Friday News Dump: Wonkbites

by Adrianna McIntyre 

Amid the AP/DOJ, IRS, and other acronym-laden scandals, health news was still happening. Yes, really. Like these stories:

  1. D is for danger. Or something. An investigation by ProPublica has revealed that Medicare’s drug program (Part D) suffers from weak oversight, resulting in unsafe prescribing behaviors.  Here’s the tricky thing about feds regulating these drugs: in 2010, 27.5 million Medicare beneficiaries filled over 1 billion prescriptions. The Center for Medicare and Medicaid Services—which oversees both public programs—only has about 5,000 employees. Quite the conundrum.
  2. Where movie stars and the Supreme Court collide. You probably heard that Angelina Jolie recently underwent a double-mastectomy as a preventive measure after learning she carries a gene that makes her more susceptible to breast cancer than most women. This resulted in a flurry of coverage about whether or not women should be screened—the test is prohibitively expensive at about $3,000. It seems like many stories skimped on why: Myriad Genetics, the company that manufactures the test, owns a patent on the gene. Yes, the gene, in everyone (in the United States). If that sounds controversial, it is: the Supreme Court is expected to decide this summer whether the company can “own” a gene, writ large.
  3. Meanwhile in Oregon… cloning, not Medicaid! Media buzz took a mad-science twist  Wednesday when a paper was published in Cell reported that scientists had finally succeeded in cloning human embryonic stem cells, which hold promise for developing replacement tissues to treat diseases. But was it overhyped? The researchers, from Oregon Health and Science University, used essentially the same technique that created Dolly the sheep—back in 1997. A more sophisticated process was developed in 2007.
  4. Something ventured, nothing gained? Republicans have voted 37 (or 38, depending on your count) times to repeal the Affordable Care Act, despite lacking the political might in Congress to succeed in their efforts. These votes have largely been dismissed as a waste of time–but Sarah Kliff has another take. Repeated talk of repeal—no matter how futile in the present—might actually be changing public perception and discourse about the law.
  5. It’s the cupcakes, stupid. The Labor Department’s most recent Consumer Price Index had some bad news for the gourmet personal dessert industry: the index for “fresh cakes and cupcakes” is falling. This sparked a hashtag (#cupcakedeflation), and hilarity ensued.

Bottom line? Keep calm and collect more data.

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Adrianna is a graduate student in public policy and public health at the University of Michigan.
Follow her on Twitter @onceuponA or subscribe to the blog.

Chargemaster Tomfoolery, Policy Responses, and Unintended Consequences (In Four Charts)

by Karan Chhabra

Nothing gets wonks going like a trove of healthcare data. On Wednesday, when the government released massive amounts of information on what hospitals charge Medicare, well, the wonks got going. Like many of them, I was skeptical of how much they mattered, since charge variations are a tired story. But a quick look at the numbers turned into a long adventure in healthcare economics, with lots of implications for state and national policy. What follows are the highlights of that adventure, without the computer crashes and med school things that fell in between.

CMS offers a useful summary by state, so I started there. The summary spreadsheet listed how much each state averaged in charges and payments for each of the most common DRGs (diagnosis-related groups). I noticed that my home state, New Jersey, seemed to be charging an awful lot. So I checked out how much hospitals charge relative to what Medicare actually ends up paying, which I’m calling “markup” [1]. Nationwide, median markup across all procedures was 216%. I ran some calculations to compare states and, sure enough, New Jersey was far above the rest [2].

average charges all states

Why? I was quick to suspect some Sopranos-style monkey business, but it appears to be—at least partly—an unintended consequence of a state law requiring insurers to cover out-of-network providers (h/t Dan Diamond). In-network hospitals confidentially negotiate rates for procedures with each insurer, which end up far below chargemaster rates. But under this law, out-of-network hospitals get payments that factor in chargemaster rates:

Patients who choose to go out-of-network will be billed based on the hospital charges, while insurers of patients who must receive emergency services at an out-of-network facility will also pay higher rates based on a “fee profile” that takes into account hospital charges … One reason for the high prices might be the state’s requirement that insurers pay for services at out-of-network providers. “It does create this perverse economic incentive for folks to charge more and more and more because they can get paid for it,” Sanders said.

