Archive for the ‘David Gorski’ Category

Guest Blog: Anti-vaccine propaganda in The Baltimore Sun

David Gorski
Friday, July 15th, 2011

David H. Gorski, MD, PhD, FACS is a surgical oncologist at the Barbara Ann Karmanos Cancer Institute specializing in breast cancer surgery, where he also serves as the American College of Surgeons Committee on Cancer Liaison Physician as well as an Associate Professor of Surgery and member of the faculty of the Graduate Program in Cancer Biology at Wayne State University.  He is the managing editor of the Science-Based Medicine blog, whose only goal is to promote high standards of science in medicine.  A collection of his posts can be found here.

The hypothesis that vaccines cause autism has been about as thoroughly falsified through research as any health hypothesis can be. Even if, by bending over backward into a back-breaking contortionist pose to be “open-minded”, some people will concede that there’s still a bit of room for reasonable doubt about whether there is no link between vaccines and autism in “susceptible” populations, there is no room for reasonable doubt left over whether vaccines caused the so-called “autism-epidemic” of the last two decades. They did not. Similarly, the mercury-containing preservative thimerosal, which used to be in several childhood vaccines until the end of 2001, when thimerosal was removed from all but some flu vaccines, has been about as cleared of being a cause of autism as it is possible for a substance to be. Basically, if thimerosal-containing vaccines were a cause of autism, we would have expected to see a decrease in autism prevalence beginning three to five years after the removal of thimerosal. Epidemiological studies have failed to find such a decline and have also failed to find evidence of correlation. I realize that anti-vaccine activists argue that there are still trace amounts of thimerosal in some vaccines, but, even so, thimerosal exposure in children fell almost overnight to levels lower than the 1980s, which was before the beginning of the “autism epidemic.” At the very least, one would expect autism rates to fall back to 1980s levels if thimerosal in vaccines were a driving force behind this “epidemic.” They haven’t. Quite the contrary, they’ve continued to climb.

So why does the manufactroversy that vaccines cause autism persist? There is no longer a scientific controversy; by and large, the question has been asked and answered. Vaccines do not cause autism, as far as we can detect. True, it’s impossible to completely prove a negative hypothesis, but if there is any way that vaccines do cause autism, it’s at a level below the ability of large epidemiological studies with tens or even hundreds of thousands of children to detect. Yet the fear persists.

One reason is that it’s very hard to eradicate a false belief, once entrenched. I’ve discussed many times how difficult it is to change people’s minds, as motivated reasoning leads them to seek confirming evidence and discount all else. Disconfirming evidence can even lead people to harden their beliefs even more. In particular, the hardcore anti-vaccine activists who persist in spreading the vaccine-autism myth have an interest and motivation in this mythology at least as potent as the interest pharmaceutical companies have in defending vaccines—more so, arguably, given the emotional attachment people have for their children. After all, all pharmaceutical companies are interested in, according to this mythology, is profit. If a parent, correctly or incorrectly, somehow comes to believe that something or someone has hurt his or her child, it is among the most potent motivations known to do something about it.

Another reason is that the concept has become entrenched in our culture—or at least parts of our culture—to the point where it appears regularly in the media, thus reinforcing the idea among those who don’t pay attention to the issue or those who do but haven’t decided if they believe that vaccines cause autism that maybe there is something to fear. Maybe there is still a controversy. A perfect example appeared in The Baltimore Sun over the weekend entitled We don’t know enough about childhood vaccines and subtitled Researcher asks: Are 36 doses of vaccine by age 2 too much, too little, or just right? I contend that the editors of The Baltimore Sun, by publishing this anti-vaccine propaganda, which would have been at home on the websites of the anti-vaccine blog Age of Autism or on the website of anti-vaccine groups SafeMinds, Generation Rescue, the International Medical Council on Vaccination or the National Vaccine Information Center (NVIC). Examining this article, written by Margaret Dunkle, described as a “senior research scientist at the Department of Health Policy at George Washington University and director of the Early Identification and Intervention Collaborative for Los Angeles County” and as having “a family member who is vaccine-injured,” is what I would consider a “teachable moment” in analyzing the tactics of the anti-vaccine movement.

