…honor achievements that make people LAUGH, then THINK. The prizes are intended to celebrate the unusual, honor the imaginative — and spur people’s interest in science, medicine, and technology.
Some scientists seek the glory of the actual Nobel prizes, some want to change the world by coming up with an XPRIZE winning idea, but I’m pretty sure that if I was a scientist I’d be shooting to win an Ig Nobel Prize. I mean, the point of the awards is “to help people discover things that are surprising— so surprising that those things make people LAUGH, then THINK.” What’s better than that?
Something didn’t seem right to epidemiologist Eric Weinhandl when he glanced at an article published in the venerated Journal of the American Medical Association (JAMA) on a crisp fall evening in Minnesota. Eric is a smart guy – a native Minnesotan and a math major who fell in love with clinical quantitative database-driven research because he happened to work with a nephrologist early in his training. After finishing his doctorate in epidemiology, he cut his teeth working with the Chronic Disease Research Group, a division of the Hennepin Healthcare Research Institute that has held The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) contract for the United States Renal Data System Coordinating Center. The research group Eric worked for from 2004-2015 essentially organized the data generated from almost every dialysis patient in the United States. He didn’t just work with the data as an end-user, he helped maintain the largest, and most important database on chronic kidney disease in the United States.
For all these reasons this particular study published in JAMA that sought to examine the association between dialysis facility ownership and access to kidney transplantation piqued Eric’s interest. The provocative hypothesis is that for-profit dialysis centers are financially motivated to keep patients hooked to dialysis machines rather than refer them for kidney transplantation. A number of observational trials have tracked better outcomes in not-for-profit settings, so the theory wasn’t implausible, but mulling over the results more carefully, Eric noticed how large the effect sizes reported in the paper were. Specifically, the hazard ratios for for-profit vs. non-profit were 0.36 for being put on a waiting list, 0.5 for receiving a living donor kidney transplant, 0.44 for receiving a deceased donor kidney transplant. This roughly translates to patients being one-half to one-third as likely to get referred for and ultimately receiving a transplant. These are incredible numbers when you consider it can be major news when a study reports a hazard ratio of 0.9. Part of the reason one doesn’t usually see hazard ratios that are this large is because that signals an effect size that’s so obvious to the naked eye that it doesn’t require a trial. There’s a reason there are no trials on the utility of cauterizing an artery to stop bleeding during surgery.
But it really wasn’t the hazard ratios that first struck his eye. What stuck out were the reported event rates in the study. 1.9 million incident end-stage kidney disease patients in 17 years made sense. The exclusion of 90,000 patients who were wait-listed or received a kidney transplant before ever getting on dialysis, and 250,000 patients for not having any dialysis facility information left ~1.5 million patients for the primary analysis. The original paper listed 121,000 first wait-list events, 23,000 living donor transplants and ~50,000 deceased donor transplants. But the United Network for Organ Sharing (UNOS), an organization that manages the US organ transplantation system, reported 280,000 transplants during the same period.
The paper somehow was missing almost 210,000 transplants.
Sharing a hotel room, however, does not a marriage make. In order to get better digital health interventions to market faster, we need what I’m calling a Partnership for Innovators, Policymakers and Evidence-generators (PIPE). As someone who functions variously in the policy, tech and academic worlds, I believe PIPE needn’t be a dream.
As I was getting ready for bed last night a friend shared a tweet that immediately caught my attention.
The tweet was of a
paper that has just been published online, titled “Does physician gender
have a significant impact on first-pass success rate of emergency endotracheal
intubation?” and showed the abstract which began,
It is unknown whether female physicians can perform equivalently to male physicians with respect to emergency procedures.
Understandably, this got the backs up of a
lot of people, myself included. Who on earth thinks that’s a valid question to
be researching in this day and age? Are we really still having to battle
assumptions of female inferiority when it comes to things like this? Who on
earth gave this ethics approval, let alone got it though peer review?
