Since this is a battle I still haven’t won, once more into the breach:
Since this is a battle I still haven’t won, once more into the breach:
Recent research with a focus on Massachusetts suggests this may actually happen, but may not last long. Several years after the coverage expansion in that state, access to care for other, previously covered residents appears to be no worse than before the expansion.
Coverage expansion would present this potential trade-off if the supply of care (the number of doctors or their productivity) does not expand to meet the greater demand for it from the newly insured.
Increasing coverage is likely to increase demand for care. A longstanding finding in the research literature is that uninsured people avoid and delay care more than insured people do, and that this can harm health. According to Gallup, uninsured Americans are about twice as likely as insured Americans to delay care, and about 30 percent have put off care because of costs. A large body of research on the effects of the coverage expansion in Massachusetts found that it increased access to care for previously uninsured residents. For example, residents in that state were almost 5 percent less likely to forgo care, compared with the expected rate without the expansion.
But improving access to care for the uninsured doesn’t necessarily reduce access for everyone else, according to a new study published in the journal Health Services Research and led by Dr. Karen Joynt of Harvard. The authors found that neither receipt of outpatient services nor quality of care suffered when coverage expanded under the state’s health overhaul, which started in 2007. The findings, which are consistent with previous work by the same authors, are based on analysis of changes in receipt of outpatient care from 2006 to 2009 for elderly Medicare beneficiaries with chronic illnesses in Massachusetts, as compared with those in other New England states.
However, other work seems to conflict with Dr. Joynt’s analysis. In an article also published in Health Services Research, Amelia Bond and Chapin White examined changes in primary care visits from 2005 to 2007 by Medicare beneficiaries in Massachusetts ZIP codes with different rates of the uninsured in 2005. They compared these differences with those from ZIP codes in surrounding states with similar characteristics. Massachusetts showed a larger gap in primary care visits by Medicare beneficiaries, suggesting that the coverage expansion came at the cost of reduced access for those Medicare beneficiaries, and presumably others who also already had insurance.
Historically, large expansions of health insurance for some tends to reduce access to care for others. Just after universal health insurance was introduced in England and Wales in 1948, receipt of care increased for most of the population, but it decreased for people with high incomes, precisely those who probably had good access before universal coverage. A similar thing happened in Quebec after Canada introduced universal coverage. Physician visits increased in general, but decreased for higher-income residents. Waiting times also increased, and more so for higher-income groups.
I am unaware of any similar research pertaining to Medicare. When it was introduced in the United States in 1965, access to care for those 65 and older improved, but we don’t know whether access to care suffered for the rest of the population. Medicare payments at that time were generous, and that may have helped spur the expansion of supply to meet the new demand.
The same cannot be said of the Affordable Care Act, in general. Indeed, it is financed in part by cuts to Medicare. However, the law does include an increase in funding for primary care training and in fees paid for primary care visits under Medicaid, albeit only through this year (Congress is considering extensions).
A potential explanation for the disparate results of the two Massachusetts studies also suggests why the Massachusetts experience may not generalize to other states. The study by Drs. Bond and White included data through 2007, while that of Dr. Joynt and colleagues included data through 2009, two years later. Perhaps health providers in Massachusetts were stretched thin in 2007, just as the new health measures were taking effect in the state. But by 2009, maybe they managed to increase their capacity, either by increasing their numbers or enhancing their productivity to meet additional demand from a larger insured population, perhaps.
For this reason, delays in care should be expected in states that are less able to increase capacity to meet additional demand from a larger insured population, perhaps because they’re not as well supplied with medical schools as Massachusetts, or because providers may not be able to increase their productivity as much as those in Massachusetts may have. Already, the Health Resources and Services Administration judges that there are regions where demand for care outstrips supply. By the organization’s estimate, 20 percent of Americans live in regions where there are not enough primary care doctors; 16 percent where there are not enough dental care providers; and 30 percent in areas where mental health providers are in short supply.
