• Patients overestimate benefits, underestimate harms of treatment. What if they knew the truth?

    The following is co-authored by Austin Frakt and Aaron Carroll. It originally appeared on The Upshot (copyright 2015, The New York Times Company). Click over to that version of the post to see the accompanying chart.

    If we knew more, would we opt for different kinds and amounts of health care? Despite the existence of metrics to help patients appreciate benefits and harms, a new systematic review suggests that our expectations are not consistent with the facts. Most patients overestimate the benefits of medical treatments, and underestimate the harms; because of that, they use more care.

    The study, published in JAMA Internal Medicine and written by Tammy Hoffmann and Chris Del Mar, is the first to systematically review the literature on the accuracy of patients’ expectations of benefits and harms of treatment. They examined over 30 studies that assessed whether patients understood the upsides or downsides of certain treatments. To a great extent, patients didn’t.

    In the 34 studies that assessed understanding of benefits, patients overestimated their potential gain in 22 of them, or 65 percent. For instance, a 2002 study published in the Journal of the National Cancer Institute asked women who had undergone prophylactic bilateral (double) mastectomy to estimate how much the procedure reduced their risk of breast cancer. On average, the women thought they had reduced that risk from 76 percent to 11 percent, an absolute risk reduction of 65 percentage points.

    For the more than 80 percent of the women in the study who did not have a BRCA genetic mutation — which drastically increases the risk of breast cancer — the real risk before surgery of developing breast cancer was 17 percent, meaning they greatly overestimated their risk reduction. Even the women with a BRCA mutation overestimated their risk reduction, but to a lesser extent.

    Another 2012 study published in the Annals of Family Medicine asked patients to estimate the benefits of screening for bowel and breast cancer, and the use of medications to prevent hip fracture and cardiovascular disease. More than two-thirds of patients overestimated the benefits of medications to prevent cardiovascular disease, and more than 80 percent overestimated the benefits of medications to prevent hip fractures.

    Further, 90 percent of patients overestimated the benefits of breast cancer screening, and 94 percent overestimated the benefits of bowel cancer screening. The researchers also asked the patients to estimate the minimum reduction in bad outcomes (like fractures or deaths) they would need to achieve to find the treatment worthwhile. For three of the four studied interventions, the minimum benefit patients would accept was higher than the actual benefit.

    In the 15 studies examined in the systematic review for which harms were a focus, patients underestimated potential downsides in 10 of them (67 percent). For example, a study published in 2012 in the Journal of Medical Imaging and Radiation Oncology asked patients to estimate the risks associated with a CT scan. A single CT scan exposes a patient to the same amount of radiation as 300 chest X-rays, and carries with it a 1-in-2,000 chance of inducing a fatal cancer. More than 40 percent of patients underestimated a CT’s radiation dose, and more than 60 percent of patients underestimated the risk of cancer from a CT scan.

    Why do patients err in assessments of risks and benefits? One reason could be that what they know is driven by the messages they hear. Doctors, direct-to-consumer ads and the media can skew our perceptions. They tend to focus on the benefits, but rarely quantify them. Health care centers, screening advocacy programs and pharmaceutical ads all push us to talk to our doctors about getting treatment without talking about actual gains.

    Doctors also aren’t always good at communicating risks. A 2013 study published in JAMA Internal Medicine found that fewer than 10 percent of patients were told about overdiagnosis and overtreatment associated with cancer screening, even though 80 percent of patients wanted to know about harms.

    This study, and others, indicate that patients would opt for less care if they had more information about what they may gain or risk with treatment. Shared decision-making in which there is an open patient-physician dialogue about benefits and harms, often augmented with use of treatment decision aids, like videos, would help patients get that information. However, a majority of patients still report that they prefer to leave medical decision-making to their doctors.

    It might also be the case that some patients would use more of certain types of care if they had more information. Many chronic conditions remain undermanaged and undertreated in the United States. It’s possible that people with these conditions who had more information would use more care, which could raise spending for these patients but make them better off.

    There’s also an argument to be made that people who overestimate the benefits of medicine to treat some conditions are more likely to take it regularly, which might lead to better outcomes, in some cases, than would occur if these patients were better informed.

    Regardless, even though some patients may benefit somewhat from being ill informed, it seems wrong to argue that we should keep them in the dark. Many of the studies in the systematic review show that people report that they would opt for less care if they better understood benefits and harms. Improved communication could better serve patients and might improve the efficiency of our health system if patients focus on getting the types of care for which the benefit outweighs risk of harm.

    It’s also possible that unrealistic expectations of care help patients cope with disease or provide them with some sense of control. Feeling hopeful about one’s future is not to be dismissed. But those unrealistic expectations don’t come cheap. We should at least consider the price that we pay for being uninformed.

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  • How to Measure a Medical Treatment’s Potential for Harm

    The following is co-authored by Aaron Carroll and Austin Frakt. It originally appeared on The Upshot (copyright 2015, The New York Times Company). Click over to that version of the post to see the accompanying charts.

    As we wrote last week, many fewer people benefit from medical therapies than we tend to think. This fact is quantified in a therapy’s Number Needed to Treat, or N.N.T., which tells you the number of people who would need to receive a medical therapy in order for one person to benefit. N.N.T.s well above 10 or even 100 are common. But knowing the potential for benefit is not enough. We must also consider potential harms.

    Not every person who takes a medication will suffer a side effect, just as not every person will see a benefit. This fact can be expressed by Number Needed to Harm (N.N.H.), which is the flip side of N.N.T.

    For instance, the N.N.T. for aspirin to prevent one additional heart attack over two years is 2,000. Even though this means that you have less than a 0.1 percent chance of seeing a benefit, you might think it’s worth it. After all, it’s just an aspirin. What harm could it do?

    But aspirin can cause a number of problems, including increasing the chance of bleeding in the head or gastrointestinal tract. Not everyone who takes aspirin will bleed. Moreover, some people will bleed whether or not they take aspirin.

