Major injury is unanticipated and expensive to treat. Therefore, trauma admissions place patients at high risk of unexpected financial strain if they lack sufficient financial risk protection. This risk is compounded by the fact that trauma often affects younger and lower-income patients1–4 who are less likely to have health insurance and thus at greater risk for high out-of-pocket (OOP) health care expenses.5,6 Prior analyses7 suggest that more than 70% of uninsured trauma patients in the United States are at risk of catastrophic health expenditure (CHE), defined by the World Health Organization and the World Bank as any OOP spending on health care that exceeds 40% of annual postsubsistence income.8–11
In the United States, the primary tool to protect against unanticipated medical spending is health insurance. Unfortunately, trauma patients are more likely to be uninsured or underinsured (high ratio of OOP spending to income) than the general population, with nearly one-in-five trauma patients lacking health insurance before the Affordable Care Act (ACA).3,7,12 Since 2010, the ACA has led to the largest gains in health insurance coverage in the United States over the last 50 years, with nearly 20 million Americans gaining health insurance coverage.13–15 This is especially true in states that elected to expand eligibility for Medicaid coverage, and over half of the gains in insurance coverage are because of Medicaid expansion (ME).13,14 For example, the state of Washington (WA) expanded Medicaid in January 2014, and the uninsured rate fell by half after newly enrolling more than 600,000 individuals.16
Despite these unprecedented gains in insurance coverage, little is known about the impact of ME on financial strain among trauma patients. In this study, we examined the association between the 2014 expansion of Medicaid eligibility in WA and financial risk protection for trauma patients. We hypothesized that ME in WA was associated with a reduction in the uninsured rate and a concomitant reduction in the risk of CHE for trauma patients.
PATIENTS AND METHODS
In this study, we assessed for prepolicy and postpolicy changes in insurance coverage, clinical outcomes, and CHE risk associated with WA's expansion of Medicaid eligibility in January of 2014. We used 6 years of data (2012–2017) from an institutional trauma registry at the only level 1 trauma center in WA.17 To focus our analysis on patients eligible for ME in WA, we restricted our analytic data set to admitted 18- to 64-year-old trauma patients who live in WA. We excluded burn patients and those with missing payer status or ZIP Code information.
Evaluating for First- and Second-Order Effects of ME
The first step in our analysis was to evaluate for first-order effects of ME on the payer mix of trauma patients in our sample. The prepolicy period was defined as 2012 to 2013, and the postpolicy period was defined as 2014 to 2017. Insurance status was obtained from the trauma registry and divided into 5 categories: private insurance, self-pay/uninsured, Medicaid, Medicare, and other (consisting of workers compensation, labor and industry, and other governmental insurance plan).
The second step in our analysis was to evaluate for potential second-order effects of a statewide insurance expansion policy on clinical outcomes, discharge disposition, and CHE risk. Previous literature has suggested that being uninsured is independently associated with inpatient mortality, length of stay, and discharge disposition among trauma patients.3,18–22 In our analyses, discharge disposition was defined as home, home with services, inpatient rehabilitation, long-term acute care, and respite care (which provides temporary housing and basic nursing care to unsheltered patients). Finally, because insurance coverage impacts patients' OOP expenses,13 and because uninsured trauma patients are at high risk for CHE,7 we evaluated CHE risk before and after ME.
In this prepolicy versus postpolicy assessment, it is possible that observed changes in insurance coverage, clinical outcomes, discharge disposition, and measures of financial risk could be related to prepolicy/postpolicy variation in the patient mix that is not directly related to the policy. As such, all analyses were adjusted for relevant covariables that could be associated with the outcomes of interest. Changes in insurance coverage were adjusted for patient demographics (age, sex, race/ethnicity), transfer status, and year. Changes in clinical outcomes were adjusted for these same variables as well as injury characteristics including injury type, injury mechanism, injury severity score, shock index at presentation, presence of traumatic brain injury, and need for mechanical ventilator support. Changes in discharge disposition were adjusted for all of these variables as well as presence of spinal cord injury and the patient's functional independence measure score23 at time of discharge (please see Supplemental Digital Content, http://links.lww.com/TA/B477, for additional details regarding definitions of these covariables).