What can be done? Two approaches come immediately to mind. First, it’s generally believed that a substantial fraction (10 percent or more) of health care delivered is wasteful, unnecessary overtreatment. By reducing that waste, we would free up resources to deliver beneficial care to those who would otherwise wait longer for it. Reductions in access to care need not be harmful if they’re purely reductions in care that isn’t of benefit anyway.
Second, we could increase primary-care capacity, for example by increasing the wages primary-care physicians earn (e.g., by increasing what Medicare pays them). Another capacity-building policy would be to allow nurses to do more of the functions that are reserved for primary-care doctors.
That delays in care in Massachusetts didn’t seem to persist beyond a year or two after coverage expansion is comforting. But other states may be able to follow Massachusetts’ lead only if they can develop sufficient capacity to meet the greater demand that the Affordable Care Act is likely to create.
This morning, the FDA released long-awaited rules requiring chain restaurants to post calorie counts. The rules appear considerably stiffer than most anticipated, and will cover movie theaters, some prepared foods in supermarkets, vending machines, and even alcoholic drinks.
All told, this is good news in the obesity wars. But it’s important to keep the news in perspective. The evidence doesn’t back up the claim that posting calorie counts will make a dent in the obesity epidemic. Aaron’s said as much many, many times here at TIE, much to the chagrin of some in the public health community. But it’s worth saying again. As I wrote in Slate back in 2011, when FDA was on the cusp of releasing proposed rules:
Since the mid-1990s, we’ve made food manufacturers print nutrition information, including calorie counts, on packaged foods. Time and again, however, studies show that few people notice nutritional information and even fewer use it effectively. As the FDA lamented in a 2004 report, “It may be that consumers do not take advantage of the available information on the food label to control their weight, perhaps because they do not appreciate how the information could be used for weight management purposes or perhaps because they find it too hard to apply the available information to such purposes.”
This shouldn’t be surprising. People may generally know that they should avoid excess calories, but they don’t often know how many are too many. Even if they do, many can’t do the math in their heads to tally the day’s calories, much less figure out which combination of dishes would stay within the daily limit. Parents who buy food for their children don’t typically keep track of the calories their kids ate earlier or will eat later. They may also have more pressing concerns than the calories in their kids’ lunches that day. And parents aren’t always in the loop. Adolescents often order their own food, and they rarely account for the long-term costs of what they eat.
Posting calorie counts works on the principle that giving people the right information will help them make good decisions. The same instinct motivates all sorts of mandatory disclosure regimes. Just tell people about risky mortgage terms, and they’ll borrow more wisely. Just tell patients about pills’ side effects, and they’ll make better choices about their meds. Just tell arrested suspects about their right to remain silent, and they’ll make smarter decisions about what to say to investigators. Yet with few exceptions, these sorts of informational solutions have failed dismally. Inundated by information that they can’t understand and don’t have time to process, people routinely ignore mandatory disclosures. And they’ll ignore calorie counts, too.
I don’t want to overstate the point. I’m glad that that FDA’s rule is as strong as it is. Calorie counts will help some people eat better. But we shouldn’t confuse the strength of these new regulations with their long-term effectiveness at reducing obesity. Consider this anecdote:
When Michelle Obama rolled out her Let’s Move! campaign against childhood obesity, the American Beverage Association—a trade group representing Pepsi, Coke, and other peddlers of liquid candy—trumpeted its ostensible support by announcing that its members would add calorie-count labels to the front of cans and bottles to make the information “clearly visible.” Yet the association has fought tooth and nail, and spent tens of millions of dollars, to kill any talk of a tax on sugary drinks. The soda companies can afford to post calorie counts because they know it won’t hurt sales. They know a soda tax might.
The FDA rule is a step in the right direction. But if we’re serious about reducing obesity in this country, we’re going to have to do a hell of a lot more.
