Abstract
Objectives. To identify the association between Medicaid eligibility expansion and medical debt.
Methods. We used difference-in-differences design to compare changes in medical debt for those gaining coverage through Louisiana’s Medicaid expansion with those in nonexpansion states. We matched individuals gaining Medicaid coverage because of Louisiana’s Medicaid expansion (n = 196 556) to credit report data on medical debt and compared them with randomly selected credit reports of those living in Southern nonexpansion state zip codes with high rates of uninsurance (n = 973 674). The study spanned July 2014 through July 2019.
Results. One year after Louisiana Medicaid expansion, medical collections briefly rose before declining by 8.1 percentage points (95% confidence interval [CI] = –0.107, –0.055; P ≤ .001), or 13.5%, by the third postexpansion year. Balances also briefly rose before falling by 0.621 log points (95% CI = –0.817, –0.426; P ≤ .001), or 46.3%.
Conclusions. Louisiana’s Medicaid expansion was associated with a reduction in the medical debt load for those gaining coverage. These results suggest that future Medicaid eligibility expansions may be associated with similar improvements in the financial well-being of enrollees.
On July 1, 2016, Louisiana became the 32nd state (including Washington, DC) to expand Medicaid eligibility to those earning at or below 138% of the federal poverty level in the year that eligibility is determined under the Affordable Care Act, as calculated by the Department of Health and Human Services. Before expansion, Medicaid coverage in Louisiana was limited to parents earning at or below 24% of the federal poverty level, pregnant women earning at or below 214% of the federal poverty level, and children in families earning at or below 255% of the federal poverty level. Nondisabled, childless adults were ineligible for Medicaid coverage.1 By December 2018, more than 475 000 individuals had enrolled in Medicaid expansion in Louisiana, and the number of uninsured in the state had fallen by more than 50%.2
Gaining Medicaid coverage has been shown to improve access to care and increase the utilization of health services; however, the association between Medicaid coverage and financial health and security is less clear.3–6 Early evidence suggesting medical debt contributed significantly to personal bankruptcy has been disputed, and estimates of the relationship between Medicaid coverage and medical debt have varied widely.7 , 8 We provide the first evidence, to our knowledge, on the association, using de-identified credit report data linked to administrative enrollment records to compare individuals gaining coverage through Medicaid expansion in Louisiana to individuals in nonexpansion states. Three notable contributions of our study are as follows.
First, we examined changes in medical debt for those who actually gained Medicaid coverage as a result of expansion. Previous work has largely relied on survey data or simulated Medicaid eligibility measures to identify those likely to have gained coverage, potentially leading to measurement error in treatment exposure.9 Second, we followed the medical debt load of those gaining Medicaid coverage under Louisiana’s expansion for 2 years before and 3 years after expansion occurred. This is a significant improvement over earlier studies that relied on limited postperiod data.8 ,10–13 As we show, the pattern of medical debt continues to evolve for several years after individuals in our sample gain coverage. Third, we compared changes in medical debt load for those gaining health insurance coverage under Louisiana’s Medicaid expansion to similar individuals in nonexpansion states. Our inclusion of this control group improves the likelihood that our results are capturing changes in medical debt load associated with Medicaid expansion and not some unobserved confounding factor.
We found that Medicaid expansion in Louisiana was associated with a reduction in the medical debt burden of those gaining coverage and that the magnitude of the reduction grew over time. Before Medicaid expansion in Louisiana, nearly two thirds of those who would gain coverage had at least 1 outstanding medical debt reported to a collection agency, and nearly half carried a medical collection of $500 or more. By June 2019, 3 years after Medicaid expansion, the share of those gaining coverage with an outstanding medical collection had fallen by 13.5%, and the average balance of a medical collection had fallen by 46.5%. We also found large reductions in the average number of outstanding medical collections and collections with balances greater than $500 and balances greater than $1000. Our findings have direct implications for policymakers in the remaining nonexpansion states, the majority of which are also located in the Southern United States.