Unadjusted prepolicy/postpolicy comparisons were performed using t tests, χ2 tests, and Wilcoxon rank-sum tests as appropriate. Adjusted prepolicy/postpolicy comparisons were performed using linear regression models. Analyses of the changes in payer mix and CHE risk were based on the entire analytic sample. Analyses of changes in clinical outcomes and disposition were focused only on the policy-relevant sample by excluding patients with Medicare. All analyses were completed using Stata 15 (StataCorp. 2017. Stata Statistical Software: Release 15; StataCorp LP, College Station, TX).
Evaluating for Patient-Level Risk of CHE
The World Bank and the World Health Organization have tracked CHE risk among surgical patients in more than 100 countries worldwide.8–10 A patient is at risk for CHE when they have OOP health expenditures amounting to 40% or greater of their annual postsubsistence income. Postsubsistence income is defined annual household income less annual spending on housing and food.8
To calculate CHE risk, we first estimated OOP spending maximums based on insurance status and hospital charges. For uninsured patients, OOP spending was defined as the full hospital bill. For privately insured patients, OOP spending was based on estimated deductibles, estimated cost sharing, and estimated annual OOP maximums based on data from the US Census and the Kaiser Family Foundation/Health Research and Education Trust Employer Health Benefits Survey (please see Supplemental Digital Content, http://links.lww.com/TA/B477, for additional details).24 For patients covered by Medicare, OOP spending was set at the national average OOP spending for dual-eligible patients, which equates to US $2,642 in 2017 dollars.25 Because WA does not require copayment for covered services and because federal law prohibits cost sharing for emergency services for Medicaid enrollees in any state, OOP spending was initially set to zero for patients covered by Medicaid. Out-of-pocket spending was also set to zero for patients covered by workers compensation. However, if any patient, regardless of payer, had reported self-payments in the trauma registry that were greater than their estimated OOP spending maximum, their estimated OOP spending was increased accordingly.
To estimate annual postsubsistence income for each patient, we used US Census data to identify the mean, median, and distribution of household incomes for each ZIP Code in WA.26 Because higher-income patients may live in ZIP Codes with low median household incomes and vice versa, we used previously established methods7,11,27,28 to estimate the income distribution for each ZIP Code (please see Supplemental Digital Content, http://links.lww.com/TA/B477, and Supplemental Figure 2 for details).
Having established OOP spending maximum, total hospital charges, and income distribution by ZIP Code, we then estimated CHE risk. For each patient, we assigned a probability-weighted annual household income from their ZIP Code–specific income distribution and then subtracted estimated annual spending on food and housing (using data from US Bureau of Labor and Statistics)29 to estimate a patient's postsubsistence income. If the estimated OOP spending was 40% or greater of their postsubsistence income, then they were determined to be at risk of CHE. This process was repeated 10,000 times using a microsimulation model in Stata that randomly assigned a probability-weighted annual household income drawn from the patient's ZIP Code–specific income distribution. The average of these 10,000 iterations provided an estimated risk of CHE for each patient, which ranged from 0 to 1.
For privately insured patients, the CHE risk model was based on national averages of OOP spending based on national distribution of private insurance coverage levels.24 To calculate a lower bound of the estimated CHE risk for privately insured patients, we ran a sensitivity analysis in which all privately insured patients were assigned an OOP spending maximum equivalent to the best “gold tier” plans (i.e., those with the lowest OOP maximums).30 To calculate an upper bound, we ran a second sensitivity analysis in which all privately insured patients had a deductible and their OOPS limit was the upper limit allowed by federal law each year (details can be found in Supplemental Digital Content, http://links.lww.com/TA/B477).
We identified 16,801 adult trauma patients under the age of 65 years from WA admitted between 2012 and 2017 (Supplemental Digital Content, Supplemental Figure 1, http://links.lww.com/TA/B477). Table 1 shows patient demographics and injury characteristics in the prepolicy and postpolicy periods.
