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Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
PEARLS BEFORE SWINE © 2020 Stephan Pastis. Reprinted by permission of ANDREWS MCMEEL SYNDICATION. All rights reserved.
Otello Stampacchia, founder, Omega Funds (illustration by Praveen Tipirneni)
I could not resist using the above, peerless comic strip from Stephan Pastis. Indeed, the two “plagues” are not just co-existing, but they are mutually reinforcing each other and making things worse.
We should take the “great wise ass on the hill” seriously and invest in science and (investigative, high quality) journalism.
About two months ago, I wrote here about “How We Are Losing the Fight Against the Virus.” At that time, the US had 2.5 million confirmed cases of COVID-19 and reported 126,000 deaths. As of this writing, we now have tallied up more than 6 million confirmed cases, and 183,000 deaths.
Sadly, those numbers will continue to grow this fall, based on my latest look at the relevant work in epidemiology, virology and immunology (and the social-political vibes you get from watching too much cable news).
I am dividing today’s contribution in four parts:
If you are, like most people reading Timmerman Report, up to date with the zeitgeist, I suggest perhaps you skip parts 1) and 2) (though a refresher never hurts) and jump to part 3).
The above picture is from the COVID Tracking Project (@COVID19Tracking): after a worrying summer that saw 7-days rolling averages of confirmed cases exceed 60,000/day for several weeks, and rolling averages of deaths exceed 1,000/ day, the situation appears to be stabilizing / mildly improving, though on a much higher plateau than in May. Rolling averages for hospitalizations and percentage of positive tests are also showing improvements, though, again, at levels that are far from reassuring (roughly around 40,000 positive cases / day and ~900 deaths / day).
Now that summer is winding down and schools, universities and workplaces are moving back into the rhythms of fall, people want to know what to expect.
It’s going to be painful.
I’ve been following an open-source AI-driven model from Youyang Gu which has been relatively accurate to date and is followed by the well-known statistical modeler Nate Silver at FiveThirtyEight.
Sadly, the model forecasts roughly 220,000 deaths by November 1st, from a current (already very grim) body count of over 180,000 confirmed cases. Please note that the current confirmed toll is already DOUBLE the number of US servicemen who died in Vietnam, Afghanistan and Iraq combined, and with no signs of stopping. (The US death toll for World War II was about 400,000).
Add to this the (very roughly) 50,000 excess deaths that are very likely already attributable to COVID-19, and the toll is very heavy indeed. This is not “normal”, it is not even remotely close to (non-pandemic) seasonal flu, and we must resist the (all too human) tendency to become numb and fatalistic about this sorry state of affairs.
According to the same model, the current best estimate of cumulative American COVID-19 infections is (roughly) 14% (close to 47 million out of 330 million people). We are (obviously, but it needs to be stated clearly) nowhere near the >70% of the population needed to achieve “herd immunity”. Please remember also that the death toll does not take into account the (unclear, but significant) number of #LongCovid sufferers (patients who have recovered from the infection but suffer long term, serious health consequences). Even ignoring all that resulting chronic disease with COVID “long haulers,” and using some very, very rough math, “letting it rip” (letting the pandemic propagate without any attempt at mitigating its spread) can lead to ~800,000-900,000 total confirmed US fatalities (more if some hospital systems become overwhelmed; possibly *many* more if we try to input “excess deaths” in the calculation).
To put this into a broader context: in the 5 days prior to Sunday, Aug. 30, the largest European countries (with a population total of 340M, roughly equivalent to the US’ 330M), reported a combined 250 deaths. During that same period, the US reported 5,500+ deaths. When you look at the death toll, the pandemic at this point is about 20x worse in the US.
This is staggering, and will have implications for the upcoming autumn.
I will attempt to summarize some of the knowledge / facts accumulated on the virus and its properties / means of propagation (and implications thereof) since the beginning of the pandemic.
a) Deaths lag hospitalizations; hospitalizations lag confirmed cases increases: the steep increase in number of cases in the US observed in the early summer led, predictably, after several weeks, to an increase in hospitalizations and then in subsequent deaths. There is an obvious lag between these events, caused by several factors: i) as the infection spreads first in the younger, less vulnerable (but not *invulnerable*) strata of the population; ii) as (unconscionable) delays in test results do not give us an accurate and timely picture of the infections spreading, and, finally (and even more unconscionably) iii) as it might take several *weeks* to report COVID-related deaths (a particular dishonorable mention to the Sunshine State, Florida, for the egregious delays, if not outright obstructionism, in reporting deaths). Remember that we are all actually living in the future versus what we normally perceive to be the present (meaning, what we could perceive the current status of the pandemic spread to be: human beings are particularly prone to recency biases). It is of particular concern when, in areas where tests are performed at scale and some speed, positive rates increase quickly. In such situations, measures such as localized / temporary lock-downs / further enforcing of social distancing / obligatory mask wearing in public are likely warranted (the latter should be enforced nation-wide ASAP).
b) This is a very infective virus: It is not as bad as measles, but it is very transmissible, especially in indoor, crowded, poorly ventilated environments. You have heard (am sure) about the dreaded R0 (R “naught”) number. Crucially, R0 is not a fixed number, but it is influenced by many factors, including human behavior (masks…) and context in which a potential transmission could occur (shouting / singing in an small / contained indoor space). The virus is spread by droplets / aerosol particles generated by breathing / talking loudly from an infected person. Worryingly enough, there is now emerging evidence that such droplets can also propagate the infection by connecting with unprotected ocular surfaces (your eyes).
By now, you should also be familiar with the importance of “super-spreading events” in transmitting / propagating the virus. As little as 10% of infected individuals could be the source of ~80% of overall infections, again especially in specific environments / context (indoor, poorly ventilated, talking loudly / singing, no masks): for example, a wedding in rural Maine, an area as yet untouched by the pandemic, led to a large outbreak in the state.
Another extremely well-researched example, very close to home for me and for many readers of Timmerman Report, was the meeting of Biogen’s executive management in Boston towards the end of February (the meeting ended on Feb 27th): the event was attended by ~175 people, from different regions in the US as well as from other countries (including Italy). Scientists have estimated that over 20,000 individual infections were originated from that single conference and then spread all across the globe.
Which leads us to:
c) Non-Pharmaceutical Interventions (NPIs) are very effective in preventing viral spread: social distancing and, especially, wearing masks / face shields in indoor areas have been proven (by a whole body of scientific literature, on this pandemic and others before it) as being very effective in preventing transmission. The graph below is a great summary of the transmission risk in various circumstances and could act as a very rough guideline (always wear a mask anyway). Please note that everything is relative and highly context-dependent: if you are in an area of high prevalence of infections (Miami-Dade County in Florida, for example, or NYC during the peak of the pandemic there in March), even low-risk endeavors, if repeated many times and by a highly susceptible individual can lead to infection. Remember the probabilities here are additive. I am not going to go into which type of masks are most effective but they are an essential component. In addition, face masks also help in reducing the amount of virus that you are infected by (viral inoculum, sometimes confused with viral load), which leads to this one essential point below:
d) Viral inoculum size is very important (and may be the factor making the difference between you ending up in the hospital or just walking it off): forgive me for perhaps stating the obvious here, but I feel this topic, of paramount importance, has not been discussed much (if at all) in the general discourse surrounding the pandemic. People seem to only assume binary outcomes from encountering the virus: you either get infected or not, and then, if you get infected, you either die or survive. This is *OBVIOUSLY* not the case (not sure if you can picture me rolling my eyes right now, but, I assure you, it is happening). How much virus you are exposed to at the beginning of your infection (viral “inoculum”) has a strong correlation with the subsequent severity of your symptoms. We have known this for a while, and yet, we are still having these inane / insane and sometime unhinged discussions / fights (including at various stores across the country) about how the obligations to wear masks infringe on people’s civic liberties. Masks do, at the very least, SUBSTANTIALLY reduce the viral inoculum you are exposed to in an infective situation, and therefore *will* either i) prevent the (non-infectious) wearer from catching a potentially lethal dose of the virus or ii) prevent the (infectious) wearer from spreading a potentially lethal dose of the virus in his/her surroundings. I honestly cannot believe we are still debating this stuff 6 months into a pandemic, but there you have it.
e) The virus is not becoming less lethal over time / the virus has not mutated into a (measurably) milder version: the (very much welcome) reduction in the mortality rates that you are seeing across countries (most of them developed: emerging countries are still seeing very high case fatality rates: see Mexico, Peru, India) is because of a variety of cumulative factors: hospitals / caregivers are no longer overwhelmed; a larger percentage of the population now being infected is younger / with less co-morbidities and risk factors; practitioners have learned / shared more information on how to deal with severely ill patients (for example, by not putting them on ventilators immediately; for those of you with an interest, I suggest joining the slack channels used to exchange know-how and tips by healthcare practitioners); more people are wearing masks / being more careful in their behaviors and therefore are being exposed to lower viral inocula when infected; finally, a few pharmaceutical interventions have since become available and are making an impact (especially dexamethasone), and with more to come: People have been focusing a lot (for good reason) on vaccines, but there is a whole bunch of other stuff that might show its effectiveness before they become widely available. As any rational person would (should?) tell you, in a(ny) pandemic you do not want to get sick early. If you’re going to get sick, it’s better that it happen later, when healthcare professionals know more about the virus and its effects.