Anti-vaccine propaganda in a major newspaper

Dunkle’s article begins rather oddly. At least, it could easily strike someone as odd if he isn’t familiar with the rhetorical techniques and bad science favored by propagandists like Dunkle:

The topics of vaccines and vaccine safety spark emotional outbursts at scientific meetings and family dinner tables alike. But many of these debates are remarkably fact-free. Surprisingly few people — not just concerned parents but also doctors, policy makers and even immunization experts — can answer this seemingly simple question: How many immunizations does the federal government recommend for every child during the first two years of life?

The answer is important because most states, including Maryland, faithfully follow the recommendations of the federal Centers for Disease Control and Prevention, codifying CDC guidelines into requirements for children to enroll in school, kindergarten, preschool and child care.

The irony of someone like Dunkle referring to debates over vaccines being “fact-free” is left for the amusement of the reader. Consider the old adage that everyone is entitled to his or her opinions but no one is entitled to his or her own facts. In the case of determining the number of vaccines that the Centers for Disease Control and Prevention recommends for children before age two, the anti-vaccine movement is a master at counting vaccines in such a manner as to make the CDC-recommended vaccine schedule appear to contain as large and scary a number of vaccines as possible. It does this by counting multivalent vaccines, such as the measles-mumps-rubella vaccine (MMR), as individual components, thus multiplying one dose or shot into three vaccines. If you count it up the right way, you can get a total of 36 vaccines, a number and anti-vaccine talking point that appears to date back to a full page ad run in USA Today by Generation Rescue three years ago:

Notice a number of fallacies all rolled up into one big poster-sized ad: “Too many too soon”; the “toxin” gambit; and confusing correlation with causation. Around the same time, Jenny McCarthy started showing up on Larry King Live! using this particular talking point. Prometheus once pointed out that he couldn’t find a way to come up with a total of 36 and thought that Generation Rescue screwed up. Be that as it may, Dunkle continues:

The critical number is how many doses of vaccine a child receives. Why? If a vaccine is strong enough to confer immunity against a disease, it is important enough to count separately.

No, the critical number is not how many doses of a vaccine a child receives. It is the number of antigens to which a child is exposed, and the number of antigens to which children are exposed is much lower than it was 25 years ago as whole cell-derived vaccines have been replaced with acellular vaccines, whatever the “true” number of vaccines as counted by Dunkle is. That’s what really matters.

Going back to find up that old chestnut of an ad on Archive.org, it occurred to me that Dunkle is a bit behind the times. Since that ad ran over three years ago, anti-vaccine groups have managed to find ways to inflate the seeming number of vaccines even higher than 36, such as 48. I tried to find the link, but I’ve even seen a blog post where the blogger was trying to claim that babies get over 60 vaccines before age two. Dunkle needs to get with the program. On the other hand, the number 36 appears to be the “official” talking point number used by most anti-vaccine groups since around 2008; so I’m not surprised that she chose it. I’m also not surprised that she’s bought into the “too many too soon” mantra that groups like Generation Rescue began promoting—surprise, surprise!—about three years ago, when Jenny McCarthy and her then-boyfriend Jim Carrey led a “march on Washington”-style rally under the banner of “Green Our Vaccines” to protest the vaccine schedule.

So where is Dunkle going with all this? Easy. She uses her complaint about the “36 vaccines” or “36 shots” as a prelude to citing a paper that claims to have found that there is a correlation between vaccine uptake and the prevalence of autism spectrum disorders:

A new Journal of Toxicology and Environmental Health study reports that the higher the proportion of infants and toddlers receiving recommended vaccines, the higher the state’s rate of children diagnosed with autism or speech-language problems just a few years later. This analysis is sure to rekindle the debate about vaccine safety.

Not really. It takes solid evidence and a quality scientific paper analyzed well to rekindle a debate. The paper to which Dunkle refers is anything but. Actually, it’s a truly execrable bit of data-mining by Gayle DeLong published earlier this year and entitled A Positive Association found between Autism Prevalence and Childhood Vaccination uptake across the U.S. Population. Prometheus and Sullivan have already had a go at this wretched bit of autism “science,” but I’ll comment as well, given that Dunkle used the paper as ammunition in her op-ed piece, that other anti-vaccine activists point to it as “proof” that “too many too soon” is a valid concern, and that DeLong’s paper has not yet been discussed here on SBM. In this case, better a month or two late than never, I say.