Medicaid expansion in WA had a large and immediate impact on the payer mix in our sample (Fig. 1). During the 2 years before ME, 20.4% of patients had Medicaid and 19.2% were uninsured (Supplemental Digital Content, Supplemental Table 1, http://links.lww.com/TA/B477). After the policy took effect in January of 2014, the proportion of patients with Medicaid in the postpolicy period doubled to 41.0%, and the uninsured rate fell to 3.7% (p < 0.001 for both). The proportion of patients with other insurance also decreased from 14.4% to 11.9% (p < 0.01). Neither private insurance nor Medicare coverage rates experienced a statistically significant postpolicy change from their prepolicy baselines of 38.3% and 7.8%, respectively (p > 0.05 for both).
Although ME was associated with a greater than 80% relative reduction in the uninsured rate, there were no statistically significant differences in intensive care unit length of stay, hospital length of stay, or risk-adjusted inpatient mortality (Table 2). Similarly, there were no statistically significant differences in risk-adjusted rates of discharge to home, with home health services, to skilled nursing facilities, to rehabilitation facilities, or long-term acute care facilities (Table 2). The small increase in the proportion of patients discharged to respite care (from 2.0% to 3.7%) was statistically significant (p < 0.05), although its clinical significance is unclear.
Across the entire study period, variations in risk of CHE were driven by insurance status (Table 3). Uninsured patients had an average CHE risk of 81.4% (95% confidence interval [CI], 80.4–82.4%), while privately insured patients had an average CHE risk of 25.9% (95% CI, 25.6–26.3%). Medicare patients had a 14.3% (95% CI, 13.9–14.7%) average CHE risk, while the risk among patients with Medicaid was less than 0.1% (95% CI, 0.0–0.1%).
The large postpolicy reduction in the uninsured rate was associated with a large reduction in the total proportion of patients at risk for CHE each year (Fig. 2). The overall proportion of all patients at risk for CHE was highest in 2013 at 27.5% (95% CI, 24.3–26.6%) and fell to a nadir of 13.6% (95% CI, 12.9–14.3%) in 2015. The risk of CHE within payer types was qualitatively similar before and after ME, with the exception of privately insured patients who had a higher CHE risk after ME (Table 3). Figure 3 shows the results of two sensitivity analyses estimating upper and lower limits to the CHE risk among privately insured patients throughout the study period.
In this prepolicy/postpolicy analysis, we used 6 years of data from the only level 1 trauma center in the state of WA to evaluate the impact of ME on insurance coverage, clinical outcomes, and financial risk protection. We identified large first-order policy effects that amounted to a doubling of trauma patients covered by Medicaid and an 80% relative reduction in the uninsured rate. These large insurance coverage gains were not associated with significant changes in clinical outcomes, including discharge to rehabilitation or skilled nursing facilities. However, financial risk protection was significantly improved as evidenced by a large reduction in CHE risk. Nonetheless, one quarter of privately insured trauma patients appear to be underinsured against hospitalizations for trauma.
Although WA's 2014 ME cut the uninsured rate among admitted trauma patients from nearly 20% to less than 4%, there were no changes in inpatient mortality, length of stay, or discharge to rehabilitation. Observational studies before the ACA has suggested that a lack of insurance may be an independent risk factor for inpatient mortality3,18,19 and that uninsured patients are less likely to receive critically important postdischarge care after injury.3,20–22 However, our findings are in line with multiple studies at the facility, state, and national level that have shown very large gains in insurance coverage after the ACA has not been associated with a reduction in inpatient mortality.31–34 The Eastern Association for the Surgery of Trauma recently published a meta-analysis of many of these studies and also concluded that the large coverage gains from the ACA have not been associated with a reduction in in-patient mortality.31 Our findings add to this existing body of work to suggest that the previously identified association between lack of insurance and inpatient mortality may not be a casual association. However, the lack of change in discharge disposition in our study is not in line with two recent nationwide analyses that demonstrated higher rates of discharge to rehabilitation facilities in states that expanded Medicaid.32,34 This highlights the fact that access to postdischarge care may be influenced by factors other than payer status, such as total number of available facilities in the state and the specific types of insurance accepted by various facilities.
