who were already insured. The reason for concern is straightforward: as insurance expansion brings new individuals into the healthcare system, the existing supply of providers—physicians, physician assistants, and nurses—may not increase proportionately. Therefore, being able to access providers in a timely fashion can become a challenge,3 not just for the newly insured, but also for the previously insured who are now competing for the same providers.

The predicted “crowd-out” phenomenon has been a key concern raised by the ACA’s critics. Examining Massachusetts, which underwent similar healthcare reform a decade ago, may be instructive in helping us understand what is likely to happen in the rest of the nation. Although some early data suggested that in Massachusetts there were delays to see primary care physicians following health reform,4,5 more careful analysis found that, at least for Medicare patients, there were no detrimental effects on access to quality outpatient care.6-9 Critics have countered that examining the effects of health reform in Massachusetts on older Americans has limited applicability, however, because older Medicare beneficiaries likely receive care from a different set of providers than those sought out by newly insured patients. Therefore, understanding what happens to previously insured patients who live in the same communities as the newly insured, and are thus likely see the same providers, will be far more instructive.

We focused on a group of patients who are highly likely to experience a “crowd-out” effect: already-insured Medicaid beneficiaries. These patients generally have poor access to healthcare services at baseline.10 Further, both Medicaid beneficiaries and the uninsured11 tend to have low incomes, include a greater share of racial and ethnic minorities,12,13 and are considered medically and financially vulnerable.14 These vulnerable individuals tend to live in the same communities, underlying the important role of safety net providers, who care for a disproportionate share of the uninsured and Medicaid beneficiaries. Safety net providers have seen an increase in demand for their services following Massachusetts health reform,15 suggesting that concerns about a “crowd-out” effect for already-insured Medicaid beneficiaries are well founded.

Given these concerns and the ongoing debate about the potential impact of Medicaid expansion on states’ existing healthcare services, we sought to answer 3 questions. First, did insurance expansion in Massachusetts, compared with control states, decrease access to effective primary care for Medicaid beneficiaries? Second, did communities in Massachusetts with the greatest uptake of insurance experience greater challenges with access to primary care than communities with a much smaller uptake? And finally, were the effects of “crowding out” particularly pronounced among previously insured African American Medicaid beneficiaries, who typically have had greater barriers to accessing care and higher rates of preventable hospitalizations?16 With these 3 questions, we sought to more comprehensively examine the impact of insurance expansion on the ability of previously insured Medicaid beneficiaries to access high-quality primary care services.


We used the Medicaid Analytic eXtract (MAX) databases for Massachusetts and 3 New England control states (Vermont, New Hampshire, and Connecticut). MAX datasets, maintained by CMS, are extracted from the Medicaid Statistical Information System. Final-action claims are used to create beneficiary-level data on demographics and Medicaid eligibility, as well as on utilization and payment for medical services. We used the Small Area Health Insurance Estimates provided by the US Census Bureau to estimate insurance rates within counties for our within-Massachusetts analysis. We used the Area Resource File to obtain county-level physician supply. Consistent with prior research, we used 2006 as the pre-health reform period,6,8 as this was when health reform legislation was passed. By 2007, key features of health reform had been implemented, and it was not until 2008 that the individual mandate penalty was implemented. We used 2009 as the post health reform period to allow for a “wash-out period” after health reform had been fully implemented.

We included nonelderly adults aged 21 to 64 years. Because claims information for Medicaid comprehensive managed care plans was unavailable in the Massachusetts MAX database for the study years,17 we limited our sample to those beneficiaries with Medicaid fee-for-service or Primary Care Case Management (PCCM) plans. PCCM plans require beneficiaries to choose a primary care provider who receives a monthly payment to coordinate care in addition to fee-for-service payment for medical services rendered. To ensure we had complete claims for our sample, we excluded those who were not eligible for the entire year, as well as those with concomitant Medicare coverage or private insurance. Additionally, we excluded beneficiaries who had a claim related to childbirth that year. We felt that the barriers to accessing care for the general Medicaid population may not be generalizable to those who became Medicaid-eligible on the basis of pregnancy. Finally, we identified eligible beneficiaries in both 2006 and 2009, including only those beneficiaries who had continuous Medicaid coverage in both years, as we sought to determine the outcomes for beneficiaries who were already insured prior to Medicaid expansion. After identifying beneficiaries who were eligible in both years, we analyzed their claims. We also obtained beneficiaries’ age, race, gender, and reason for Medicaid eligibility from the MAX database personal summary file. We collapsed the 26 subgroups of Medicaid-eligible populations in our sample into 3 main categories: disabled, adult, and other.