How bad can an epidemiology paper be?

Dunkle is actually a bit late promoting this study, as it’s been floating around the anti-vaccine blog underground for well over a month now. One thing that is very apparent from the article and DeLong’s analysis: DeLong is not a scientist. A quick perusal of almighty Google reveals that she is, rather, a faculty member in the Department of Economics and Finance in the Zicklin School of Business, Baruch College/City University of New York. As always, the fact that DeLong is clearly not a scientist doesn’t necessarily mean that she is wrong. Rather, her poor study design, clear lack of some very basic background knowledge about her study subject, and biased presentation are far more likely to indicate that she is wrong. Even so, somehow DeLong managed to get her manuscript accepted to the Journal of Toxicology and Environmental Health, a journal I only vaguely remember having heard of.

I can’t resist pointing out a bit of misinformation right in the abstract. For example, the reason for the rapid rise of autism in the U.S. is not, as DeLong characterizes it, really much of a “mystery.” It’s very likely the result of diagnostic substitution in the wake of the broadening of the diagnostic criteria for autism and autism spectrum disorders that occurred in the early to mid-1990s, as Paul Shattuck has shown and Steve Novella has discussed. Yes, there may have been a genuine increase in autism prevalence over the last 20 years (although even that is debatable), but, if such an increase has occurred, it appears to be so small that it’s not even clear that there was one.

DeLong carries on this sort of misinformation right in the text. Here’s one thing you should know about reading scientific papers. The introduction is where the authors try to “frame” the issue that led them to do the research and the hypothesis that derives from that issue in the most favorable way possible. To the knowledgeable reader or reviewer, a botched up introduction section that misrepresents the scientific consensus and the issues is almost always a sure sign that the science that follows will either (1) not support the authors’ hypothesis; (2) be of such poor quality that it doesn’t really support or refute any hypothesis at all; or even (3) cast doubt upon the authors’ hypothesis, even though the authors spin it otherwise. In this paper, for instance, DeLong argues that there “are several reasons why vaccines may trigger autism,” after which she lists a veritable laundry list of long-discredited anti-vaccine notions, bringing up (naturally!) old anti-vaccine bogeymen like mercury and aluminum. Nowhere is it mentioned that DeLong’s view is not the scientific consensus. Only one side is presented, the anti-vaccine side.

Another way you can recognize a bad introduction to a research paper is by the quality of the research that is cited. In DeLong’s case, the research cited is awful indeed, with citations to papers by SafeMinds and Age of Autism stalwart Mark Blaxill, the anti-vaccine father-son tag team of Mark and David Geier (otherwise known as the doctor with a suspended license and his son busted for practicing medicine without a license), Russell Blaylock (who counts HIV/AIDS denialism, antivax, and many other forms of pseudoscience as part of his repertoire), and Laura Hewitson, whose “monkey business” research was also published in the very same journal in which DeLong’s study appears. There’s more, but these are just some of the examples, perhaps the most egregious of which is a reference by anti-vaccine homeopath James Compton Burnett writing in 1884.

Then there’s the design of the study itself. First (and most egregious), there’s the issue of why DeLong combined speech or language impairments (SLIs) with autism diagnoses to do her analysis. DeLong appears to have used statistics that states are required to maintain under federal legislation, the Individuals with Disabilities Education Act (IDEA). Under IDEA, every school is required to provide data on children who have an Individual Education Plan (IEP), including the students’ primary classification. As Liz Ditz pointed out, IDEA classifications are not medical diagnoses. A child with a diagnosis of autism under IDEA may or may not actually have autism. Also, children with an IDEA classification of SLI are most commonly children with problems in fluency, articulation, or voice, not autism. Examples include apraxia and aphasias, voice disorders, stuttering, and language-based learning disabilities. It’s not for nothing that James Laidler characterized IDEA data as not being a reliable measure that can be used to track autism prevalence accurately.