BACKGROUND
How medical debt contributes to household financial strain and the extent to which that strain is alleviated by gaining Medicaid coverage remain open questions. Between 20% and 60% of all personal bankruptcy filings have been attributed to a medical event,7 ,14–17 and several studies have concluded that Medicaid expansion was associated with fewer bankruptcies.10 , 11 , 18 , 19 Recent studies have also linked Medicaid eligibility to improved financial health, including fewer medical collections, payday loans, evictions, and unpaid bills.3 , 8 ,10–13,20–23 However, a notable shortcoming of nearly all previous work is a reliance on probabilistic measures of Medicaid eligibility or self-reports of insurance coverage. Probabilistic eligibility, rather than actual Medicaid enrollment, has the potential to induce measurement error resulting in bias. Comparisons of survey responses on Medicaid coverage to administrative records have found error rates as high as 35%, underscoring the potential for significant measurement error in studies that rely on self-reports.9
Only 2 studies have used administrative Medicaid enrollment data matched to credit reports.3 , 11 Results from the Oregon Health Insurance Experiment showed no effect of gaining Medicaid coverage on personal bankruptcies, tax liens, or judgments for unpaid bills, but indicated reductions in medical collections, money owed for medical expenses, and money borrowed to pay medical bills.3 In a study that most closely resembles our work, researchers linked credit report data and Medicaid enrollment records in Michigan and found that gaining Medicaid coverage was associated with reductions in overall collections, medical collections, and past due debt.11
Although these studies provide the best evidence to date that Medicaid eligibility expansions are associated with reduced medical debt, they are not without their limitations. For one, a relatively small number of people gained coverage as a result of the Oregon lottery, and fewer than 70% of lottery participants could be matched to their credit reports.3 Moreover, the study was able to track the financial health of lottery participants for only 14 months, on average, after coverage approval. In the study of Michigan’s Medicaid expansion, the authors relied on the timing of individual enrollment to identify changes in medical debt rather than a comparison with a group unaffected by Medicaid expansion. This strategy is problematic if the decision to enroll in Medicaid is related to a medical event, as is often the case with presumptive eligibility enrollment.
METHODS
We used Medicaid enrollment records from the Louisiana Department of Health to identify individuals gaining Medicaid coverage in July 2016. The majority of those gaining coverage in the first year of expansion did so in the first month because of Louisiana’s system-assisted enrollment, which used data from existing aid programs to determine Medicaid eligibility.24
We worked with Experian Information Solutions to match expansion enrollees to their credit reports after assigning each beneficiary a randomized identifier and removing personal information that would jeopardize anonymity. Experian located credit reports for approximately 98% of the expansion population, for an initial sample of 213 581 individuals. The credit report data contained several measures of medical debt, including the total number of unsatisfied medical collections and the total balance on all unsatisfied medical collections.
We also randomly selected the credit reports of approximately 1.4 million individuals living in zip codes with high rates of uninsurance in the following states that had yet to adopt Medicaid expansion as of July 1, 2019: Alabama, Florida, Georgia, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, and Texas. We considered a zip code to have a high rate of uninsurance if the share of individuals living in that zip code who reported that they lacked insurance coverage in the 2015 American Community Survey was above the 75th percentile of zip code–level uninsurance rates in the state. Approximately two thirds of the zip codes in our sample are also above the 75th percentile of zip code–level poverty rates. We used these data to construct a control group composed of individuals who would likely gain coverage if the states in which they lived adopted Medicaid expansion. By comparing changes in medical debt between those gaining Medicaid coverage in Louisiana and those in our control group, we were better able to eliminate the influence of potential confounding factors (e.g., overall improvements in the macroeconomy).
We gathered information on medical debt for our expansion and control samples in the month of June each year from 2014 through 2019 (25 months before Medicaid expansion in Louisiana and 36 months after). Finally, we dropped anyone who moved out of the state in which they lived in June 2016 to eliminate changes in exposure to different state policies and anyone with missing credit information in any study period. These restrictions resulted in a sample of 1 170 230 individuals, of whom 196 556 gained Medicaid coverage through Louisiana’s expansion.
We compared changes in medical debt for the Louisiana expansion sample to our control sample using a difference-in-differences (DID) research design. DID is a quasiexperimental method that compares changes for a treatment group (i.e., the expansion population) to those for a control group (i.e., those in nonexpansion states). We modified the standard DID design to allow estimates of the association between Medicaid expansion in Louisiana and medical debt to vary over time by interacting an indicator variable for an individual’s inclusion in the expansion population with indicators for each year of our credit report data.
