Category Archives: Public Health

When a grand old scientist talks, you listen: Maynard Olson and Genomic Medicine

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All opinions are my own and do not necessarily reflect those of Novo Nordisk.

Last night I had the great opportunity to hear Maynard Olson give a public lecture on Genomic Medicine.  As one of the founders of the Human Genome Project, he’s been around in a pivotal role for much of the revolution in our understanding of the genome.  A revolution, as he himself points out, that we are still just beginning.

He gave his speech as part of the UW Genome Sciences Department’s summer lecture series, and spoke to a packed auditorium about how the information we are learning about the genome has implications for diagnostics, therapeutics, and public policy.  I’ve heard Maynard speak before, and he’s always refreshingly down-to-earth, candid and measured in his descriptions and comments.  Not for him are flights of speculation or hyperbole, and he actually ended his talk with a call to stop the hype.  As he said, “The product is solid.  It doesn’t need hype.”  Maynard, who is slim, with a fringe of red hair that’s silvering at the sides (kind of like Reed Richards), does not look at all near his age of about seventy years. Continue reading →

Maternal immune systems, autism and the value of prediction

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All opinions are my own and do not necessarily reflect those of Novo Nordisk

Following on these two papers (1, 2) published in Translational Psychiatry.

When I wrote about Gene by Environment (GxE) interactions and the possible health of children, I was describing how changes in maternal health might have an effect on child health at the level of what genes are turned on and off.  In that situation, there may be the possibility that actions by potential mothers before conceiving could positively impact child health.  In many other cases, though, the actions for predicted problems can only take place after birth.  Key point: in many cases these interventions are best done early, which is why states have newborn screening programs (although surprisingly the number of tested conditions varies from state to state).  In the context it’s interesting that a couple of recent papers have identified what may be a way to predict the development of autism in children.

The papers describe findings that may, if corroborated, have a large impact on autism prediction and, eventually, possible treatment and prevention for a subset of patients.  First, the team demonstrated through study of non-human primates that these human autoantibodies, when given to pregnant rhesus monkeys, led to significant changes in both maternal and infant monkey behavior.  Mothers in the experimental group showed more protective behavior toward their infants, and those offspring more frequently approached known and unknown monkeys despite not receiving commensurate social responses.  Male offspring also showed measurable increases in brain volume.  Second, the research team discovered that autoantibodies  to combinations of fetal brain proteins are found in a significant fraction of mothers who have Autism Spectrum Disorder (ASD) children, while mothers from the control group rarely have such autoantibodies to so many of these proteins. Continue reading →

Fielding percentage for UK surgeons

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All opinions are my own and do not necessarily reflect those of Novo Nordisk.

Last week I posted on how our measurements of defense in baseball have become a lot more sophisticated, and how that gave me hope for the evaluation of innovation.  If baseball, one of the most tradition-bound of US sports can adopt to new metrics, surely business can too.

I was reminded of this with the publication of a recent article about the National Health Service (NHS) in the United Kingdom and their plan to publicize the surgical success rates of clinicians across their country.  Surgeons in eight different specialities will have their mortality rates for specific procedures, length of hospital stays post surgery, and other elements published in tables for anyone to access.  The first group to have this information released is vascular surgeons.

A fascinating aspect of how this is being done is that publication of one’s rates is voluntary, but if a surgeon chooses not to have his or her rates published, that surgeon will be named.  It’s not quite putting people into stocks in the public square, but it is definitely a form of public shaming meant to increase participation.

Nevertheless, six surgeons have opted out and been named.  Game theory might predict these are surgeons on the low end of the measured metrics, who are taking a calculated risk that the negatives associated with not publishing their rates are less than the negatives that would come with disclosure of their rates.  But that’s not the case.  The NHS has stated that none of these surgeons lie outside the normal range for the reported metrics.

Instead, these doctors are protesting that the metrics are not measuring the right things.   They suggest the metrics don’t take into account the subtleties involved in surgical cases, how procedure names alone don’t properly capture how difficult or easy a procedure might be for a given patient.  Are there comorbidities?  Is a patient in generally poor health?  Is a surgeon one who specializes in tricky, difficult cases which would therefore lead to a lower success rate even though the surgeon him or herself might be highly skilled and effective?  Could these metrics scare new surgeons away from performing more difficult procedures?