Naturally, DeLong cites papers to justify lumping together SLIs and autism for purposes of her analysis. None of them support her hypothesis, and her citing them demonstrates that she does not have even a very basic understanding of her subject. For example, she confuses SLI (speech or language impairment) with SLI (specific language impairment). True, this nomenclature can be confusing, but if you’re going to write a scientific paper involving these topics, you need to know the language. DeLong clearly doesn’t know the language. At the very least, that this remained in her paper is a massive failure of peer review on the part of the journal, whose peer reviewers should have picked up on this. Finally, one of the three papers DeLong cited was apparently an error, but she later stated that she had meant to cite other papers, neither of which actually support her decision to lump SLIs together with autism either.

I’m left with the not-so-sneaking suspicion that the only reason that SLIs were lumped together with autism and ASDs for purposes of correlation with the percentage of children in each state receiving their full vaccine schedule is because the numbers somehow worked out the way that DeLong wanted them to. Otherwise, DeLong’s looking at mostly unrelated phenomena that have some degree of overlap. Certainly there appears to be no valid scientific or medical justification for combining the data from the IDEA classifications of SLI and autism.

Then there’s the methodology chosen for trying to find correlations, described here:

Children who are vaccinated at age 2 years may not develop autism until they are older. To determine the prevalence of autism for a specific cohort of children, the vaccination data from when the children were 2 years old is compared with autism prevalence when they are 8 years old. The relevant vaccination data for children who were 8 years old in 2001 are those from 1995, when the children were 2 years old. For children who turned 8 years old in 2002, the relevant vaccination data are from 1996, and so on. The earliest available data–vaccination data from 1995–were matched with autism prevalence up to 2007.

Besides DeLong’s having fallen for the ecological fallacy (group level comparisons rather than individual-level comparisons), she doesn’t provide much in the way of a good justification for why she chose ages 2 and 8 as their vaccine time point and prevalence time point. Then there’s the issue of confounders. DeLong tried to control for ethnicity, but in explicably she used the CDC’s National Immunization Survey rather than, say, U.S. Census data to derive ethnicity figures. Other potential confounders examined included family income, other disabilities, and the number of pediatricians in each state. Of course, states range in size from small to very large, and it can easily be argued that state level data are not “fine” enough to be used for this purpose. After all, many states are quite large, with huge differences in urbanicity. Think, for instance, California, with several large cities separated by huge swaths of rural and mountainous land. Or think Pennsylvania, which is in essence a 360 mile wide state with two very large cities, one east and one west, and several medium-sized cities clustered mostly in the east, all separated by miles upon miles of farm land or mountains. Urbanicity, as you might recall, can have a huge effect on the number of autism diagnoses, as I discussed three years ago. Naturally, DeLong made no attempt to control for urbanicity.

In other words, there’s no reason to put any real credence in DeLong’s study, especially given how small the observed effect appears to be. After reading this study, I was left wondering why on earth DeLong did it. After all, most of DeLong’s previous work appears to involve the study of banking, the FDIC, and financial risk taking. Why did she embarrass herself so by moving out of her specialty? After all, I would never think of trying to do a paper on economics or business and expect it to be accepted to peer-reviewed journal in the relevant academic discipline. As Dirty Harry Callahan once said, “A man’s got to know his limitations,” and I do, for the most part, know my limitations. DeLong apparently does not, and unfortunately Dunkle doesn’t recognize DeLong’s limitations,

Sprinkle in anti-vaccine fallacies, mix, and bring to simmer

Besides the invocation of yet another bad study, the rest of Dunkle’s article is a concise listing of a number of common anti-vaccine fallacies. There is, of course, repetition of the “too many too soon” mantra. Then, of course, there’s the “aluminum” gambit:

In addition to the number of doses, vaccine ingredients can be problematic, especially for susceptible subgroups. First are adjuvants, substances added to boost effectiveness and allow smaller doses of vaccine antigen to be used. The most common adjuvant is aluminum, which is found in vaccines for hepatitis and diphtheria-pertussis-tetanus.

There is no convincing evidence that aluminum adjuvants in vaccines are dangerous or cause autism as administered, and there is a lot of evidence that they are safe.