I then took a deep breath and asked myself
why a respected journal, The American Journal of Emergency Medicine,
would publish such idiocy. Maybe there was something else going on. The best
way to find out is to read the paper so I got a copy and started reading. The
first thing that struck me was the author affiliations – both are associated
with hospitals in Seoul, South Korea. The second author had an online profile,
he is a Clinical Professor of Emergency Medicine. I couldn’t find the first
author anywhere which made me think they are probably quite early in their
career. The subject matter wasn’t something I could imagine a male early career
researcher being interested in so figured they are probably female (not knowing
Korean names I couldn’t work out if the name was feminine or masculine).
This month, we saw historic turnout at the polls for midterm elections with over 114 million ballots cast. One noteworthy observation regarding voter turnout is record rates of participation by younger voters aged between 18 to 29 years old. Around 31 percent of people aged 18 to 29 voted in the midterms this year, an increase from 21 percent in 2014, according to a day-after exit poll by Tufts University.
Surely their political engagement counters the criticism that millennials are disengaged and disconnected with society and demonstrates that millennials are fully engaged when issues are relevant to them, their friends, and their families. Why, then, do we not see the same level of passion, engagement and commitment when young adults are asked to consider their health and well-being?
I have had the privilege of being a member of the National Heart, Lung and Blood Institute-funded Coronary Artery Risk Development in Young Adults (CARDIA) study research team. In over 5,000 black and white adults who were initially enrolled when they were 18 to 30 years old and have now been followed for nearly 35 years, we have described the decades-long process by which heart disease develops. We were able to do this because, in the 1980s when these studies began, young adults could be reached at their home telephone numbers. When a university researcher called claiming to be funded by the government, there was a greater degree of trust.
Unfortunately, that openness and that trust has eroded, particularly in younger adults and those who may feel marginalized from our society for any number of valid reasons. However, the results—unanswered phone calls from researchers, no-shows at the research clinic and the absence of an entire group of adults today from research studies, looks like disengagement. Disengagement is a very real public health crisis with consequences that are as dire as any political crisis. Continue reading…
In my three-part series on why we know so little about ACOs, I presented three arguments:
We have no useful information on what ACOs do for patients;
that’s because the definition of “ACO” is not a definition but an expression of hope; and
the ACO’s useless definition is due to dysfunctional habits of thought within the managed care movement that have spread throughout the health policy community.
Judging from the comments from THCB readers, there is no disagreement about points 1 and 3. With one exception (David Introcaso), no one took issue with point 2 either. Introcaso agreed with point 1 (we have no useful information on ACOs), but he argued that the ACO has been well defined by CMS regulations, and CMS, not the amorphous definition of “ACO,” is the reason researchers have failed to produce useful information on ACOs.
Another reply by Michael Millenson did not challenge any of the three points I made. Millenson’s point was that people outside the managed care movement use manipulative labels so what’s the problem?
I’ll reply first to Introcaso’s post, and then Millenson’s. I’ll close with a plea for more focus on specific solutions to specific problems and less tolerance for the unnecessarily abstract diagnoses and prescriptions (such as ACOs) celebrated today by far too many health policy analysts.
Summary of Introcaso’s comment and my response
I want to state at the outset I agree wholeheartedly with Introcaso’s statement that something is very wrong at CMS. I don’t agree with his rationale, but his characterization of CMS as an obfuscator is correct.
Every once in awhile on the wards, one of the attending physicians will approach me and ask me to perform a literature review on a particular clinical question. It might be a question like “What does the evidence say about how long should Bactrim should be given for a UTI?” or “Which is more effective in the management of atrial fibrillation, rate control or rhythm control?” A chill usually runs down my spine, like that feeling one gets when a cop siren wails from behind while one is driving. But thankfully, summarizing what we know about a subject is actually a pretty formulaic exercise, involving a PubMed search followed by an evaluation of the various studies with consideration for generalizability, bias, and confounding.