f) There is no pre-existing, foolproof, 100% protective “immunity” to this virus. I have seen over the last few weeks the spreading of this fascinating, completely unproven “theory” that there is a large amount of existing immunity to COVID-19 in the population due to the fact that, supposedly, up to 50% of people have T cells with cross-reactivity (in Petri dishes, in a lab) to this virus because of previous exposure to other (seasonal) coronaviruses. This reminds me of the late ‘90s, when curing cancer in mice was enough to make stock prices for certain (mildly overpromoting) biotech companies skyrocket… (yes, I know this statement ages me quite a bit, thanks very much). Even given this (completely unproven) “theory” (I think “opinion” might be closer to the mark) the benefit of the doubt, again we come back to the *fact* that a smaller viral inoculum (and subsequent viral load in your bloodstream) is probably more important in determining your progression towards the worst symptoms. Even some mild degree of cross-reactivity and therefore (possibly, not surely) protection will not help you if you are inhaling infectious particles by the millions without masks at an (indoor, poorly ventilated) bar. In the words of Ricky Gervais (if you do not watch his comedy stand-ups, you should): “You can have your own opinions, but you cannot have your own facts”.
g) Children (especially very young children) are not, by and large, severely affected by the virus: what I mean by saying “by and large”, is that they are not as affected as adults, probability-wise. There is, also in children, a continuum in the risk probability, again increasing with age, and with other pre-conditions predisposing to severe symptoms (obesity etc.). That said, the risk is not exactly zero even for very young children. To provide some quantification of the probabilities here, Dr. Stephanie Graff (@DrSGraff), described a recent State of Florida pediatric report (using data since March)… OK, HOLD ON. I know what you are thinking. So let me address the elephant in the room here first: far be it from me to rely upon, let alone celebrate, “Florida statistics” on the pandemic, which is as close to an oxymoron as I can think of, but this is all I got so far. At any rate, according to said (possibly very flawed) Florida statistics, 48,928 children statewide have tested positive; of these, 600 have been hospitalized (~1.2%), with most of them recovering, and 8 have tragically died (~0.01%). 50 children <18 have been diagnosed with MIS-C, which is a severe inflammatory disorder caused by the virus (~0.1%, or 1/1000 for those of you who suck at math). I have not been blessed with children, so will leave to parents their own risk-reward calculations analysis. At any rate, it is also abundantly clear that children are infected by the virus and can transmit it, sometimes very efficiently: young, college-age adults effectively seem to transmit as well as older adults. This has severe implications for community transmission as soon as schools reopen (see below), and particularly for inter-generational households with grandparents or people with other pre-existing conditions.
Most of the unknowns that still plague us are related to this incredibly complex organ of the human body, the immune system. This is a new virus for the human species, and, notwithstanding the incredible progress made to date, there are still lots of things we do not know, some of which are (unfortunately) essential before being able to plan a return to some semblance of normal life.
a) How long does immunity last? In early July, a British study pre-print showed declines in neutralizing antibody titers (antibody amounts / volume of blood) observed during a ~3 months follow up period post-infection. Cue (predictably) apocalyptic commentaries. However, there are reasons (I know, this is unlike me, but bear with it for a bit) to be a bit less pessimistic: as eloquently described by Derek Thompson in The Atlantic in this very thoughtful piece as well as by his (very witty) colleague Ed Yong in many great articles (this one in particular is worth a read) the immune system is extremely complex and has many components. First, even a lower antibody titer later on might still be sufficient to forestall a severe re-infection (see below) DEPENDING ON THE VIRAL INOCULUM (see? Keep wearing a mask please). Also, the authors of the British study only looked at B cell antibody responses, and did not measure T cell responses (to be fair, that is a much harder measurement to make): there are reasons to believe that T cell responses might provide the strongest / longest-lasting immunity to COVID-19 (read here if you want to geek out). Perhaps some very rough calculations might also provide some guidance: we know that existing, seasonal coronaviruses (responsible for ~25% of seasonal common colds) are capable of re-infecting people every year or so which means immunity might last for a few months; on the other hand, SARS and MERS (the other recent coronavirus epidemics) *seem* to provide some degree of immunity for ~24 months. That said, both SARS and MERS only infected (luckily: their fatality rate was very high) a few individuals, so we do not have a deep data set to use here. If I were a betting man, I would work under the operating assumption that exposure to infection (and subsequent recovery) might confer some protection for anywhere between 12 and 24 months. The uncertainty is due to myriad factors (how much virus were you exposed to; the infected individual’s sex and the overall state of his / her immune system; the amount of virus exposed to in a subsequent re-infection; etc.). Which brings us to the latest developments:
b) People can get re-infected: note, this should technically be in section 2), but it is a very new development and there is still a lot of uncertainty / debate on the possible consequences. As described by more and more reports in the last few days, there have been (some more, other less “properly” confirmed) cases of COVID-19 re-infection (in Hong Kong, Belgium, Netherlands, and now Nevada): in these cases, the virus genetic sequence in the second infection was sufficiently different from the one from the earlier infection to rule out artifacts of detection and other explanations. Before I go on, I would like to highlight the sheer “amazingness” of this: we are testing and sequencing viruses of many infected individuals, globally. This is leading us to important learnings on the virus (the above is a not-so-snide retort to people / organizations who would like to reduce testing volumes or only test symptomatic people; apparently one such organization included, until recently, the US CDC). I’d start with the (important) premise that such cases, at the population level, are in my opinion quite rare and likely to remain so and be restricted to individuals who have decided to have the questionable bad luck of i) living in areas of very high pandemic prevalence, ii) being particularly susceptible to infection, and / or iii) engage in very risky behaviors. That said, the Nevada case is of particularly concern, since the individual had much more serious / severe disease symptoms the second time around than during the first infection: normally, even if the immune response generated after the first infection is unable to prevent a second one, it should, however, conceivably, at least forestall more severe symptoms. However, as brilliantly put by Dr. Sarah Cobey (epidemiologist / evolutionary biologist from U. Chicago, quoted in the article above), “Infection is not some binary event”, and with a reinfection, “… the question is how much is the immune system getting engaged?” Which, again, takes us back to viral inoculum size and its interaction with any individual’s immune system. As a corollary of this (extremely important) topic and the consequences on testing, I would strongly suggest you follow Prof. Michael Mina on Twitter (@michaelmina_lab): in particular, this thread (on the consequences of using PCR testing alone in guiding public health decisions) is very important.
c) Implications for vaccines: the above questions have huge implications for vaccines’ development / testing / distribution logistics: if immunity generated by the virus is indeed short-lived, then we might have to become accustomed to annual booster shots, for example. Add to this huge uncertainty some other important complexities, such as i) the mRNA vaccines currently under development are likely to require -70 Celsius or -20 Celsius cold chains; ii) that as much as 30% of the US population does not believe in vaccines and, finally, and perhaps very importantly, iii) that the recent public communication blunders by the FDA and the CDC are very likely to further increase the suspicion that any vaccines approved in a hurry just ahead of a close, contested election might be more motivated by politics than science, and you have all the ingredients for a very messy situation in which not enough people get vaccinated. [Clarification: 9:34 am ET, Sept. 1. The cold chain conditions can vary depending on the mRNA vaccine candidate. A previous version of this article grouped them together.]
First of all, thanks for sticking with me so far. Obviously what follows is speculation. That said, I am quite concerned about the upcoming autumn / winter season. As mentioned above, the virus spreads readily and easily indoor, virus inoculum sizes become probably much bigger, and as we transition to indoor classes / living in general, in a much more generally dry environment, this is very dangerous, especially as the baseline of people already infected in the US is extremely high, driving a steady rate of community transmission (compared to most other countries). As a data point that further corroborates this, we should look at Australia: as well discussed by Silvia Merler (@SMerler), and as shown below, Australia is now truly undergoing a “second wave”, with much higher mortality rates than the first. For those of you who have not been to Australia (and who are not familiar with the fact that our planet has opposite seasons in the two hemispheres: yes, am talking to you, flat-earthers…), Australia underwent its first wave around the end of their summer and is now undergoing their second wave in their winter. With the usual lag, the second wave appears to be having a disproportionately high fatality rate vs the first. At the same time, we cannot say Australia is governed by a “negationist” government or was not warned about the virus ahead of its second wave.
To conclude, some suggestions:
a) I am extremely glad that a number of jurisdictions, including some universities, seem to be starting to administer the current season’s flu vaccine early, to prevent a flooding / overrun of their healthcare systems. I suggest this should become mandatory across the country, as soon as possible.
b) I understand the economic imperative for universities and schools to reopen (also, which parent would want their teenage children brooding at home for another year???). However, we need to be extremely mindful for the potential to further increase spread due to disorderly behavior usually associated with young adults (who are not usually showing the most compliant and obedient behavior at the best of times). It is truly amazing to observe the wave of innovative solutions being tested by various universities / colleges. Some examples of such innovation are shown here: I believe more colleges should follow the University of Arizona and University of North Carolina approach and implement wastewater virus testing from college dorms to have an early warning signal which allows them to implement contact tracing and isolation of infected / asymptomatic individuals very early in the process. Amongst many reasons to use this testing is that i) there is evidence that the virus shows in stools before or at least concomitantly with the early infectiousness period and ii) the method does not require asking teenagers to be compliant with various testing / masking / social distancing guidelines.
c) The recent launch of a fast, cheap and easy to perform antigen test format (from Abbott Laboratories) could also finally introduce a much-needed game changer on the playing field: again as discussed by Michael Mina this test format could really be useful to break transmission chains, therefore limiting / containing local outbreaks early on in their spread (which is not the case, still, with the current test types available in the US, due to their lack of reliability and the incredible, mind-boggling delays in obtaining results). This test, and hopefully soon others like it, should be deployed at scale across the country ASAP. What do I mean by “at scale”? Looking at the scope of the pandemic in the US, we should test tens of millions of people A WEEK to have a chance of contain local outbreaks / superspreading events.
d) Wear a mask.