To measure access to primary care, we used Prevention Quality Indicators (PQIs), developed by the Agency for Healthcare Research and Quality, to measure potentially avoidable hospitalizations.18 We used PQIs that might be particularly sensitive to effective primary care access—including acute PQIs (urinary tract infection, bacterial pneumonia, and dehydration) and chronic PQIs (diabetes, chronic obstructive pulmonary disease, hypertension, heart failure, angina)—and overall PQIs, both before and after reform. We also looked at a composite marker for control conditions (acute myocardial infarction, stroke, hip fracture, and gastrointestinal bleeding) that have been used in prior research6,19-22 to capture conditions that are less likely to be affected by changes in primary care access. Our predictor of interest was exposure to health insurance reform.


We used multiple different strategies to isolate the impact of the reform program. Initially, we chose a control group of 3 New England states (Vermont, New Hampshire, and Connecticut) in which healthcare would be most similar to that of Massachusetts. Because Medicaid eligibility criteria vary substantially by state, we calculated the proportion of total beneficiaries in each state that met our inclusion criteria. Next, we conducted an analysis comparing rates of PQIs and control conditions among counties in Massachusetts with high versus low insurance uptake after health reform. If health reform had a detrimental effect, then we expected to see a larger increase in PQIs in the high-uptake counties compared with the low-uptake counties. Finally, we repeated this analysis for the control condition composite (total number of admissions for control conditions per 100,000 beneficiaries) in order to see whether changes in Massachusetts for the control composite were comparable with changes in the control states.

We calculated our primary outcome—the absolute number of acute, chronic and overall PQIs—for the pre- and post periods in Massachusetts and the control states. In our first analysis, we compared PQI rates among Medicaid patients in Massachusetts in 2006 (pre-reform) with PQI rates for the same population in 2009 (post reform). Since this simple comparison could be confounded by a time trend, our second and primary analysis used a difference-in-differences (DID) approach to compare the change in PQIs in Massachusetts with the change in PQIs in the control states. The analyses were carried out at the beneficiary-year level using longitudinal Poisson regression models, allowing for correlated pre-post measurements within each beneficiary. The outcome for each beneficiary was the number of PQIs in a given year, and the primary predictors were indicators for time (pre vs post), condition (Massachusetts vs control), and the interaction between time and condition. To try to further balance the comparison between Massachusetts and the control states, we allowed the 3 control states to have differing initial PQI rates and we adjusted for the following available patient characteristics: age, gender, race, reason for Medicaid eligibility, and county-level physician supply. The models were implemented using the Glimmix procedure in the SAS version 9.4 statistical package (SAS Institute Inc, Cary, North Carolina).

In our within-state analysis,6 we identified counties with rates of baseline health insurance below the median for the state as high potential effect counties. We then calculated the change in insurance rate before (2005-2006) and after (2007-2009) health reform, and identified those counties with rates of insurance uptake that were above the median as “high-uptake” counties. We compared the change in rates of PQIs before and after health reform in high- versus low-uptake counties using the same longitudinal Poisson regression model as above. In order to address our specific interest in “crowding out” in the African American population, we repeated the analysis comparing Massachusetts with control states looking specifically at the potentially more vulnerable African Americans.

Sensitivity Analysis

In addition to the primary analysis using Poisson regression modeling, we also conducted all analyses using linear regression to investigate if our findings were sensitive to modeling choice. Results were considered significant at a 2-sided P value of less than .05. The Office of Human Research Administration at the Harvard T.H. Chan School of Public Health approved this study.


Sample Characteristics

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