Our regression models included controls for age and education along with individual fixed-effects terms, which controlled for time-invariant individual characteristics, including observable characteristics such as sex and race/ethnicity and unobservable characteristics such as an individual’s rate of time preference or risk tolerance that could be associated with insurance coverage and medical debt. We attempted to address potential bias from changes unrelated to Medicaid expansion by including controls for state-level unemployment rates, poverty rates, per capita household income, and year fixed effects that controlled for correlates of medical debt that vary over time but are common to all individuals in our sample. We estimated our DID models using ordinary least squares estimation and clustered SEs at the state level to account for unobserved in-state correlations of the error terms. More details on our empirical specification can be found in the Appendix (available as a supplement to the online version of this article at http://www.ajph.org).
RESULTS
Table 1 details baseline means for each of our outcome variables. Although we randomly selected individuals for our control group from zip codes with high rates of uninsurance in nonexpansion states, we could not explicitly condition on health insurance coverage status. As a result, the medical debt burden among our control group was lower than the burden among our treatment group before July 2016. On average, more than 60% of those in the expansion population had at least 1 outstanding medical collection on their credit report compared with 46% of those in the control group. The average total outstanding balance on all medical collections was $2300 for those in the expansion population compared with $1450 for those in the control group.
TABLE 1—
Summary Statistics For Treatment and Control Group: Southern United States, 2014–2019
| Baseline Sample Characteristics | Treatment Group | Control Group |
| Average age, y | 37 | 8 |
| % female | 54 | 43 |
| % with < high school education | 17 | 25 |
| % with a college degree | 10 | 10 |
| % with any medical collection | 60 | 46 |
| Average no. of medical collections | 3 | 2 |
| Average no. of medical collections > $500 | 1 | 1 |
| Average medical collection balance, $ | 2308 | 1451 |
| Average medical collection balance, log | 4 | 3 |
| % with any medical collection > $500 | 44 | 32 |
| % with any medical collection > $1000 | 35 | 24 |
Note. Our analytic sample included 196 556 Medicaid expansion beneficiaries in Louisiana and 973 674 individuals from high-uninsured zip codes in nonexpansion states. We observed each of these individuals once per year from 2014 through 2019 for a total of 7 021 380 person-year observations. The baseline period was 2014–2016.
Figures 1 and 2 plot unadjusted trends for 2 of our measures of medical debt from 2014 through 2019. Figure 1 displays the share of individuals in the sample with at least 1 outstanding medical collection. In June 2017, 1 year after Medicaid expansion, the share of the expansion population with a medical collection remained steady at just above 60% but began a pronounced decline in 2018 that continued through 2019. The share of those in the control group with a medical collection remained largely stable over the sample period.
FIGURE 1—
Percentage of Medicaid Expansion Enrollees With Any Medical Collection in Louisiana vs Southern Nonexpansion States: Southern United States, 2014–2019
Note. The control group is composed of a random sample of individuals living in zip codes with high uninsurance rates in Southern nonexpansion states. Both the Louisiana and control samples follow a balanced panel of the same individuals over time. The vertical line represents the last observation before Medicaid expansion in Louisiana.
FIGURE 2—
Medical Collection Balance in Louisiana vs Southern Nonexpansion States (Log Points): Southern United States, 2014–2019
Note. The control group is composed of a random sample of individuals living in zip codes with high uninsurance rates in Southern nonexpansion states. Both the Louisiana and control samples follow a balanced panel of the same individuals over time. The vertical line represents the last observation before Medicaid expansion in Louisiana.
Figure 2 shows that medical balances for the expansion population were higher, on average, than those for the control group but fell dramatically following Medicaid expansion. We transformed debt values using a logarithmic scale to minimize the influence of outliers, and Figure 2 shows that average medical collection balances hovered between 4 and 5 log points from 2014 through 2017 (approximately $2300) for individuals in the expansion population, but by 2019, had fallen by nearly 1 full log point (to just over $1700, on average).
The trends seen in Figures 1 and 2 are consistent with a negative association between Medicaid coverage and medical debt, although they lack controls for potential confounding factors. Consequently, we turned to our regression estimates (Table 2), which extended the number of outcomes related to medical debt that we examined. All estimates should be interpreted as changes for the treatment group compared with the control group relative to June 2016 (the omitted period in our regression model).