The main finding of this study is that large insurance coverage gains can lead to large reductions in financial risk for trauma patients. Our finding that over 80% of uninsured trauma patients were at risk of CHE is in line with previous national estimates of greater than 70%.7 Our study, however, extends the previous literature by showing the impact that ME has had on reducing this risk among trauma patients. Because the gains in Medicaid coverage appear to have come almost exclusively from previously uninsured patients, CHE risk among the uninsured fell precipitously. However, our findings also highlight the fact that approximately one fourth of patients with private insurance coverage are still at risk for CHE after trauma admission (Fig. 3). Because CHE risk is driven by high OOP spending relative to one's income, the change in CHE risk among insured patients over time may be driven, in part, by increases in cost sharing in the form of higher deductibles and increased coinsurance.24 In 2018, the national limit on OOP spending for private plans was US $7,350 for individual plans and US $14,700 for family plans.24 Given that a recent national survey found that nearly half of working adults could not pay an unanticipated US $1,000 medical bill within 30 days,35 it is not surprising that an unanticipated trauma admission, which may very often require all of a patients' annual OOP maximum payment, places privately insured patients at risk for significant financial strain.
While these findings highlight the role of insurance coverage expansion in reducing the burden of OOP expenses for patients, the implications for trauma center reimbursement are uncertain. At our own institution, the proportion of patients with bills referred to some sort of charity care decreased significantly (Supplemental Digital Content, Supplemental Figure 3, http://links.lww.com/TA/B477), but that is just one component of the total facility-level financial impact. It is possible that ME could increase hospital reimbursement, as overall reimbursement from Medicaid patients is greater than reimbursement from uninsured patients, and prior nationwide analyses have suggested that the ACA-related insurance coverage gains could lead to over US $1 billion in additional revenue for nonelderly adult trauma care alone.7 In addition, a recent national analysis (not limited to trauma care) suggests that ME has led to a US $6.2 billion reduction in uncompensated care among expansion states.36 However, it is also possible that this increase in reimbursement may not entirely cover the costs of care, and thus, the planned reductions to the Medicaid Disproportionate Share Hospital program could actually lead to increased levels of uncompensated care at some safety-net hospitals.37 While state-level ME status has been associated with a decreased risk of hospital closures,38 the impact of these changes on the financial well-being of trauma centers is not yet known and will require rigorous evaluation.
Our findings must be interpreted in light of the study's limitations. First, we are only able to estimate the risk of CHE for each patient because our trauma registry does not report patients' actual household adjusted gross income or the amount they actually paid to their insurer or the hospital. It is possible that some patients at this institution (or others) may receive financial assistance through the form of discounts, payment plans, charity care, or other similar programs. As a result, we used a population-level approach and relied on established methods for estimating household income and estimated each patient's potential risk of CHE after 10,000 rounds of microsimulation modeling. Relatedly, our analysis does not account for the fact that some of our patients may be homeless or have no income. This limitation would bias our data toward an underestimation of the overall CHE risk for all patients. Finally, because of differences in injury patterns, average income, insurance products available, practice patterns, and costs to charge ratios, all of which could impact the CHE risk, the generalizability of our findings may be limited to facilities with similar patient demographics and injury characteristics. Of note, however, our findings are very similar in magnitude to previously reported national estimates of CHE risk among uninsured trauma patients.7
Medicaid expansion in WA state had a significant impact on insurance coverage and financial risk protection for trauma patients. Medicaid expansion led to a doubling of trauma patients on Medicaid and cut the uninsured rate by 80%. Because uninsured trauma patients are at a high risk for CHE, large gains in insurance coverage were associated with equivalent-sized gains in financial risk protection. However, this analysis also highlights the fact that not all insured patients are fully protected from significant financial strain as over one quarter of privately insured trauma patients were at risk for CHE by 2017. Additional financial risk protection strategies are needed to help the remaining uninsured, and the increasingly underinsured, address the lasting impacts of injury beyond hospital discharge. Further work is also needed to evaluate the effects of insurance expansion and anticipated changes in reimbursement for uncompensated care on the financial viability of the trauma centers that these patients rely on for lifesaving care.
All authors took part in the study design, data interpretation, writing, critical revision. J.W.S. and M.G.S. performed the data analysis.
The authors declare no conflicts of interest.