Follow Otello Stampacchia on Twitter: @OtelloVC
This article expresses the personal views and perspectives of the author. The views and perspectives expressed here do not necessarily represent the views or perspectives of Omega Fund Management, LLC or any officer, director, partner, member, manager or employee of Omega Fund Management, LLC or any of its affiliated entities.
Mike Pellini, managing partner, Section32
Please help me understand the value of OUR Centers for Disease Control and Prevention in 2020.
It’s baseball season — sort of. We have been waiting and waiting for the CDC to step up to home plate. The bases have been loaded for 7 months, and their fans have been waiting. The CDC is supposed to be our national cleanup hitter.
Yet, instead of trying to hit the ball out of the park for the grand slam, they have either struck out each time or tried for the occasional bunt single. This week, they decided to not even leave the on-deck circle.
Here’s the situation. Millions of kids and parents are seeking solid, science-based recommendations on what to do about school this fall during the pandemic. The CDC responded to this challenge by issuing revised guidance on Aug. 26 that said asymptomatic people who have been exposed to an infected person don’t necessarily need to get tested.
That revised guidance — to do less, not more testing — reportedly came from the White House and top levels of the Department of Health and Human Services. Maybe it did; maybe it didn’t. What matters is that the CDC issued the information.
Scientists were baffled, as the new guidance makes no sense. Within hours, CDC director Robert Redfield backpedaled, telling reporters that “testing may be considered” for anyone who has been exposed to the virus.
In a statement, Redfield said:
“Everyone who needs a COVID-19 test, can get a test. Everyone who wants a test does not necessarily need a test; the key is to engage the needed public health community in the decision with the appropriate follow-up action.”
The public needs firm guidance. This statement is so wishy-washy, it is meaningless.
Is there any real value left for this aging, high-priced player?
Stats in baseball don’t lie. Stats in healthcare generally don’t lie either. Roughly 40% of the spread of COVID-19 virus is from asymptomatic people. In a country with 5.9 million confirmed cases of COVID-19, and many more unconfirmed cases, it’s safe to say we have several million people capable of spreading this disease without even knowing they are sick. That’s undebatable at this point (though I know even the most basic facts are seemingly up for debate these days.)
Let’s recap some of the most visible and critical missteps by the CDC since the start of the pandemic, and let’s consider the consequences.
Now we have to deal with this latest blunder — actually discouraging testing for asymptomatic individuals who have been exposed to a known infected person. Clearly, these people are at high risk of infection to themselves, and they pose a risk of infecting others — potentially many, many others if they show up unmasked, at a large event, especially indoors.
Why not test these people to reduce the chance of them becoming spreaders, or even superspreaders? Instead of making this common-sense recommendation, the CDC just walked off the field and did not even demonstrate an effort to hit the ball.
Our nation is at a loss. On what scientific justification did they make this decision?
We know “The key to containing the virus is how effectively a country builds up systems for testing, tracing and isolating potential virus carriers, while establishing clear safety rules as the economy reopens.” (WSJ)
Let’s face it; the CDC has clearly demonstrated it either doesn’t have its head in the game, or it is a cleanup hitter that has been forced onto the bench.
Ruth Etzioni, Full Member, Division of Public Health Sciences, Fred Hutch Cancer Center
In the last few days I have been wondering how Michael Joyner and Arturo Casadevall have been feeling.
Joyner and Casadevall are the first and senior authors, respectively, of the report, “Effect of Convalescent Plasma on Mortality among Hospitalized Patients with COVID-19: Initial Three Month Experience,” posted on Medrxiv on August 12. The preprint server allows researchers to make their work public before peer review, and has become a popular source for producers and consumers of evolving research on the SARS-CoV-2 pandemic.
The report describes the experience of an Expanded Access Program, providing convalescent plasma from recovered COVID-19 patients to roughly 35,000 hospitalized patients over 3 months. All received plasma transfusions, so patients receiving the product could not be compared with those who did not get it.
Despite the lack of any comparison between patients who did and did not receive plasma transfusions, this report has formed the basis for the FDA’s Emergency Use Authorization for convalescent plasma to treat COVID-19 patients. It’s the report that was embarrassingly misinterpreted by the commissioner of the FDA, Stephen Hahn, and by administration officials. And it is the report that may well have spelled the death knell for controlled trials that were hoping to give us a convincing answer as to whether and how well the treatment actually works.
It is hard to imagine that the report’s authors actually intended for all of this to happen. Yet, by doing what medical researchers do all the time, they played into the hands of an administration hungry for anything that would show they were making progress against an intractable virus that has killed at least 200,000 Americans.
To understand how this unfolded and why it bodes so badly for the future, we need to examine the story that the report lays out. The report itself is mostly reasonably transparent, at least about the patients involved (roughly 35,000 over three months from April to July) and how sick they were (52% ICU and 27% on ventilators). The authors obviously recognized that they were reporting an experience rather than a randomized study, and that there was no comparison group since all included patients received at least one unit of plasma. But they still concluded that their findings provided “signatures of efficacy for convalescent plasma.”
What were these “signatures of efficacy”? According to the report and the FDA announcement, there were two: that patients given the product earlier in the course of their illness and those with high antibody levels in their donated plasma were less likely to die than those given plasma later or whose transfusions had low antibody levels.
More specifically, 8.7% of those given plasma within the first three days following their COVID-19 diagnosis died within a week, whereas 11.9% of those given plasma four days or later died. And, of the few patients (roughly one out of ten) for whom information on antibody titer could be retrieved, 8.9% of those in the top 20% and 13.7% of those in the bottom 20% died within a week. Notwithstanding the modest absolute numbers, the relative differences are clinically significant.
At first glance, these results might seem somewhat convincing.
I mean, if you wait to get plasma you do worse, and if your plasma is antibody rich you do better. Surely this must mean plasma works!
Not so fast.
There are major issues with these results that go beyond the (utterly justified) criticism that the data doesn’t come close to the established standard for evidence about novel treatments, the randomized trial. Indeed, the report’s authors lacked not only a randomized comparison between a treated and an untreated group, they were missing the untreated group entirely. So, they presented two alternative comparisons.
Each is problematic for its own reason.
The question of whether it is better to treat a patient earlier rather than later is universally challenging. In prostate cancer, a major controversy has raged for years about whether high-risk prostate cancer cases should be offered additional (adjuvant) radiation treatment right after primary surgery, or wait until their cancer relapses. A similar question occurs in breast cancer. But we can’t just compare patients given radiation in the adjuvant setting with patients given radiation for recurrent disease. They are simply not the same; indeed, some of the patients treated early would never have progressed to relapse even in the absence of radiation therapy.
In general, patients treated early in their disease course are a mix of those who would and would not be eligible for treatment later. In the case of COVID-19, patients at day 2 include those who would make it to day 5 or 10 without treatment and also those who would not! So, the analysis in the report that compares the patients treated within three days against those treated at four days or later is ill conceived. In the end, the results are basically useless.
Alright, you say, but what about the plasma titer comparison? The authors go to great pains to note that the titer was not known at the time of treatment and even use the term “pseudo-randomized” to describe this comparison.
I am going to leave aside the fact that the antibody titer analysis only included about 10% of the sample, and that it is therefore unclear how representative this group might have been, because there is a bigger issue here. This is the matter of how the high- (and low-) titer groups were defined.
There is no established threshold to define high- versus low-antibody titer for SARS-CoV-2. The authors chose the top 20% to be the high-titer, and the bottom 20% to be the low-titer group. This seems reasonable until you realize that they could have chosen the highest and lowest 25%. Or the highest and lowest 33%! Maybe they even tried these thresholds but presented just the results provided. We don’t know. Regardless however, the results show only that survival was improved in the high-titer group compared with the low-titer group. We don’t know if those who received low-titer samples did any better than if they had not been treated at all. In fact, there are real concerns about whether low-titer plasma might induce a harmful effect, referred to as antibody-dependent enhancement (ADE) of coronavirus infection.
At best, we can only conclude from the titer comparison that high-titer plasma, amounting to approximately one-fifth of the plasma samples, might possibly constitute an effective treatment. There is absolutely no signal of efficacy for low-titer samples. But the FDA’s Emergency Use Authorization actually emphasizes that it covers all plasma samples, reflecting the report’s conclusions which also do not differentiate.
I don’t have a crystal ball and I can’t read the minds of the report’s authors in terms of what other comparisons they may have considered, or what they might be working on now to deliver more reliable answers. But their choices and their conclusions thus far are entirely consistent with an academic culture that rewards the finding of significant, positive effects associated with new treatments. It is a culture that encourages positive spin about novel medical interventions and overstatement of marginal clinical results.