    Aspirin’s N.N.H. for such major bleeding events is 3,333. For every 3,333 people, just over two on average will have a major bleeding event, whether they take aspirin or not. About 3,330 will have no bleed regardless of what they do. But for every 3,333 people who take aspirin for two years, one additional person will have a major bleeding event. That’s an expression of the risk of aspirin, complementing the fact that one out of 2,000 will avoid a heart attack.

    Granted, one out of 3,333 is a pretty tiny risk. But remember that the chance of benefit is pretty small, too.

    Sometimes, though, the N.N.H. can be much lower, even lower than that of N.N.T., which suggests the chance of harm is greater than the potential benefit. Consider screening mammograms, which are considered so essential that they are the only screening tests specifically mentioned in the Affordable Care Act, and coverage for them with no cost sharing is required by the law.

    If you look at the data for all randomized controlled trials of breast cancer screening, the N.N.T. for recommending screening to prevent one death from breast cancer after 13 years of follow-up is 1,477. But further analyses show that the one woman would have probably died of other causes anyway. There may be no benefit at all with respect to preventing death from all causes.

    Screening with mammograms can cause harm, though. They lead to overdiagnosis, encouraging the provision of therapies that provide no benefits — but do carry risks, and therefore are considered harms.

    If we look at those same studies, for every 333 women who are assigned to have a screening mammogram, one extra will undergo a lumpectomy or mastectomy as a result. One in every 390 women assigned to have a screening mammogram will undergo an extra course of radiation therapy as a result. (In these randomized controlled trials, patients are either assigned to get screening mammograms or they are not. The study then usually looks at the outcome for all who were assigned to get the mammogram, whether they actually did or not.)

    In other words, for about every 1,500 women assigned to get screening for 10 years, one might be spared a death from breast cancer (though she’d most likely die of some other cause). But about five more women would undergo surgery and about four more would undergo radiation, both of which can have dangerous, even life-threatening, side effects.

    Thus, N.N.H., paired with N.N.T., can be very useful in discussing the relative potential benefits and harms of treatments. As another example, let’s consider antibiotics for ear infections in children. There are many reasons that parents and pediatricians might consider treatment. One commonly cited reason is that we want to prevent serious complication from untreated infections. Unfortunately, antibiotics don’t do that, and the N.N.T. is effectively infinite. Antibiotics also won’t reduce pain within 24 hours. Antibiotics have, however, been shown to reduce pain within two to seven days. Not all children will see that benefit, though. The N.N.T. is about 20 for that outcome.

    Antibiotics can cause side effects, however, including vomiting, diarrhea or a bad rash. The N.N.H. for side effects in this population is 14.

    This means that when a child is prescribed antibiotics for an ear infection, it’s more likely that he will develop vomiting, diarrhea or a rash than get a benefit. When patients are presented with treatment options in this manner, they are sometimes more likely to agree to watchful waiting to see if the ear infection resolves on its own. For most children with ear infections, observation with close follow-up is recommended by the American Academy of Pediatrics.

    A wealth of N.N.T. and N.N.H. data based on clinical trials is available on a website developed by David Newman, a director of clinical research at Icahn School of Medicine at Mount Sinai hospital, and Graham Walker, an assistant clinical professor at the University of California, San Francisco. But it’s important to understand that results from clinical trials do not always reflect what happens in the real world. As criteria for treatment become more permissive beyond those applied in trials, the N.N.T.s can go up. But importantly, N.N.H.s often do not. Healthier people are less likely to see a benefit from antibiotics or an aspirin. But they are not less likely to have a side effect or complication.

    This is because the harms associated with treatment usually have nothing to do with the underlying illness. They are caused by the therapy, regardless of the reason for use. Children will develop diarrhea, vomiting or rashes from antibiotics in the same relative amounts no matter why we are using them. Put another way, clinical trials are designed to target the class of patients that most likely benefits from treatment, but they are not targeted to those more or less likely to experience harm. When treatments are applied in real-world clinical settings, we generally don’t see changes in the proportion of patients harmed by them relative to trials.

    When we stray from recommendations for therapies, and broaden the population given studied treatments, the N.N.T.s often go up, but the N.N.H.s stay the same. Things are often even worse than the data in studies make them look. Fewer people benefit, but just as many are harmed.

    We hope that every therapy has a benefit. The N.N.T. shows us that benefits are often much less likely than many might think. The N.N.H. can show us how likely we are to have a harm compared with a benefit. Considering both, especially in light of how practice often differs from studies, can help us make better decisions about how to care for ourselves and those we love.

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  • Can This Treatment Help Me? There’s a Statistic for That

    The following is co-authored by Austin Frakt and Aaron Carroll. It originally appeared on The Upshot (copyright 2015, The New York Times Company). Click over to that version of the post to see the accompanying charts.

    In his State of the Union address last week, President Obama encouraged the development of “precision medicine,” which would tailor treatments based on individuals’ genetics or physiology. This is an effort to improve medical care’s effectiveness, which might cause some to wonder: Don’t we already have effective drugs and treatments? In truth, medical care is often far less effective than most believe. Just because you took some medicine for an illness and became well again, it doesn’t necessarily mean that the treatment provided the cure.

    This fundamental lesson is conveyed by a metric known as the number needed to treat, or N.N.T. Developed in the 1980s, the N.N.T. tells us how many people must be treated for one person to derive benefit. An N.N.T. of one would mean every person treated improves and every person not treated fails to, which is how we tend to think most therapies work.

    What may surprise you is that N.N.T.s are often much higher than one. Double- and even triple-digit N.N.T.s are common.

    Consider aspirin for heart attack prevention. Based upon both modifiable risk factors like cholesterol level and smoking, and factors that are beyond one’s control, like family history and age, it is possible to calculate the chance that a person will have a first heart attack in the next 10 years. The American Heart Association recommends that people who have more than a 10 percent chance take a daily aspirin to avoid that heart attack.