In other words, NJ hospitals can inflate their chargemaster rates without any repercussions to the average insured patient (the state law will make sure their care is covered). So what we have here is a law designed to protect out-of-network patients from inflated charges, but with the apparent consequence of inflating chargemaster charges across the board. The Affordable Care Act includes similar protections for emergency room visits, enacted one year after NJ’s—will they have the same effect? The ACA provisions only apply to insurance plans written after September 2009, and CMS’s data are from 2011, so it may be too early to tell.

I still wondered if New Jersey’s charges appeared inflated because of one single hospital, or if it appeared true across the board. So to Jersey I went. I averaged the markups for every DRG in the dataset, by hospital, to see whether any hospitals systematically inflate charges across their entire chargemasters (blue columns in the graph below). Again, I broke out the markups for uncomplicated strokes (in red), just as an example. The results are in:

NJ charges by hospital

One hospital, Bayonne Medical Center, is orders of magnitude above the rest. For an uncomplicated stroke, they charge an average $101,394—but Medicare only pays them $5,667. The gap between charges and payments shouldn’t surprise anyone following healthcare delivery; Uwe Reinhardt explains it effectively and humorously here. But at this hospital, that gap is astronomical. (The median markup in NJ is 555% whereas the national median is 216%; since median calculations are insensitive to extreme values, Bayonne’s probably not solely responsible for NJ’s eminent position.)

The kicker here? Bayonne Medical Center is the only for-profit hospital in the chart. I’m not suggesting this implies that all for-profit hospitals charge equally obnoxious rates, but someone with nice grants and research assistants should crunch the numbers for all 50 states and let us know.

Here’s the funny thing: New Jersey has laws that protect the poor uninsured, requiring hospitals to bill them only 115% of Medicare rates [3]. The feds have actually proposed a similar regulation but one that would apply exclusively to not-for-profits—under this regulation, those hospitals could only bill patients qualified for financial assistance at the rate paid by Medicare or commercial insurance [4]. The cutoffs for financial assistance vary at each hospital, but they tend to reflect some multiple of the federal poverty line. If this regulation is finalized, not-for-profit hospitals that violate it could lose their tax-exempt status—a threat hospital execs do not take lightly.

The federal law doesn’t apply to for-profit hospitals, though. Since New Jersey’s doesn’t make the same distinction, I wonder why the federal law does—the feds aren’t trying to influence the charges for hospitals like Bayonne in any other state. Yes, the federal policy uses tax exemption as a stick, whereas NJ is using hospital licensure, but the federal government has required other things of hospitals irrespective of tax exemption—why not use Medicare/Medicaid dollars instead, like EMTALA?)

NJ’s law and the federal regulation matter to this debate because they protect the uninsured from chargemaster tomfoolery. Oddly enough, in a way they also disincentivize getting health insurance altogether. Since Medicare pays less than private insurance (115% of Medicare may also be a bargain), it could be cheaper to the patient to go uninsured under the protection of these laws, than to pay insurance’s overheads in the off chance that an emergency admission happens.

Insurance expansion is a crucial part of Obamacare. It also may be in jeopardy thanks to the IRS’s relatively toothless enforcement power for the individual mandate. At the end of the day, if you’re pretty healthy and of limited financial means, refusing to buy health insurance on the individual market is not the craziest financial decision you could make [5].

So what’s a policymaker to do? The two policies I’ve discussed—requiring out-of-network reimbursement, and limiting charges to the uninsured—both create perverse economic incentives. The first encourages chargemaster inflation, and the second undermines health insurance altogether. This is when cynics like me smugly invoke the law of unintended consequences. But there has to be a policy alternative that protects the poor and keeps the system sane, right? It turns out there is, but it’s a bit of a political grenade. So don’t say I didn’t warn you.