In normal times, we might take the admittedly cautious but still overstated conclusion of benefit in this report with a grain of salt and modulate our expectations of effectiveness in practice accordingly. Doctors wouldn’t rush off and change prescribing habits instantly on this thin body of evidence – not until they could see a clear and convincing difference between patients who got convalescent plasma, and patients who didn’t.
Clearly, these are not normal times. Scientists need to do whatever they can to make sure that their science is not misinterpreted, exaggerated, or even hijacked to make a political point. I sure hope that Drs Joyner and Casadevall are alarmed that their research featured so prominently in what can only be considered a dress rehearsal for the vaccine study results that are expected later this fall.
David Shaywitz
As the summer draws to a close, I thought TR readers might enjoy a final August distraction. I’ve always been an avid reader, and lately, I’ve found myself increasingly drawn to the history and science of American politics.
On the history front, and inspired by Stanford professor Jeffrey Pfeffer (an expert on power and leadership), I’ve started Robert Caro’s famously comprehensive multi-part biography of Lyndon Johnson. I’ve completed the first volume: The Path To Power (1982), which describes how the Johnson family first arrived in Texas, and takes us from Johnson’s rough childhood in the unforgiving hill country, through his election as a Congressperson in 1937, and concludes with his razor-thin loss in the special Senate election of 1948.
Johnson emerges as a striking if deeply flawed individual, with indomitable ambition, a relentless work ethic, a need to be in charge, and a drive to win at all costs. A student of human nature, he cajoles those whose support he craves and dominates those beneath him, tirelessly manipulating all who he encounters.
Johnson steals a series of elections, starting in college. Ironically, his defeat in the Senate 1948 race apparently reflected not the propriety of the election, but rather the ability of his opposition to cheat more effectively. 
It becomes immediately apparent that ambition, narcissism, and the privileging of victory over ethics is not the sole provenance of a particular person, party, place, or time. I’m just beginning the next volume (Means of Ascent [1990]), and the author cautions in the preface that (somehow) it promises to be even darker than the first.
For those interested in a deeper understanding of the contemporary political state, the obvious must-read here is Tim Alberta’s American Carnage (2019), describing the transformation of the Republican party into the party of Trump. Alberta is now the Chief Political Correspondent at Politico, and previously wrote for the conservative National Review.
Alberta brings unusually deep insight into his subject, and the sense that he’s reporting about the party’s current state more in sorrow than in triumph. You can get a feel for his style from his deeply perceptive recent essay about the state of the GOP, lamenting that no one really knows what the party believes in anymore.
Technology and the sophisticated use of data and social media is often said to have played a central role in Trump’s 2016 victory. We’re afforded an insider view of this in Targeted, Brittany Kaiser’s account of her involvement with the infamous data firm Cambridge Analytica (CA), and its close relationship with the Trump campaign. Aspects of this are also covered in the Netflix documentary, “The Great Hack.”
While Kaiser suggests Cambridge Analytica played a key role in Trump’s victory, many others are more doubtful. Writing in The Atlantic, Ian Borogos and Alexis Madrigal dismiss the contribution from Cambridge Analytica, and emphasize the role of the Facebook algorithm itself.
Even here the impact isn’t clear; Hugo Mercier, a cognitive scientist immersed in this literature, tells me that “the effects of advertising [including online ads] for political campaigns in general elections are small at best.”
He adds “the effects of online ads are small and noisy, indeed, so small and noisy (as a rule) that even researchers at Google admit that it’s impossible to know whether online ads bring positive ROI [return on investment].” I recently downloaded his new book, Not Born Yesterday, and look forward to his discussion of why persuasion is so difficult.
The challenge of voter persuasion in particular was highlighted by a high-profile 2017 study from two academic researchers: David Broockman, now at UC Berkeley and Joshua Kalla, at Yale. They examined data from 49 field trials, and concluded that political campaigns have essentially no detectable impact on candidate choice in general elections. Most people ultimately vote along familiar party lines, and it’s apparently much more effective to mobilize your own partisans compared to trying to get partisans on the other side to switch.
It also turns out that the fraction of persuadable voters is probably also a lot less than you might think. A key research finding is that while ever-more people self-identify as “independent, if you push them, most of these acknowledge they lean left or lean right. Data suggest these leaners are at least as partisan in their ultimate voting as the voters who affiliate from the outset as either Democrat or Republican. Thus the number of true independents is comparatively small.”
There’s also the added challenge of how to think about this middle group, often referred to as “undecideds.” This can refer to likely voters who are actively weighing who to vote for, but could also encompass voters who feel alienated from politics (and the increasingly partisan nature of politics), and opt not to vote at all; such “low frequency” voters are notoriously challenging to draw to the polls.
Two fascinating political scientists who’ve thoughtfully discussed aspects of these issues: Rachel Bitecofer (here) and David Shor (here).
The difficulty of changing votes meanwhile doesn’t mean persuasion is entirely useless. Work from Todd Rogers applying psychology to election strategy through an iterative series of field trials has resulted in a series of ways to “nudge the vote,” as Sasha Issenberg of the New York Times put it in 2010. (Issenberg subsequently wrote a book on the topic, The Victory Lab (2012), examining the science behind Obama’s 2008 win).
One successful method: closing the intention/action gap by asking voters questions that force them to think through how they’ll actually go about voting; this approach has been shown to increase turnout in a statistically significant fashion.
Importantly, while it can be difficult to convince voters to change their minds, research suggests that some efforts to persuade people to change their opinion – particularly outside the partisan signaling associated with a general election – can be successful. For example, Broockman and Kalla published a study in 2016 that found a small but significant number of respondents (about 10%) could be persuaded to embrace a more sympathetic view of transgender rights, a change that endured for at least three months.
The key, it turned out, was “deep canvassing,” meaning the field volunteers engaged in an extended, empathetic conversation with the respondents, listening intently and asking thoughtful follow-up questions.
In these fractious times, this is a hopeful message from which we all can learn. When everyone is so busy trying to talk, there can be remarkable power in attentive listening.
Peter Kolchinsky, managing partner, RA Capital
With today’s Aug. 24 deadline looming, it’s important to explain how President Trump’s “most favored nations” executive order to purportedly lower drug prices would actually backfire, and hurt patients both at home and abroad.
The order, which ties prices for certain drugs paid for by Medicare to the lowest prices paid in other countries, including Canada and much of Europe, also conspicuously ignores the real problems faced by the American healthcare system.
Trump says that “Americans are funding the enormous cost of drug research and development for the entire planet,” which is why he doesn’t like that other countries are getting lower prices.
The trouble is that the president is wrong. Right now, other countries do pitch in – and his new order isn’t going to make other countries pay their fair share for pharmaceutical R&D.
Instead, it’s going to result in them paying no share at all.
Even a bad roommate that chips in a bit of money here and there helps defray the cost of a mortgage. For drug companies, some profit from Europe and elsewhere is better than nothing. In the meantime, Americans continue to benefit as Europeans, Canadians, and many others defray at least some cost of paying off the mortgage on pharmaceutical innovations that American patients need.
But instead of ensuring that Americans pay less for drugs, Trump’s order – while intending to “import” prices from abroad – would actually force companies to “export” U.S. prices for drugs to other countries in an attempt to protect their most important market. And when European and other countries refuse to pay, patients suffer the world over.
Turning Europe and Canada into entirely untapped markets would mean that instead of benefiting from other countries pitching in for, as President Trump has said, “the enormous cost of drug research and development,” Trump’s order will leave America standing by itself to shoulder those costs.
The downstream impact is obvious: because he doesn’t seem to understand basic economics, the president’s “favored nations” order would literally cost Americans more.
The dynamic is pretty simple. To make up for the loss of revenue in Europe, Canada and elsewhere, pharmaceutical companies would respond by raising US drug prices higher than they already are. And given our dysfunctional healthcare system, this would translate directly into higher out-of-pocket drug costs for the people that Trump claims to be fighting for.
Other nations have demonstrated that they’re willing to deny life-saving medicines to their citizens on the basis of cost. They have demonstrated that they’re willing to wait longer for access to new, lifesaving drugs – even when they do eventually decide they’re worth paying for.
Those kinds of price controls are simply un-American and mistakes we do not want to import or normalize.
Nevertheless, since President Trump’s orders do nothing to lower out-of-pocket costs, Americans would still be saddled with high deductibles and copays.
Some might be inclined to just fall back on blaming the drug industry, mistaking high drug prices for large profits. But the drug industry’s collective profits are only 10-12% of revenues when the successful companies are combined with all the unprofitable biotechs still investing tremendous amounts of money, at risk, on developing new drugs, including treatments and vaccines for COVID-19.
Because drugs eventually go generic, in order to stay in business, drug companies have to invent new drugs that Americans need. If they don’t invent, they die.
Meanwhile, insurance companies invent nothing. Insurance companies generated greater profits when COVID-19 struck because they were able to continue collecting monthly premium payments while patients stopped going to doctors and getting treatments. Even in the pandemic when so many people had to sacrifice so much, insurers still had the gall to reject coverage for COVID-19 testing and care when people got sick.
The diagnosis for what ails America is clear – our health insurance plans.
