The Incidental Economist

Colleagues say Dafny’s examination of health-insurance markets has helped mold the state and federal exchanges that are central to President Barack Obama’s 2010 health-care law, which allows qualified individuals and small businesses to purchase coverage. Her studies show a lack of competition in insurance markets drives up premiums, part of the reason “the goal is to get more than half a dozen plans in each exchange,” said David Dranove, director of the Health Enterprise Management Program at Northwestern University’s Kellogg School of Management. “Leemore identified that just having two or three plans in a market is just not enough.”

I recommend you read the rest.

Here’s a TIE post about her work on the effect of consolidation in the insurance industry premiums. Here’s one on the value of choice among plans. Leemore has done important and creative work. I’m proud to know her.

@afrakt

A new paper by Keyhani et al. titled “Overuse and Systems of Care: A Systematic Review” caught my eye. I’m a sucker for systematic reviews, particularly one on overuse as it relates to systems of care. This is TIE candy.

Background: Current health care reform efforts are focused on reorganizing health care systems to reduce waste in the US health care system.

Objective: To compare rates of overuse in different health care systems and examine whether certain systems of care or insurers have lower rates of overuse of health care services.

Data Sources: Articles published in MEDLINE between 1978, the year of publication of the first framework to measure quality, and June 21, 2012.

Study Selection: Included studies compared rates of overuse of procedures, diagnostic tests, or medications in at least 2 systems of care.

Data Extraction: Four reviewers screened titles; 2 reviewers screened abstracts and full articles and extracted data.

Results: We identified 7 studies which [*] compared rates of overuse of 5 services across multiple different health care settings. National rates of inappropriate coronary angiography were similar in Medicare HMOs and Medicare FFS (13% vs. 13%, P=0.33) and in a state-based study comparing 15 hospitals in New York and 4 hospitals in a Massachusetts-managed care plan (4% vs. 6%, P>0.1). Rates of carotid endarterectomy in New York State were similar in Medicare HMOs and Medicare FFS plans (8.4% vs. 8.6%, P=0.55) but nonrecommended use of antibiotics for the treatment of upper respiratory infection was higher in a managed care organization than a FFS private plan (31% vs. 21%, P=0.02). Rates of inappropriate myocardial perfusion imaging were similar in VA and private settings (22% vs. 16.6%, P=0.24), but rates of inappropriate surveillance endoscopy in the management of gastric ulcers were higher in the VA compared with private settings (37.4% vs. 20.4%–23.3%, P<0.0001).

Conclusions: The available evidence is limited but there is no consistent evidence that any 1 system of care has been more effective at minimizing the overuse of health care services. More research is necessary to inform current health care reform efforts directed at reducing overuse. [Emphasis added.]

Over nearly 35 years there were only seven studies comparing overuse across systems of care. Actually, all of them were published since 1995 and all but three since 2006. Still, is this enough evidence to go on, especially considering the studies focused on specific types of overuse, not overuse writ large? I’m underwhelmed.

That grain (or shaker) of salt aside, the results are plausible enough. (Enter speculation mode.) The US health system seems to be, in general, roughly equally inefficient no matter where you look. I buy that. It’d be a stretch to make policy recommendations based on such a thin bed of evidence, but one might be that pushing the system a bit more toward managed care or a bit more toward integrated delivery systems isn’t likely to cause major efficiency gains.

Something more fundamental is likely needed, something that embeds more deeply into the culture of provision of care or the culture of patients. Somehow, “we” need to care a lot more about cost, quality — value — than we do. We need to stop mistaking more for better. Of course, not paying for more is a start, but that alone seems insufficient, not to mention hard to do properly.

The US medical culture, either from a practitioner or patient view, will be hard to change. Beyond the myriad ideas both in law and suggested, I don’t know how to do it. I don’t know which ideas will do any good. Maybe many of them will help. Maybe none of them. My best bet at the moment is that, coupled with better alignment of incentives on both sides of the medical transaction (patient and provider), the continued drum beat of evidence pointing to the large amounts of low value care, and even to specific instances of low value care, will eventually penetrate. That’s my hope, anyway.

* This use of “which” is incorrect. It should be “that.” This really irritates me.

@afrakt

At first, it seemed like the study by Hyo Jung Tak, Gregory Ruhnke, and David Meltzer challenged the idea that shared decision making (SDM) is associated with lower health care resource use.