Dunkle follows this up with a combination of the “mercury” gambit and the “toxins” gambit:

Second are preservatives — such as thimerosal, which is 49.6 percent mercury. Thimerosal is still contained in many flu shots, although it was, except for trace amounts, removed from other child vaccines a decade ago. Many child vaccines (including those for diphtheria-pertussis-tetanus, HIB, and hepatitis) contain formaldehyde, which was just added to the government’s list of known human carcinogens.

The hypothesis that mercury in vaccines somehow causes autism or autism-spectrum disorders is a failed hypothesis.

Moreover, the attempt to scare mothers with claims of all sorts of nasty chemicals in vaccines is nothing more than a toxic myth. I once chastised Santa Monica pediatrician to the stars’ children (including Jenny McCarthy’s son Evan), Dr. Jay Gordon, for invoking the “formaldehyde” bogeyman. Formaldehyde is actually a normal byproduct of human metabolism, and a typical 5 kg two-month-old infant has about 1.1 mg of formaldehyde circulating in his blood, which is five times more than any vaccine contains. As for formaldehyde’s recent addition to the list of carcinogens by the National Toxicology Program, it should be noted that this is for higher exposures. As the National Toxicology Program itself points out on its fact sheet:

Studies of workers exposed to high levels of formaldehyde, such as industrial workers and embalmers, found that formaldehyde causes myeloid leukemia, and rare cancers including sinonasal and nasopharyngeal cancer.

As always, the dose makes the poison, and the tiny amount of formaldehyde in vaccines is not the same thing as the amount of formaldehyde that to which industrial workers are exposed in industries where formaldehyde is manufactured or used extensively or to which embalmers are exposed. If you peruse the scientific report issued by the National Toxicology Program, you’ll see that all the supporting studies involved rather large exposures, far more than any vaccine exposure. Dunkle’s citing formaldehyde as a carcinogen has about as much relevance to vaccine safety as the observation that people can drown in lakes does to discussions of the optimal amount of water people need to drink each day.

Unfortunately, either through advocacy, knowing an editor, or taking advantage of an editor’s desire to publish something interesting and controversial, anti-vaccine groups and activists manage to get articles like this one by Margaret Dunkle into major newspapers. Sometimes, it’s reporters themselves who fall for harmful pseudoscience like this. (Sharyl Attkisson, Steve Higgs, and Steve Wilson, I’m talking to you.) In either case, such articles and reporting represent massive failures of fact-checking, objectivity, and journalistic responsibility, and that’s exactly what The Baltimore Sun is guilty of by publishing Margaret Dunkle’s propaganda.

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Guest Blog: Integrating patient experience into research and clinical medicine: Towards true “personalized medicine”

David Gorski
Friday, November 12th, 2010

David H. Gorski, MD, PhD, FACS is a surgical oncologist at the Barbara Ann Karmanos Cancer Institute specializing in breast cancer surgery, where he also serves as the American College of Surgeons Committee on Cancer Liaison Physician as well as an Associate Professor of Surgery and member of the faculty of the Graduate Program in Cancer Biology at Wayne State University.  He is the managing editor of the Science-Based Medicine blog, whose only goal is to promote high standards of science in medicine.  A collection of his posts can be found here.


We advocate science-based medicine (SBM) on the Science-Based Medicine blog. However, from time to time, I feel it necessary to point out that science-based medicine is not the same thing as turning medicine into a science. Rather, we argue that what we do as clinicians should be based in science. This is not a distinction without a difference. If we were practicing pure science, we would be theoretically able to create algorithms and flowcharts telling us how to care for patients with any given condition, and we would never deviate from them. It is true that we do have algorithms and flowcharts suggesting guidelines for care for a wide variety of conditions, but there is wide latitude in them, and often a physician’s “judgment” still ends up trumping the guidelines. While it is also true that sometimes physicians have an overinflated view of the quality of their own “clinical judgment,” sometimes to the point of leading them to reject well-established science, as Dr. Jay Gordon frequently does, what I consider to be physician’s judgment is knowing how to apply existing medical science to individual patients based on their circumstances and, yes, even desires and values.