A more interesting question, in my opinion, is to ask why we do not know what we do not know. To delve into is a question requires some understanding of how research is conducted, and it has implications for how clinicians make decisions with their patients. Below, I hope to provide some insights into the ways in which clinical research is limited. In doing so, I hope to illustrate why some topics we know less about, and why some questions are perhaps even unknowable.Continue reading…
The competition to get into medical school is fierce. The Association of American Medical Colleges just announced that this year, nearly 50,000 students applied for just over 20,000 positions at the nation’s 141 MD-granting schools – a record. But medical schools do not have a monopoly on selectivity. The average student applies to approximately 15 schools, and many are accepted by more than one. Students attempting to sort out where to apply and which admission offer to accept face a big challenge, and they often look for guidance to medical school rankings.
Among the organizations that rank medical schools, perhaps the best-known is US News and World Report (USNWR). It ranks the nation’s most prestigious schools using the assessments of deans and chairs (20%), assessments by residency program directors (20%), research activity (grant dollars received, 30%), student selectivity (difficulty of gaining admission, 20%), and faculty resources (10%). Based on these methods, the top three schools are Harvard, Stanford, and Johns Hopkins.
Rankings seem important, but do they tell applicants what they really need to know? I recently sat down with a group of a dozen fourth-year medical students who represent a broad range of undergraduate backgrounds and medical specialty interests. I posed this question: How important are medical school rankings, and are there any other factors you wish you had paid more attention to when you chose which school to attend?
A couple of weeks ago, President Obama launched a new open data policy (pdf) for the federal government. Declaring that, “…information is a valuable asset that is multiplied when it is shared,” the Administration’s new policy empowers federal agencies to promote an environment in which shareable data are maximally and responsibly accessible. The policy supports broad access to government data in order to promote entrepreneurship, innovation, and scientific discovery.
If the White House needed an example of the power of data sharing, it could point to the Psychiatric Genomics Consortium (PGC). The PGC began in 2007 and now boasts 123,000 samples from people with a diagnosis of schizophrenia, bipolar disorder, ADHD, or autism and 80,000 controls collected by over 300 scientists from 80 institutions in 20 countries. This consortium is the largest collaboration in the history of psychiatry.
More important than the size of this mega-consortium is its success. There are perhaps three million common variants in the human genome. Amidst so much variation, it takes a large sample to find a statistically significant genetic signal associated with disease. Showing a kind of “selfish altruism,” scientists began to realize that by pooling data, combining computing efforts, and sharing ideas, they could detect the signals that had been obscured because of lack of statistical power. In 2011, with 9,000 cases, the PGC was able to identify 5 genetic variants associated with schizophrenia. In 2012, with 14,000 cases, they discovered 22 significant genetic variants. Today, with over 30,000 cases, over 100 genetic variants are significant. None of these alone are likely to be genetic causes for schizophrenia, but they define the architecture of risk and collectively could be useful for identifying the biological pathways that contribute to the illness.
We are seeing a similar culture change in neuroimaging. The Human Connectome Project is scanning 1,200 healthy volunteers with state of the art technology to define variation in the brain’s wiring. The imaging data, cognitive data, and de-identified demographic data on each volunteer are available, along with a workbench of web-based analytical tools, so that qualified researchers can obtain access and interrogate one of the largest imaging data sets anywhere. How exciting to think that a curious scientist with a good question can now explore a treasure trove of human brain imaging data—and possibly uncover an important aspect of brain organization—without ever doing a scan.
As the new year started, all kinds of predictions come to our attention, mostly of things that will enter our lives.
How about things that will dissolve from our lives ?
Of all species that became extinct the Dodo has become sort of synonymous with extinction. To “go the way the Dodo”means something is headed to go out of existence. (picture and quote source The Smithsonian)
So this goes not only for species but also stuff we use or things we do.
You might want to have a look at the extinction timeline and find things you did, ‘some’ time ago, and don’t anymore.
But what about health care? What will vanish, will the doctor due to all of this new technology disappear, or the nurse? Will we no longer go to a hospital or to the doctors office? I don’t think so.
We still will be needing professionals with compassion and care. However shift is happening and some things will start getting obsolete. In the following I am in no way going to try to be exhaustive, so feel free to add in comments or thought on what you think will disrupt from our lives in terms of health(care).