Importance  Patient participation in medical decision making has been associated with improved patient satisfaction and health outcomes. However, there is little evidence concerning its effects on resource utilization. Patient participation in medical decision making has been hypothesized to decrease excess utilization but might be expected to increase utilization when other decision makers have incentives to reduce utilization, as under prospective payment systems for hospital care.

Objective  To examine the relationship between patient preferences for participation in medical decision making and health care utilization among hospitalized patients.

Design and Setting  Survey study in an academic research setting.

Participants  A survey that included questions about preferences to receive medical information and to participate in medical decision making was administered to all patients admitted to the University of Chicago Medical Center general internal medicine service between July 1, 2003, and August 31, 2011, and completed by 21 754 (69.6%) of admitted patients.

Main Outcomes and Measures  The survey data were linked with administrative data, including length of stay and total hospitalization costs. We used generalized linear models to measure the association of patient preference for participation in decision making with length of stay and costs.

Results  The mean length of stay was 5.34 days, and the mean hospitalization costs were $14 576. While 96.3% of patients expressed a desire to receive information about their illnesses and treatment options, 71.1% of patients preferred to leave medical decision making to their physician. Preference to participate in decision making increased with educational level and with private health insurance. Compared with patients who had a strong desire to delegate decisions to their physician, patients who preferred to participate in decision making concerning their care had a 0.26-day (95% CI, 0.06-0.47 day) longer length of stay (P = .01) and $865 (95% CI, $155-$1575) higher total hospitalization costs (P = .02).

Conclusions and Relevance  Patient preference to participate in decision making concerning their care may be associated with increased resource utilization among hospitalized patients. Variation in patient preference to participate in medical decision making and its effects on costs and outcomes in the presence of varying physician incentives deserve further examination.

It’s important to recognize that the results on length of stay and hospital costs are associated with the response to a single question at time of admission: “I prefer to leave decisions about my medical care up to my doctor.” There was neither any random assignment nor any test of an SDM intervention. Indeed, we don’t even know if patients who expressed greater interest in participating in decision making actually did so, or, if so, to what extent. Perhaps in an environment without an SDM protocol, patients who seek to guide their own care end up making poor decisions, leading to longer stays and higher costs.

This leaves open the possibility that SDM could reduce health care utilization among patients who self-identify as having a lower preference for leaving medical decisions up to their doctors, among others. Moreover, by its nature it would do so in a way consistent with patients’ values.

Of course, if it led to higher utilization, that would be consistent with patient values too. In this study, we have one measure of patients’ values and measures of resource use and cost, but no confidence that the latter are really connected to more fully informed and articulated versions of the former, as might be obtained in an SDM process.

@afrakt

European countries often look to the United States for inspiration and innovation in ways of organizing and paying for health care.

I know! I can hardly believe it either. But that’s the first sentence of a recent Health Affairs paper that is the subject of my latest post on the AcademyHealth blog.

@afrakt

In a follow-up to my EconTalk discussion with Russ Roberts about the Oregon Health Insurance Experiment (OHIE), he interviewed Jim Manzi about it this week. Russ invited me to submit a written response to the interview, which you’ll find below. It is linked to from the EconTalk page for the Manzi interview. I have no substantial disagreement with most of the content of the interview. The purpose of this reply is to add more information, not to debate any points.

Like Jim, I did not have any numerically specific prior views about how much expansion of Medicaid would affect the physical health of non-elderly adults over two years. However, as Jim pointed out, the OHIE investigators suggested that we compare their diastolic blood pressure change results to findings from specific, prior work. Since my conversation with Russ, my colleague and physician Aaron Carroll examined that prior work and shared his thoughts in two posts. He concluded that, for a variety of reasons, we should not have expected the OHIE to reveal the size of change observed in those prior studies, the approximately 5 mm Hg in diastolic blood pressure change that Jim suggested as a rough average. You can read the details at the links for yourself.*

A key point is that blood pressure reduction should only be expected in a population with initially elevated blood pressure, which was the focus of the prior literature referenced above. In contrast, the headline OHIE result is for all study subjects, only a small percentage of whom had elevated blood pressure at baseline. Unfortunately, there is no reported OHIE subanalysis focused exclusively on subjects with hypertension at time of randomization. Depending on which metrics from the published results you examine, between 3% and 16% of the sample had elevated blood pressure at baseline. Taking the high end, 16% x 5 mm Hg = 0.8 mm Hg is in the ballpark of a reasonable expectation of the reduction in diastolic blood pressure the OHIE could have found (it was also the study’s point estimate) were it adequately powered to do so. Was it?