Look back at the first chart in this post. I talked about the biggest inflator, New Jersey, more than you probably wanted. What about that blip to the far right? Nope, no math error—Maryland’s markups are almost a hundredfold lower than NJ’s. It’s a story policy wonks know well: the state instituted an “all-payer” system for its hospital pricing in the 1970s, wherein every provider in the state is required to charge every payer the same price for the same service. This isn’t socialism–cash is still coming from the same places, and going to the same hospitals, and everyone is allowed to make money—but it is textbook rate-setting. The result? They’ve closed the gap between charges and payments, and slowed down cost growth in the process:

maryland cost growth

maryland charges versus costs

There are some flaws in Maryland’s system, but they can be remedied—Austin Frakt outlines them smartly here and here. First, it doesn’t make a ton of sense for each hospital to be paid the same amount when there are obvious differences in their amenities (and potentially their quality). It’s as if an upscale restarant were required to charge the same amount for a bacon cheeseburger as Burger King. Price controls by central dictat are also an ungodly administrative hassle. But it does make sense for each payer (insurance, Medicare, etc.) to pay the same amount to each hospital, the same way one restaurant patron shouldn’t pay less (at the same restaurant) because they’re bigger or better-insured. Thus Frakt, Uwe Reinhardt and others turn to Germany’s more market-based approach, where payers negotiate together for one rate for each procedure from each hospital.

This is a huge difference from the current system, where private insurers negotiate separately and confidentially, on the basis of concerns pretty irrelevant to patient care: most of all, market power. But it’s not “price controls,” because prices are reached through negotiation. Assuming we allow the uninsured to pay the same price, it essentially quashes the problem of price discrimination against uninsured patients, affords private payers the same power to bend the cost curve as Medicare, and makes plain sense—where else in the economy are different people forced to pay different prices for the exact same goods from the exact same seller? I haven’t fully come around to all-payer as the solution to all our woes, but 1500 words and four charts later, the proposal has definitely earned my interest.

 

Update #1, 5/17/13: The New York Times has just published a deeper dive into what’s going on at Bayonne Medical Center, specifically its use of out-of-network status to milk more from the system, and its switch to for-profit status. They have an accompanying editorial here. Go read, but tell ‘em I sent you!

Update #2, 5/17/13: There’s an answer to my question in the last paragraph, “where else in the economy are different people forced to pay different prices for the exact same goods from the exact same seller?” It happens a lot when customers are bidding for goods that different people value differently (say, an auction). But the problem here is that with third-party payment, the patients are not the customers and bidders—insurers are. So the advantages of auction-style pricing—namely, pricing that accurately reflects the value of a product to each consumer—don’t apply in the hospital context. Instead, the system we have rewards market power at the expense of those who are most vulnerable. (Thanks to Austin Frakt for his insightful comments.)

 

Footnotes

1.  If you really want to know what I did in Excel, kudos for your bravery. I defined “markup” as (average charge – average payment) / average payment. I calculated that for each DRG, for each hospital in NJ. So if a hospital billed $2000 and Medicare paid $1000, the markup was 100%. Then I averaged those markups in all the DRGs from each hospital, to arrive at the percentages you see in the chart’s blue column. I broke out strokes in the red column just to make the data a little more concrete. You can access my spreadsheet here.

2. As I mentioned, I calculated the “markup” of every DRG in every state. Then averaged those markups within each state, and again broke out strokes as an example.

3.  Thanks to recent legislation, NJ limits the ability of hospitals to overcharge uninsured people under 500% of the federal poverty line—hospitals can only bill them 115% of what Medicare would’ve paid. But as Reinhardt notes, a family of four with gross income over $117,750 wouldn’t be protected. And more importantly, this isn’t true in every state.

4. For the proposed federal regulation, see page 17 here.

5. Not the craziest financial decision. But for the sake of your own health, as well as the ethics of living in society, it’s pretty dubious.

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Karan is a student at Robert Wood Johnson Medical School and Duke graduate who previously worked in strategic research for hospital executives.

Follow him on Twitter @KRChhabra or subscribe to the blog.