What the president could do – but hasn’t done – is push for all Americans to have insurance with low out-of-pocket costs. He might also consider eliminating out-of-pocket costs for drugs when there is no clinically equivalent, inexpensive, high-quality, generic alternative.
This, however, would require an acknowledgement of a simple but elusive truth: what makes any aspect of healthcare affordable for patients is a function of the insurance they have. If an insurance plan sticks patients with massive out-of-pocket costs, then treatment is expensive and inaccessible. If an insurance plan covers treatment fully, treatment is accessible.
The bottom line: America needs insurance reform. We need our next president and Congress to reform health insurance so that Americans’ insurance plans actually offer what they advertise – affordable access to healthcare.
Peter Kolchinsky, a biotechnology investor and scientist, is Managing Partner of RA Capital Management, L.P., and author of The Great American Drug Deal.
All of us need some time and space for spiritual renewal. Especially in a year like 2020.
Mountain climbing does it for me.
Last weekend, I was fortunate to get out to Mt. Shuksan in the remote North Cascades of Washington. It was a gorgeous climb, led by my friends and partners at Alpine Ascents International.
The climb was a charity fundraiser for Facing Homelessness. It’s a Seattle-based group that uses the power of photography to expand our sense of empathy for our fellow humans. It’s an important first step.
I hope you enjoy these mountain photos, and that you may find some inspiration in the good work of Facing Homelessness.
Watch for the Frontpoints column next week.
Thanks for reading, and stay well.–Luke
Mt. Baker, shot from the Shannon Ridge Trail up Mt. Shuksan.
Mt. Shuksan, from a distance.
Sunrise on Mt. Shuksan, looking East at the North Cascades.
The view of Mt. Baker, near the summit of Shuksan (elev. 9,127 ft).
Summit of Mt. Shuksan, Aug. 17, 2020. Photo by David Gottlieb, Alpine Ascents International
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Today’s guest on The Long Run is Rosana Kapeller.
She’s the president and CEO of Cambridge, Mass.-based ROME Therapeutics.
ROME aims to discover and develop drugs based on emerging science in what is sometimes called the “repeatome.”
Rosana Kapeller, president and CEO, ROME Therapeutics
These are long repeat stretches of DNA that scientists until recently knew very little about, and still have a lot to learn about with regard to its function. ROME made its official debut in April with a $50 million Series A financing from GV, Arch Venture Partners and Partners Innovation Fund. The stated plan is to interrogate this territory to look for new targets to treat cancer and autoimmune disease.
Rosana was born and raised in Rio de Janeiro, and went to medical school there before coming to the Boston area to do her PhD in molecular and cell biology. She got in early at Millennium Pharmaceuticals, was there during its rise to prominence in the first genomics boom, and then took on increasing roles of responsibility at a couple of startups – Aileron Therapeutics and Nimbus Therapeutics.
She has serious science and technology chops, and is now learning to adjust to the new role of being a CEO.
Rosana is a warm person and has a wonderful laugh, which you’ll hear from the start. This is an enjoyable conversation with a consummate scientific entrepreneur.
Now, please join me and Rosana Kapeller on The Long Run.
Are you a fan of The Long Run podcast? Trying to raise awareness of your company, your organization, or your services with a high-powered crowd of entrepreneurs and venture investors who listen to The Long Run? My business representative, Stephanie Barnes, can tell you about sponsorship opportunities of this show. But first, tell me about your company in a brief email – luke@timmermanreport.com.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Bonnie H. Anderson, chairman and CEO, Veracyte
There are very few things many of us can say with 100 percent certainty. But after three decades of experience in molecular diagnostics and the life science industry, I can say this: never before has the healthcare profession, and even the American public, been so laser-focused on diagnostics.
For all the tragedy it has caused, the COVID-19 pandemic has shone a much-needed spotlight on the value of medical diagnostics. It has forced important discussions about how we think about and deploy these tools.
Challenges in running enough tests to quickly and accurately diagnose everyone infected with COVID-19 in the United States – in order to properly treat those in need and gather the information necessary to slow the spread of infection – have forced our country to take a crash course in diagnostic tests.
Everyone from the United States’ leading infectious disease experts and our country’s most influential healthcare reporters to a broad swath of the general public are intensely engaged in COVID-19 tests, weighing accuracy against turnaround time, sensitivity against specificity, or the appropriate time to use a molecular test to spot an active infection versus an antibody test to determine whether an individual was previously exposed to SARS-CoV-2.
Beyond the education factor, COVID-19 has revealed the broader utility of these tests. Diagnostics have become the gatekeeper to getting the economy back on track, sending children back to school and allowing American citizens to travel into other countries.
COVID-19 has also – finally – forced our hand in terms of making testing more accessible. Diagnostic testing is no longer limited to clinical centers. Instead, people are getting creative, expanding test access in order to meet a previously unfathomable demand for tens of millions of tests. Around the country, we’ve seen the dramatic expansion of mobile testing centers, pop-up testing locations, and point-of-care testing. Employers are testing their workforce — a move that’s been largely lauded by both groups as a means of getting people back to work safely and responsibly.
In some of these settings, a doctor’s prescription isn’t always necessary to get these tests. The surge in demand forces us to ask ourselves basic questions, such as when a prescribing healthcare professional is necessary to maintain safe and effective healthcare decision-making and when having to see one creates an unnecessary bottleneck in the system. In this case, the RT-PCR tests marketed by reputable companies and performed by well-equipped academic and private industry labs are so simple and reliable that it makes more sense in the long term to open the floodgates for consumers to directly order up tests themselves, have sample collection kits shipped straight to their homes, and then ship their samples back to a lab for analysis.
As diagnostics have become a central character in the pandemic story, public and private funding has poured in for the development of new tests.
Valuations for companies designing and manufacturing diagnostic tests have soared. New tests are cheered by reporters on the national news. The success we’ve seen has come from a massive, concerted effort by diagnostic developers, healthcare providers, regulatory agencies, and insurance companies.
As a veteran of the diagnostics industry, I am pleased to see that these tests are finally getting their due.
But it also makes me wonder why it took a global pandemic to get us here. And when COVID-19 is finally conquered, will the interest in diagnostics fade along with it?
Funds started flowing freely to diagnostic development when it became clear these tests would be necessary to get the economy back up and running at full capacity. Unfortunately, we have never seen this response when just as many lives were at stake, but the economy was functioning as usual.
Consider lung cancer, an area that I have focused on for more than a decade. The American Cancer Society estimates that more than 228,000 people will be diagnosed with lung cancer and nearly 136,000 people will die from it in the U.S. this year. The vast majority of funding dedicated to lung cancer goes toward research efforts focused on developing new therapeutics. But diagnostic tools that could detect lung cancer at much earlier stages and significantly improve patients’ chances of survival might offer more benefit and take a lot less funding to bring to market.
The most common objection I hear about introducing new diagnostic tests is that they will add to the already unsustainable costs of healthcare. This is a fundamental misunderstanding of diagnostics; good tests, developed carefully and targeted at specific health needs, will in fact reduce costs.
Today, just 2.3% of healthcare spending in the U.S. comes from in vitro diagnostics. Think about that for a second – it’s a statement about our traditional view of the value of diagnostics. This is the number one reason why we see so many therapeutics startups receive large sums of venture capital, while relatively little is directed toward diagnostics.
We pay a lot for drugs, not so much for diagnostics.
Looking a little further into the healthcare spending data, we can presume that advanced genomic tests from specialty laboratories – one of the most technologically promising aspects of the diagnostics industry — account for an even smaller slice of overall spending. If we doubled or even tripled that spending in order to detect health problems earlier — to start treatment of diseases when they are far less expensive to manage than they are in later stages — we would still be coming out ahead on overall expenditures. Testing is cheap compared to long-term illness, treatment, and death.
Placing a higher priority on the development and availability of advanced genomic tests for early disease detection would represent a major step in shifting our healthcare system’s focus from treatment to wellness. Diagnostics can help us catch diseases early, prevent the spread of illness, keep people healthy at work, and reduce all kinds of costs associated with undiagnosed disease and therapies. While those of us in the diagnostics community have known this all along, the COVID-19 pandemic has raised awareness of it for a much larger audience.
We need payers to buy into the value of early diagnosis through more sensitive and less invasive testing. This model can save lives and lower costs. Yet in the past, government agencies and insurance companies have often balked at covering such tests.
Consider the example of idiopathic pulmonary fibrosis (IPF), a serious and progressive lung disease. It takes the typical patient years to get diagnosed, often through multiple invasive, costly and potentially dangerous procedures. During that time, more than 20% of these patients will be given treatments that can actually harm them. My company has developed a clinically validated genomic test that can help end the diagnostic odyssey and get patients on the right treatment faster. This would improve health and reduce healthcare costs, and yet private payers have been very slow to cover it.
If we can conceive of pandemic-related diagnostic strategies to determine which people are safe to go to school, get on a plane, or return to work, then surely we can see the value in using diagnostic tests to tackle other leading causes of death.
We need to start waking up and realizing the benefits of early diagnosis of disease — the idea that we can test to stay well, instead of counting on therapies to make us well again.
Perhaps the new models of testing established for the pandemic will jump-start a different approach to diagnosis.
