Putting a Price Tag on the Decision to Opt-Out of the Medicaid Expansion

Conservative opposition to the Affordable Care Act is old news. Even after the Supreme Court ruling last year, it was quickly established that–given the option–many traditionally Republican states would not be participating in the Medicaid expansion. I’ve written before about the irony that the very states choosing to opt-out of the Medicaid expansion are the ones who would benefit from it the most, because a higher proportion of their population is uninsured, their residents’ health is generally worse, and the amount of federal matching funds they’d bring into their states is higher. But new research from the RAND Corporation puts a price tag on the states’ decision to opt-out and it has me wondering if what we’re seeing isn’t just farsighted politics at its best. Let me explain.

In a recent Health Affairs article, Carter Price and Christine Eibner report the results of a microsimulation model to predict insurance coverage and federal and state spending under several different scenarios tied to the Medicaid expansion. They find that if the 14 states currently vowing to opt-out of the Medicaid expansion keep their word, there will be a number of consequences compared to a scenario in which all states participate in the expansion. First, there will be an additional 3.6 million persons who remain uninsured. Second, the Federal government will save about $8.4 billion. That figure comes about because the Feds will spend $21.3 billion less on Medicaid (remember, fewer people are enrolled), but $11.2 billion more on subsidizing premiums for individuals who would have been eligible for Medicaid but instead ended up enrolling in the health insurance exchange, and $1.7 billion more on uncompensated care. Third, and finally, states that opt-out will lose out on $1 billion, because while their Medicaid costs decrease slightly, they will also have to pick up the tab for uncompensated care when Federal disproportionate share hospital payments are cut.

If you assume that these effects are spread evenly over the 14 non-participating states (they’re not, of course), it works out to the following: $71,428,571 per state in additional state spending for 257,143 fewer persons with insurance. In other words, each state is willing to pay about $278 for a person not to be insured. According to the Kaiser Family Foundation, in 2009, the average amount paid per Medicaid enrollee per year was $5,527. Thus, by 2020, when states would be responsible for 10% of these costs, that would equal $553. The conclusion: states would rather limit their total spending to $278 per person and get nothing in return, than pay an additional $275 so that each person could have health insurance, and face the possibility that that amount could increase over time.

But I think there’s a bit more to it than that. As the authors write: “The decision to expand Medicaid would not have substantial effects on state budgets prior to 2017.” What we are seeing is less about math, and more about politics. First, with the mid-term elections in 2014, the GOP is hoping that the health insurance exchanges that will begin enrollment in October of this year will be a disaster and that they will pick up seats in Congress as a result. Then, with the Presidential election in 2016, the GOP is hoping that their resistance to the Medicaid expansion will have caused further disruptions to the implementation of “Obamacare” and they will be able to leverage that to win the White House. If they are successful on both counts, they may find that they are closer to repealing or seriously dismantling the Affordable Care Act. That matters to them. A lot. And they’re willing to gamble away billions of dollars in Federal matching funds in the hope that doing so will reclaim the Senate and the Presidency for Republicans. If at least one of those things doesn’t happen, I think you’ll see at least some states have a change of heart in 2017, and opt-in to the Medicaid expansion at that later date.

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Racial Segregation, Hospital Quality, and Disparities in Surgery

A new study from Justin Dimick and colleagues appearing in the June issue of Health Affairs builds on the established evidence that blacks are more likely than whites to have surgery at low-quality hospitals, but adds an interesting twist: the degree of racial segregation in an area–and not distance to the nearest hospital–is what matters.

In fact, the authors find that black patients actually lived closer to the high quality hospitals than white patients did. So this isn’t about convenience based on proximity, and it isn’t about segregating in such a way that the low-quality hospitals and the minority neighborhoods are lumped together geographically. Instead, it is about something much more systemic: residential segregation. In other words, if the black and white residents of your community are spread around pretty evenly, then blacks aren’t having surgery in low-quality hospitals quite as often. If, on the other hand, blacks and whites live on different sides of town in your community, then this study finds that black patients are up to 96% more likely than whites to have surgery in a low-quality hospital. In their paper, the authors suggest ways of reducing these disparities, but also conclude that current payment reforms, including pay-for-performance, may actually exacerbate the disparities by further diverting resources away from the low-quality hospitals.

 

Latest Health Wonk Review

The latest installment of the Health Wonk Review is up over at Wing of Zock. It features a Wright on Health post, along with many other great posts from other top-notch health policy bloggers. Stop reading this and go check it out already!

 

Do Readmission Rates Really Indicate Hospital Quality?

Unplanned readmissions to the hospital have been the focus of much attention in recent years for obvious reasons: First, they are relatively easy to measure using administrative claims data. Second, like all inpatient hospitalizations, they cost a lot of money–and are therefore a target for reducing spending. Third, they are a proxy for quality of care, as at least some portion of them are likely avoidable if the hospital does its job well. On this last point, many disagree, citing the lack of continuity of care that exists post-discharge as a major source of readmissions. According to the folks in this camp, the patients themselves and their primary care physicians–not the hospital–are to blame for many of the unexpected returns to the hospital.

While this debate rages on, however, the federal government is taking action. Since 2009 they have published data on hospital quality using the Hospital Compare website, so that the public can be better informed. Then, starting last October, readmission rates for three conditions (heart attack, congestive heart failure, and pneumonia) were tracked, and hospitals with higher than expected rates were subjected to a reduction in Medicare reimbursement.

But a recent study from Matthew Press and colleagues in the June issue of Health Affairs finds that hospital readmission rates may not be such a good indicator of hospital quality after all. First, they found that across all hospitals, readmission rates for heart attack ranged from a low of 15.3% to a high of 25.6%. When they divided the hospitals up into quartiles, they found that only 1.7 percentage points separated the bottom 25% from the top 25%. Then, not surprisingly given the limited distance between the groups, they found that in just two years, many of those in the best performing group moved into the worst performing group and vice versa. Part of the explanation is what statisticians and econometricians call “regression to the mean.” In short, if you’re at the top of the pack, it is statistically more likely that you will move down than move up, just because you’ve got much more room to move in one direction than the other. The same is true in the reverse for the low performers. The investigators also found that, with few exceptions (e.g., teaching status), risk-standardized readmission rates were not correlated with other measures of hospital quality.

So what does this mean? Well, the authors suggest, there could be quite a few problems with policies that rely heavily on readmission rates alone as an indicator of hospital quality. Instead, they argue that other measures should be considered in addition to readmission rates when comparing hospital quality and that it is important to take regression to the mean into account by adjusting accordingly. In short, when it comes to measuring hospital quality, the more ways in which it is measured, the better.

 

Shafrin Hosts Latest Health Wonk Review

If your looking for some of the most pressing questions in health policy–and some awfully good answers to those questions–check out the Jeopardy Edition of the Health Wonk Review, hosted by the Healthcare Economist, Jason Shafrin.