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So, Medicaid improved depression in Oregon. What else can we say about that?

by Adrianna McIntyre

The purpose of my last post was to present the argument that the mental health findings in Oregon are of very real clinical consequence: empirical research suggests a causal link between depression and illness, physical limitations, and mortality. No one has disputed evidence on that front. However, I failed to address something critical: we don’t have a clear picture of the underlying mechanism by which Medicaid improved depression rates in the Oregon population. That’s a very fair—and nuanced—point. It deserves its own post. Say, this one.

Obligatory recap for newbies; everyone else skip ahead. Two weeks ago, leading economists released the results of a two-year study where individuals were randomized onto Medicaid in Oregon through a lottery, marking the first time we’ve been able to compare Medicaid and uninsurance in the context of a controlled trial. Due to limitations (time, sample size, magnitude of effects) the study lacked the  statistical power necessary to draw conclusions on physical health outcomes. However, depression rates markedly declined among those who received Medicaid coverage through the lottery. The change—a relative decrease of 30% compared to the uninsured group—was “significant” in both technical and colloquial terms. The study suggests that Medicaid enrollment plays a causal role, but available evidence does a poor job of addressing why.

Maybe it’s “all in their heads.” That’s still a real effect. It’s possible that knowing one has insurance does a lot of heavy lifting in the mental health department. Such a phenomenon has been described as a “placebo effect,” by some (I disagree with this characterization on purely semantic grounds, but that’s unimportant). The argument makes sense when it’s tied into the study’s findings that Medicaid substantially reduces catastrophic health expenditures, which is what happens when insurance functions as insurance. Medicaid reduces financial strain and uncertainty, which in turn likely reduces stress—cortisol is a nasty bugger—leading to an improved sense of well-being. That it’s psychological doesn’t make the effect any less “real”.  But it creates space for a reasonable debate: is there a more cost-effective means to the same end? Catastrophic coverage, perhaps? More on that toward the end of the post.

Related criticisms point to the authors’ report that a month after enrollment (before substantive care utilization), patient surveys found “evidence of an improvement in self-reported health of about two-thirds the magnitude of our main survey estimates from more than a year later” (p. 1061, gated). A caveat: these findings didn’t include the depression screen. I don’t mean that changes in the depression screen weren’t statistically significant; the screen actually wasn’t part of the one-month survey instrument.

Still, let’s say if it had been included, we would have seen a similar pattern. Should we then dismiss the depression findings as some artifact of a “winner’s high”? The authors—as is mentioned in almost every commentary on this study—are some of the best in the field. This quirk did not escape their notice; here’s what they had to say on the matter (emphasis mine):

[T]he event of winning (or losing) the lottery may have direct effects on the outcomes we study, although it seems unlikely to us that any such effects both exist and persist a year after the lottery (p. 1081) … It is not clear that the immediate effects are directly comparable to those from one year later. Some of the immediate improvements may reflect “winning” effects that are less likely to be picked up in the estimates one year later. (p. 1099)

So, yes, the authors accept that “winning the lottery” might have influenced self-report measures in the month following randomization. But they’re skeptical that winners would stay that jazzed for a year—certainly not two—unless something else was at play. And yet, the results on depression and mental well-being stayed fairly consistent across the study’s duration, so something else must have contributed to the sustained improvements (the financial security discussed above being among the contenders).

Increased medication use probably played some role. Positive depression screens declined by 9.2% and depression medication use increased by 5.5%.  It’s true that this latter change was not statistically significant (though it represents a relative increase of roughly one-third), but it was actually pretty close, with a p-value of 0.07. This is much nearer to the value needed to achieve significance (0.05) than we observed with the physical health outcomes. Your views on how to interpret that will vary based on your statistical philosophy—you have one of those, right?—but I figure it’s a tidbit worth offering.

Also, the Oregon Medicaid plan has mental health coverage that extends beyond pharmaceuticals. The plan’s covered services include evaluations and consultations, therapy, case management, medication management, hospitalization, and emergency services.  That said, we have no idea whether this contributed to improvement in depression screening results. The problem is that information about outpatient care was obtained through surveys, not medical records, and we don’t know how much of that, if any, was related to mental health (ie: cognitive behavioral therapy). The survey asked subjects how many office visits they’d had over the past year (with a physician or other health care professional), but didn’t explore the nature of those visits.