Could the new model of workplace testing be an avenue that opens up diagnostics beyond just COVID-19 testing? For example, does it open the door for broader screening and earlier detection of lung cancer? There are obvious issues with how much access employers have to their employees’ health information, and those will have to be managed carefully. But if employees come to trust their employers to deliver high-quality diagnostic tests — perhaps better tests than those they could access through typical healthcare channels — this could transform the valuation of and payment for important tests designed to keep people healthier and keep insurance premiums in check.
Self-insured employers could be the first to adopt this approach, and if it proves successful, other large employers might quickly follow suit. This is not enough, but it could be an interesting place to start.
COVID-19 has changed the way we are delivering healthcare and has — at least for the moment — dramatically increased the value we place on diagnostics and early detection. I truly believe if we can hold onto the lessons we are learning from this pandemic, there could be a brighter future for improving health and reducing healthcare costs through the use of diagnostic tests across a wide range of diseases and health conditions.
Bonnie H. Anderson is Chairman and Chief Executive Officer of Veracyte, a global genomic diagnostics company that improves patient care by providing answers to clinical questions that inform diagnosis and treatment decisions.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
Medicine is plagued by a feedback gap, or perhaps more accurately, a feedback paradox.
On the one hand, clinicians are bombarded by feedback. Every day, there are a slew of process and billing metrics to review, providing an accounting of the volume of patients seen, and the intensity of each visit.
How thorough was the exam? What procedures may have been performed?
Even beyond the measures related to billing are an ever-growing number of measures related to guideline adherence. Doctors have an increasingly long set of questions to answer, boxes to check.
Did you ask about seatbelts use? Did you ask about home safety? Have you scheduled an eye exam for your patient with diabetes? These are often, individually, sensible and relevant considerations. In aggregate, however, they can be overwhelming and lead to guidance fatigue and ethical slippage, as Bill Gardner (here) and Drs. David Blumenthal and J. Michael McGinnis (here) have discussed, and as I recently reviewed in the context of COVID-19.
Yet for all these metrics and assessments, what’s often lacking is any sense of how the patients are actually faring – the thing that matters most.
It’s not at all clear whether all the carefully documented and billed work is leading to improved outcomes for patients. Are there approaches for some patients that are leading to better outcomes than other approaches, and which could be generalized? Are there other approaches that should be abandoned?
The heart of the problem is how most clinical research is done. On the one hand, to demonstrate, scientifically, that a particular approach (whether a novel drug or a new approach to therapy, like placing sick COVID-19 patients on their stomachs instead of their backs when they’re in the hospital) works, a clinical trial – ideally, a randomized, double-blind, placebo-controlled, to the extent possible – is performed. This is a highly choreographed exercise, where the exact characteristics of the subjects to be enrolled, the evaluations to be performed, and the criteria for success are all spelled out, clearly, in advance (or at least, ought to be). Enrolled subjects are tracked meticulously, and ultimately, you should be able to determine if the intervention was more effective than your control at achieving the pre-specified outcomes.
This is the way medical science advances – through rigorous clinical studies, and then the adoption of these results in clinical practice.
The conceit of this approach is that the results from a rigorous clinical study will be widely generalizable, meaning that the patient you’re treating will respond like the subjects in the study, and the treatment you’re providing will mirror that offered in the study.
Of course, we all know side effects can emerge over time that weren’t seen in clinical trials, and we recognize that certain groups – women and minorities in particular – have historically been underrepresented in clinical trial populations.
To the extent that you accept the basic premise that a well-run clinical trial yields results that can guide care, then it makes sense for health systems to impose processes that try to ensure patients with a particular condition receive the treatment indicated by clinical trials.
This approach has given rise to the idea of “disease pathways,” that provide a template for managing patients with a particular condition, and the embrace of process metrics, that try to ensure doctors are following the updated guidance – rather than, say, doing what perhaps they’ve always done because it’s what they know.
In practice, there are two problems with this system: one stemming from doctors who disregard these pathways, the other related to doctors who adhere to them.
Many physicians (perhaps especially in august academic institutions) worry most about their colleagues, especially those who practice in the community.
These physicians are often presumed by their university colleagues to be less attuned to the latest scientific literature, and hence neither of relevant advances in care nor practicing in a setting that encourages them to do so. Consequently, their patients may not be receiving the most optimal care, and have no way of knowing it.
But a second category of challenge involves the physicians who are attuned to guidelines, perhaps because they closely follow the literature, or perhaps because their health system nudges them to adhere. While arguably the patients in the care of these physicians are on balance better off than those in the first group, there are still important challenges – and embedded opportunities.
For starters, subjects in clinical studies are notoriously unrepresentative of the more general population – enrolled subjects tend to be healthier and to have fewer co-existing conditions, in effort to enable a clearer evaluation of the intervention, and provide a sort of “best case” read-out.
In addition, subjects in trials tend to be followed unusually closely – they may be reminded to take their medicine, for example, and encouraged along the way by study staff. While inclusion of a placebo group controls for the therapeutic impact of the extra attention (known as the “Hawthorne Effect”), it doesn’t account for the combined effect of experimental intervention plus attention – the increased likelihood that a subject in a study will reliably take a given medicine, say, compared to a patient in a doctor’s office, where the medication adherence rate can be astonishingly low. (It’s also why tech-enabled health service companies that feature a strong coaching and tracking component, like Omada and Virta, have gained considerable traction.)
As UCSF’s Dr. Fred Kleinsinger noted in a 2018 publication:
“Medication nonadherence for patients with chronic diseases is extremely common, affecting as many as 40% to 50% of patients who are prescribed medications for management of chronic conditions such as diabetes or hypertension. This nonadherence to prescribed treatment is thought to cause at least 100,000 preventable deaths and $100 billion in preventable medical costs per year.”
Finally, some patients in clinical practice may actually respond better than expected to a particular intervention – perhaps because of an intrinsic characteristic (involving their genetics or their microbiome, for example), or perhaps because of something subtle a particular physician or care provider did, a tweak that might goose the patient along a little bit. These are opportunities to learn from “happy accidents,” to institutionalize serendipity, so to speak, and benefit from the ingenuity of inquisitive physicians (medicine’s lead users, to use von Hippel’s term) –opportunities that are easily missed in the course of routine care.
The common feature of all these clinical care scenarios is the almost complete absence of tracking the one thing that matters most – actual patient outcomes over the long term.
A new medicine may get approved on the basis of robust clinical trials, but the approval of a drug, as I’ve argued, doesn’t necessarily equate with the improvement of outcomes of real world patients.
Why aren’t outcomes tracked in routine care the way they are in clinical trials? For one, it’s surprisingly hard to track patient journeys, especially as patients often get care from multiple medical systems – though even rigorously determining the outcome for a patient entirely within one medical system can be remarkably difficult, especially in the context of real life, when appointments are missed, and assessments can be spotty and inconsistent, and documentation unreliable.
But the second reason the information isn’t tracked is, essentially, no one (besides the patient!) really cares, in the sense of being personally invested in (and accountable for) the outcome. Certainly not in fee-for-service systems, where doctors (who, to be clear, obviously try their best for each patient) charge based on their activity rather than patient outcome. Hence, the extended push for approaches that seek to prioritize improved care, and specifically, improved outcomes. But even here it’s challenging – doctors will always argue that their specific patients are different – more complex or sicker, say, so of course they’ll do worse.
Often, the doctors may be right, and efforts to increase transparency around the success of certain surgical procedures has naturally led to notorious gaming of the system, where a mediocre heart surgeon who selects only the easiest cases scores higher than an exceptional surgeon who takes on the most difficult ones.
Attempts to adjust for complexity, inevitably, go only so far.
What emerges from this is a health system that, despite what may be the best intentions of providers, essentially, is flying blind, and lacks the basic ability to see what’s it doing, a system that lacks the fundamental ability to iteratively optimize and improve.
Yes, care improvement occurs – but over incredibly long periods of time, driven by the slow pace of robust clinical trials, rather than by the opportunities to systematically learn from patients and providers every single day.
For years, we’ve championed the concept of a “learning health system,” and today, it remains largely an unrealized aspiration.
What is to be done?
Mark McClellan, professor, Duke University
A remarkable recent two-day conference organized by Dr. Mark McClellan and colleagues at the Duke-Margolis Center (all materials available here) dug into exactly this challenge, drilling into the question of whether there can be some kind of convergence between the process of collecting and analyzing data for clinical trials and doing the same for clinical care; data collected outside of traditional clinical trial is often called “real world data,” or RWD.
While it would be a disservice to condense such a rich conference into tidy conclusions, there were nevertheless several powerful lessons that I took away.
For one, I was reminded yet again of the rigor and meticulousness of clinical trials. I knew this, of course, having designed, written and executed a number of studies, and having been immersed for years in research environments focused on this process. Even so, it was instructive to go through the challenges of defining who, exactly, has a particular condition – what is the definition of a “case?” What is the definition of an “outcome?” In the context of clinical trials, the criteria tend to be exceptional explicit, and evaluation of results can often require a deliberate, pre-defined process of adjudication.
The challenge, many speakers emphasized, is that doctors, busy taking care of patients – perhaps seeing patients every 15 minutes or 20 minutes (not uncommon, and I’ve heard of less) – barely have enough time to perform the minimum services (and documentation) required to get paid.
