Book Club: Risky Medicine...

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In my ongoing attempt to better understand the inner workings of the US healthcare industry, I've read yet another book: Robert Aronowitz's Risky Medicine (2015). Within, the author argues that medical practice has undergone a consistent shift away from treating disease and towards 'managing risk' via an ever expanding array of preventative screenings and medications. To summarize the message: Risk reducing drugs are incredibly appealing to pharma companies as their markets are vast, advocacy groups often blindly support screenings irrespective of their efficacy because they help manage fear, and regulation generally has a much lower bar for risk-prevention than actual disease treatment.

It can take a long time for epidemiologists tease out these factors, and the true value of preventative services often take decades to evaluate. By then, they can become entrenched within the culture of advocacy groups, who will often trot out versions of the above, naïve statistics in order to argue for continued screening. Furthermore, the above example (and many advocacy groups [2]) focus entirely on sensitivity of screenings to the exclusion of specificity: no test is completely without false-positives, which can offset the value of the test if the condition that it's trying to detect is sufficiently rare (see positive/negative predictive values).  

Another major argument made by the book is that the standards for approving risk-reducing drugs are too low, thus ever increasing the pool of preventive treatments. Disease risk factors identified during the course of clinical research are rarely as predictive at the whole population-level as they are in study groups. Furthermore, statistically significant association says nothing about the magnitude of the effect of reducing risk factors. Nevertheless, when pharma companies develop a drug that reduces a risk factor for a disease (say blood pressure), they need only show safe efficacy in targeting the risk factor, and not that it actually lowers incidence of the ultimate disease. Therefore, as above, drugs can become entrenched in medical practice long before we realize that their efficacy is marginal or non-existent.

While thought-provoking concepts, Risky Medicine never gave me a sense of the actual magnitudes of these issues. Most of the discussion is theoretical, calling for increased skepticism in the face of new preventative strategies. While some examples of unnecessary focus on risk are discussed, the case-studies given chapter-length treatment are quite-complex, spanning a large range of issues that muddy the main message and make it difficult to form an opinion without more knowledge. This likely explains why ~45% of the book consists of detailed footnotes, which aren't citations so much as additional background necessary to understand the circumstances being discussed [3].

Controversies such as the efficacy of the prostate-specific antigen test or hormone-replacement therapy in post-menopausal women indicate that both the definition of 'risk' as well as how we determine what risk factors are worth addressing are worthy of continued consideration. Furthermore, as the Aronowitz indicates, we haven't comprehensively addressed the quality-of-life effect of moving millions of people out of what was classically regarded as a state of 'good health' to an endless maze of varying levels of risk. Hopefully, someday I'll read a book that discusses these topics more cogently, with explanations of actual research.

 

[1] In reality, values are rarely this large and obvious, and it's often more complicated. For example, according to Aronowitz, the tendency in the medical community has been to broaden the definition of diseases over time, generally increasing the number of diagnoses regardless of treatment efficacy and therefore further reducing the apparent mortality.

[2] According to the author, patients rarely complain about false-positive diagnoses, even when it's clear that such a case occurred. For example, if a test falsely suggests that a patient has cancer and leads to painful biopsies that rule out disease, it's more likely that the patient will feel elated for having 'dodged a bullet' than ask why the original test was positive.

[3] Only at the very end of the book is it indicated that its various chapters are collected from previously published essays, chapters, and papers. Unfortunately, it doesn't make for a very clear presentation of the argument and leads to a lot of repeated examples and redundancy.


Book Club: The Patient Will See You Now...

Eric Topol is a cardiologist known for his advocacy for technology-based disruption of the healthcare industry. I heard him make some provocative statements about creative destruction in medicine on the Econtalk podcast, and since I'm now now working in the broader healthcare industry, I decided to read his book, The Patient Will See you Now (2015; Basic Books).

I'm not sure for whom this book is intended: it covers a lot of ground, and many of the technical concepts that it discusses are far more controversial than presented. Topol tries to tackle a multitude of weighty subjects in a single book, and I'm sure that the general exuberance for all things 'omic' and 'big data' are going to ruffle a few feathers. In the interest of my time and yours, I'll comment on three major themes.