This echoes the debate about defense in baseball, and whether standard metrics such as fielding percentage are the best for measuring defensive ability, or if more elaborate measures better reflect reality.

Still, while I agree with the viewpoint that we should always try to improve metrics, I also think the NHS is doing the right thing.  I think in this case the proper analogy might be baseball defense back at the time before the invention of fielding percentage.  In the practice of medicine world-wide there is a surprising lack of information about measures like success rates and efficacy.  As Sir Bruce Keogh said to the BBC: “This has been done nowhere else in the world, and I think it represents a very significant step.”  To take another quote from the article, Professor Ben Bridgewater commented, “We’ve been collecting data on cardiac surgery since 1996 and we’ve been publishing it at individual surgeon level since 2005, and what we’ve seen associated with that is big improvements in quality: the mortality rates in cardiac surgery today are about a third of what they were ten years ago.” That which we don’t measure, we can’t improve.

In the US, that idea is becoming more prominent.  Recent articles in Time and the New York Times have highlighted how transparency is lacking in the United States healthcare system, and the Obama Administration’s emphasis on comparative effectiveness is another thrust in that direction.  What the NHS is doing is a great model and a great start, and I hope they continue to both make these aspects of healthcare more transparent and work to refine their metrics so that they accurately reflect the difficulty of practicing good medicine.

Metrics and the Heisenberg quality of gathering data about behavior

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All opinions are my own and do not necessarily reflect those of Novo Nordisk

Thinking more about the Global Health Metrics Conference, one element that resonated was that measurement does not occur in a vacuum.  When metrics are gathered, and especially when they are gathered out in the open by global health surveys, for example, there’s the real issue of the act of measuring changing the validity of what’s being measured.  I’ve been thinking about this in the context of hiring and workplace management.  For example, if the media were to report that viewership of Khan Academy videos on YouTube was found to correlate highly with creativity in the workplace, I expect two things would happen.  One, viewership of Khan Academy would spike, and second, the metric would rapidly begin to lose what correlative and predictive power it had.  People would try to game the system.

In Global Health, where countries are incentivized to meet certain milestones, it requires real thought to either make sure the milestones are strongly causally related to the health goals, or else that the metrics undergo continual fine-tuning to ensure the desired effect.  If the metric were something like number of healthcare facilities, a country could ensure that number increases but there wouldn’t necessarily be a concomitant increase in actual health services delivery.  I’m sure these are topics the Global Health community wrestles with every day.

It’s kind of like with relationships.  While on the one hand, we can tell our partners what we want, and often see them do it, on the other hand don’t we really secretly want them to already know and behave accordingly, because somehow that’s more genuine?  It’s certainly why social science researchers often mislead their study subjects on the actual purpose of behavioral experiments.  Or, to quote from the movie Buckaroo Bonzai, “Character is what you are in the dark.”

Ultimately, it seems best to try to measure behavior as closely as possible to the desired outcome.  That’s why baseball is nice.  We want good hitters, and to find good hitters it’s simple:  we measure how well a player can hit.

A Genomics Researcher’s Take on the Global Health Metrics Conference 2013

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All opinions are my own and do not necessarily reflect those of Novo Nordisk

Over the past three days I had the opportunity to attend the Global Health Metrics Conference here in Seattle.  This is not my field; I’m a genomics researcher working in biomedical research and drug development, but I’ve also been curious about what’s going on in the area of public and global health.  This seemed like a good place to get a crash course.  The Lancet has kindly published all the abstracts and I wanted to give my impressions of what I heard.

First takeaway:  I was surprised and intrigued by how many parallels I saw between the work I do (primarily transcriptomics and genomics) and the work I saw reported.  Sure, global health researchers use surveys rather than high throughput sequencing, and gather data on nations rather than patients, and deal with the complexities of culture and government instead of human biology, and work in the public sphere as opposed to the private, and use a completely different vocabulary than I do, but other than that it was really similar.  So similar I put together this table:

Biomedical Genomics Research Global Health  Metrics
Increasing amount and types of data Yup
Biomarkers Indicators
Growing emphasis on efficacy measurements ditto
Lots of Acronyms, NIH, AMA, EULAR, ADME GBD, DALYs, CDVS, USAID
Struggle to understand what tissue, cell, analyte to measure Struggle to characterize the right metric to demonstrate effects/efficacy
Gene X Environment interactions poorly understood Local environment effects beginning to be captured
Personalized medicine Nation specific solutions
Noisy data, lots of unknowns Maybe even noisier data and, yeah, unknowns
More focus on longitudinal studies Already there

And so on.  I’ll elaborate on a few more below.  Another immediate takeaway:  I wasn’t even aware of the Institute for Health Metrics and Evaluation (sorry guys).  Now that I am, it’s a place I’d like to visit.