The detailed evaluation typically required in clinical trials feels like a luxury few busy physicians have as they fly through their day. Many speakers highlighted the importance of not contributing to provider burden, and several noted the increasing rate of doctor burnout, which some have attributed to the “death by a thousand clicks” nature of existing interactions with the electronic health record system.
Another huge challenge is that, while clinical trial data is typically captured in a dedicated database explicitly built with the desired analysis in mind, patient care data is often scattered across a healthcare system, or multiple healthcare systems, where it can be difficult to even know what other relevant data exist and might be germane.
For at least one speaker, UCSF breast surgeon and clinical trial innovator Dr. Laura Esserman, the right solution would involve the radical re-engineering of care delivery — “a sea-change in how clinicians practice,” so that clinical-trial quality data are routinely captured. By doing a much better job collecting data, and by improving how we gather and share data, she argues, we can ultimately both save time — by entering data once, and using it many times — and improve care.
Laura Esserman, professor, surgery; UCSF
As Esserman points out (and I couldn’t agree with this point more), “Imagine a business where you have no idea what your outcomes are, no idea what your metrics are. We must be in the business of quality improvement.”
For many other speakers, the priority was searching for ways to improve the system while not requiring extensive changes in the way doctors practice. For all the discussion of stakeholder alignment, the intrinsic tension between providers and researchers was palpable, and acknowledged by a number of the presenters.
In one of the day’s best talks, Chhaya Shadra of Verana Health, a startup focused on real world data in several specialties including ophthalmology, neurology, and urology, argued “If you have to take time to improve documentation to help researchers, it’s not fair to clinicians whose primary responsibility is patient care.”
Similar sentiments were expressed by UCSF’s unfailingly insightful and articulate health IT policy researcher Julia Adler-Milstein, in a recent Annals of Internal Medicine commentary on a paper revealing that physicians spend around 16 minutes per patient engaging with the EHR system, about a third of the time on chart review (i.e. trying to find information), another quarter of the time on documentation (adding their own notes), and another 17% of the time on order entry – all told, a remarkable amount of hunting and pecking.
The dilemma we face, Adler-Milstein observes, is:
“How do we generate the foundation of clinical data needed to support the EHR’s many high value uses (including but not limited to clinical care) while doing so efficiently (for example, improving user interface design, using digital scribes, and simplifying documentations)? Even with the most efficient approach, physicians (and many other types of clinicians) will never obtain a direct return from future use of their documentation equal to their time cost of documentation. At a minimum, acknowledging this mismatch and making physicians feel valued for the time they spend in the EHR is needed. (emphasis added)
Many of the mitigation approaches Adler-Milstein described were also highlighted by speakers at the Duke conference: for instance, the founder of Google Glass company Augmedix described the company’s focus on serving as a digital scribe; other presenters emphasized the need for improved EHR user interface, and more generally, for more human factors research and design.
Julia Adler-Milstein, professor of medicine, UCSF
A number of presenters also emphasized the importance of understanding the questions you are trying to ask, and pointing out that for a number of applications – some population trends, for example – you may not need perfect data, and may be able to extract real value from what you have. But, even there, you still need to have a sense of both the quality of the underlying data and the nature of the existing imperfections, so that you don’t subsequently use the data inappropriately, and draw misguided conclusions.
Dr. Paul Friedman, chair of cardiology at the Mayo Clinic, offered examples that highlighted both the possibility and the limitations of using existing data. On the bright side, he highlighted a project demonstrating the use of artificial intelligence to deduce, with remarkable accuracy, a patient’s ejection fraction (a measure of heart output typically determined using cardiac echocardiography) from routinely collected electrocardiograms (ECGs) archived by the Mayo.
On the other hand, Friedman described the frustration of trying to predict COVID-19 infections (which may impact heart cells) using the sort of detection technology commonly available on smartphones and some wearables. In this case, he says, a key barrier turned out to be the lack of underlying standardization among these tools.
While all may report out something looking like a “standard” ECG, their approaches to deriving this are so different that you really struggle to develop an algorithm from these disparate data. Alignment around some set of standards involving sampling rate and dynamic range would help, he suggested.
Speakers evinced particular interest in the ability to stitch together multiple data types (say EHR data with claims data and specialty clinical data) using privacy-preserving technology like that offered by Datavant, a Bay Area startup cited by several presenters. This approach obviously won’t solve issues related to underlying quality of data that’s being linked, but it can help not only develop the rich data set representing a patient’s longitudinal journey, but by bringing together a range of sources, the technology may help surface — and in some cases, resolve — data entry errors and other ambiguities.
The opportunities – and the risks, especially around privacy – of collecting and including more data from wearables and other “consumer” products in a patient’s health record were also highlighted by several speakers. This 10-minute talk by Elektra’s Andy Corovos may be among the best overviews I’ve seen on this topic — a topic that for years has been especially near and dear to my heart.
As Veradigm’s Stephanie Reisinger noted in (another) compelling presentation, the consumerization of healthcare, including the increased use of devices and apps, represents a major healthcare trend, “empowering us to see and share health data,” and “driving informed consumers to demand a greater say over health journeys. Human bodies are becoming big data platforms.”
Of course, this possibility also relates directly to the sorts of security concerns Coravos and others highlighted.
What’s also clear is that pharma is looking at these large integrated datasets in different ways than they once did. Initially, pharma companies turned to real world evidence primarily in the context of health economics – typically “health technology assessments.” Primarily, pharmas were seeking evidence of real-world performance, to facilitate engagement (and negotiations) with payors (and, ex-US, regulators who require a discussion of relative value).
Today, Veradigm’s Reisinger says, the most common questions from pharma partners involve protocol optimization (understanding the impact of various inclusions and exclusion criteria and the number of patients you might be able to recruit, for example), patient finding (recruitment – an effort to connect eligible patients with suitable trials) and the development of synthetic control arms for some studies (for situations when a suitable control arm may not be feasible or ethically appropriate but a relevant basis of comparison is required, for example).
Finally, several speakers, including FDA’s Dr. Amy Abernethy, highlighted the potential value of real world data in understanding and developing effective therapeutics for COVID-19; this is the focus of the COVID-19 Evidence Accelerator, for example. The recent Surgisphere scandal involving COVID-19 data (I discuss this in detail here) was cited by several speakers including Abernethy, who highlights the need for “ruthless transparency.”
I left the conference feeling neither entirely elated nor thoroughly despondent – though perhaps with a healthy mixture of the two emotions. As Abernethy observed, “doing science is messy, and we’re doing science together as a community.”
There are real opportunities here, as well as intrinsically difficult hurdles – many presenters pointed out that the social/political/”psychological” (as one speaker said) hurdles are far greater than the technology hurdles. That said, it’s also quite possible that improved technology could catalyze advances that might highlight the potential value here and motivate further collaboration.
Another hope would be that an innovative health delivery system, perhaps reimagined along the lines that I’ve described here, and that Esserman has championed, emerges somewhere and proves so compelling to patients that it becomes the immediate gold standard, and is widely adopted.
A more immediate step might be the voluntary adoption of some of the technical standards for devices that Friedman describes.
In short, I came away impressed by the urgent need to improve how our healthcare system captures and shares data, and thus learns – or, presently, fails to learn – from patient experience. I also appreciate, along a range of dimensions, just why this seemingly urgent and obvious problem has remained such a stubbornly difficult nut to crack.
Above all, I’m struck, by the need – in the phrase of legendary NASA flight director Gene Kranz – to continue to work the problem.
David Shaywitz
Entrepreneurial stories, often told through the narrative framework of the hero’s journey, are by now well past the point of cliché.
Yet, it’s important, perhaps even instructive, to remind ourselves every now and again about the exceptional, transformative power of entrepreneurship, the can-do thinking it motivates, and the remarkable progress it can propel.
This potential emerged as a central theme of Dr. Jill Hagenkord’s recent appearance on the Tech Tonics podcast that Lisa Suennen and I regularly host, as Hagenkord shared her journey that Suennen nicely characterized as “a rags to riches story in the most American of ways.”
Hagenkord grew up in rural Iowa, an already difficult childhood made worse, she says, by her parents divorce when she was ten. Dependent on government assistance for housing and food, she was a self-described “behavior problem” throughout school.
Jill Hagenkord
She chose to attend college — an almost unheard of decision for girls in her area, according to Hagenkord — because she wanted to get away, and heard there were “great parties and cute guys” at the University of Iowa, in Iowa City, 90 minutes away.
At college, she partied hard but also worked hard, both in her classes — she made the Dean’s List first semester, despite the nearly complete absence of college prep in high school — and beyond, where she typically held down 2-3 jobs to make ends meet.
Initially, she focused on fortifying the humanities education she realized she missed in high school.
However, her life direction was then profoundly influenced, as she tells it, by a “jerk boyfriend,” a medical student who she dated for six years. When the guy told her she was only doing well because she was taking easy humanities classes, she took science classes in order to prove him wrong – which she did.
Then he bet her she could never succeed in the most difficult pre-med classes, a biology lab. She aced it, despite lacking any of the prerequisites. She won the wager and claimed the prize: dinner at the “nicest restaurant in town” – the local Red Lobster.
Unimpressed, Hagenkord’s perversely significant other assured her she could never get into Harvard or Stanford University. So she applied and won admission to the MD/PhD program at Stanford.