TABLE 2—
The Effect of Medicaid Expansion on the Number of Medical Collections: Southern United States, 2014–2019
| Any Medical Collection, b (95% CI) |
No. of Medical Collections, b (95% CI) |
No. of Medical Collections > $500, b (95% CI) |
Medical Collection Balance, b (95% CI) |
Any Medical Collection > $500, b (95% CI) |
Any Medical Collection > $1000, Estimate (95% CI) |
|
| 25 mo before expansion | –0.01 (–0.02, 0.01) | –0.07 (–0.31, 0.17) | –0.05 (–0.01, 0.003) | –0.09 (–0.21, 0.04) | –0.01 (–0.02, 0.001) | –0.01 (–0.026, 0.01) |
| 13 mo before expansion | 0.01 (–0.004, 0.03) | 0.19 (–0.004, 0.38) | 0.07 (–0.002, 0.15) | 0.11 (–0.03, 0.24) | 0.02 (0.001, 0.03) | 0.02 (–0.002, 0.03) |
| 12 mo after expansion | 0.01 (–0.01, 0.02) | 0.19 (0.06, 0.32) | 0.09 (0.03, 0.14) | 0.09 (–0.02, 0.20) | 0.01 (–0.003, 0.02) | 0.01 (–0.002, 0.02) |
| 24 mo after expansion | –0.02 (–0.05, 0.02) | –0.16 (–0.46, 0.13) | –0.07 (–0.17, 0.04) | –0.15 (–0.40, 0.09) | –0.02 (–0.04, 0.01) | –0.02 (–0.04, 0.01) |
| 36 mo after expansion | –0.08 (–0.11, –0.05) | –0.90 (–1.13, –0.67) | –0.26 (–0.35, –0.18) | –0.62 (–0.82, –0.43) | –0.06 (–0.08, –0.04) | –0.05 (–0.07, –0.03) |
| Louisiana baseline mean | 0.60 | 3.12 | 1.16 | 4.25 | 0.44 | 0.35 |
Note. CI = confidence interval. Study size was n = 7 021 380. The 1-month before expansion group was omitted from analysis. b values are from interaction terms between an indicator for Louisiana Medicaid expansion enrollment and survey periods. Individual controls included sex, age, education, and state unemployment rate. State controls included the unemployment rate and per capita household income. All models included individual and survey period fixed effects, and SEs are clustered at the state level.
Table 2 contains estimates of changes in the share of the expansion population with at least 1 medical collection. Before expansion, changes in this outcome were small for the expansion population compared with the control sample. One year after Louisiana’s Medicaid expansion, the share of the expansion population with at least 1 medical collection grew by 0.009 percentage points (95% confidence interval [CI] = –0.005, 0.023; P = .180), or 1.5%, before declining in 2018 and 2019. By 2019, the share of the expansion population with at least 1 medical collection had fallen by 0.080 percentage points (95% CI = –0.106, –0.054; P < .001), or 13.3%.
Table 2 provides estimates of changes in the average number of medical collections and the average number of medical collections with a balance greater than $500. In the first year following expansion, the average number of medical collections increased by 0.188 (95% CI = 0.058, 0.317; P = .01), or 6.0%, and the average number of medical collections with a balance greater than $500 increased by 0.087 (95% CI = 0.033, 0.142; P = .006), or 7.5%. By 2019, however, the expansion population had experienced a 0.896 percentage point (95% CI = –1.125, –0.667; P < .001), or 28.7%, reduction in the average number of medical collections and a 0.264 percentage point (95% CI = –0.350, –0.179; P < .001), or 22.7%, reduction in the number of medical collections greater than $500.
Table 2 displays changes in the average balance of medical collections for those in the expansion population compared with those in the nonexpansion control states. Because we log-transformed the medical collection balance outcome, the estimates are changes in balances measured in log points. Like the patterns in Table 2, medical collection balances rose slightly for those gaining Medicaid coverage in Louisiana in the year following expansion and then began to fall in 2018. By 2019, balances on medical collections for the Louisiana Medicaid expansion population had fallen by 0.621 log points (95% CI = –0.817, –0.426; P < .001), or 46.3% ([e–0.621 – 1] × 100), compared with those in nonexpansion states. Table 2 presents changes in the probability of a medical collection of $500 or more and a medical collection of $1000 or more. In both cases, Medicaid expansion in Louisiana was associated with reduced medical debt by 2019.