Indeed, if there’s one area where SBM has all too often fallen short in the past, it’s in taking into account the patient’s experience with various treatments. What got me thinking (again) about this issue was an article by Dr. Pauline Chen in the New York Times last Thursday entitled Listening to Patients Living With Illness. She begins her article with an anecdote:

Wiry, fair-haired and in his 60s, the patient had received a prostate cancer diagnosis a year earlier. When his doctors told him that surgery and radiation therapy were equally effective and that it was up to him to decide, he chose radiation with little hesitation.

But one afternoon a month after completing his treatment, the patient was shocked to see red urine collecting in the urinal. After his doctors performed a series of tests and bladder irrigations through a pencil-size catheter, he learned that the bleeding was a complication of the radiation treatment.

He recalled briefly hearing about this side effect three months earlier, but none of the reports he had been given or collected mentioned it, and once he had recovered from the angst of the emergency room and the doctor’s office visits and the discomfort of the clinical work-up, he didn’t give it more thought — until a few weeks later, when he started bleeding again.

By the time I met him, he was in the middle of his third visit to the hospital. “I feel like I’m tied to this place,” he said. He showed me a plastic jug partly filled with urine the color of fruit punch, and he described a post-treatment life marked by fear of going to the bathroom and discovering blood. “If I had known that my life would be like this after radiation,” he sighed, “I would have chosen the surgery.”

To this, I’ll add a little random bit of personal experience of my own. No, I wasn’t a patient who had to face something like this patient, but I do see something similar in my patients. Back when I was in my surgical oncology fellowship — and before that, in my general surgery fellowship — I was always taught that lumpectomy was preferable to mastectomy because it saves the breast and most women want to save their breasts. After all, lumpectomy plus radiation therapy results in the same chance of survival as mastectomy; so we should offer lumpectomy whenever tumor characteristics (the main one being size relative to the rest of the breast) permit it. Yet this assessment often neglects to acknowledge that, for some women, undergoing six or seven weeks of radiation is horribly inconvenient, and that there are often complications. It also often neglects to acknowledge that there is a price for saving the breast besides having to undergo radiation therapy: there’s the possibility of more surgery to achieve clear surgical margins, not to mention a higher risk of local recurrence in the breast. For some women, this latter possibility is a deal-breaker. Even though they acknowledge that their chances of survival would be the same with lumpectomy or mastectomy, the thought of an approximately 8% local recurrence rate eats at them to the point that they opt for mastectomy.

Then there is the issue of chemotherapy. We frequently recommend cytotoxic chemotherapy for women with relatively early stage breast cancer, even though the addition of chemotherapy in such patients only increases the chance of survival by perhaps 2–3% on an absolute basis, depending upon the tumor. Of course, as I’ve pointed out before, the benefits of chemotherapy are more marked in more advanced operable tumors, but in early stage tumors they are rather modest. This is therapy that causes hair loss, increased risk of infections, and can cause damage to the heart, but it is the standard of care. Most women are willing to undergo this sort of therapy, too; I can’t locate the study, but I’ve seen one survey where women respond that they would be willing to undergo chemotherapy for a 1% increased chance of survival.

The point is that these sorts of questions are value judgments that often depend upon what patients consider important. The patient described by Dr. Chen, for instance, would apparently prefer risks of surgery rather than peeing blood all the time and having to go back to the doctor’s office and hospital time and time again for this problem. Science can tell a physician and patient like this that radiation or surgery will produce an equivalent chance of surviving his cancer. It can tell them what the complications of each choice are likely to be, and what the odds are of each complication. That’s part of what I mean when I refer to science-based medicine. What it can’t tell the patient and doctor is which constellation of risks would be more easily bearable by the patient. The same is true for whether to choose mastectomy or radiation or whether to opt for chemotherapy after breast cancer. Science provides the numbers and the “price” of each choice, but it can’t — nor should it — tell the patient what to value. Moreover, what the patient values may not be what the physician values. As Dr. Chen points out:

Whether conducted at a laboratory bench or in clinical trials, medical research has long been driven by a single overriding goal — the need to find a cure. Usually referred to more modestly as a search for “the most effective treatment,” this standard has served as both a barometer of success and a major criterion for funding. Most published studies are marked by a preponderance of data documenting even minor blips in laboratory values or changes in the size of a spot of cancer or area of heart muscle damage on specialized X-rays. Some studies bolster the apparent success of their results with additional data on societal effects like treatment costs or numbers of workdays missed.