Hagenkord was excited about coming to the Bay Area, but never felt “culturally comfortable” at Stanford’s campus in suburban Palo Alto. So she lived in the city, almost an hour away, and never showed up for class, just for the exams, which she apparently crushed anyway.
She dropped the PhD part, and decided to pursue a medical residency in pathology — a discipline without a lot of one-on-one contact with (living) patients. She had found aspects of clinical medicine — like telling a patient or a family that there was nothing that could be done — overwhelming sad.
She started her pathology residency training at UCSF, but was drawn away by the lure of a startup. It was the boom of the late 1990s, she said, and it seemed everyone in the Valley was joining a startup, where new millionaires appeared to be minted by the day.
The experience, she said, was “exhilarating.” She was surrounded by “all of these super bright science PhDs and tech people, super bright and super ambitious.”
She added, “I learned so much and it opened my mind up so much to the possibility of how can I be an entrepreneur in medicine.”
Hagenkord joined the company when it was relatively small, stayed with it through growth to over 500 people, saw it expand internationally, and was there for its IPO. While it doesn’t sound like she saw a large financial return, the startup experience clearly affected the way she thought about problems.
Even when she returned to finish her pathology residency — which she did back in Iowa — “every step of the way,” she said, “I was constantly thinking — this could be done better, this could be done more efficiently, how do we do this better?”
As Suennen points out on the podcast, this must have represented a huge contrast for Hagenkord: “After hearing all your life that ‘things cannot be done’ or ‘you cannot do this,’ on the entrepreneurial side, things are characterized by ‘you can do anything, if you work hard enough.’”
It sounds like this more hopeful attitude suited Hagenkord. “It’s such a long shot but you can will it to happen,” she explains. “It was something that had never been done before. And I did get criticized — but it gave me all the courage I needed to think, ‘I can start my own company.’”
Even so, she initially elected to continue her training in Iowa because:
“I tried to be a ‘normal’ pathologist, live a quiet life, make a good paycheck pushing glass slides — but as I finished my residency, there was too much happening on the bleeding edge. What I saw coming were these massively parallel testing technologies, where instead of looking at one analyte at a time, we were looking at hundreds or millions at a time, and were going to need computational tools to distill this information into medically meaningful nuggets. That’s what I was the most excited about and wanted to learn the most about.”
Consequently, she pursued a customized dual pathology fellowship program. The faculty at the University of Pittsburgh Medical Center liked her proposal so much that they “invented” the fellowship for her. The program allowed Hagenkord to gain specialized pathology training in both molecular genetics and oncology informatics – a combination she says has since become more common.
When she told the head of her residency program about her plans, Hagenkord says, he told her “Jill, why don’t you do a real fellowship? You’re never going to get a job.”
She adds, with a grin, “When the old curmudgeonly white dude is telling you that you don’t know what you’re doing, you should feel reassured that you’re onto something.”
She then returned to Silicon Valley, in a series of roles at genetics companies. Early on, she got to know the co-founders of 23andMe. As she tells the story, “I got to know [23andMe co-founders] Anne [Wojcicki] and Linda [Avey — see her Tech Tonics episode here] through the Silicon Valley network. We’re around the same age, have kids, are working moms, live in the same town.”
She continues, “I could look at what they were doing from a regulatory point of view and recognize gaps. I would always say [to Anne and Linda] the consumer aspect of health is so exciting and you are leading the charge, let me help you any way I can. It wouldn’t hurt you to fix these gaps.”
For while, it seems, Hagenkord offered occasional, informal advice. But “after they got the warning letter [from the FDA] in November 2013, this led to another long conversation and wound up in a job offer [as Chief Medical Officer]. So I joined the company after they were shut down by the FDA, to try to re-build bridges with medical community.”
Hagenkord worked through that thorny set of issues with the team at 23andMe. These efforts paid off, and over time the company worked itself back into the Agency’s good graces. While Hagenkord left the company, she distinctly remembers the day she sold her 23andMe shares (on the secondary market, as the company hasn’t sold shares to the public). Her shares in the private company were worth enough to be “truly life-changing.”
On vacation in Mexico, when she heard the funds had been deposited in her account, she says she left a $1,000 tip for the bartender.
“It felt awesome — and we got really good service rest of time we were there.”
Reflecting on the experience, she describes it as “one of those Silicon Valley moments — not a ‘founder of Google Silicon Valley moment,’ but a decent Silicon Valley moment, when you have more money that you ever, ever, ever thought you’d have shows up in your bank account one day.”
She then reveled in the opportunity to play what she called, tongue-in-check, “Hillbilly Millionaire”:
“I called family — in Iowa — flew back, and said ‘everybody, get in the car, we’re going to the mall, buy anything you’ve ever, ever wanted.’ They were really bad at it, at first. They went into Target, went back to the clearance sale rack — spent like $250, and I was like, ‘I didn’t fly back here so you could buy $250 at the clearance sale rack — now get out there and really shop!’ They really did.”
The experience, she says, was “the best feeling ever” — though she also apparently enjoyed the 50th birthday present she treated herself to — a trip with 22 family members on a private yacht around coastal Turkey,” which she viewed as a chance to appreciate the “people who really supported me.”
After several more years advising health tech companies, she recently took on a new role, as head of genetics at Optum a subsidiary that’s been described as “the information technology and services arm” of UnitedHealth Group.
Once again, she is thinking of moving back to Iowa.
In addition to her inspiring story, Hagenkord shared several lessons about her experiences as an entrepreneurial physician working with technology entrepreneurs in Silicon Valley.
Although my own life journey could not have been more different from Hagenkord’s, we seem to have come to remarkably similarly conclusions from our experiences at the intersection for technology and medicine.
“In the last ten years,” Hagenkord says,
“tens of billions of dollars have been invested in healthtech, with very, very few success stories. It’s like watching the same movie over and over again, and it’s pretty torturous, especially as I’m usually the only person with a medical background in the company.
I know the recipe — there are no shortcuts, and if you try to skip a step, you’re going to set yourself back three years. But to watch these brilliant tech entrepreneurs, who’ve been wildly successful in consumer tech, try to take the recipe that’s worked in consumer tech — go fast and break things, fake it ‘til you make it. If you get it wrong trying to sell somebody a pair of shoes online, and they have to wait three days for their shoes instead of one day, it’s going to be OK.
The same recipe does not work well when you apply it to a real health product –what we call that is ‘unconsented human subjects research’ in healthcare. You can’t sell someone a finished product that you know is full of bugs without telling them. The philosophical and cultural differences between tech and health need to be better balanced.
Taking the best of these brilliant tech and design people and combining it with the best of the people with the medical experience and the understanding about how to access the medical market — having that balance are the healthtech companies that are going to be the most successful.
Hagenkord also weighed in on whether coastal entrepreneurs think about people in the middle of the country, and whether one can imagine Silicon Valley-type innovation in places like Iowa.
On how Silicon Valley tech entrepreneurs tend to see the world:
“One of the things you see is someone in love with their own technology looking for a place to stick their technology, as oppoed to looking at a problem and trying to solve the problem.
The Bay Area is such a weird bubble place — so not like middle America where I grew up. To be able to really understand — especially when you’re thinking about your commercialization strategy — the idea that ‘people will just buy this.’ People who can’t really pay their mortgage, and have their lights turned off, and are struggling to get milk — for them, looking at their genetics is a huge luxury that’s beyond comprehension, and I think that gets lost sometimes. But often times there are representative people like me in [startups] to remind them of that aspect.”
And the idea of entrepreneurship in the Midwest, as promoted by champions like J.D. Vance and Steve Case?
“I did spend a lot of time thinking about it — it’s really too hard to be an entrepreneur in the middle of the country, because none of the venture money is there.
There are very smart people in the Midwest; if I had a good loving family, I’d have wanted to stay there and raise my family there. It’s a matter of access — [to capital] and to mentors — so much of the Silicon Valley game is who you know, and who can introduce you to who, and who can vouch for you. When you’re living in Des Moines, it’s hard to make those connections.”
I raised a final point with Hagenkord, by email, after the podcast, asking her about the thought process leading to her recent decision to join not a venture capital firm or a startup, but rather at a massive insurance company — over the summer, she took at new job as Vice President of Genetics at Optum.
My own top of mind reaction echoed Hamilton’s King George III’s response to the news that President Washington would be succeeded by John Adams: “Good luck.”
But here’s how Hagenkord sees it:
“Health tech startups can raise an ungodly amount of funds, but they often struggle to 1) identify a real, tractable problem in health, and/or 2) try to apply a consumer tech go to market strategy to a health-related product. They are smart people and they learn and iterate, but often they waste valuable time and money learning how the health care market works and what kind of evidence they need to achieve widespread adoption.
At Optum, I can help tech entrepreneurs understand how to get on the market legally and how to successfully get to widespread adoption and payer coverage (if desired). Since Optum invests, partners with, and acquires health-related startups, the tech entrepreneurs and investors have a vested interest in engaging with Optum. They are more inclined to generate proper evidence data when Optum says they need it. I also have the support of an amazing team at Optum Genomics who are all aligned with this same vision. In short, I could do more good more quickly at Optum as part of a team than as an individual consultant.”
I hope her lived experience at Optum ultimately matches up to these lofty, worthy expectations.
One thing I know for sure: I wouldn’t bet against her!