I worked with Aaron and fellow health economist Sam Richardson on this question. We found that the study had 80% power (the standard minimum for clinical studies) to detect a change in diastolic blood pressure of 2.82 mm Hg. Put another way, this means that the probability of failing to detect a true change of this size, the false negative rate, is 20%. For the more reasonable, expected 0.8 mm Hg change calculated above, the probability of a false negative is about 86%, or 14% power. This is underpowered by any reasonable criterion.

But that’s just diastolic blood pressure. What about another measure? In the discussion of their paper, the investigators calculated the reduction in glycated hemoglobin level one might have expected from the clinical literature, 0.05 percentage points. That’s well within the 95% confidence interval of their estimate and corresponds to a false negative rate of 75%, or 25% power. So, the study was underpowered for this measure too.

Aaron, Sam, and I have calculated power for other physical health measures reported from the OHIE and will share results soon. If you can’t wait, I have posted methods so you can do power calculations yourself. This is possible because power analysis methods are well-known and all necessary parameters are readily available in the published paper. The only difference between the methods I’ve posted and what Aaron, Sam, and I are doing is that we are incorporating a few higher-order nuances, like adjusting for the effect of the study’s survey weights.

Now for the lightning round. Here are a few quick responses to other aspects of the interview:

• Jim noted that 40% of lottery winners didn’t apply for Medicaid coverage. As discussed, this might be due to an expectation of low value from Medicaid or lack of follow-through skills (jointly, low prudence). However, half of the 60% who did apply were deemed ineligible. The investigators report that this was largely due to income above the 100% FPL threshold, but other potential reasons include moving out of state, securing other coverage within a six month look-back period, or aging out of eligibility. One might reasonably presume a similar proportion (half) of those who did not apply would have also been ineligible. Perhaps some knew that to be the case and spared themselves the fruitless exercise of completing the forms. It seems reasonable to me that those capable of weighing the value of Medicaid would also know whether their incomes were too high, they moved out of state, they secured other health insurance coverage, or were too old. Therefore, it is likely that substantially fewer than 40% of the non-applicants suffered a lack of prudence. Judging from the proportion of applicants deemed ineligible, perhaps the number of imprudent non-applicants is closer to 20%. This is speculation, but no less plausible than that Jim or Russ offered.
• The RAND Health Insurance Experiment was not a study of health insurance coverage since it did not include any uninsured subjects. It was a study of cost sharing, capped at $1,000 (circa mid-1970s dollars) for all participants.
• The OHIE depression reduction result was not observed largely or entirely in the first month after enrollment. The investigators didn’t conduct a depression screen in a one-month survey, but did in later surveys, as Adrianna McIntyre explains. However, self-reported health did improve substantially in the first month.
• To what extent the findings are informative about Obamacare’s Medicaid expansion would be an excellent topic of discussion. Neither Jim, Russ, nor I got into this question very deeply. It’s properly a question of external validity, not bias, which is something else.

In conclusion, I applaud Russ for devoting two episodes to the OHIE. It is an important study, both for its subject and methods, and it deserves at least that much attention. I also praise Jim for his addition of substantial value to the conversation. I hope I have helped clarify a few points.

* Aaron’s posts largely focus on systolic blood pressure, though diastolic is mentioned and is also included in the cited studies. Suffice it to say, the same issues of expected effect size and insufficient power arise for systolic blood pressure as I discuss for diastolic. I focused on diastolic because it is what Jim mentioned and the lead investigator emailed me about.

@afrakt

From interfluidity and via Tyler Cowen:

@afrakt

Zachary Marcum, Mary Ann Sevick, and Steven Handler in JAMA:

Medication nonadherence is widely recognized as a common and costly problem. Approximately 30% to 50% of US adults are not adherent to long-term medications leading to an estimated $100 billion in preventable costs annually. The barriers to medication adherence are similar to other complex health behaviors, such as weight loss, which have multiple contributing factors. Despite the widespread prevalence and cost of medication nonadherence, it is undetected and undertreated in a significant proportion of adults across care settings. According to the World Health Organization, “increasing the effectiveness of adherence interventions may have far greater impact on the health of the population than any improvement in specific medical treatments.” How can adherence be improved? We propose that the first step is to view medication nonadherence as a diagnosable and treatable medical condition.