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JAY J. DOUCET, M.D., M.Sc. (San Diego, California): Thank you Chair Bulger and Recorder May for the opportunity to discuss this interesting and illustrative study on the effect of insurance status to financial risks for trauma patients.
I would like to thank the authors for having the paper to me on time and it was a good read and a well-written paper.
Dr. Scott and his coauthors have continued their investigations on the Affordable Care Act by studying the effect of Medicaid expansion in Washington state.
And in that state the authors have shown that Medicaid expansion has significantly reduced the total number of patients at risk for catastrophic health expenses.
And this is largely because legislation limits the copay for Medicaid patients to much lower costs as compared to private insured or uninsured patients.
Medical bills, as you have heard, comprise a huge problem in the United States. A majority of bill collection activity by bill collectors in the United States is from medical debt. That’s 60 percent of the profit made by bill collection agencies is from medical bills.
Medical debt is not automatically cancelled. And although the statute of limitations may limit the ability to sue the payer up to 15 years, the debt is still collectable, even after the patient’s death. And in many cases and in many states they can still go after the patient’s relatives.
Hospitals quickly sell off their debt to bill collectors. And they use controversial practices to obtain payment. Even after the ACA, medical bankruptcy still comprises about two-thirds of the bankruptcies in the United States.
And medical debt affects about half-a-million families a year in the United States. Many of these families that are affected were not eligible for Medicaid in their state.
I would suggest that the real risk of catastrophic health expenses really depends on the action of the hospital and its bill collectors. Although the uninsured group are at the highest risk for catastrophic health expenditures, a patient with low or little income or who does not need a high credit rating may not be a good target for the bill collectors and may actually be forgotten or forgiven.
So I have a few questions for my esteemed colleague.
First off, what is the effect of your institution’s Chargemaster on your study? For instance, I know in my center we charge about 3.3 times our actual costs, which is about the national average. The national median is about 2.2. So obviously charges are much different than costs. And some hospitals have much higher Chargemasters as a result.
What is your institution’s policy – your new one or your old one if you like – on medical debtors? Do they collect their own debts? Do they quickly sell these off to bill collectors? Do they treat trauma patients differently who may not even have had a chance to personally sign a contract with the hospital themselves?
And I would disagree on your point on bankruptcy. I think medical bankruptcy is a good indicator of catastrophic health expenditure because it is not a theoretical thing, it’s a discrete event, not a calculation or score.
And somebody who suffers a medical bankruptcy is someone who was solvent, had a credit rating and is somebody who has probably been pursued by creditors into declaring bankruptcy; and, therefore, has had a additional pretty adverse outcome as a result of their trauma.
I think that medical bankruptcy be a better metric for the success of expanded Medicare coverage. And I think that decrease in bankruptcy would be very interesting measure in a future paper I hope to see.
I’d like to thank the Association for the honor of discussing this important work.
DAVID HARRINGTON, M.D. (Providence, Rhode Island): Thank you. Your last slide provoked an interesting question for you.
We found in Rhode Island that the initial expansion and revenue to the center in the Medicaid expansion was helpful.
But we’ve seen now a profound secondary migration of commercial payers or commercial insurance patients into the ACA and Medicaid. And so what is happening is that while the original ACA was a flush of money, we’re now seeing declining reimbursements.
And I wonder whether the more aggressiveness on the parts of hospitals to collect debt is a reflection of the migration of commercial insurance payers to Medicaid. Do you see this trend in your state?
DAVID H. LIVINGSTON, M.D. (Newark, New Jersey): That was very nice presentation. I think there is no doubt for the states that adopted it, the Medicaid expansion following the implementation of the ACA was really a great thing.
But your data ends in 2017 which is, unfortunate as there has been marked changes since then and as you know administrative data takes a while to be populated. In Newark, until the ACA came in, were consistently 20–25 percent uninsured institution which fell almost into single digits. In the last two years, as it’s been chipped away, we are back up to double digits. These data is much more timely as I get it from our billing service monthly and quarterly.
In addition, in our area we have patients who either are undocumented or more importantly, have people in their household who may be undocumented. With the aggressive posture of the new ICE and the rise of ICE as a negative force in this country, they’re not coming near the Medicaid people again. Even if they themselves are documented and qualify. They’ll just stay uninsured.