Few studies, however, focus on the patient experience.

She then refers to a study by Dr. Albert W. Wu, lead author and a general internist and professor of health policy and management at the Johns Hopkins Bloomberg School of Public Health in Baltimore published in the journal Health Affairs entitled Adding The Patient Perspective To Comparative Effectiveness Research. In this study, Wu et al argue for the inclusion of the patient’s perspective in comparative effectiveness research. What this involves is patient-reported outcomes. To illustrate the concept, Wu et al use this chart for patients with chronic obstructive pulmonary disease (COPD).

These sorts of measures are particularly appropriate for comparative effectiveness research (CER). For the reason, consider what CER is: basically CER compares existing treatment modalities already determined to be effective in prior clinical trials in order to determine which is more effective. Other important measures include cost-effectiveness. However, although some efforts go into assessing patient-reported quality of life outcomes of the sort listed above, all too often it’s hit-or-miss whether these sorts of measurements are included in clinical trials. One initiative that this article describes is the Patient-Centered Outcomes Research Institute, whose mandate is to:

  • Establish an objective research agenda;
  • Develop research methodological standards;
  • Contract with eligible entities to conduct the research;
  • Ensure transparency by requesting public input; and
  • Disseminate the results to patients and healthcare providers.

Wu et al suggest that the PCORI can only realize its potential if it supports initiatives that integrate measures of patient experience into not just research but into routine clinical care. A number of possibilities are suggested, including how to integrate general and disease-specific tools into clinical trials in order to measure patient-reported outcomes. Also suggested are various means of integrating these tools not just into clinical research but into routine clinical care, including using them in administrative claims data, linking this data to electronic medical records, and even promoting the collection of such data as being required for reimbursement.

One problem I can perceive immediately in trying to use the PCORI is that it has no real power. In fact, the health insurance reform bill known as the Patient Protection and Affordable Care Act (PPACA), which mandated the creation of the Patient-Centered Outcomes Research Institute, provides no power to it. Indeed, its main charge is to assess “relative health outcomes, clinical effectiveness, and appropriateness” of different medical treatments, both by evaluating existing studies and conducting its own. Even given that huge mandate, the law also states that the PCORI does not have the power to mandate or even endorse coverage rules or reimbursement for any particular treatment. Indeed, so toothless is the PCORI, at least in its present form, that it has been disdainfully described as being like the UK’s NICE but without any teeth, which is all too true. Basically, the law says that Medicare may take the institute’s research into account when deciding what procedures it will cover, as long as the new research is not the sole justification and the agency allows for public input. Moreover, if the political reaction to the USPSTF’s revision of the guidelines for mammographic screening last year is any indication, if politicians don’t like a PCORI recommendation, you can be quite sure that they’ll behave similarly. After all the ranting about “rationing” that was used to attack the PPACA, it was not politically feasible to make the PCORI a government agency or to imbue it with any real authority.

Politics aside, let’s get back to the sorts of initiatives suggested by Wu et al. One that in particular interests me is the concept of using patient portals to collect this information. Patient portals are websites that offer a variety of services to patients, including secure e-mail communication with the clinician, the ability to schedule appointments and request prescription refills, as well as the opportunity to complete intake and other forms that used to be completed on paper in the office. The authors propose using such portals to collect patient-centered quality of life measurements and give an example of how this might be done in the case of a hypothetical breast cancer patient:

In one possible scenario, a woman with breast cancer is being followed by an oncologist who would like to know how she is doing on the chemotherapy regimen she is receiving. The oncologist logs on to PatientViewpoint.org, enters the patient’s number, and orders the BR-23 Breast Cancer–Specific Quality of Life Questionnaire for her to complete online before her next visit. The patient receives an e-mail notification to do this, logs on to PatientViewpoint.org, and completes the survey.

The patient’s results are automatically calculated and are made available both on the website and within the hospital’s electronic health record alongside all of her other laboratory test results. At the visit, the oncologist pulls up the results and asks the patient about an increase in her depression scores. It would also be possible to aggregate all of the patient’s questionnaire results with those of other patients receiving chemotherapy for similar breast cancer cases and to use these data to help compare the effectiveness of different regimens.

