Objective: To examine the effect of cost, a traditionally “inactive” trait of intervention, as contributor to the response to therapeutic interventions.
Methods: We conducted a prospective double-blind study in 12 patients with moderate to severe Parkinson disease and motor fluctuations (mean age 62.4 ± 7.9 years; mean disease duration 11 ± 6 years) who were randomized to a “cheap” or “expensive” subcutaneous “novel injectable dopamine agonist” placebo (normal saline). Patients were crossed over to the alternate arm approximately 4 hours later. Blinded motor assessments in the “practically defined off” state, before and after each intervention, included the Unified Parkinson’s Disease Rating Scale motor subscale, the Purdue Pegboard Test, and a tapping task. Measurements of brain activity were performed using a feedback-based visual-motor associative learning functional MRI task. Order effect was examined using stratified analysis.
Researchers took a bunch of patients and gave them injectable saline (placebo). They told half of them they were getting a “cheap” new drug and the other half an “expensive” new drug. Then they measured them on a number of physical tasks. Four hours later, they reversed the groups.
Everyone got better with the placebo injection. That’s placebo effect number one. But those who got the expensive drug got more of an effect. In fact, quoting the study, “Expensive placebo significantly improved motor function and decreased brain activation in a direction and magnitude comparable to, albeit less than, levodopa”.
I’ve read way too much reporting, and way too many tweets, parsing how the latest measles outbreaks happened. Some are missing the point. This is how outbreaks of a disease that isn’t endemic to the United States occur:
Someone travelling/living abroad contracts the disease and comes to the US
Other people who are susceptible to the disease come into contact with them here at home
What we CAN do is try to prevent other people here from getting the disease. That’s where vaccination comes in. If everyone is vaccinated against measles, for instance, than – yes – a very small number of people might contract the disease when (2) occurs, but the vast, vast majority of people who come into contact with the infected person will be fine.
The system breaks down, and outbreaks occur, when more people are susceptible. Everyone, for instance, is susceptible to Ebola at a certain point in the illness. So we have to be careful to quarantine people who are infected when they are sick. But Ebola is relatively hard to catch. It has an R nought of 2, meaning that an infected individual might infect, on average, 2 others. But measles has an R nought of 18. It’s one of the most infectious pathogens around.
Quarantining is difficult, if not impossible. The virus is unbelievable hardy and easy to catch. So the absolutely, positively best thing you can do it to be vaccinated. Period.
I should point out that it also doesn’t matter to the outbreak why people remain unvaccinated and susceptible. It can be because of religious reasons. It can be because of irrational fear. It can be because they’re “hippies”. I don’t care – the outbreak is the same. (1) is going to happen. But if everyone was vaccinated, then the infected person wouldn’t make national news because it would be very hard for it to go much beyond themselves.
The important part of stopping an outbreak of measles isn’t (1). That’s going to happen every once in a while. The important part is that too many people in the United States remain unvaccinated and susceptible to measles for any number of reasons. That’s what’s “causing” the outbreak. That’s what we need to focus on. Full stop.
If the Supreme Court rules against the government in King v. Burwell, what would happen next? Many states without their own exchanges may not be able to move quickly enough to establish exchanges for 2016, as I explained at the New England Journal of Medicine. But what about Congress? It has the power to fix the problem with a stroke of the pen. Would it do so?
You could try to tell an optimistic story. The Republicans in Congress will come under considerable pressure from constituents who’ve lost their tax credits. And, in exchange for a fix, the White House might cave to some Republican demands: axing the employer mandate, lifting the medical-device tax, relaxing some exchange rules, or whatever. Maybe there’s room for a deal.
But any suggestion Congress will just clean up the mess should be taken with a huge grain of salt. As Randy Barnett has said, the Supreme Court is more likely to rule against the government if it believes that Congress has a replacement bill in hand. So it’s no surprise that congressional supporters of the lawsuit claim that they’re “working on possible legislation to respond to the verdict, along with the relevant Senate committees, the Republican Policy Committee, and House Ways and Means Chairman Rep. Paul Ryan.”
It ain’t gonna happen. Ace reporting from Sahil Kapur over at Talking Points Memo gives the lie to the view that congressional Republicans have any kind of plan in the works. You should read the whole thing, but here’s a taste:
[C]onversations with more than a dozen GOP lawmakers and aides indicate that the party is nowhere close to a solution. Outside health policy experts consulted by the Republicans are also at odds on how the party should respond.
The party that has failed to unify behind an alternative to Obamacare for many years now has five months to reach an agreement. It’s an unenviable predicament, especially for the congressional Republicans leading the effort to devise a response — all of whom hail from states that could lose their subsidies. …
One big challenge, the Republican aide said, is that a GOP plan would be unlikely to cover as many people, making it an easy piñata for Democrats to pound. “That’s the brutal truth. We have a problem with that for very specific reasons. We don’t have good responses,” the aide said. “Show me the constituent in a town hall meeting who you can tell it’s OK for them to lose their health insurance.”