Whoa! That was unexpected. It ought to spark conversation at the next cocktail party, that is if you happen to attend a cocktail party for doctors and health policy wonks.

I don’t have a lot to add other than some questions: Are behaviors themselves medical conditions? Or are behaviors usually symptoms of the thing we call a condition? For instance, selling all your possessions to get high is an unhealthy behavior, but it isn’t the condition. Drug addiction is. Which do we treat? (That’s rhetorical.) Is medication nonadherence more like drug addiction or more like the thing drug addiction makes you do? If the latter, what’s the real, underlying condition?

Related to all this is, why don’t drug companies do more to promote medication adherence? Don’t they have a business interest in people using more of their products? I think someone once wrote me about some legal obstacle to drug companies promoting adherence in some way, but I don’t recall what the issue was.

Also, isn’t the hospital that is now at the center of an ACO much more interested in medication adherence than it used to be? Pre-ACO, the hospital earned greater revenue if its patients didn’t take their meds and ended up back in the ED or OR. Now, as an ACO, perhaps the hospital has a greater incentive to treat the condition more cheaply. If that’s possible with drugs, adherence should be paramount.

Your thoughts welcome.

@afrakt

Levy and Meltzer’s update of their 2004 book chapter on the effect of health insurance on health is not new. It was published in 2008, but I’ve never blogged on it. It’s a great and concise resource. Here’s an ungated PDF.

Their Table 1, below, rounds up the best evidence available at the time of publication (click to enlarge).

Most, but not all, of these studies find that expansions of health insurance result in health improvements. The fact that only some of these studies find an effect on health illustrates one important limitation of this type of study: The results of natural experiments may be specific to the population studied. As a result, different natural experiments may yield different conclusions. For example, Card et al. (3) show that the transition onto Medicare at age 65 does not reduce mortality at that age, but Currie & Gruber (4, 5) show that Medicaid expansions reduced child and infant mortality. These varying results are not necessarily contradictory, however, because they apply to different populations. But these differences underscore the fact that the question “How does health insurance affect health?” is complicated, and the answer will depend on (among other things) what we mean by health insurance and whose health is being considered.

Does health insurance improve people’s health? You’ll find Levy and Meltzer cited by both those who answer “yes” and “no” to this question. Of course, as they plainly write, the real answer is nuanced. It depends on which type of people you examine and what you mean by “health.” Just mortality and all Medicare beneficiaries, no discernible effect. Other physical health outcomes and/or other (perhaps sicker) populations, the effect is apparent.

Are those benefits worth the expense? That’s a completely different question and a subjective one. Your answer to it cannot change the objective facts about whether insurance improves health.

@afrakt

In early April I offered an analogy to illustrate reference pricing. The post was provocatively titled “Copayments are stupid.” Perhaps for that reason it garnered a considerable number of comments, to which I respond in a new post on the AcademyHealth blog.

@afrakt

How would you debate a wing nut? Cass Sunstein has a very encouraging and interesting answer.

For a positive answer, consider an intriguing study by Philip Fernbach, a University of Colorado business school professor, and his colleagues. Their central finding is that if you ask people to explain exactly why they think as they do, they discover how much they don’t know — and they become more humble and therefore more moderate. [...]

Interestingly, Fernbach and his co-authors found no increase in moderation when they asked people not to “describe all the details you know” about the likely effects of the various proposals, but simply to say why they believe what they do. If you ask people to give reasons for their beliefs, they tend to act as their own lawyers or public relations managers, and they don’t move toward greater moderation. The lesson is subtle: What produces an increase in humility, and hence moderation, is a request for an explanation of the causal mechanisms that underlie people’s beliefs.

Interestingly, what we do on this blog — provide evidence — is not what the investigators suggest works. I don’t know if they tested this approach, but it is well-known that evidence is confirming but not convincing. If the Fernbach study is right, what one should be doing is a lot more listening and asking than telling.

Sunstein’s column is worth a full read. Before commenting, at least do that  much. If you disagree, I ask that you explain exactly why and how much you know about this subject. The published study is gated, but what appears to be an ungated working paper version is here (PDF). It’s in my pile.

@afrakt