One thing that really impressed me was the work that IHME has put into making the Global Burden of Disease survey lucid, simple and accessible.  The data presentation by Kyle Foreman and Peter Speyer (@Peterspeyer) was terrific.  Not so much for any specific piece of data (although the trends and findings are all pretty fascinating), but rather for their demonstration of the power of dynamic presentation and facile web-based tools.  Static powerpoint charts are clearly so last decade.  Anyone wanting to check out their presentation can go here, or even better just go directly to the site.  As a scientist who also works with large, multifactorial datasets, I know the struggle to condense that data into a usable, comprehensible form.  I think Peter and Kyle have done a great job, and I also like the potential crowdsourcing aspect of it.  As I’ve commented on before, crowdsourcing methods, whether via games or other techniques, have a real potential to fully utilize large datasets and also to solve big problems.

Of the many talks I heard, a few I’ll highlight, just for the specific points I took away.  On the first day, Tanya Marchant showed interesting and cautionary data about making sure that what you’re measuring really measures what you think you’re measuring.  In this case, measuring the presence of skilled birthing assistants as a proxy for maternal care during childbirth turns out to be incomplete because of other factors such as availability of basic medical supplies.  Reminds me of debates over things like how best to measure drug efficacy in clinical trials–for example, response versus progression free survival in oncology.

Joseph Dieleman presented his work on looking on the effects of external aid to developing nations for health.  In a perfect world, external aid would just be added to pre-existing health expenditures, and after aid expired, local governments would maintain spending at pre-aid levels, or even higher.  Well, turns out this isn’t always the way this happens.  Aid comes in, local health budget gets shifted “temporarily,” but temporarily turns to permanently when the external aid leaves.  One of the thoughts that went through my head during this conference was to remember the law of unintended consequences.

I enjoyed Michael Wolfson‘s talk on functional health status.  Coming from an industry that really likes it’s tried and true measures like HDL/LDL levels, the concept of looking holistically at factors relating to actually feeling good was a nice contrast, and food for thought.

Bruce Hollingsworth had a great quote in his part, “People need incentives to provide accurate data.”  Yeah.  Tell me about it.  In transcriptomics it’s been a mantra for years that “Garbage in, garbage out,” in terms of incoming biological sample integrity and resulting data quality.  From what I saw, the data you can get trying to measure Global Health is maybe even noisier than the kinds of data that I normally deal with.  My main conjecture for why all hope is not lost due to data quality in Global Health is that GH researchers are able to bias the indicators they sample towards things with (hopefully) real meaning, else they would be adrift in a sea of not very useful data.  Maybe they feel that way anyway?  Bruce also made the point that there are external factors, again, which influence health.  Even people who know where to go for the best treatment may not because the facility is too far away.  Location, location, location.

Speaking of garbage (but not in a bad way), David Phillip‘s talk later that day referred to the problem of trying to extract useful data out of vital health records full of things like garbage codes.  That is, causes of death that are supremely unhelpful from a public health perspective, such as (I’m exaggerating here) death by lack of life.  His work on extracting useful proportions from this data based on the overall data distribution reminded me of imputation techniques that are used in genomics.

There were many more engaging talks, and I also had great conversations at lunch with different people. I suppose I shouldn’t be surprised by the similarities.  I think many research fields these days are converging on a similar emphasis on big data, analytics, efficacy, and finding the right metrics.  I also appreciated the long view shown by so many of these programs.  One of the drawbacks of private industry is the prevalence, often, of the short term view.  I could wish we had the decades-long commitment shown by various Global Health initiatives.

The aspect I find daunting in Global Health is how much uncertainty that community is dealing with, which greatly affects efficacy and efficiency.  An intervention might be exactly the right one when viewed in isolation, but can be so easily derailed by external factors.  Like biology, like baseball, it seems the key thing is to find the metrics that at least tell you that you made the change you hoped for, with the understanding that what happens at the end is so often, unfortunately, out of our control.