It gets worse, as Loren Adler noted on Twitter: after King, CBO’s new baseline would be a world without tax credits in 34 states. Any fix would be sure to cost a lot, complicating the politics for the Republican Congress. Combine that with the fact that negotiations over a fix would be taking place against the backdrop of a presidential primary, and it’s really hard to see how Congress reaches a grand compromise with the White House.
The posturing around a post-King fix reminded me of this terrific column from Ezra Klein, where he explained “the central problem for conservative health reformers” who are pushing to get the Republican Party to coalesce around an alternative to the ACA:
[B]ecause Republicans don’t care that much about health reform and because so much of what health reform demands offends conservative sensibilities or constituencies, the party doesn’t want to make the sacrifices necessary to unite behind an alternative to Obamacare, much less actually pass and implement it.
Which is to say, if the government loses King, Congress probably won’t lift a finger to help pick up the pieces.
The association between policy changes and subsequent outcomes is often evaluated by pre-post assessments. Outcomes after implementation are compared with those before. This design is valid only if there are no underlying time-dependent trends in outcomes unrelated to the policy change. If clinical outcomes were already improving before the policy, then using a pre-post study would lead to the erroneous conclusion that the policy was associated with better outcomes.
The difference-in-differences study design addresses this problem by using a comparison group that is experiencing the same trends but is not exposed to the policy change. Outcomes after and before the policy are compared between the study group and the comparison group without the exposure (group A) and the study group with the exposure (group B), which allows the investigator to subtract out the background changes in outcomes. Two differences in outcomes are important: the difference after vs before the policy change in the group exposed to the policy (B2 −B1, [see] Figure [click to enlarge]) and the difference after vs before the date of the policy change in the unexposed group (A2 −A1). The change in outcomes that are related to implementation of the policy beyond background trends can then be estimated from the difference-in-differences analysis as follows: (B2 −B1) −(A2 −A1). If there is no relationship between policy implementation and subsequent outcomes, then the difference-in-differences estimate is equal to 0 (Figure, A). In contrast, if the policy is associated with beneficial changes, then the outcomes following implementation will improve to a greater extent in the exposed group. This will be shown by the difference-in-differences estimate (Figure, B).
As I posted previously, many studies have pointed to technology as a principal driver of health care spending growth. Those studies also credit third party payment (i.e., insurance) and income for some of the blame too. More interesting, coverage, income, and technology interact; their intersection is explored (by me) in a new post on the AcademyHealth blog.
Building on the ambitious Combatting Antibiotic-Resistant Bacteria (CARB) process, the President’s budget request this week called for dramatically increased funding, $1.2 billion. This funding request is at the correct magnitude and demonstrates appropriate balance between various priorities. While Congress will surely have views on the specifics, I hope for broad consensus that very bold action must be taken along the general lines described by the President. Paying $1.2 billion dollars a year as an “insurance premium” to avoid the end of antibiotics is a critical policy priority. I suspect that every scientist and policy wonk working on these issues would agree with this statement.
This week the House Energy & Commerce Committee released a discussion draft of legislation under the 21st Century Cures Initiative (full text here). The proposals fall short of what we need. Solving this problem will require spending real money.
Section 1061 permits early release of antibiotics with less data on safety and efficacy, together with a more restrictive label. This provision is no surprise and has been in the works for a while (prior versions here and here). While it will undoubtably get antibiotics to the market more quickly, that will not be a panacea for antibiotic innovation unless we fix reimbursement. We will get some drugs several months earlier with thinner data packages on safety and efficacy; as a result, these drugs will not sell well until better data is available. Innovation will not be rewarded.
Section 1062 updates how we test for antibiotic susceptibility and how that is communicated on the drug label. My concern is whether this provision would further encourage off-label use of antibiotics. Antibiotic should be used with better evidence of safety and effectiveness.
Section 1063 creates “wildcard exclusivity,” a radical and controversial departure from our 226-year history with US patent law. The Constitution (Art. I, sec. 8, cl.8) gives Congress the authority to create IP:
To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries.
The “exclusive right” is tied to the invention itself (“their respective writings and discoveries”). If you discover a new drug, you get a patent on that drug.
Section 1063 breaks from the Constitutional standard and historical practice by offering a 12 month period of exclusivity on a completely unrelated drug. Create a new antibiotic, and the reward is a fully transferrable 12 months of exclusivity that could be given to a drug for cancer, heart disease or Hepatitis C. This reward is very indirect and inefficient, and can be quite costly as it will protect billions of dollars of drug sales from generic entry. The provision also calls for “donations” to the NIH and patient access programs, but understand that all of these funds come from our health insurance system through higher drug prices when generic drugs are delayed. It will also be very difficult to control this idea. If antibiotics are worthy of wildcard exclusivity, why not cancer, Alzheimer’s and every orphan drug?
Data supports the need for billion-dollar incentives for antibiotics; wildcard exclusivity is just a poor way to achieve that goal.