    How effective is aspirin for that aim? According to clinical trials, if about 2,000 people follow these guidelines over a two-year period, one additional first heart attack will be prevented.

    That doesn’t mean the 1,999 other people have heart attacks. The fact is, on average about 3.6 of them would have a first heart attack regardless of whether they took the aspirin. Even more important, 1,995.4 people would never have a heart attack whether or not they took aspirin. Only one person is actually affected by aspirin. If he takes it, the number of people who remain heart attack-free rises to 1996.4. If he doesn’t, the number remains 1995.4. But for 1,999 of the 2,000 people, aspirin doesn’t make any difference at all.

    Of course, nobody knows if they’re the lucky one for whom aspirin is helpful. So, if aspirin is cheap and doesn’t cause much harm, it might be worth taking, even if the chances of benefit are small. But this already reflects a trade-off we rarely consider rationally. (And many treatments do cause harm. There is a complementary metric known as the number needed to harm, or N.N.H., which says that if that number of people are treated, one additional person will have a specific negative outcome. For some treatments, N.N.T. can be higher than the number needed to harm, indicating more people are harmed than successfully treated.)

    Not all N.N.T.s are as high as aspirin’s for heart attacks, but many are higher than you might think. A website developed by David Newman, a director of clinical research at Icahn School of Medicine at Mount Sinai hospital, and Dr. Graham Walker, an assistant clinical professor at the University of California, San Francisco, has become a clearinghouse of N.N.T. data, amassed from clinical trials. Among them, for example, are those for the effects of the Mediterranean diet.

    The Mediterranean diet, which is heavy in vegetables, fruits, nuts and olive oil; moderate in fish and poultry; and light in dairy, meat and sweets; has long been advocated as a means to avoid heart disease. In people who have never had a heart attack, but who are at risk, the N.N.T. is 61 to avoid a heart attack, stroke or death. And that is for people who adhere to the diet for about five years. For those at higher risk, who have already had a heart attack, to avoid one additional death, the N.N.T. is about 30. That’s the number of people who would have to adhere to the diet for four years so that one extra person survived. About 1.4 people out of 30 such people will die no matter what they eat; 27.6 will not die no matter what they eat. Only one will benefit from sticking to the diet.

    But it’s not easy for everyone to stick to such a diet for that many years. Some — for example, those who enjoy steak and ice cream — will feel that it diminishes their quality of life. When you hear that the diet prevents heart attacks, then it might sound worth it. But does it still sound worth it when you consider that 29 out of 30 people who stick to the diet for several years see no benefit at all? Will you stick to it for years and be the lucky one for whom that matters?

    As treatments go, an N.N.T. of 30 is pretty good. Very few are as low as 10, though some are. For instance, the use of steroids in people having asthma attacks to prevent admission to the hospital has an N.N.T. of eight. This is so obvious, and so powerful a treatment, that there are no commercials and no op-eds preaching steroid use for asthma. (Maybe there should be. It’s likely that this therapy is being underutilized, perhaps because cost-sharing discourages some people with asthma from seeking care when they might need it.) Steroids work very well for asthma attacks — better than many treatments for other conditions. But still, seven of eight people suffering an asthma attack see no benefit at all from steroids with respect to preventing hospitalization.

    Even more concerning, N.N.T.s as calculated from clinical trial data are probably higher than those based on real-world medical care. In clinical trials, treatments are applied to a select population for whom they’re intended. In medical practice, it’s very common for treatments to be applied to a much broader population, including many people for whom they’re less likely to be effective, which increases the N.N.T. This is, perhaps, because doctors would rather offer an explicit treatment — perhaps to harnessplacebo effect — even when it’s not likely to be of additional benefit.

    In fact, as recently reported in The Times, a new study showed that many people who are prescribed aspirin for the primary prevention of cardiovascular disease don’t meet the criteria described above for its use. Because of this use in a population beyond that targeted in clinical trials, the N.N.T. in practice is most likely higher than the 2,000 suggested by those trials. (It’s worth noting that our best estimates of N.N.T.s can rise or fall as more data are collected and as treatments or how or to whom they’re delivered change.)

    Antibiotics are a classic example of overuse. For instance, the N.N.T. for antibiotics to treat radiologically diagnosed acute sinusitis is 15, meaning that 14 out of 15 who take them derive no benefit. But physicians often write prescriptions for antibiotics in situations when the diagnosis of sinusitis is far less assured. This leads to antibiotics being overprescribed and overused, raising their N.N.T. in practice.

    The use of stents to open up clogged arteries in patients who are not actively suffering a heart attack is another treatment that is employed too often. (Stents are considered appropriate in patients who are having a heart attack.) Many more patients believe they extend life than their N.N.T. suggests. The N.N.T is effectively infinite, relative to treatment with medications, for people not suffering a heart attack.

    Until health care technology improves, there’s not a lot we can do about N.N.T.s that are larger than we might hope. It’s just a fact of current medical technology that not everyone benefits from treatment, even when well targeted. President Obama’s push for “precision medicine” is an attempt to change this, by using genomics to focus treatments on people who would most benefit from them. That will take time.

    In the meantime, we would all be better served by a more informed understanding of exactly how much, or how little, benefit is reasonably to be expected by taking a drug, changing our lifestyle or undergoing a procedure. Especially since the chance of benefit, as expressed by N.N.T., might not be worth the risk of harm, as expressed by N.N.H. We’ll discuss that more next week.

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  • The cost of resistance to the ACA in Mississippi

    This post is co-authored by Bill Gardner and Austin Frakt.

    Lower+Mississippi+Delta+Mired+Poverty+KNvxHX1JNhclIn PoliticoMagazine, Sarah Varney details the consequences of Mississippi’s rejection of the Affordable Care Act’s (ACA’s) Medicaid expansion.

    The background is that it is hard to find a list of state public health or well being indicators

    where Mississippi doesn’t rank last: Life expectancy. Per capita income. Children’s literacy.