Have you’ve seen what trends have been occurring on a more granular level at your institution. Because I think we need to not only document the rise of people’s insurance but we’re going to need to document the failure and loss as it begins to chip away. And there is no doubt I think it has been slowly chipping away in the last two years.
R. LAWRENCE REED, M.D. (Indianapolis, Indiana): I enjoyed this presentation. I was most impressed, though, about the need hospitals have to go and garnish wages and repossess homes and so forth for patients who were unable to pay their bills.
Seemingly that insurance-for-all process fails. And I think we had a good clue from Dr. Fabian’s exceptional talk this morning at the Fitts Oration about what is going on here.
If you recall, he had a graph showing the costs of health care rising over the past several decades. But included in that cost was the number of physicians and the number of health care administrators.
You know, three-quarters of health care might go to patient care. But at least a quarter to a third has been estimated as going to administration including, of course, government administration which you don’t see on the health care side of that graph that Dr. Fabian showed.
So it’s massive, this huge structure that has had to be created. And part of the big problem there is because we’re not actually paid for what we do. We only get paid for what we write.
Physicians through our notes, our RVUs, the better the note, the better the payment. And the hospital also has clinical documentation rules that coders have to depend upon to actually code the case properly.
And why should you have to code something when you’re coming in for a hernia. That should have a procedure code that’s already there and you shouldn’t have to write any specific things about it.
When your car mechanic changes a sparkplug there is no operative procedure note that goes with that. We need to be paid for what we do.
The whole system of payment is behind the problem. That sets up all this administration, this excess costs. There is no way a government can ever keep up with it except by printing lots of money.
So I think we need to turn our heads into a new direction, as physicians actually drive the process of health care and seek a solution to a different way of getting paid.
JOHN W. SCOTT, M.D., M.P.H. (Ann Arbor, Michigan): Great. Thank you all for these thought-provoking questions. And thank you, Dr. Doucet. So I will try to hit these quickly.
Regarding the Chargemaster, I don’t know what Harborview’s Chargemaster is but, as you mentioned, the national average is around two to three. I would imagine it is pretty similar.
I’m glad you brought up Chargemaster, though, because the irony of Chargemaster is when they look nationally it’s huge variations across the country. The places with highest Chargemaster are the places with the most uninsured patients.
And so you have more patients who are at risk for getting an overinflated bill that’s hurting their credit but that’s because the hospital is not getting the payment. And so it is just like this really nasty cycle that really doesn’t help anybody.
Regarding bankruptcy, I want to be clear. I think it’s absolutely critical. We have to trend it. We have to follow it. It’s a good objective measure. I think it just doesn’t tell the whole story. And it specifically excludes lower-income families I think we need to be cognizant of.
Regarding Harborview’s policy of what they do, so it’s actually wonderful. I went and sat down and met with the folks that you would call if you had a bill that you couldn’t meet with.
And they work as hard as they can to find some way to reduce the charges or there is a bit charity or there is a charity fund at the county and state level.
We did look at charity care utilization after the ACA and it went down 80 percent, just like it. But, as was mentioned, I’m kind of mixing questions now, reimbursement is driven by payer mix as things exist in America right now.
And so the hospitals that were doing pretty good before the ACA are doing worse because they were getting really like a pretty darn good payer mix that’s getting a little bit worse.
But the hospitals at the bottom that were doing worse the ACA are actually doing better because having any type of insurance is better than a lack of insurance. So it’s a heterogeneous mix around the country.
Regarding Dr. Livingston’s point, I think it’s vital that we trend this, we follow it, and we watch as things got a little bit better and they’re getting a little bit worse.
One thing I think is not very political that has been somehow framed as this really political idea is that people having insurance is a good thing. I think most people in America would say, yes, it would be good if people were insured. Nobody is excited about the uninsured rate going up.
But to Dr. Reed’s point, you hit the nail on the head. We absolutely, absolutely, absolutely have to get rid of waste and have to make costs something that is actually relevant because we can put out all the insurance we want but it’s not going to fix the problem that these bills are just too, too high.
So thank you very much.