Effect of Medicaid Expansion on Health Insurance Coverage and Access to Care Among Adults With Depression
Abstract
Objective:
Multiple studies have detailed the relationship between Medicaid expansion under the Affordable Care Act and various health and financial outcomes. However, fewer studies have examined Medicaid expansion’s effects on individuals with psychiatric diagnoses. This study sought to determine the relationship between Medicaid expansion and various health and financial outcomes among low-income adults with depression.
Methods:
This quasi-experimental study used a random-digit-dial survey of U.S. citizens ages 19–64 with incomes below 138% of the federal poverty level. Surveys were conducted in three southern states (two expansion states, Arkansas and Kentucky, and one nonexpansion state, Texas) between 2013 and 2016. The study sample consisted of those with a positive screen for depression—score of ≥2 on the two-item Patient Health Questionnaire (N=4,853). Survey-weighted difference-in-differences regressions were conducted with insurance status, health care access and utilization, and affordability of care as outcomes of interest. Subgroup analyses stratified the sample on the basis of the respondent’s residence in a health professional shortage area (HPSA) in mental health and severity of depression.
Results:
Medicaid expansion was associated with a significant reduction in the proportion of adults with depression who lacked health insurance (−23 percentage points, 95% confidence interval=–32 to –14, p<.001). Medicaid expansion was also associated with significant reductions in delaying care and medications because of cost. These changes were similar regardless of residence in a mental health HPSA and severity of depression.
Conclusions:
Medicaid expansion was associated with improved access to care and medication among persons with depression, even in areas with relative shortages of mental health professionals.
Beginning in 2014, states could choose to provide Medicaid coverage to low-income adults through the Affordable Care Act (ACA). Since 2014, more than 11 million low-income adults in 32 states have newly enrolled in Medicaid coverage through the ACA (1). A number of recent studies have detailed the association between state Medicaid expansions and health and financial outcomes (2–5). This research has found that Medicaid expansion is associated with lower rates of uninsured individuals and increased health care utilization, as well as modest improvements in health outcomes (2,3,6,7). In addition, expanded Medicaid coverage has been associated with a reduced likelihood of medical bankruptcy, fewer delays in care because of cost, and less trouble with other bills (3,8). These studies have analyzed the general adult population; however, there has been less research focused explicitly on the Medicaid expansion’s effects on those with mental illness, including depression.
Prior studies that were focused on mental health conditions showed that after Medicaid expansion, there were significant reductions in severe psychological distress, undiagnosed depression, unmet medical needs regarding depression, and increased medication use for depression (9–12). Other research has examined changes in health care access and utilization for persons with mental illness after private coverage expansion, which may differ in its effects from Medicaid (13). Given the high prevalence of and comorbidities associated with depression in the United States (14–18), a continued focus on the relationship between Medicaid expansion and measures of health care access and utilization and health outcomes for individuals with depression is warranted.
Although many individuals with depression are diagnosed and treated by a primary care physician, mental health specialists play an important role in the provision of high-quality care for individuals with severe depression or comorbidities (19,20). Inadequate physician capacity is a significant concern of policy makers regarding the ACA’s coverage expansions. Without an adequate number of clinicians to care for the newly insured, individuals may gain insurance but remain without adequate access to health care services. These concerns may be even more pronounced among specialists (including mental health specialists), who typically are fewer in number, less likely to accept Medicaid, and less evenly distributed geographically, compared with primary care physicians. Thus evidence regarding the connection between the supply of mental health specialists and patient-level outcomes is needed.
Previous research suggests that nearly half of all individuals with a behavioral health condition remained uninsured after the ACA’s full implementation (21). Additional research is needed on the implications for health insurance coverage, access to care, and financial support for treatment options among those with severe depression compared with those with mild to moderate depression. Specifically, the functional limitations associated with severe depression may make access to health insurance and health care services more difficult because of cost, even among individuals with Medicaid coverage (22).
This study used survey data from low-income adults with depression in three southern states to examine the relationship between Medicaid expansion and health and financial outcomes. The study also sought to assess whether these changes are different for respondents who live in a mental health professional shortage area (HPSA) and those with more severe depression.