Paternalism in medicine

The first section of the TPWSYN(?) criticizes the problem of pervasive paternalism in medical practice. While all professions are expected to be self-promoting (and self-serving), medicine is somewhat unique in its degree of self-congratulation and self-importance. In particular, Topol is critical of the field's lack of interest in 'democratizing' the healthcare process: essentially, there's a lot of information available for patients to make informed decisions about their care, but they rarely have access to their own medical data [1].

I understand why physicians could be weary of too much patient 'involvement': doctors already complain about patients citing 'Dr. Oz' when questioning diagnoses and prescriptions, and it's easy for desperate patients to fall for misinformed woo that they read online. But ultimately I agree with Topol: patients are already organizing support groups and sharing information online and MDs can either be there to shape the process, or allow it to happen without their involvement (the lack of practitioner involvement goes a long way to explaining why so-called Electronic Medical Records, or EMRs, are so physician-unfriendly [2])

Omics will revolutionize everything

Anyone who's followed the literature on things like genome-wide association studies (GWAS) [3] knows that, for many complex diseases, they've been quite controversial and/or disappointing with little of the phenotypic variance explained by genomic factors (see Visscher et al. 2012, for example). They're also very expensive. Regardless, Topol presents them without any controversy as if they're going to explain the root causes of everything - he's firmly on the side of 'we just need more data'. 

But that's the rub: the root cause of every disease isn't purely genomic. Rather, disease phenotypes result from the interaction between genes and the environment. Furthermore, no law says that these interactions need be 'additive', so saying that this disease is 40% genetic and 60% environmental doesn't make sense. More data may be good from an academic perspective - but much more work needs to be done to translate this into clinically actionable findings - there's a big difference between the statistical significance of an effect and its magnitude [4] .

It's also worth pointing out that a major challenge in applying the results of GWAS in a clinical setting is that in addition to the results being sample and size-specific, they are also often very population-specific. This means separate studies are required to identify risk loci associated with cancer in caucasians (the most well-studied group), versus africans, or asians, or latinos, etc. So unless the diagnostic value of these studies increases dramatically, it may be difficult to justify the costs.

The smartphone as the all-in-one medical diagnosis device

TPWSYN spends a lot of time discussing how technological advancement is shrinking the cost and size footprint of complex medical devices. In particular, there are apparently several excellent proof-of-principle technologies that can attach to your smartphone and collect information on things like blood-pressure, temperature, or the visual status of your inner ear, nose, or throat, among others. Via software and/or telemedecine, there's a possibility that such devices could allow routine diagnoses of minor conditions without the need for expensive, time-consuming hospital visits.

Clearly, there's a lot of exciting potential in such devices: as an example, diabetics have been able to monitor their own blood-sugar levels for years now. However, I think that this type of technology brings up one of the major caveats of the entire book: 'More data' is only useful if clinicians know what to do with it. Consider the following: maternity wards have largely adopted continuous fetal monitors that affix to the mother's belly, over the traditional 'checking in' every so often. This has coincided with a large spike in the number of unplanned, emergency C-sections. However, there  has been no corresponding drop in rates of infant mortality. Most likely, continuous monitors exposed a large number of 'normal' fluctuations in prenatal heart-rates and contractions, which spooked unfamiliar medical staff into performing unnecessary operations [5].  

In the fullness of time, we'll likely figure out how to perform analytics on 'big data' in order to produce meaningful effects on individual patient outcomes (not simply 'statistically significant', but actually noticeable in magnitude at the individual level). A lot of this is going to come from combining 'omics' and monitoring with work unraveling the underlying mechanisms of disease. But the results that one obtains from data are only as good as the data themselves, as well as the hypotheses under which they are interpreted. I'm not sure whether the best place upon which to focus the bulk of our efforts is in collecting ever more data of untested quality. 

Ultimately, much of what Topol discusses in his book will likely come to pass - at least in implementation if not in actual value to patients. But without serious discussion of the subtleties of the underlying science, it amounts to much more hype than information.

[1] Topol also criticizes medical associations for levying non-evidence-based criticisms against things like allowing registered nurses to handle diagnosis and prescription in 'routine' practice.  

[2] See The Digital Doctor, by Robert Wachter.

[3] e.g. The entire journal called Nature Genetics.

[4] Consider the types of results that you (used to) get from 23&me: If you have a variant that increases your risk of disease X by 2%, are you going to change anything about the way you live your life? Would it even help at an individual level or are you only going to see an effect in aggregate?

[5] See Expecting Better, by Emily Oster.