DISCUSSION
Our findings indicate that Louisiana’s Medicaid expansion was associated with a substantial reduction in the medical debt load for those gaining coverage. Compared with those in nonexpansion states, the probability of a medical collection fell by 13.3% and medical debt balances fell by approximately $1000, on average, for those in the expansion population. This is a slightly larger absolute reduction in medical debt than has been reported by other studies.3 , 8 , 11 For example, previous work found that medical debt fell by an average of $511 in the first 21 months for those gaining coverage through Medicaid expansion in Michigan.11 Another study reported that a Medicaid eligibility expansion in Oregon resulted in a $390 reduction in medical debt over the first 14 months that individuals gained coverage. The larger reductions in medical debt associated with Medicaid expansion in Louisiana can likely be explained by 2 factors. First, medical debt was higher, on average, for individuals gaining Medicaid coverage in Louisiana ($2308) than for those in Michigan ($1002) and Oregon ($1999). Second, we observed changes in medical debt reported to collections over a longer period of time following coverage gains for our sample (36 months) than for the samples used in Michigan (21 months) and Oregon (14 months). As our results indicate, medical debt continues to decline over time so that observing affected individuals at longer follow-up intervals will lead to large estimates of debt reduction.
Notably, the pattern of medical debt following expansion for those gaining coverage evolved over the 3-year follow-up period. One year following expansion, every indicator of medical debt load that we analyzed had initial relative increases—a finding at odds with other studies reporting reductions in medical debt associated with Medicaid coverage in the first year (or even 6 months) after eligibility expansions. We did not observe average medical debt loads begin to decline until the second year following expansion, after which medical debt continued to decline further in the third year. We lack a definitive explanation for this pattern and leave this as an area for future research. However, we have confirmed that the higher average debt loads in the first year following expansion in Louisiana are not driven by fewer individuals with low levels of medical debt.
Limitations
This research has several limitations. We were able to measure the association between Medicaid expansion and medical debt for individuals gaining coverage only in Louisiana, raising questions about the external validity of our findings. For example, Louisiana residents had a disproportionately high level of medical debt compared with residents in other Affordable Care Act Medicaid expansion states. However, like Louisiana, the majority of states that have yet to expand Medicaid eligibility are located in the Southern United States, with population demographics that more closely resemble those in Louisiana than in earlier expansion states. As a result, our estimates can guide expectations for policymakers considering expansion.
We observed Medicaid enrollment for those in Louisiana at a single point in time coinciding with the state’s July 1, 2016, expansion. Therefore, we do not know whether individuals gaining coverage maintained that coverage throughout our sample period. However, were those in the Medicaid expansion population to subsequently move to private coverage or uninsurance, cost sharing for medical care would likely increase, suggesting that our estimates would represent the lower bounds of the relationship between Medicaid coverage and medical debt.
Data limitations did not allow us to construct a control group composed of individuals who would have qualified for Medicaid coverage if the states in which they lived expanded Medicaid under the Affordable Care Act. Instead, we approximated potential eligibility by selecting individuals living in zip codes with high rates of uninsurance. Although this limitation means that we do not know the insurance status of those in our control group, this shortcoming would not bias our estimates unless any changes in insurance status for those in nonexpansion states were systematically related to Louisiana’s Medicaid expansion. We have no reason to believe this would be the case, and Figures 1 and 2 indicate no break in trend for the control group associated with Medicaid expansion in Louisiana.
Finally, every estimate of the association between Medicaid expansion and medical debt in Louisiana in the first year following expansion was positive and statistically significant. Unfortunately, we were not able to empirically identify the mechanism for this observed pattern, although it is likely that collections are a lagged indicator of medical debt.
CONCLUSIONS
Medicaid expansion in Louisiana was associated with substantial medical debt relief for those gaining coverage. Our findings provide insight into the implications for individual financial well-being of future Medicaid eligibility expansions.
ACKNOWLEDGMENTS
This research was supported in part by the Louisiana Department of Health (contract VCAF-19-128-006).
CONFLICTS OF INTEREST
The authors have no conflicts of interest to declare.
HUMAN PARTICIPANT PROTECTION
This research was determined to pose minimal risk to the research participants and was approved through expedited review by the Tulane University institutional review board (IRB REF# 2019-1057). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for observational studies.
Footnotes
See also Gee, p. 1385.
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