    There are many social and cultural factors that contribute to Mississippi’s catastrophic public health. One factor is a pervasive lack of health insurance.

    Small businesses dominate [Mississippi’s] economy. [They] typically don’t offer health insurance, and Mississippi’s public health program for the poor is one of the most restrictive in the nation. Able-bodied adults without dependent children can’t sign up for Medicaid in Mississippi, no matter how little they earn, and only parents who earn less than 23 percent of the federal poverty level—some $384 a month for a family of three—can enroll. As a result, one in four adult Mississippians goes without health coverage. For African-Americans, the numbers are even worse: One in three adults is uninsured.

    As passed, the ACA required states to extend Medicaid eligibility to 138% of the Federal Poverty Line and provided substantial federal funds in support. But the Supreme Court made that extension optional and Mississippi opted out.

    Refusing Medicaid had serious effects on access to health care in rural Mississippi. It isn’t just that poor residents couldn’t get care because there were uninsured. They also had fewer places to go when local clinics had to close.

    The Medicaid gap hit hospitals hard, too. Without the cash infusion that a Medicaid expansion would have brought, Mississippi hospitals are being strained to a near breaking point, with a number of them shuttering entire departments and laying off staff. Poor people often flocked to the emergency room at Montfort Jones Memorial Hospital in Kosciusko, for instance… Earlier this year, the hospital shut down its intensive care unit and laid off 38 employees. Next, the psychiatric unit for seniors closed. One in five people who come to the hospital can’t pay their medical bills, and Montfort Jones had relied on supplemental Medicaid payments to defray the costs. But under the health law, federal aid for uncompensated care trails off. Without those payments, and with no softening in the demand for uncompensated care, Montfort Jones had been losing up to $3 million a year, and couldn’t meet payroll…

    [A Kosciusko physician] led me down a darkened hallway [in the Montfort Hospital Emergency Department] and pushed open the doors to the ICU. It looked as though the nurses, doctors and janitors had just gotten up and left. Scanning the bay of ghostly patient rooms, Alford said mordantly, “This is a state-of-the-art ICU.” Now, patients with pneumonia, blood clots or infections are sent 70 miles away to Jackson.

    hospitalpicture1_jonlowenstein

    Montfort Jones Hospital in Kosciusko, MS.

    If you are unfamiliar with rural poverty, be aware that not everyone has a car, public transportation is not always available, and ambulances are unaffordable if you are uninsured. If you are concerned about health care efficiency, think about making the capital investment required to build an ICU and then just letting it depreciate unused.

    So how should we think about the ACA in the light of Mississippi? First, let’s acknowledge that many people’s views about the ACA reflect their philosophical commitments. For Michael Cannon, who may have influenced Mississippi’s decision making, freedom itself was at stake. Progressives disagreed: Bill argued that the ACA expands human freedom. Progressives also emphasized the importance of equal access to health care.

    However, conservatives and progressives also disagreed on the likely outcomes of the ACA for the health system. The data are now coming in and they pose important questions for each side.

    For conservatives: Many opponents believed both that the implementation of the ACA would fail and that it would result in worse rather than better health care. But after a rough start, the ACA is largely meeting its implementation goals. It is significantly reducing the number of the uninsured. And whereas health outcomes are improving in Massachusetts, the first adopter of ACA-like reform, Varney’s article suggests that in Mississippi the health care system is showing signs of collapse. It’s hard to look at Mississippi and conclude conservatives were right, for the ACA was hardly given a chance in that state, as Varney details.

    Yet, Mississippi, though it’d be better off in many respects, would probably never be like Massachusetts if it fully embraced the ACA. Massachusetts started with a strong economy, a well-educated citizenry, and first world rather than developing world population health. As Varney shows, part of what has happened to Mississippi is likely the result of specific policy choices by elected officials. But a lot of it was baked into a terrible history.

    Nevertheless, Governor Kasich’s question is, we believe, on point:

    “The opposition to [Obamacare] was really either political or ideological,” Ohio Gov. John Kasich (R) told the Associated Press earlier this month. “I don’t think that holds water against real flesh and blood, and real improvements in people’s lives.”

    Principles matter, but how do you weigh principles that dictate opposition to access to basic health insurance against the costs borne by Mississippi’s rural poor?

    For progressives: The Supreme Court’s ruling increased the powers to the states decide their own health policies. Progressives must ask: How important is it that Medicaid expansion proceed in Mississippi according to the standards in the ACA? Would you be willing to grant greater flexibility for Mississippi (and other Red states) to expand the program with features you don’t like? Granting flexibility looks like complicity in allowing unequal standards of health and health care, depending on the accident of where they live. But look at the vast differences between Mississippi and Massachusetts now. They are vast. For worse or (in our view) for better, we have a federal system. What’s happening in Mississippi reflects the (fully legal) choices politicians have made at all levels. If refusing to compromise leads to a political blockage of reform, we end up with outcomes like Mississippi’s. Is the fight for equal standards worth that cost?

    Mississippi should prompt both conservatives and progressives to assess the cost of their principles. How much are they worth in “flesh and blood, and real improvements in people’s lives”?

    @Bill_Gardner and @afrakt

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  • Employers love workplace wellness programs, but they generally don’t work

    The following originally appeared on The Upshot (copyright 2014, The New York Times Company) and is coauthored by Austin Frakt and Aaron Carroll.

    Most news coverage of the new Kaiser Family Foundationannual survey on employer-sponsored health plans has focused on the fact that growth in premiums in 2013 was as low as it has ever been in the 16 years of the survey. But buried in the details of the report are some interesting insights into how employers think about controlling health care costs. One example is that they’re very fond of workplace wellness programs. This is surprising, because while such programs sound great, research shows they rarely work as advertised.

    Wellness programs aim to encourage workers to be more healthy. Many use financial incentives to motivate workers to monitor and improve their health, sometimes through lifestyle-modification programs aimed at lowering cholesterol or blood pressure, for instance. Some programs offer a carrot, like discounts on health insurance to employees who complete health-risk assessments. Others use a stick, penalizing poor performance, or charging people more for smoking or having a high body mass index, for example.