Yesterday I wrote that auto-renewal, as it’s currently conceived, has me worried. I must not be the only one, because a new strategy was proposed in a parcel of regulations released on Friday. Sam Baker has a helpful write-up:
To tackle this problem, HHS said it’s considering offering a menu of new options when people sign up for the first time. Instead of being automatically renewed for their existing plan, consumers could ask to be switched into the cheapest plan with comparable benefits. Or, if their plans’ premiums rise by more than a certain amount—say, 5 or 10 percent—they could be automatically assigned to one of the three cheapest plans with similar benefits.
So, upon enrollment, people will prospectively choose how they want their auto-renewal to work—do they want to prioritize premium costs or provider network? One thing that’s not clear to me is what “similar benefits” guidance would look like—surely the same metal tier, but cost-sharing, drug formularies, and other features can vary across plans in the same tier. The regulations are for 2016 at the earliest (for states who want to pilot auto-renewal alternatives) and 2017 for federally-run marketplaces.
This seems like a step in the right direction: surely a menu of different renewal options is better than no menu at all? There are obvious problems—a new plan will mean a new provider network—but people who really love their doctors can keep their plan, or specify financial parameters under which they’d like to keep their plan. In theory, someday there could be a “cheaper plan but only if it has an overlapping network” option, but for now HHS is still pressing insurers to make up-to-date provider lists available at all.
Automatically moving people to lower-cost alternatives also isn’t a new idea. In Medicare Part D, some low-income enrollees have a “switching” default for drug plan renewal; if the cost of their plans increases beyond a certain threshold, they are automatically enrolled in one of the cheapest plans available, unless they sent paperwork to affirm that they wanted to keep their current coverage. Admittedly, the “network” stakes are lower in a drug plan than for traditional coverage—but Massachusetts used a similar auto-switching paradigm with health insurance for state employees in the past.
For empirical work related to this problem, you might want to start with this working paper by Keith Marzilli Ericson (a grateful hat-tip to Timothy Layton). Ericson lays out two broad reasons that people, left to their own devices, don’t change insurance plans when doing so would be financially beneficial:
People who don’t switch because of psychological factors—I’d venture that this is a wide swath of static enrollees—would be better off electing a renewal scheme that automatically moves them into cheaper alternatives. It’s important to emphasize that the rules propose making this an option for enrollees, not a requirement; the six-page summary of the rules isn’t very clear on that point.
Per Ericson’s analysis, introducing an automatic switching default would increase the elasticity of demand. Higher elasticity of demand may lead to a more competitive market, which could plausibly mean lower federal spending, in the aggregate, on tax credits.
That’s the good news. The bad news is that it sounds like a logistical nightmare for pricing plans.
Better financial certainty for enrollees comes at the expense of risk pool certainty for insurers. When submitting rates for the following enrollment cycle, insurers don’t know where their plans fall in the pricing hierarchy (though some states will share preliminary rates and allow insurers to submit new bids). Unless enrollee preferences are shared with them, insurers will face another complex variable for setting rates: how many beneficiaries do they stand to lose—if they become one of the more expensive plans—or gain, if they become one of the cheaper alternatives?
People who want to keep their networks seem more likely to be high-acuity patients, so we would expect the “switching” population to be consistently healthier, on average. Unless the risk-adjustment program—which is permanent and budget-neutral—is very effective at pooling risk across all insurers, it’s not entirely clear to me how plans left with high-cost patients could make themselves more competitive in future years to attract price-sensitive healthies. Would insurers discontinue high-cost plans and introduce new plans to re-select their desired risk pool?
Brendan Saloner raised another good question: will this lead to a proliferation of low-premium, high-cost-sharing plans? I think this is a credible worry, but it’s worth noting that low-premium options have already proven disproportionately popular; according to ASPE, 60-65% of bronze and silver enrollees selected the lowest or second-lowest cost (premium) plan. So the incentive for low-premium plans is already there. Would this amplify that incentive? Probably. How much? I’m not sure.