Methods
Survey and Population
From 2013 to 2016, we contracted with a survey research firm to conduct random-digit-dial telephone surveys of low-income adults in Texas, Arkansas, and Kentucky in November and December of each year. The survey was a repeated cross-section and used both landline and cell phone numbers to maximize our ability to reach our target study population. Each year’s sample contained between 750 and 1,000 respondents per state, for an overall sample of 10,574 that was evenly divided among the three states. Kentucky and Arkansas expanded coverage to low-income adults in 2014—Kentucky via traditional Medicaid and Arkansas by providing enrollees with free private health insurance coverage through the ACA’s health insurance exchanges. We selected Texas, which did not expand Medicaid under the ACA, as a control state because it is in the same U.S. Census region as Arkansas and Kentucky and because its demographic make-up is similar to that of the other two states.
Eligibility for expanded Medicaid coverage was determined by screening respondents on the basis of age (19–64 years), citizenship status (U.S. citizen), and household size and income (<138% of the federal poverty level for a given household size). The survey response rate was 22%. We used postsurvey weighting to mitigate nonresponse bias (23), and responses from this survey have been validated against two other national data sources—the Behavioral Risk Factor Surveillance System and the American Community Survey (ACS). To account for the demographic characteristics of each state’s low-income adult population, data from the ACS were used to apply state-specific survey weights to each response. We adjusted the ACS-based weights to account for household size and rates of dual cell phone and landline use among respondents. Item nonresponse was handled by multivariate imputation based on respondent-level demographic characteristics. Further details of the survey methodology have been published previously (6). [A copy of the survey instrument is available in an online supplement to this article.]
In each year, we screened respondents for depression with the two-item Patient Health Questionnaire (PHQ-2), which consists of two questions: “Over the past 2 weeks, how often have you been bothered by any of the following problems? Little interest or pleasure in doing things; and Feeling down, depressed, or hopeless.” The possible total score ranges from 0 to 6 (0–3 for each question; 0, not at all; 3, nearly every day). We categorized respondents with a score of ≥2 as screening positive, which prior research indicates has a 93% sensitivity and 74% specificity for depression (24). We then asked respondents about their access to care and health care use in the previous 12 months.
Verbal consent to participate in the survey was obtained prior to the administration of the survey, and the study received approval from the Harvard T. H. Chan School of Public Health Institutional Review Board.
Outcomes of Interest
Study outcomes included health insurance status (uninsured, Medicaid coverage, and private health insurance), having a personal doctor, cost-related delays in care, skipping medications because of cost, difficulty obtaining specialist appointments, and several measures of utilization over the prior 12 months (emergency department visits, office visits, specialist visits, and any hospitalization). All outcome variables were dichotomous.
Statistical Analysis
We conducted a difference-in-differences analysis to examine the relationship between Medicaid expansion and each of the outcomes detailed above. The difference-in-differences approach is a pre-post comparison of an intervention with a control group. In our case, we compared changes in outcomes before (2013) versus after (2014–2016) Medicaid expansion in the expansion states to changes during the same period in the nonexpansion state. When implemented as a multivariate regression, the difference-in-differences analysis included a dummy variable for expansion status and the postintervention period and an interaction between these two variables (expansion state*postexpansion period). This last coefficient is of primary interest in difference-in-differences analyses [see the online supplement ].
In our primary analysis, we treated 2013 as the preimplementation period and pooled 2015–2016 data as the postimplementation period (2014 was treated as a transitional period and therefore was included in our regression as a separate indicator variable). Regressions were linear probability models, as is standard in difference-in-differences designs with a binary outcome (25), and our models adjusted for gender, race-ethnicity, marital status, income, age, educational attainment, and rurality. Standard errors were clustered at the county level, following previous studies with this data set (6). All analyses used Stata 14.0.
Subgroup Analyses
In addition to the main analyses above, we conducted analyses with two prespecified subgroups on the basis of the respondent’s residence in a mental health HPSA and severity of depression. To test whether the supply of mental health professionals led to differential changes in outcomes after coverage expansion, we conducted a subgroup analysis, stratifying the sample into respondents who resided in a mental health HPSA versus those who did not. Mental health HPSAs are defined by the Health Resources and Services Agency (HRSA) (26). We used zip code–level designations from HRSA to create a binary indicator of residence in a mental health HPSA.
To test whether the changes in outcomes associated with Medicaid expansion varied by severity of depression, we stratified our sample into two groups and repeated the same difference-in-differences regression analyses described above. In one group were individuals who had a PHQ-2 score of 2 or 3 (moderate depression), and in the other group were those with a PHQ-2 score of ≥4 (severe depression) (24). For both sets of stratified analyses, we conducted postestimation hypothesis testing (using suest in Stata) to determine whether the difference in regression-adjusted difference-in-differences estimates between the two subgroups was statistically significant.
Sensitivity Analysis
In sensitivity analyses, we included 2014 in the postimplementation estimate, pooling all three years (2014–2016). We also assessed the effect of defining our sample based not only on the PHQ-2 but also on whether a respondent had been given a diagnosis of depression. We conducted analyses in which we limited our sample to individuals who either screened positive for depression or reported a history of depression.
Results
Survey and Population
Of the 10,574 low-income adults interviewed over the study period (2013–2016), 4,853 screened positive for depression (PHQ-2 score of ≥2). Of those, 2,645 were classified as having moderate depression (PHQ-2 score of 2 or 3) and 2,208 as having severe depression (PHQ-2 score of ≥4). In the full sample, roughly half (N=2,111) lived in a mental health HPSA.
Table 1 presents summary statistics for the sample, by expansion status, in the preexpansion period (2013). Compared with respondents in the nonexpansion state (Texas), those in the expansion states were more likely to live in a rural area (54% versus 11%, p<.001) and were more likely to be white (76% versus 33%, p<.001) and less likely to be Latino (3% versus 39%, p<.001). In addition, the rate of individuals who screened positive for moderate depression (26% versus 23%, p=.04) and for severe depression (22% versus 18%, p<.001) was higher in the expansion states. No significant differences were noted by expansion status in mean age, gender composition, marital status, educational attainment, and the percentage of individuals living in a mental health HPSA. In addition, 2013 rates of comorbid conditions were similar for the full survey sample and for respondents who screened positive for depression [see online supplement ].
| Nonexpansion (N=377) | Expansion (N=968) | ||||
|---|---|---|---|---|---|
| Characteristic | % | SE | % | SE | p |
| Mean age | 39 | 1.20 | 42 | .65 | .11 |
| Female | 65 | .04 | 64 | .02 | .80 |
| Living in a rural area | 11 | .02 | 55 | .20 | <.001 |
| Living in a mental health HPSAb | 38 | .04 | 40 | .02 | .67 |
| PHQ-2 scorec | |||||
| 2 or 3 | 23 | .01 | 26 | .01 | .04 |
| ≥4 | 18 | .01 | 22 | .01 | <.001 |
| Race-ethnicity | |||||
| White, non-Latino | 33 | .04 | 76 | .02 | <.001 |
| Black, non-Latino | 25 | .03 | 19 | .02 | .08 |
| Latino | 39 | .04 | 3 | .01 | <.001 |
| Educational attainment | |||||
| Less than high school | 31 | .04 | 27 | .02 | .38 |
| High school diploma | 40 | .04 | 46 | .02 | .20 |
| Some college or college degree | 30 | .04 | 28 | .02 | .61 |
| Marital status | |||||
| Married | 38 | .04 | 38 | .02 | .98 |
| Single | 36 | .04 | 31 | .02 | .31 |
| Widowed, divorced, or separated | 26 | .03 | 31 | .02 | .23 |
| Income statusd | |||||
| Under 50% FPL | 26 | .03 | 35 | .02 | .04 |
| 50%–100% FPL | 45 | .04 | 38 | .02 | .13 |
| 100%–138% FPL | 16 | .03 | 20 | .02 | .17 |
a
Results are from 2013 and are survey weighted. N=4,834 minus item nonresponse.
b
Health professional shortage area
c
PHQ-2, two-item Patient Health Questionnaire
d
FPL, federal poverty level
Difference-in-Differences Analysis
Medicaid expansion was associated with a significant decrease in the proportion of adults who reported having no health insurance (–23 percentage points, p<.001) and a significant increase in the proportion of adults reporting having Medicaid (13 percentage points, p=.02) (Table 2). Medicaid expansion was also associated with an increase in the proportion of respondents who reported having a personal doctor (11 percentage points, p=.02) and a decrease in the proportion who reported delaying care because of cost (−16 percentage points, p=.002) or skipping or not taking medications because of cost (−18 percentage points, p=.008). Medicaid expansion was not associated with significant changes in utilization in the prior 12 months nor with any significant change in difficulty getting appointments with a specialist.
| Nonexpansion state | Expansion states | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Preexpansion (N=377) | Postexpansion (N=775) | Preexpansion (N=968) | Postexpansion (N=1,772) | Estimatea | ||||||
| Outcome | % | 95% CI | % | 95% CI | % | 95% CI | % | 95% CI | % | 95% CI |
| Has Medicaid coverage | 32 | 25 to 39 | 38 | 33 to 42 | 31 | 27 to 36 | 50 | 47 to 54 | 13* | 2 to 24 |
| Has no health coverage | 36 | 29 to 44 | 30 | 25 to 34 | 39 | 34 to 43 | 10 | 8 to 12 | –23*** | –32 to –14 |
| Has private coverage | 24 | 17 to 31 | 27 | 23 to 31 | 19 | 15 to 22 | 43 | 40 to 47 | 8 | 0 to 17 |
| Has a personal doctor | 56 | 48 to 64 | 53 | 48 to 578 | 64 | 59 to 68 | 71 | 68 to 74 | 11* | 3 to 20 |
| Delayed care because of cost | 46 | 38 to 53 | 47 | 42 to 52 | 51 | 46 to 56 | 37 | 34 to 40 | –16** | –25 to –6 |
| Delayed medications because of cost | 42 | 34 to 50 | 43 | 38 to 48 | 55 | 50 to 59 | 37 | 34 to 40 | –18*** | –27 to –9 |
| Had trouble getting appointment with specialist | 33 | 25 to 40 | 24 | 20 to 28 | 21 | 17 to 25 | 20 | 17 to 23 | 8 | –2 to 17 |
| Had any office visit | 48 | 40 to 55 | 50 | 45 to 55 | 63 | 59 to 68 | 63 | 60 to 67 | –2 | –11 to 8 |
| Was hospitalized | 23 | 17 to 29 | 24 | 20 to 28 | 24 | 20 to 27 | 24 | 22 to 27 | .2 | –9 to 9 |
| Had an emergency department visit | 21 | 14 to 27 | 24 | 20 to 28 | 24 | 20 to 28 | 21 | 18 to 24 | –6 | –16 to 4 |
a
Difference-in-differences estimates comparing nonexpansion state (Texas) to expansion states (Arkansas and Kentucky) pre-Medicaid expansion (2013) and postexpansion (2015–2016) (N=4,834 minus item nonresponse). Data from 2014 were treated as a transitional year. Coefficients are from the difference-in-differences estimator, shown as the percentage point change. Linear probability models were controlled for age, gender, race-ethnicity, rurality, political identification, income, marital status, and educational attainment. (2015–2016 versus 2013)
*
p<.05, **p<.01, ***p<.001
Reductions in the uninsured rate and cost-related delays in care and medication use were significant both for the group living in a mental health HPSA and the group not living in a mental health HPSA (Figure 1). However, no significant between-group differences were noted for any study outcomes.
FIGURE 1. Changes in coverage and access to care after Medicaid expansion among low-income adults who screened positive for depression, by residence in a mental health HPSAa
aDifference-in-differences estimates comparing nonexpansion state (Texas) to expansion states (Arkansas and Kentucky) pre- and post-Medicaid expansion (N=4,834 minus item nonresponse). Solid circles represent the difference-in-differences estimates in percentage points, and lines are the 95% confidence intervals for each estimate. Linear probability models were controlled for age, gender, race-ethnicity, rurality, political identification, income, marital status, and educational attainment. No significant (p<.05) between-group differences were found. HPSA, health professional shortage area
Decreases in the uninsured rate and cost-related medication delays associated with Medicaid expansion were significant both among respondents with severe depression (PHQ-2 score of ≥4) and among those with moderate depression (PHQ-2 score of 2 or 3) (Figure 2). Both decreases in cost-related delays and increases in having a personal physician were significant for the severe-depression subgroup and not significant for the moderate-depression subgroup; however, none of these changes were significantly different between the two groups.
FIGURE 2. Changes in coverage and access to care after Medicaid expansion among low-income adults who screened positive for depression, by depression severitya
aDifference-in-differences estimates comparing nonexpansion state (Texas) to expansion states (Arkansas and Kentucky) pre- and post-Medicaid expansion (N=4,834 minus item nonresponse). A score of 2 or 3 on the two-item Patient Health Questionnaire indicates moderate depression, and a score of ≥4 indicates severe depression. Solid circles represent the difference-in-differences estimates in percentage points, and lines are the 95% confidence intervals for each estimate. Linear probability models were controlled for age, gender, race-ethnicity, rurality, political identification, income, marital status, and educational attainment. No significant (p<.05) between-group differences were found.
Sensitivity Analyses
When we pooled 2014–2016 into a single postimplementation period, the magnitude of the difference-in-differences estimates were slightly smaller than in our primary model, although these estimates were not statistically different from our main results [see online supplement ]. When respondents who reported a history of depression were included in the sample, effect sizes were smaller than those obtained in our primary model, but again the overall estimates were not statistically different from our main results [see online supplement ].
Discussion
In our analysis of primary survey data from nearly 5,000 low-income adults who screened positive for depression, we found that the ACA’s Medicaid expansion was associated with significant improvements in coverage rates and financial access to care. Prior to the ACA, Medicaid participation rates among eligible adults were roughly 50%. Thus evidence suggests that eligibility for Medicaid does not automatically result in enrollment or receipt of coverage—the enrollment process may be particularly burdensome for individuals with mental or substance use disorders. We also found that Medicaid expansion in Kentucky and Arkansas was associated with an increased probability of having a personal doctor and a decreased probability of delaying care or medications because of cost. These changes are consistent with prior findings for the general low-income population (6–8) but extend these results to a high-risk population with active symptoms of depression. Given the high prevalence and substantial morbidity associated with depression, these results have important public health implications.
Although we found that Medicaid expansion was associated with increased health insurance coverage and access to care, we were not able to determine whether the care sought was related to the respondent’s depression or for general medical issues. Further research should examine the relationship between Medicaid expansion and depression-specific care.
Our findings also shed new light on the issue of adequate access to clinicians with respect to mental health. Some policy makers have expressed concerns that Medicaid expansion could exacerbate physician shortages or that shortages might limit the effects of expanding coverage (27,28). Our findings suggest that individuals residing in a mental health HPSA did not experience any significant change in their ability to obtain appointments with specialists, compared with those did not live in a mental health HPSA. The positive changes in access to care after Medicaid expansion were evident even among those with depression who were living in a mental health HPSA. Taken together, this evidence suggests that constraints due to physician shortages did not outweigh the advantages conferred by having health insurance. However, our survey and data did not enable us to draw inferences about how this pattern of results occurred—whether Medicaid expansion led to increases in provider hours worked, changes in the number of providers accepting Medicaid, or reductions in the types of coverage provided to other populations. Future research exploring these issues would be valuable.
Our study provides new information on the relationship between Medicaid and the severity of an individual’s depression. When we compared those who screened positive for severe depression with those with moderate depression, we identified gains in coverage and access to care in both groups. Although some outcomes were significant for only one of the two severity subgroups, we did not find any significant between-group differences for these changes.