    Wellness programs are popular among employers. An analysis by the RAND Corporation found that half of all organizations with 50 or more employeeshave them. The new survey by the Kaiser Family Foundation found that 36 percent of firms with more than 200 workers, and 18 percent of firms over all, use financial incentives tied to health objectives like weight loss and smoking cessation. Even more large firms — 51 percent of those with 200 workers or more — offer incentives for employees to complete health risk assessments, intended to identify health issues.

    Medium-to-large employers spent an average of $521 per employee on wellness programs last year, double the amount they spent five years ago, according to a February report by Fidelity Investments and the National Business Group on Health. The programs are generally offered not directly by insurance companies, but by specialist firms that tell employers they will reduce spending on employees’ care by encouraging the employees to take better care of their health.

    Wellness programs have grown into a $6 billion industry because employers believe this. In fact, asked which programs are most effective at reducing costs, more firms picked wellness programs than any other approach. The Kaiser survey found that 71 percent of all firms think such programs are “very” or “somewhat” effective, compared with only 47 percent for greater employee cost sharing or 33 percent for tighter networks. (Recent research on public employee plans in Massachusetts found that tighter networks were associated with large savings.)

    What research exists on wellness programs does not support this optimism. This is, in part, because most studies of wellness programs are of poor quality, using weak methods that suggest that wellness programs are associated with lower savings, but don’t prove causation. Or they consider only short-term effects that aren’t likely to be sustained. Many such studies are written by the wellness industry itself. More rigorous studies tend to find that wellness programs don’t save money and, with few exceptions, do notappreciably improve health. This is often because additional health screenings built into the programs encourage overuse of unnecessary care, pushing spending higher without improving health.

    However, this doesn’t mean that employers aren’t right, in a way. Wellness programs can achieve cost savings — for employersby shifting higher costs of care onto workers. In particular, workers who don’t meet the demands and goals of wellness programs (whether by not participating at all, or by failing to meet benchmarks like a reduction in body mass index) end up paying more. Financial incentives to get healthier sometimes simply become financial penalties on workers who resist participation or who aren’t as fit. Some believe this can be a form of discrimination.

    The Affordable Care Act encourages this approach. It raises the legal limit on penalties that employers can charge for health-contingent wellness programs to 30 percent of total premium costs. Employers can also charge tobacco users up to 50 percent more in premiums. Needless to say, this strikes some people as unfair and has led to objections by workers at some organizations, as well as lawsuits.

    Another way that wellness programs can help employers is by putting a more palatable gloss on other changes in health coverage. For instance, workers might complain if a company tries to reduce costs through higher cost sharing or narrower networks that limit doctor and hospital choice. But if these are quietly phased in at the same time as a wellness program that’s marketed as helping people become healthier, a company might be able to achieve those cost reductions with less grumbling.

    At least one study has shown that a wellness program can achieve long-term savings. In 2003, PepsiCo introduced what was to become its Healthy Living program, which included lifestyle management (weight, nutrition and stress management along with smoking cessation and fitness) and disease management components (targeting participants with asthma, coronary artery disease, atrial fibrillation, congestive heart failure, stroke, hyperlipidemia, hypertension, diabetes, low back pain and chronic obstructive pulmonary disease). A study published in Health Affairs examined the outcomes of the program seven years after implementation, the longest such study of a wellness program to date.

    Researchers found that participation in the PepsiCo program was associated with lower health care costs, but only after the third year, and all from the disease management components of the program. This suggests that wellness programs that target specific diseases that may drive employer costs could achieve savings, though perhaps only after several years. When more broadly implemented and focused on lifestyle management, as many wellness programs are, savings may not materialize, and certainly not in the short term.

    Employers may misunderstand the research if they think that just any wellness program, by itself, is the surest route to reducing overall health care spending. That just isn’t the case. It may be true that, if designed well, some programs can save money for both the employer and employees in the long run, but not by focusing on lifestyle changes. Programs that merely do that may cut employer costs, but only by shifting them to employees. If firms wish to count that as a victory in the battle against health care costs, they may do so, but their employees may look at it differently.

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  • How to pay for only the health care you want

    The following originally appeared on The Upshot (copyright 2014, The New York Times Company) and is jointly authored by Austin Frakt and Amitabh Chandra.

    One reason health insurance is expensive is that most plans cover just about every medical technology — not just the ones that work, or the ones that are worth the price. This not only drives up costs, but also forces many Americans into purchasing coverage for therapies they may not value. But there’s no reason things couldn’t be different, and better for consumers.

    Consider the latest technology for treating prostate cancer: the proton beam. It’s delivered with a football field-size machine costing well over $100 million. Per treatment, this therapy costs at least twice as much as alternative approaches, but is no more effective. Many health plans cover it and other therapies of low or uncertain value because they pay for anything that physicians deem medically necessary even when evidence suggests otherwise. And, without even knowing it, Americans pay for it in higher premiums.

    It doesn’t have to be this way. If plans could compete on the basis of the therapies they cover, consumers could decide what they wish to pay for. This sounds complicated, but it need not be.

    Health plans could define themselves at least in part by the value of technologies they cover, an idea proposed by Professor Russell Korobkin of the U.C.L.A. School of Law. For example, a bronze plan could cover hospitalizations and visits to doctors for emergencies and accidents; genetic diseases; and prescription drugs that keep people out of hospitals. A silver plan could cover what bronze plans do but also include treatments a large majority of physicians find useful. A gold plan could be more inclusive still, adding coverage, for instance, for every cancer therapy shown to improve patient outcomes (no matter the cost) as long as it was delivered at a leading cancer center. Finally, a platinum plan could cover experimental and unproven cancer therapies, including, for example, that proton beam.

