The impact of the Affordable Care Act on health care access and self‐assessed health in the Trump Era (2017‐2018)

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Associated Data

Abstract

Objective

To estimate the impact of the major components of the ACA (Medicaid expansion, subsidized Marketplace plans, and insurance market reforms) on health care access and self‐assessed health during the first 2 years of the Trump administration (2017 and 2018).

Data Source

The 2011‐2018 waves of the Behavioral Risk Factor Surveillance System (BRFSS), with the sample restricted to nonelderly adults. The BRFSS is a commonly used data source in the ACA literature due to its large number of questions related to access and self‐assessed health. In addition, it is large enough to precisely estimate the effects of state policy interventions, with over 300 000 observations per year.

Design

We estimate difference‐in‐difference‐in‐differences (DDD) models to separately identify the effects of the private and Medicaid expansion portions of the ACA using an identification strategy initially developed in Courtemanche et al (2017). The differences come from: (a) time, (b) state Medicaid expansion status, and (c) local area pre‐2014 uninsured rates. We examine ten outcome variables, including four measures of access and six measures of self‐assessed health. We also examine differences by income and race/ethnicity.

Principal Findings

Despite changes in ACA administration and the political debate surrounding the ACA during 2017 and 2018, including these fourth and fifth years of postreform data suggests continued gains in coverage. In addition, the improvements in reported excellent health that emerged with a lag after ACA implementation continued during 2017 and 2018.

Conclusions

While gains in access and self‐assessed health continued in the first 2 years of the Trump administration, the ongoing debate at both the federal and state level surrounding the future of the ACA suggests the need to continue monitoring how the law impacts these and many other important outcomes over time.

Keywords: access to care, affordable care act, health, health care access, health insurance, medicaid expansions, self‐assessed health, self‐reported health

What is already known on this topic

The ACA led to significant improvements in coverage and access to care throughout 2014 to 2016, as well as a lagged emergence of improvements in self‐assessed health.

This study evaluates the impact of the ACA on insurance coverage, access to care, and self‐assessed health including 2017 and newly released 2018 data.

What this study adds

Despite a political shift and changes in the administration of the ACA beginning in 2017, gains in coverage and access to care remained stable in 2017 and 2018 compared to 2016.

In addition, we also continue to observe improvements in excellent self‐assessed health in 2017 and 2018.

1. INTRODUCTION

In 2014, the major components of the Affordable Care Act (ACA), including the individual mandate, subsidized Marketplace coverage, and state Medicaid expansions, were implemented. 1 , 2 , 3 A recently published review 2 summarizes the growing literature on the impact of the ACA on insurance coverage, 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 access to care, 17 , 18 , 19 and self‐assessed health 3 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 among other outcomes. This review suggests that the ACA, including the Medicaid expansions, increased coverage and access to care after one (2014) to four (2014‐2017) postreform years, but did not have as clear an effect on self‐assessed health. Some studies focusing on the ACA Medicaid expansions alone found mixed results, with some showing improvements in health, while others found no effect. 2 , 3 One study examining both the Medicaid expansions and the non‐Medicaid expansion components of the ACA found that an increase in health (as measured by the probability of reporting excellent health) emerged with a lag in 2015. 28

In this paper, we estimate the causal effects of the ACA on access to care and self‐assessed health during 2017 and 2018, the first 2 years of the Trump administration, using data from the Behavioral Risk Factor Surveillance System (BRFSS). Ours is among the first publications to include both 2017 and 2018 data. 25 , 27 , 28 , 29 , 30 , 31 Our access outcomes are the likelihoods of having insurance coverage, costs being a barrier to seeking care, a primary care doctor, and a checkup in the past year. Our health outcomes include overall self‐assessed health and days of the previous month not in good physical health, not in good mental health, and with health‐related limitations.

There are multiple reasons why adding data from the first two years of the Trump administration to the prior analysis is important. First, even in the absence of any changes to the ACA, it would be interesting to see whether the lagged emergence of improved health continues into 2017 and 2018. This may be the case as enrollees become more familiar with their new coverage over time and how to best navigate the health care system. In addition, if upfront investments in medical care take time to translate into better health, then we might expect further improvement in self‐assessed health in 2017 and 2018. 32 Second, 2017 marked a change in the administration of the ACA with a new president taking office in January. President Trump's first executive order encouraged the federal government to waive or delay the implementation of any components of the ACA that would impose a financial or regulatory burden. 33 In addition, funding for ACA outreach and education programs, including funding for navigators, was reduced for open enrollment periods associated with 2017 and 2018 coverage. 34 Potentially most consequential, in October 2017, the administration discontinued cost‐sharing reduction (CSR) payments to insurers for silver Marketplace plans, at a time when insurers already submitted premium rates for the coming plan year with an expectation of receiving CSR payments in return for reducing the cost‐sharing in plans for low income enrollees. 35 Political debate surrounding the ACA was prominently featured in the news, including the failed vote to repeal the ACA in July 2017 and the vote to pass the tax reform package that included a repeal of the ACA individual coverage mandate in December 2018. 36 Thus, the addition of 2017 and 2018 data allows us to examine the initial causal impact of these events. This is important as recent descriptive evidence suggests that the national coverage rate actually fell by 0.5 percentage points between 2017 and 2018. 37

Following a recently established literature, we estimate difference‐in‐difference‐in‐differences (DDD) models with the differences coming from time, state Medicaid expansion status, and local area pretreatment uninsured rate in order to estimate the impact of the full ACA. 11 , 14 , 15 , 16 , 25 , 27 , 28 , 38 This approach stands in contrast to many studies that use a simpler difference‐in‐differences (DD) model comparing changes in expansion states to changes in nonexpansion states in order to identify the effect of the ACA Medicaid expansion alone. Identifying the impact of the national components of the ACA, such as the individual mandate and subsidized Marketplace coverage, requires a different approach because they were implemented in every state at the same time. The inclusion of a third difference in our model handles this issue because the national components of the ACA should provide the most intense “treatment” in local areas with the highest uninsured rates prior to the ACA.