Dr. Wu’s site is currently only set up to accommodate breast and prostate cancer patients, but it could be expanded. There now exist a large number of tools like the BR-23 to assess quality of life, and, with what appears to be the nigh inevitable infiltration of the electronic medical record into medicine over the next several years, integrating such tools into routine clinical care should become increasingly easy and inexpensive. On the other hand, one problem with such tools is that clinicians are already buried in “information overload.” Whether they would actually read and use the results of such studies outside the context of clinical trials is not assured, at least not if there is no incentive to do so. If this sort of approach is going to work, the government and insurance companies are going to have to pony up. Another problem is that a lot of doctors don’t like this sort of measurement. They consider it unscientific and “squishy” or they don’t know what to do with the information. Whether these attitudes will change or not as CER becomes increasingly embedded in clinical research is impossible to say.

Dr. Wu’s article leads me to reflect upon two things. First, it’s important to remember that the reason these “softer,” “squishier” measures are becoming more important is precisely because SBM has been so successful. Diseases that were once fatal are now chronic. A prime example is HIV/AIDS. Back when I was in medical school, HIV was invariably fatal. AIDS patients died rapidly — and in most unpleasant ways. Thanks to SBM, which developed the cocktails of antiretroviral drugs, HIV/AIDS has become a chronic disease, so much so that babies born with HIV are now approaching adulthood. What this success means is that, although not completely, by and large mortality is no longer the be-all and end-all of HIV treatment. Now, we are seeing quality of life issues coming to the fore. The same is true for some cancers, and it’s certainly true for diabetes and heart disease. As Wu et al point out:

Patient-reported outcomes directly support the primary goal of much of health care: to improve health-related quality of life, particularly for people with chronic illnesses. No one can judge this better than the patient. For example, the main objective of hip replacement surgery is to reduce pain and improve the capacity to get around. The main goal of cataract extraction is to improve visual functioning—that is, the ability to perform activities that require eyesight, such as reading, walking without falls, and working on a computer.

In addition, there are often trade-offs between the length and quality of life. Important considerations are the side effects of treatment of HIV disease, the temporary diminution of functioning after coronary bypass surgery, or fatigue resulting from cancer chemotherapy. Even for life-saving treatments, this kind of trade-off can influence a patient’s decision making among alternative courses of care.

Once again, these decisions and the trade-offs patients decide to accept should be informed by the science. The options presented to the patient and their cost in terms of potential complications and impact on the patient’s ability to go about his daily activities and in essence live his life must be based on science. However, that does not mean that the final determination will always be purely based on estimates of efficacy. If the patient decides, for instance, that the survival advantage that chemotherapy will provide after her breast cancer surgery is not sufficient to be worth months of hair loss, fatigue, and the risk of heart damage, then that is her choice. The key is that we as clinicians must make sure that she has accurate, science-based information upon which to base that choice. Informed consent must be based on sound, scientifically verified information. Anything else, such as the sorts of “informed consent” advocated by “health freedom” groups is in reality misinformed consent. It is our responsibility as science-based practitioners to do our best to make sure that the treatments we offer our patients are based in science and that the information about the relative benefits, risks, and costs about these treatments is also based in science.

The second thing that comes to my mind is the complete contrast between the sorts of efforts that Wu et al are undertaking and what purveyors of unscientific so-called “complementary and alternative medicine” (CAM) do. SBM is, through CER, undertaking systematic measurements of quality of life measures, and the use of genetic tests that provide information about prognosis and predict response to therapy, making its first real steps towards truly “personalized” medicine. Yes, these steps are halting — stumbling at times, even — but they are steps towards the day when SBM can offer patients treatment options based on science and personalized to the characteristics of the biology of their disease that are unique to them, all while taking the patients’ own values and desires into account. Contrast that to so-called CAM, where “personalized medicine” basically means making it up as the practitioner goes along, and I think you’ll see what I mean. Whatever the deficiencies and faults of SBM (and it’s impossible not to concede that there are many), SBM is far closer to true “personalized medicine” than any CAM, and it is using CER to come even closer still. CAM has nothing to compare.