    This way, nothing would be concealed or withheld from consumers. Someone who wanted proton-beam cancer treatment coverage could have it by selecting a platinum policy and paying its higher premiums. Someone who did not want to pay higher premiums for lower-value care, in turn, could choose a bronze or silver plan. This gives a different, but more useful, meaning to the terms “gold,” “silver” and “bronze” than they have in the new insurance exchanges today.

    The idea of ranking plans by value of care they cover has some limitations. One impediment is that it’s not in a specific plan’s interest to fund the research to discover the value of health care technology. Such information is a public good — meaning once learned, it can be known and used by all plans. But public investment in research can avoid this problem. Additional funding for studying what works in health care and what does not would help enormously, as would regulatory changes to allow plans to use the fruits of that research to exclude low-value technology from coverage.

    A second concern is that as people become sick, they will prefer plans that cover more treatments, including experimental ones. As sick people disproportionately choose more generous plans, their expenses and premiums will have to rise. This phenomenon, known as adverse selection, is familiar in most health insurance markets, including those for employer-sponsored plans, private plans that participate in Medicare and in the Affordable Care Act’s new marketplaces. One common way to address it is to permit individuals to switch plans only once per year, during an open enrollment period. This locks people into their choice for some time, so they can’t suddenly upgrade their plan after getting sick. If a once-per-year enrollment period proves insufficient in this case, a longer period could be imposed.

    Structuring health plans according to value would give Americans the ability to buy whatever health care technologies they choose — including, if they want it, unproven and expensive care — without forcing others to pay for that choice. This would help address the key, though under-recognized, problem in American health care today: Not that Americans spend a lot on health care, but that they spend a lot without always getting good value for the money.

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  • Job lock: What the ACA does

    Links to all posts in the series to which this post belongs are in the introductory post. This post is jointly authored by Nicholas Bagley and Austin Frakt.

    As the posts in this series have shown, the available studies provide ample support for the theoretical prediction that many people will experience job lock. In an ideal world, the fear of losing employer-sponsored health insurance shouldn’t drive decisions about where, whether, or how much to work. Attentive to that concern, the drafters of the ACA took a number of steps to mitigate job lock.

    Most fundamentally, the Act prohibits any health plan—employer-sponsored or not—from refusing to cover a preexisting condition. (Only grandfathered plans get a pass.) Employers can still impose up to a 90-day “waiting period” before a group health plan kicks in, but that’s it. The elimination of preexisting-condition exclusions should ease somewhat the concerns of workers who might have hesitated to switch jobs because, if they had a gap between jobs of more than two months, a new employer could have declined to cover such a condition for up to a year.

    Of even greater importance, the Act revolutionizes the individual market for private insurance. By prohibiting medical underwriting and guaranteeing issue, the ACA makes it more likely that those with preexisting conditions or high anticipated medical expenses can find affordable insurance in the private market. That should encourage those workers to retire earlier, to switch to firms that don’t offer health coverage, or to become entrepreneurs. The overall effect on job lock will be mixed, however. Community rating will make exchange plans more expensive for the young and healthy, which may discourage them from ditching their employer-sponsored coverage.

    In any event, employer-sponsored coverage will still be tax-advantaged, meaning that many people will still prefer to get coverage through their employers. That’s particularly true for higher-income individuals for whom the tax exclusion is especially valuable. As a result, job lock won’t go away altogether. On the other hand, the Cadillac tax will gradually claw back some of the tax advantages of more plans over time, beginning with those with very high premiums.

    For those under 400% of the poverty line, however, the ACA extends tax credits and cost-sharing subsidies to buy exchange plans. Those credits and subsidies will partly offset the exclusion, mitigating job lock. For low-income workers, the effect could be particularly dramatic: the subsidized cost of insurance on an exchange may be lower than the wage reduction necessary to purchase employer-sponsored coverage. For those workers, exchange coverage could be a better deal than employer-sponsored coverage. Indeed, such workers might shy away from jobs that offer such coverage and pay commensurately less than alternative employment with fewer benefits.

    For those under 133% of the poverty line, the expanded availability of Medicaid coverage—at least in states that have expanded their Medicaid programs—could also diminish job lock. Consider the breadwinner in a family of four making just $30,000 at a job that offers health coverage. Prior to the ACA, he or she may have been unable to afford a job at a comparable wage that didn’t offer health coverage. With Medicaid as a fallback, a different job at the same wage would be considerably more attractive.

    The ACA also encourages small employers to provide employer-sponsored coverage, which could make it more attractive for workers to take a job with a small firm. The Act extends sizeable tax credits to employers with fewer than 25 employees that offer health coverage. By eliminating medical underwriting, the Act should make it easier for small firms with bad claims experience to secure affordable coverage. (Some firms with good claims experience, however, will see their premiums go up.) The Act also organizes the market for small-business coverage onto an exchange—a SHOP exchange—which, by encouraging price transparency and making it easier to shop for coverage, may encourage small employers to offer health coverage to their workers.

    Finally, the ACA broadens a rule that makes the tax exclusion available only to those employers that don’t discriminate in favor of highly compensated workers. Previously, the rule only applied to self-insured employers, which allowed fully insured employers to offer health plans to only a slice of their workforce and still take advantage of the tax exclusion. Once the IRS issues rules to implement the non-discrimination rule, those fully insured firms will have a tax incentive to offer coverage to most of their workforce—including to lower-income workers who might otherwise have been reluctant to take a job with the firm.

    Putting all these and other ACA reforms together, the law should substantially reduce job lock. At the same time, we can confidently say it won’t eliminate it. For some people—particularly those with incomes too high for exchange subsidies and who also benefit substantially from the exclusion of employer-sponsored insurance premiums from income taxation—coverage through work will still be the best deal possible. And a lot of small firms still won’t offer coverage, which may lock those people into jobs that offer health coverage.