2. DATA AND METHODS

2.1. Data

We use data from the BRFSS, an annual telephone survey of health and health behaviors conducted by state health departments in collaboration with the CDC. The BRFSS is the largest continuous health survey in the United States, collecting information on more than 300 000 adults per year. Having a large sample size is critical to obtaining meaningful precision because the ACA affected insurance coverage for only a fraction of the population, limiting plausible effect sizes. The BRFSS is therefore a commonly used data source in the ACA literature on access and self‐assessed health. 23 , 25 , 27 , 28

Our sample period is 2011‐2018. The sample starts in 2011 because this is the first year in which the BRFSS included cell phones in its sampling frame. The sample ends in 2018 because this is the last year currently available. This timeframe gives us three years of pretreatment data and five years of post‐treatment data. We limit our sample to individuals 19‐ to 64 years old who were interviewed between 2011 and 2018. As is common in the literature, we drop observations with missing values for the variables used in our analysis. 23 , 39 , 40

Our outcome variables measure access to care and self‐reported health status. Access outcomes include indicators for any health coverage, having a primary care doctor, having a regular physician checkup in the past 12 months, and having any care needed but foregone because of cost in the past 12 months. Self‐reported health status is based on a rating of overall health as poor, fair, good, very good, or excellent. We use this to construct indictors for whether overall health is good or better (ie, good, very good, or excellent), very good or excellent, and excellent. Other health measures include number of days of the last 30 not in good mental health, not in good physical health, and with health‐related functional limitations. These sorts of subjective self‐assessed health variables have been shown to be correlated with objective measures of health, such as mortality. 41 , 42 , 43

We construct a Medicaid expansion indicator that is based on information collected by the Kaiser Family Foundation. 44 A total of 31 states and Washington, DC expanded Medicaid by 2016 and no state expanded in 2017 or 2018. The majority of states expanded Medicaid in January 2014, with some exceptions. Michigan expanded in April 2014 and New Hampshire in August 2014. Pennsylvania, Indiana, and Alaska expanded in January, February, and September of 2015, respectively. Montana and Louisiana expanded in January and July of 2016, respectively. We classify states as part of the Medicaid expansion beginning the month‐year of their expansion. Other state‐level variables include indicators for whether states set up their own insurance exchanges, whether these exchanges experienced glitches, 44 , 45 and seasonally adjusted monthly state unemployment rate from the Bureau of Labor Statistics. The exchange glitch indicator flags the six states that had severe upfront technology problems when they rolled out their state exchange in 2014. 45

We measure the intensity of the non‐Medicaid components of the ACA using the uninsured rate in the respondent's “local area” in the pretreatment year of 2013. This measure captures the “dose” of ACA treatment the local area could have received. We compute each respondent's “local area” pretreatment uninsured rate within our BRFSS sample of nonelderly adults. The publicly available BRFSS does not include geographic identifiers narrower than the state, but does tell us whether the respondent resides in the center city of an MSA, outside the center city of an MSA but inside the county containing the center city, inside a suburban county of an MSA, or not in an MSA. We use this variable to construct four subgroups within each state: those living within a central city, suburbs, non‐MSA, and within‐state location unavailable (this is the case for respondents interviewed on their cell phone). Based on these four geographic categories, we calculate the pretreatment average uninsured rates by “location” (considering “cell phone” to be a location for the sake of convenience) within a state. To ensure that each area contains enough respondents from our sample to reliably compute pretreatment uninsured rates, we follow the previous literature and combine the seven areas with fewer than 200 respondents in 2013 with other larger areas. Specifically, we combine the central city and suburban parts of Wyoming into one area, and do the same for Vermont, South Dakota, and Montana. In addition, we combine the suburban and rural parts of the states of Massachusetts, Arizona, and California. This process, which exactly mirrors that used in prior BRFSS studies of the ACA’s two‐ and three‐year effects, 25 , 27 generates 194 areas with 2013 uninsured rates that are computed from 219 to 5,804 respondents, with the average being 1,475 respondents and the median being 1,205.

We use responses from several other BRFSS questions to construct individual‐level controls. Specifically, we control for age using indicators for five‐year increments (from 25‐29 to 60‐64, with 19‐24 as the reference group), female, race/ethnicity (non‐Hispanic black, Hispanic, and other; non‐Hispanic white as the reference), married, education (high school degree, some college, and college graduate; less than high school degree as the reference), household income ($10 000‐$15 000, $15 000‐$20 000, $20 000‐$25 000, $25 000‐$35 000, $35 000 $50 000, $50 000‐$75 000, and >$75 000, with

Table 1 provides pretreatment means and standard deviations of our ten outcomes of interest between 2011 and 2013, and Appendix Table S1 reports the means and standard deviations for the controls. We stratified our entire analytic sample into four groups based on whether the respondent's state expanded Medicaid and whether the local area's pretreatment uninsured rate was above or below the median within the sample. According to Table 1, 79 percent of the sample had some form of coverage prior to 2014. Individuals in expansion states (columns 2 and 3) were slightly more likely to have insurance prior to 2014 than those in nonexpansion states (columns 4 and 5). Residents who live in expansion states with prereform uninsured rates below the median (column 3) had, on average, better health care access and self‐assessed health than the rest of the sample even before 2014. Our DDD model will account for these baseline differences. Our online Appendix describes trends in our outcome variables over time.

TABLE 1

Means and standard deviations of dependent variables by state medicaid expansion status and pretreatment uninsured rate