Importantly, the regulations are about new enrollees; they don’t address what would happen for people who already have exchange coverage. I think—though I defer to the lawyerly types on this—that there could be legal impediments to changing the default renewal process for existing beneficiaries (but that they could be vigorously encouraged to update their preferences with their exchange).
Right now the proposed rules are a bit amorphous, as regulations awaiting comment often are. Insurers and consumer advocate groups alike are certain raise valid, complicated, and sometimes competing concerns. That’s because there isn’t a tidy solution to the auto-renewal problem—we’re looking for the least-worst strategy.
I’ve heard that Twitter has been abuzz this week with pics of nasty school lunches. As if in response, JAMA Pediatrics has a study looking at lunches brought from home:
Importance: The nutritional quality and cost of lunches brought from home are overlooked and understudied aspects of the school food environment.
Objectives: To examine the quality and cost of lunches brought from home by elementary and intermediate school students.
Design, Setting, and Participants: An observational study was conducted in 12 schools (8 elementary and 4 intermediate) in one Houston, Texas, area school district from October 6, 2011, to December 5, 2011. Participants included 242 elementary and 95 intermediate school students who brought lunches from home.
Exposures: Lunches brought from home.
Main Outcomes and Measures: Foods brought and amounts eaten were recorded along with student grade level and sex. Nutrient and food group content were calculated and compared with current National School Lunch Program (NSLP) guidelines. Per-serving prices for each item were collected from 3 grocery stores in the study area and averaged.
Pretty simple study. Researchers looked at lunches students brought from home and figured out their nutritional content. They also compared what they found to National School Lunch Program (NSLP) guidelines.
Lunches brought from home has fewer servings of fruits, vegetables, whole grains. They had more sodium. And they also had less milk (AND DON’T GET ME STARTED ON THAT). Almost all of the lunches from home had desserts, chips, and caloric beverages (which reimbursable school lunches can’t have). The cost of the lunches were $1.93 in grade school and $1.76 for middle school. Interestingly, students in the lower-income middle schools brought more expensive ($0.31 more) than students from middle-income schools.
Bottom line, lunches brought from home were nutritionally worse than NSLP guidelines would warrant. If we want to combat obesity, we need to pay attention to more than school-provided food.
Muthiah Vaduganathan and Vinay Prasad in a JAMA Viewpoint:
From a pragmatic standpoint, drug development programs conducted in broad populations poorly prioritize which patients should start therapy first. If provided regardless of risk, expensive new first-in-class agents may overwhelm health care budgets. In hepatitis C management, novel drug therapies broadly indicated for most patients with chronic hepatitis C, such as sofosbuvir, cost approximately $1000 per pill, presenting major cost challenges to drug implementation and distribution. In an effort to balance access and affordability, recent hepatitis C clinical practice guidelines have encouraged use of these agents primarily in the sickest subgroup of patients. In the current financial environment, emerging clinical trials should consider selecting the groups at highest risk to guide an economically viable and practical approach to drug utilization.
This gets at the point that even a very cost-effectiveness therapy can overwhelm budgets. The allocative efficiency problem still needs to be addressed.
Robert Langreth reports:
Unless more is done about a wave of new and expensive drugs, some priced at as much as $50,000 a month, Miller [the chief medical officer at Scripts Express] says that health plans are going to be swamped as costs double to half a trillion dollars as soon as 2020. […]
Drug companies that “think they can charge whatever they want” in competitive categories “run the risk of being excluded,” said Glen Stettin, 50, Miller’s colleague responsible for clinical products, including formularies.
Correction: In my first draft, I had written that CVS will keep 95% of drugs, not 95 drugs, off its main formulary. My bad.
Paul Demko reports:
[CMS] wants to require insurers to establish “pharmacy and therapeutics” committees that would meet at least four times a year to review drug formularies. […]
If an insurer rejects a customer’s request, under the proposed rule the individual would have the right to appeal that decision to an independent panel.