    Quantifying how much the ACA will remove job lock requires extrapolating the research findings to a post-ACA world. That involves some educated guesswork, but Linda Blumberg, Sabrina Corlette, and Kevin Lucia have taken a run at one aspect of the problem and based estimates of what the ACA will do for entrepreneurship and self-employment on some of the entrepreneurship-lock literature covered earlier in the series. They “make a rough estimate that the number of self-employed individuals will increase by about 1.5 million, a relative  increase of more than 11 percent.”

    That’s a step in the right direction. Even after the ACA, however, job lock will remain an issue. Indeed, it will remain an issue, at least to some extent, so long as employer-sponsored coverage is subject to different rules than coverage secured in the private market. When it comes to job lock, the ACA is just the latest chapter. It’s not the end of the story.

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  • Zombie Medicaid arguments

    The following is co-authored by Aaron and Austin.

    They just won’t die. Evidently, the House Republican budget is going to take another whack at Medicaid reform. Today, from the WaPo:

    Medicaid, which provides health coverage to low-income families, is the object of a sharply worded review. “Medicaid coverage has little effect on patients’ health,” the report says, adding that it imposes an “implicit tax on beneficiaries,” “crowds out private insurance” and “increases the likelihood of receiving welfare benefits.”

    There are studies documenting circumstances under which Medicaid can substantially “crowd out” private insurance. But, as has been explained on TIE, those circumstances don’t necessarily apply to the ACA. Moreover, many people at the low end of the socio-economic spectrum have the option of Medicaid or nothing. They make less than 138% of the poverty line. They aren’t able to afford insurance without massive subsidies.

    But, as always, we want to focus on the first statement, the one that declares that Medicaid doesn’t improve patients’ health. That’s not true.

    Does anyone really dispute that having health insurance is better than not having health insurance? Anyone who does should put their money where their mouth is. Mediaid isn’t welfare. You don’t get cash. It pays for health care if you need it. And, like all health insurance, it makes people healthier and saves lives. Lots of people say so.  Studies confirm this.

    A lot of the research that “shows Medicaid is bad” is flawed or misunderstood

    That research could be improved with the use of better research design, and methodologically stronger studies have shown that Medicaid is good for HIV mortalitychild healthinfant mortality, and more.

    Which brings us to the Oregon Health Study, an actual randomized controlled trial of Medicaid. We have both written on early results. We’ve also commented on the later results, which are the ones people often seize upon to discredit Medicaid. Again.

    People say that it does little to improve the health of people who have diabetes, who are at risk for heart disease, who have high cholesterol, or who have high blood pressure. There are real problems with those assertions. The Oregon study was not powered to detect improvements in those domains. We’re sorry, but it wasn’t. Here’s Austin explaining how it wasn’t set up to detect major changes in cholesterol or the Framingham Risk Score. Here’s Aaron talking about how it couldn’t detect changes in hypertension because the vast majority of people didn’t have it, and the assumptions that underlie arguments for being able to see a change aren’t on point. Same goes for diabetes.

    Here’s a summary of those issues.

    Why do people have insurance? Most people have it to protect themselves from financial ruin should they get really ill. But they also get it because it provides them the ability and incentive to get health care if they need it. Medicaid is about access. It’s just the first step in the chain of events that leads to better health and wellbeing. It’s not sufficient, but it is often necessary.

    Many who argue that insurance should immediately and significantly make a population healthier are glossing over these other issues. They also seem not to care that there are no good RCTs proving that private insurance (or Medicare) do this.

    There are lots of legitimate claims to make against Medicaid. It under-reimburses physicians, for instance, causing access problems in some areas and for some beneficiaries. (Guess what. Those problems are even worse for the uninsured, though.) But the natural response to saying docs don’t get paid enough would be to increase Medicaid funding to improve that. Gutting the program will do the opposite.

    And let’s live in the real world here. Cutting Medicaid will be hard and painful. It will have serious consequences.

    We look forward to a continuing and lively debate on how to reform the health care system. But declaring that health insurance in the form of Medicaid hurts people or “doesn’t work” ignores the real good that it does for so many people. (And, come on, health insurance is just pushing money around—it isn’t medicine or procedures.) Let’s listen to each other’s arguments and respond to them, instead of repeating talking points past each other.

    @aaronecarroll and @afrakt

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  • On CMMI and our tendency to “over-mythologize” RCTs

    The following is jointly authored by Adrianna and Austin.

    Randomized controlled trials are the gold standard in empirical research, but that doesn’t mean they’re the only standard worth paying attention to. If we only find value in RCTs, researchers are wasting an awful lot of time and headspace on alternate methods. So, that recent NYTimes hit piece on the Center for Medicare and Medicaid Innovation strikes us as troubling.

    Aaron covered some important technical points yesterday. RCTs can have fantastic internal validity—when they’re conducted well, we can say with relative certainty how treatment did or did not affect the study population—but our capacity to generalize those results is often limited. Dan Diamond has a piece worth reading, too:

    CMMI’s approach isn’t totally above reproach; the data that the center is seeing from its pilots could be confused by secular trends, like changes in population, practices, and so on. That’s why, Harvard’s Jha acknowledged, it’s important to design studies with a contemporary control group and statistical testing.

    But under CMMI’s ambitious charter, researchers are attempting to track a range of payment and delivery reforms. And it’s hard to think of how the center could use an RCT for some of its projects.

    For example, I asked a half-dozen different researchers to construct a hypothetical RCT to test how accountable care organizations would work. All were stumped.

    These aren’t clinical trials where you can pass out pills and placebos and carefully record individual health outcomes; CMMI is all about changing institutional practices. And just because health policy is closer in proximity to medicine (and its many RCTs) doesn’t actually make health policy more amenable to this kind of study than any other policy domain.

    Take ACOs as an example. What would we supposedly randomize: patients, physicians, or entire hospital systems? Can you imagine the backlash if Medicare tried to foist the program on randomized-but-disinterested providers? Patients would be tricky, too. The way ACOs work now, a Medicare beneficiary is passively “assigned” to an ACO if their physician belongs to that ACO. But that beneficiary isn’t required to limit their care to the ACO—an acknowledged wrinkle—and they may not actually realize that they’re taking part in a new delivery paradigm. (That, itself, is a source of natural (and imperfect) randomness that could be exploited.) According to one Health Affairs brief, critics “believe that patients should have a choice about participating in an arrangement that could reward providers for reducing services.” That sort of rhetoric hardly bodes well for implementing randomized trials in the health services delivery setting.

    Moreover, CMMI wasn’t designed to focus on cumbersome, time-consuming, and relatively static experiments. This is a good thing.

    These demonstrations aim to do two things to health services delivery: improve quality while maintaining or decreasing costs, or reduce costs while maintaining or improving quality. The emphasis is on “rapid-cycle” evaluation—collecting and analyzing data in near-real time, providing feedback on the programs. Far from wasting resources, CMMI is actually bound by law to modify or terminate demonstrations that have insufficient evidence of success.

    Well-conducted policy trials are important and we can learn a lot from them. That said, they don’t come easy or cheap, so they’re not very common. Nor are they immune to threats to internal validity from contamination/crossover/attrition, problems that can be addressed by—wait for it—observational study techniques.

    The Oregon Medicaid experiment was a terrific empirical exercise. It’s also paradigmatic of limitations that policy RCTs face—an entire methods course could probably be taught on it. In order to correct potential biases inherent in the design, the authors employed instrumental variables, an observational technique. A constrained sample size meant power problems. A focus on Portland restricts the results’ external validity. Scholars have (and will continue to) debate the study’s findings and their generalizability.

    Empirical science, and every technique thereof, is imperfect and incremental. But it’s the best we have. Insisting on only one research modality—the RCT—and overlooking the potential gains and relevance of other approaches is costly, both in dollars and applicable knowledge.

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  • Helping research inform legislation

    The following post is coauthored by Sarah Jane Reed, Sarah K. Emond, and Austin Frakt. Sarah Jane Reed serves as Program Director for the Institute for Clinical and Economic Review (ICER), where she oversees operations and strategic planning for the New England Comparative Effectiveness Public Advisory Council (CEPAC).  She holds a Masters of Science in International Health Policy from the London School of Economics. Sarah K. Emond is responsible for the strategic direction of ICER as its Chief Operating Officer, including the implementation of ICER’s research through its flagship initiatives, CEPAC and the California Technology Assessment Forum (CTAF).  Sarah has a Masters of Public Policy from the Heller School at Brandeis University.

    Disclaimers: The views expressed here are the authors’ own and do not necessarily represent the views or opinions of ICER or CEPAC. Austin serves as a member of CEPAC.

    Increasingly, lawmakers are influencing medical policy through patient notification laws and insurance coverage mandates. Such laws are intended to benefit patients, but their inflexibility can cause them to be out of step with sound interpretations of clinical research.

    Consider breast cancer screening. Thirteen states have recently passed breast density notification legislation requiring radiologists to inform women when their mammogram results reveal they have dense breast tissue, which may mask abnormalities. (Approximately 50% of women have dense breasts.) Dozens more states have similar legislation pending. Some states have gone further, requiring insurance coverage of supplemental ultrasound screening for women with dense breasts.

    The issue has also caught the attention of Congress, where similar breast density notification legislation has been introduced.

    Notably, no states with laws such as these and none of the legislation introduced in Congress stratify their requirements by patient risk. Yet sensitivity to risk may, in fact, be what’s best for patients.

    We often think more health information is better. However, notifying women at low risk of breast cancer of their density status may raise more questions than it helps answer. To make informed decisions about future screening options for women with dense breasts, patients and providers need to weigh the benefits and risks of additional screening. Does supplemental screening catch more cancers? Does it help save lives?

    The New England Comparative Effectiveness Public Advisory Council (CEPAC) recently addressed these questions. CEPAC is an independently recruited Council of 18 practicing physicians, methodologists and public representatives from all six New England states who meet in public to discuss and vote on evidence reviews covering test and treatment options in high-impact clinical areas.

    Through its process, CEPAC discusses how evidence can be interpreted on a regional basis, taking into consideration factors such as prevalence, workforce issues, and utilization patterns that are unique to New England but affect how evidence can best be applied in policy and practice. The body also accepts and considers public comments, thereby incorporating a diverse range of stakeholder views and concerns.

    (CEPAC, and its sister organization, the California Technology Assessment Forum, are the flagship implementation initiatives of the independent non-profit, the Institute for Clinical and Economic Review.)

    At its last meeting in December, CEPAC deliberated on the latest evidence on supplemental breast cancer screening for women with dense breasts. In weighing the benefits and risks of supplemental screening, CEPAC examined the evidence on additional cancers detected, reduced mortality rates and the risks of further testing, including the possibility of false alarms.

    A majority of CEPAC voted that for women at low-risk for breast cancer, the evidence does not demonstrate a benefit of supplemental screening. During the deliberation, Council members highlighted the dearth of evidence on long-term outcomes, such as mortality, for these women. However, in women at a moderate- or high-risk for cancer, CEPAC voted that the benefits of supplemental screening outweigh the risks, with the strongest evidence supporting additional screening in women at higher risk for breast cancer. You can read the full report here.

    A discussion at the December meeting of how the evidence should influence policy and practice focused on changes needed in guidelines, clinical practice workflow. A common refrain during this discussion was, “is the policy ahead of the science?” In other words, in light of CEPAC’s votes, are laws that mandate dense breast notification to low-risk women doing more harm than good? This touches on the divisive issue of just how much of medical care should be shaped by legislation. Though CEPAC cannot resolve that question, it is clearly relevant in the case of dense breast tissue notification, as well as others.

    As states in New England, and nationally, contemplate legislation mandating that women be notified if they have dense breasts, more attention should be paid by policymakers to expert, fair, transparent, and publicly deliberative assessments of the current state of the relevant evidence. There is a real danger of laws getting ahead of science. And, all good intentions aside, that is not to the benefit of patients.

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