There’s evidence to suggest that improvements in well-being cannot be attributed to other major social services. I alluded to this at the end of my last post: social policy is believed to have a huge impact on downstream health outcomes; for a better discussion of that than I can offer in a paragraph, check out this NYT op-ed. One concern was that subjects randomized to Medicaid might be more attuned to—and thus consume more of—other social services, which could confound results. However, the authors were able to track TANF (cash welfare) and SNAP (food stamps) benefits. The data suggest these programs did not factor into observed improvements:

[S]election by the lottery is not associated with any substantive or statistically significant change in TANF receipt or benefits. However, lottery selection is associated with a statistically significant but substantively trivial increase in the probability of food stamp receipt (1.7 percentage points) and in total food stamp benefits (about $60 over a 16-month period, or less than 0.5% of annual income).  (p. 1082)

All that brings us to the trillion-dollar-over-ten-years—the cost of expansion—question: should we look at facilitating expansion of catastrophic coverage instead? It’s a defensible argument and a debate worth having: this is quintessential “insurance functioning as insurance”; it might offer the same psychological benefits from improved financial security. I wasn’t able to find any empirical evidence on this front. I see this resting on two issues: whether a high deductible would undermine feelings of financial security, and how important mental health services were to Oregon’s improvements (we don’t know the answer to either of these questions). And then, the math: would it be more cost-effective than traditional Medicaid? That depends a lot on how you intend to structure such  a program.

Proponents of having an HDHP/HSA system in place of traditional Medicaid have pointed to the “Healthy Indiana Program” (HIP), which operated under a Medicaid waiver from 2008 to 2011 and is probably our most instructive example. I’ve read conflicting reports about whether the program is cost-effective: an analysis from the Kaiser Family Foundation found higher per-capita costs than traditional Medicaid when you include individual contributions. A different report from Milliman Inc., asserts that the program achieved lower spending.

HIP was strong in many ways: the program offered first-dollar coverage of preventive services (addressing one of the harshest criticisms of catastrophic insurance), improved access, and it enjoyed overwhelming public support. But it was also subject to conditions that aren’t permitted under the Affordable Care: HIP didn’t cover dental or vision, and had annual ($300,000) and lifetime ($1M) contribution caps. Health reform requires Medicaid to offer these supplemental  benefits and eliminates caps for all insurance, public and private.  In order to accomodate the ACA, Milliman has confirmed that the program would  cost 44% more than a traditional expansion. Also salient: HIP did not cover prescription drugs or other mental health services until the deductible was met; there’s evidence that patients put off care, including drugs, if they must use HSA funds. We can’t rule these out as factors in the Oregon depression findings—to the extent that’s true (another unknown), it’s possible  traditional Medicaid performs better  for depression than an HDHP/HSA alternative.

Here’s how I see the evidence balance out, though I’m certain others will disagree: Something about traditional Medicaid coverage in Oregon substantially improved depression rates. We don’t know what that is, but it’s  associated with the program—improved well-being due to more financial security or “knowledge” of insurance, access to mental health services (both drug and other therapies), or something else I failed to account for. We don’t have similar evidence about catastrophic care and mental health. And Indiana’s experience does not suggest that an HDHP/HSA system would be more cost-effective, so what would we have to change to make that true? Would benefits be preserved?

We don’t understand the differential effects of mental health/drug coverage, financial security, and subjective well-being on depression. Whatever the magic cocktail is, Oregon’s Medicaid has it. The causal uncertainties obviously complicate a full-throated defense of traditional Medicaid’s effect on the disease—but they don’t offer much harbor for the catastrophic insurance argument, either.

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Click here to subscribe, for the latest news in and around health policy. 

Adrianna works in clinical research and is a graduate student in public policy & public health at the University of Michigan. Follow her on Twitter @onceuponA.

Friday News Dump: Wonkbites

by Karan Chhabra

Another whirlwind week of wonkery! Here’s what you might’ve missed:

  1. Breaking: What’s Michigan doing to Medicaid? Hot off the press, there’s a new proposal from Michigan lawmakers to expand Medicaid, but with some major strings attached. Michigan House Republicans are okay with expansion if it limits “able-bodied adults” to only four years of coverage. I’m not the blog’s correspondent on all things Michigan (that’s Adrianna), and details are scant, but at the moment this smells fishy. Aside from the dubious ethics and economic implications, how much do “able-bodied adults” cost Medicaid anyway?
  2. Skepticism on the chargemaster data: Wonks went wild Wednesday, when the feds released gobs of data on hospitals’ charges to Medicare for their 100 most common procedures. Among other things, they revealed massive variation in the charges for the same procedures at hospitals just miles apart (anyone surprised?). And they signal that the feds are making efforts toward transparency. But this smart post by Paul Levy steps back from the fuss, highlighting how little those charges reflect actual payments and concluding that “the release of bad data is worse than having no data at all.”
  3. Kids, get inIn this editorial, Ezekiel Emanuel (who advised the President’s healthcare reform effort) voices his fear that “young invincible” millennials, especially us males, may turn their noses at Obamacare’s individual mandate to buy health insurance. He explains why health insurance exchanges need young and healthy enrollees, and offers some ideas to support the cause.
  4. Scratch that: You probably don’t remember when I told you, just a few weeks ago, that the healthcare cost growth slowdown was largely the result of the recession. I’m glad you don’t remember, because new reports say the recession played a smaller role than once thought—welcome news for those hoping that the system is moving toward more efficient and cost-effective care.
  5. Oh no you didn’t: Time‘s cover story this week courts us millennials with the charming title “The Me Me Me Generation: Millennials are lazy narcissists who still live with their parents.” Any other day we’d fire back on the offensive, but this The New Republic post does it better, so read it. Time did get one thing right, though—how to spell millennial. Two Ls and two Ns, people!

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Karan is a first-year student at Robert Wood Johnson Medical School and Duke graduate who previously worked in strategic research for hospital executives.

Follow him on Twitter @KRChhabra or subscribe to the blog.

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The Medicaid experiment showed a significant effect on depression. That’s a big deal.

by Adrianna McIntyre

I stand corrected. It’s been a week since the Oregon Medicaid study’s two-year results were published, and there hasn’t been much discussion in the media or among policy bloggers about what a big deal the mental health findings are—even though there’s an extensive literature documenting how detrimental depression is to physical health and productivity. Even though Jonathan Gruber, who is among the nation’s leading health economists (and co-authored the study), called the results “astounding.” Fine, I’ll give it a shot.

clinicalsidebysideFirst, the study results. Medicaid coverage was associated with positive depression screening declining by nearly one-third—that is to say, the control (uninsured) group screened positive for depression 30% of the time, but the rate among those who enrolled in Medicaid through the lottery fell to about 21% [1, 2]. This was statistically significant at a p-level of 0.02. The chart at right is from the Oregon study authors; I added a red box around the relevant clinical results.

Why were the depression screening results statistically significant when others weren’t? Depression was “by far the most prevalent of the four conditions examined.” The observed sample of patients was larger, and the effect was huge. Voilà! Stunning results with statistical power.

Depression is seriously detrimental to everyday functioning. Poor mental health has a measurable impact on “functional disability,” a metric that accounts for days in bed and days where an individual suffered activity limitations due to illness. One of the earlier studies I looked at (from 1992) examined trends in disability levels over a year for subjects who were high-utilizers of the health care system. The authors found that “persons with depressive symptoms that did improve showed large reductions in levels of disability during the 1-year follow-up.” Subjects who improved from severe depression had a 45% reduction in disability scores while those with moderate-improved depression had a 40% reduction. And this is in one year! There was no improvement in functional ability among subjects whose depressive symptoms remained the same over the course of the study.

Depression is also an independent predictor of mortality. In laymen’s terms, the depressed are more likely to die younger, even when we control for other factors that contribute to ill health. In a six-year study of older adults (in whom it’s easier to study mortality), depression was found to increase the risk of mortality by 24% [3]. Depression did not need to be severe (“major depression”) for this to be true—mild and subclinical depression also showed an effect. This is consistent with past literature suggesting that depression results in “vital exhaustion and decreased emotional vitality”, which contribute to functional decline. The authors also argue that studies using self-report measures to identify depressive symptoms (like the Oregon study) are likely to underestimate true prevalence of the disease.

Employers benefit financially from reduced depression among employees.  A cost-benefit analysis found that enhanced depression care (a one-time screening plus telephone management as needed) yielded a net savings to employers of $2895 over five years. This is because the cost of lost productivity and depression-associated medical treatment outweighed the cost of offering screening and follow-up management. “But this is about employers,” you might protest, “and Medicaid is not employer-sponsored insurance.” That’s true. But it’s also true that three-quarters of the uninsured are from working families; don’t forget, only about 68% of employers offer health insurance. Many of the working uninsured would qualify for the Medicaid expansion, which—suggested by the Oregon study’s robust findings—ought to reduce prevalence of depression in this population, easing burdens on our health care system and the economy. 

Depressive symptoms—even modest ones—sensitize the body’s inflammatory response. I’m not the blog’s token medical student (that would be Karan), so bear with me. The authors helpfully sum up clinical effects of depression:

Depression is associated with enhanced production of proinflammatory cytokines that influence a spectrum of conditions associated with aging, including cardiovascular disease, osteoporosis, arthritis, type 2 diabetes mellitus, certain cancers, periodontal disease, frailty, and functional decline.

The study goes on to test the effect of depressive symptoms on the body’s reaction to something pretty common: the flu virus. Sure enough, investigators found higher cytokine levels (cytokines are molecules that help regulate immune responses) among those who exhibited depressive symptoms, even though the number of depressive symptoms was low before exposure to the virus (and did not change after exposure). Two weeks out, people who reported more depressive symptoms still had higher cytokine levels. The authors argue that depression could have maladaptive effects on the immune and endocrine system, leading to prolonged infection, chronic stress, and added health risk.

***

I’m not trying to argue that supporters should try selling the entire expansion on its mental health merits alone—I happen to agree with those commentators who provide evidence that the study was far too underpowered in the physical health domains to accurately spin its results as some damning indictment of the Medicaid program. It’s hard not to agree with them; we’re talking math, not politics. My point here is that the mental health benefits are a hugely important, and they deserve more than a passing mention when we talk about the study.

There’s another thing about the “health insurance doesn’t make us healthier” argument, if you buy that: something creates health disparities across the socioeconomic gradient. If we don’t need more insurance, then we definitely need stronger social policy to help our most vulnerable (and I’m certain you’ll find many who argue we need both). That’s a choice—but I’m not sure it’s one that opponents of the Medicaid expansion are willing to make.

Footnotes

1. Technical note on the methodology: depression was assessed by eight questions in a self-reported survey. This might sound tenuous to those inclined to be skeptical, but a two-question version of the measure has actually been empirically validated as an effective tool for depression screening.

2. Curiously, the decline in depression did not correlate to increased use of medication. This suggests that the financial security of having insurance has potential to improve mental health. Depression medication use did increase, but not at a statistically significant level (p = 0.07; it needed to be 0.05 or smaller). Even if it were significant, the change in medication was substantially smaller than the change in positive screening for depression (raw changes were +5.5% and -9.2%, respectively). 

3. For the people who are interested in such nuance, the study offered relative risk calculations that controlled for sociodemographic factors (RR, 1.43), prevalent clinical disease (RR, 1.25), subclinical disease indicators (RR, 1.35), and biological/behavioral risk factors (RR, 1.42), all statistically significant at a 95% level. I’ll add the caveat that this study was conducted with subjects over the age of 65—that is to say, the Medicare-eligible population, which could impact the generalizability of the findings to the prospective Medicaid population, but I still think they were meaningful to establish general trends.

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Click here to subscribe, for the latest news in and around health policy. 

Adrianna works in clinical research and is a graduate student in public policy & public health at the University of Michigan. Follow her on Twitter @onceuponA.

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