In addition, the agency offered more insight into what might constitute discrimination when it comes to drug formularies. For example, if a health plan places most or all drugs that treat a specific condition on the highest cost tier, the CMS warned that it would likely constitute discrimination. […]
The CMS indicated that it is also considering a requirement that plans post data on provider networks and drug formularies in a “machine-readable file.” That would allow third parties to extract the data and use it to create tools to help consumers make informed choices about what products will meet their needs.
This is relevant to two points I’ve made before: (1) There is good innovation and bad innovation. (2) For this very reason, there is a tension between innovation and consumer protection.
(Finally, I do not like “the CMS,” but I do not dispute that there’s a sense in which it is grammatically correct. There is good grammatical innovation and bad grammatical innovation …)
Pamela Hartzband and Jerome Groopman have a recent New York Times editorial arguing that quality incentives for doctors are corrupting medicine:
[F]inancial forces largely hidden from the public are beginning to corrupt care and undermine the bond of trust between doctors and patients. Insurers, hospital networks and regulatory groups have put in place both rewards and punishments that can powerfully influence your doctor’s decisions.
This large claim deserves extensive discussion.
In a quality-based incentive, a doctor’s medical practice is measured based on the patterns of care recorded in her patients’ medical records and evaluated against a standard of good care. For example, diabetic patients should be screened periodically for retinopathy, so this element of quality might be measured by counting how many of the doctor’s diabetic patients were screened at the appropriate times. The doctor then gets an additional payment if she meets or exceeds a recommended standard. Quality-based incentives play an important role in many health care reform schemes, including Accountable Care Organizations.
Hartzband and Groopman have four arguments for their view that pay-for-performance is corrupting medicine. Confusingly, three of them are packed into this sentence:
These metrics [used to measure the quality of doctors’ practice] are population-based and generic, and do not take into account the individual characteristics and preferences of the patient or differing expert opinions on optimal practice.
Unpacked, these arguments are that: (a) experts differ on optimal practice. (b) Individual patients vary biologically such that what is good for the typical patient may not be good for this patient. And, (c) variation among patients in risk preferences and life goals are such that what good means for one patient may not be what good means for the next patient. According to (c), even identical twins with identical diseases may have different optimal treatment strategies.
Argument (a) is not compelling. Experts’ opinions vary and so what? Many treatments based on opinion alone have proved disastrous. This is why we seek evidence-based treatment guidelines.
Argument (b), however, points to an important challenge for evidence-based medicine. Ben Djulbegovic and Gordon Guyatt argue that too many ‘evidence-based’ guidelines underlying physician quality metrics are not based on enough evidence to be trustworthy.
But surely the solution here is to develop better guidelines while weeding out the untrustworthy ones. Are all guidelines untrustworthy? I would be surprised if Hartzband, an endocrinologist, does not believe in screening diabetic patients for retinopathy.
Suppose, however, that the guideline is based on enough data to provide sound guidance for the ‘average’ patient. Hartzband and Groopman are right: the guideline will be of limited use if it fails to track important dimensions of biological variation among patients. However, high quality guidelines for complex disorders are longitudinal: they enjoin doctors to carefully assess outcomes and to adjust treatment to address individual variation in patient response. And here again, the urgently needed solution is to develop the science to produce better algorithms that personalize treatment to the unique biology of the individual.
Argument (c) is that what is optimal for an individual patient may vary depending on the preferences of that patient. This is absolutely the case, it’s important, and in other writings, Hartzband and Groopman have deep observations about doctor-patient communication and decision making. But are personal definitions of human well-being so variable that guidelines are useless? Are there, for example, significant numbers of sighted diabetic patients who are indifferent to whether they lose their vision?