The study had some limitations. The survey response rate (22%) was a potential limitation. Although it was similar to those of other ACA-related surveys, our survey may have been subject to nonresponse bias. Postsurvey weighting, which this study used, can mitigate nonresponse bias (23). In addition, the survey responses used in this study were externally validated by using two federal government household surveys, which demonstrated state-level trends similar to those in our survey for key outcomes of coverage and access to care (6).
Second, as with any quasi-experimental study, unobserved variables may have changed differentially in expansion versus nonexpansion states. Although our estimates were similar to those in a number of other quasi-experimental comparisons of health outcomes between expansion and nonexpansion states, we still cannot rule out the possibility of residual confounding (6–8).
Third, there may be some confusion in our survey regarding health insurance coverage source, especially because Arkansas’s approach used Medicaid dollars to purchase private insurance. In our survey, we tried to mitigate this confusion by including multiple names of Medicaid coverage as well as names of the health insurance marketplaces in each state. Despite these efforts, there may still be measurement error in our coverage estimates of public versus private insurance.
Fourth, some respondents may not have known their annual or monthly household income or may have omitted relevant sources of income. However, we used a threshold method for assessing income, which eliminated the need for a respondent to provide a specific figure, and again, our survey produced estimates for the low-income populations in these states that are similar to those obtained in large government surveys.
Finally, our results may not be generalizable to other states or populations. For instance, the use of a phone survey may systematically leave out groups of interest, such as unhoused and transient individuals, with high rates of mental health conditions. Our study included only three states, which may not be representative of the nation as a whole. Of particular concern is Arkansas’s decision to expand its Medicaid program by providing vouchers for beneficiaries to purchase private insurance in the health insurance marketplace, a policy that creates a distinct health coverage and access environment compared with most other states. However, previous work suggests that changes in most of the outcomes we studied did not significantly differ between the greater expansion populations in Kentucky (expansion via Medicaid) and Arkansas (“private option” expansion).
Conclusions
A number of studies have examined the effects of Medicaid expansion on access to care, health outcomes, and health care–related financial distress, in general. However, fewer studies have examined the effect of Medicaid expansion on individuals with depression or other mental disorders. We found that Medicaid expansion was associated with increased access to health care services and decreased cost-related delays in care in a sample of adults with active symptoms of depression.
These findings have important implications as state and federal policy makers consider future changes in Medicaid. For instance, both Arkansas and Kentucky received approval from the Centers for Medicare and Medicaid Services to require beneficiaries to work as a condition of enrollment, which may be particularly challenging for individuals with mental illness (29). Arkansas has recently implemented its work requirement, but this work requirement is now being challenged in court. Although Kentucky’s initial work requirement was struck down by the court system, it recently proposed an amended version of work requirements. In addition to these changes, continued threats to the ACA and its Medicaid expansion may have detrimental effects on access to and utilization of health care services for individuals with depression (30). Our results provide new insights into the relationship between Medicaid expansion and health care access for individuals with depression and could serve as an important baseline for future analyses of changes in Medicaid policy.
Footnote
The content is solely the responsibility of the authors and does not necessarily represent the views of the Commonwealth Fund, AHRQ, or the National Institutes of Health.
Supplementary Material
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Information & Authors
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Published In
Cover: Untitled, by Willem de Kooning, 1944. Oil and charcoal on paper laid on board. © Art Resource, New York City.
History
Received: 11 April 2018
Revision received: 27 June 2018
Accepted: 20 July 2018
Published online: 28 August 2018
Published in print: November 01, 2018
Keywords
Authors
Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
National Institute of Mental Health10.13039/100000025: T32MH019733
Commonwealth Fund10.13039/100000905:
Agency for Healthcare Research and Quality10.13039/100000133: K02HS021291
This project was supported by a research grant from the Commonwealth Fund. Ms. Fry completed this project with support from training grant T32MH019733 from the National Institute of Mental Health. Dr. Sommers’ work on this project was also supported in part by grant K02HS021291 from the Agency for Healthcare Research and Quality (AHRQ).Metrics & Citations
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