Finally, Section 1064 boosts the hospital DRG for the cost of new antibiotic drugs. But this fix only helps inpatient antibiotics (typically IV drugs) and we also need new oral antibiotics. While it increases payments to hospitals, there is no guarantee of any increase to companies unless they market to hospitals and convince them to pay more. Finally, this does nothing for reimbursement for infection control, diagnostics, vaccines, and antibiotic stewardship. Medicare should pay for those things too: we know that infection control has been essential in bending the curve on MRSA.
The US Congress and the Administration have an opportunity to work together to truly reform the broken business model for antibiotics. Let’s make policy based on the best available evidence.
As I’ve discussed before, insurance companies are very, very good at what they do. Even with guaranteed issue and community ratings, formularies can be used to deter sick patients and attract healthy ones. Go learn more about this in my latest piece over at the AcademyHealth blog.
The following is an announcement from AcademyHealth about a student competition for which I will be a judge.
AcademyHealth along with its Translation and Communication Interest Group will formally kick-off the second Annual Student Competition, “Presenting Research in Compelling Ways: A Student Competition” with a Google Hangout on Wednesday, January 28 from 4 – 4:30 p.m. EST. This competition will be held during our Annual Research Meeting (ARM) in Minneapolis, June 14-16, 2015. Students will present on the same prominent and recently published HSR study. Presenters will be given a time limit and creativity will be a focus. There will also be cash prizes awarded at the ARM to the top three presentations. Students may use various creative presentation styles such as PechaKucha, PowerPoint, Presentoon, Prezi, “Shark Tank” and more.
Student members and Student Chapters of AcademyHealth are encouraged to attend. At the conclusion of the Google Hangout, participants will be able to:
Understand the format and guidelines of the Student Competition
Learn helpful hints and best practices from last year’s competition winner
Hear from Translation and Communication IG Member and the competition’s moderator, Dr. Felicia Mebane, regarding various options of presentation styles
As an added bonus, those who participate in the Hangout will be the first to learn which published HSR study has been selected for this year’s competition piece. You won’t want to miss this great opportunity to get a leg up on your fellow competitors! The link to the Hangout may be found by clicking here. Please confirm your attendance by accepting the invitation. Feel free to share the invite with other student members of AcademyHealth who might be interested. (Note: A Gmail or Google+ account is recommended as Google users can post questions and RSVP before the event.)
If you’re not an AcademyHealth member, students may join AcademyHealth for $40/year and take advantage of free educational webinars, learning development and networking opportunities, competitions like this one and more. Contact email@example.com for more information.
In my junior year at boarding school, I went for weeks wearing nothing but black, had a brief but intense involvement with methedrine, disappointed or enraged everyone who cared about me, and was nearly expelled. Otherwise I had a great time.
What I remember most clearly, though, was an inescapable self-loathing and a desire to punish myself that emanated, it seemed, from the center of each cell of my body. I was, in short, clinically depressed. Depression is the anti-matter to hope. If you have never been depressed, you likely do not recognize how your everyday (healthy) experience is fueled by positive expectations. Depression is the starvation of hope, experienced as pain. Or as William Styron put it:
In depression… faith in deliverance, in ultimate restoration, is absent. The pain is unrelenting, and what makes the condition intolerable is the foreknowledge that no remedy will come- not in a day, an hour, a month, or a minute. If there is mild relief, one knows that it is only temporary; more pain will follow. It is hopelessness even more than pain that crushes the soul.
Depression is one of the major health problems of adolescence. It’s important, therefore, to get some precision on how large this problem is. Shelli Avenevoli, Joel Swendsen, Jian-Ping He, Marcy Burstein, and Kathleen Merikangas provide data in the Journal of the American Academy of Child and Adolescent Psychiatry. They studied a sample of over ten thousand adolescents aged 13 to 18 years using the Composite International Diagnostic Interview, a widely respected mental health epidemiological survey instrument.
Lifetime and 12-month prevalence of MDD were 11.0% and 7.5%, respectively. The corresponding rates of severe MDD were 3.0% and 2.3%. The prevalence of MDD increased significantly across adolescence, with markedly greater increases among females than among males. Most cases of MDD were associated with psychiatric comorbidity and severe role impairment, and a substantial minority reported suicidality.
Depression is more common among girls (10.7% of adolescents) than boys (4.6%). The prevalence of depression increases with age during adolescence and appears to hit a peak in the later teens.
Teens depressed during the last 12 months.
Depressed kids are substantially more likely than non-depressed kids to have other mental health problems, including substance use disorders. Among severely depressed kids, 21% will have had a suicide attempt in the past year. Adolescent depression is not always a precursor to adult depression, but many adults with depression had their first experience in adolescence.
These data do not tell us whether the rates of depression among adolescents are changing and I discourage talk about an epidemic. Similarly, you should not attribute the prevalence of adolescent depression to any recent cause, such as cyberbullying. Adolescent depression is worldwide and the risks are built into the physiology of puberty and the stress of the transition to adulthood.
The key finding from this paper is simply that significant numbers of kids are experiencing intense suffering from a serious mental illness. In later posts, I plan to discuss problems in identifying these kids and getting them effective care.
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. TheAmerican 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; haslong 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 asthmaattacks 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-sharingdiscourages 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 harness aplacebo 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.