I agree with Hartzband and Groopman that a doctor could experience a moral tension when a well-informed patient wants a course of treatment that deviates from a guideline and would threaten his quality incentive. However, the solution to this problem is not to eliminate the guideline. Instead, the quality measure should provide the doctor and patient a mechanism to resolve the tension in the patient’s favor. The doctor should be able to document that she discussed the standard with the patient and that the decision reflects the patient’s preferences. The patient would then be excluded from the quality calculation, as is presently done when there is a medical contraindication to the recommended treatment.
Finally, Hartzband and Groopman have an argument (d), which is that a doctor’s duty is exclusively to the patient in front of her, not the population as a whole.
When a patient asks “Is this treatment right for me?” the doctor faces a potential moral dilemma. How should he answer if the response is to his personal detriment? Some health policy experts suggest that there is no moral dilemma. They argue that it is obsolete for the doctor to approach each patient strictly as an individual; medical decisions should be made on the basis of what is best for the population as a whole.
We fear this approach can dangerously lead to “moral licensing” — the physician is able to rationalize forcing or withholding treatment, regardless of clinical judgment or patient preference, as acceptable for the good of the population.
Here Hartzband and Groopman are arguing not only with insurance companies and anonymous health policy experts, but also with the Ethics Manual of the American College of Physicians (ACP). The Manual says that
Physicians have a responsibility to practice effective and efficient health care and to use health care resources responsibly. Parsimonious care that utilizes the most efficient means to effectively diagnose a condition and treat a patient respects the need to use resources wisely and to help ensure that resources are equitably available.
What the ACP is urging, however, is not that the individual patient be deprived to benefit the population. Rather, the ACP recognizes that physicians have a duty to practice with the awareness that medical resources are finite and need to be available to all, rather than selectively allocated to the affluent. This can occur only if physicians practice parsimoniously, that is, as Jon Tilburt and Christine Cassell (and see Austin here) write,
delivering appropriate health care that fits the needs and circumstances of patients and that actively avoids wasteful care—care that does not benefit patients.
The needs of the population, on this view, are legitimately addressed by actively avoiding wasteful care. This ethic is consistent with doctors being at the same time zealous advocates for their patients’ needs. Do Hartzband and Groopman think that physicians lack the moral discernment to combine the two?
Finally, Hartzband and Groopman write with a strange insensitivity to historical context. First, they write with an urgency that suggests that pay-for-performance incentives are rapidly deforming medical ethics. But as Aaron has noted, so far the evidence suggests that pay-for-performance schemes have little effect on physician behavior.
Second, I am baffled by their claim that “financial forces are beginning to corrupt care.” Fee-for-service payment systems give physicians an obvious incentive to overtreat. On Hartzband and Groopman’s logic, prior generations of physicians must have been deeply corrupt. Yet they express no concern about the effects of those incentives. My view is that fee-for-service does affect physician behavior, sometimes in ways that harm patients. But it would be hyperbolic to call traditional physicians ‘corrupt’. Similarly, we should carefully study the effects of quality incentives on physician behavior. But we shouldn’t panic. The management of moral tension is part of every complex human endeavor.
So it seems likely that present and future physicians can negotiate the moral tensions between incentives tied to well-supported professional guidelines, the need for stewardship of scarce medical resources, and the priority of helping patients make autonomous medical choices. The cause here must be advanced by improving quality measurement, not abandoning it.
For discussion the ACP Ethics manual, see Aaron (here and here), Ezekiel Emanuel, and Paul Kelleher. See here for more TIE writing on pay-for-performance and be aware that skepticism about the effectiveness of P4P is not the same as skepticism about evidence-based medicine.
It’s Thanksgiving this week in America. Last year, we talked about the myth that turkey makes you sleepy. I argued that there’s nothing special about turkey that would make you tired. But, we pointed out that a super large meal, and excessive alcohol consumption, could make you want to take a nap. But how caloric is the feast? Let’s get some answers. This is Healthcare Triage:
For those of you who came here for more information, here you go: