A Medical Ferguson

December 12, 2014

CHW LogoDisparities in the criminal justice system have replaced health disparities in the headlines recently.  Is this because health disparities have improved?  Some recent articles confirm my suspicion that the answer is mostly no.

Two New England Journal of Medicine articles and one editorial examine this question.  In one article, researchers examined racial differences in performance on 17 process-of-care quality measures – for example, did patients with heart attacks or pneumonia receive appropriate medications.  In 2005, quality of care was substantially worse for black and Latino patients compared with non-Latino whites.  In 2010, the performance gap had improved substantially.  That seems like good news.  However, in another article in the same issue, racial differences in actual health outcomes (such as control of high blood pressure or diabetes) among Medicare recipients persisted from 2006 to 2011 nationally, though there were improvements in some regions.

How to reconcile these two reports?  Of course the populations and methods are somewhat different.  But a larger point is that reducing disparities in health care does not necessarily translate to reducing disparities in health.  Health care is one of many determinants of a person’s health, and only accounts for about 10% of health status.  The remaining 90% is due to genetics, behavior, and environment.  Improving health takes a lot more than improving health care.

Even in regard to disparities in care, the evidence is not necessarily encouraging.  The findings of an article in Pediatrics were fairly provocative.  Using national data, authors found that adherence to prescribing guidelines for otitis media was actually better for black children than non-black children, which seems like good news.  However, the difference was due to less prescribing of broader-spectrum, more expensive (and not recommended) antibiotics for black children.  These national data confirm early, localized findings from other studies.  While it would be nice to attribute this to more diligence by providers when treating blacks, a more realistic explanation is less parental pressure for expensive antibiotics – or more likely less anticipation by the provider of such pressure – for blacks compared with whites.  The authors of an accompanying editorial describe this as an example of “structural racism.”

Eliminating health disparities is going to take more than changing prescribing.  It will require addressing those behavioral and environmental factors that are the primary determinants of our health.  It means Ferguson, MO needs to have more in common with the nearby but much more affluent suburb of Ladue.

 


Healthy Care

December 5, 2014

CHW LogoThere are many ironies of the term “health care” as currently used, especially in the US.  Not the least is that actually keeping people healthy is financially punished.  But an irony that is not as often discussed is the enormous adverse environmental impact that hospitals and other medical facilities typically have.  For example, hospitals produce nearly 12,000 tons of waste per day, an average of 26 pounds per bed.  They are also major consumers of water and energy.  Hospitals are among the most energy-intensive facilities; they account for 8% of all of the nation’s energy use.  The resulting waste and emissions of carbon and other pollutants adversely affect the health of a community, in the name of providing health care.

Some systems are working to change that.  In October, Gundersen Lutheran Health System in La Crosse, WI, became the first health system in the US to be energy independent, generating more energy than it consumed.  In 2008, Dr. Jeff Thompson, Gundersen CEO (and a pediatrician!) set a goal of energy neutrality as way to reduce cost and lower the negative impact of the facility on the health of the community.   They achieved this through a combination of conservation (a 40% reduction in energy use, saving roughly $2 million a year to the system despite a 25% increase in the size of the facility over the same time) and development of renewable, non-polluting energy sources like geothermal, wind, and biomass, through both local projects and regional partnerships.

We want the kids in Wisconsin to be the healthiest in the country.  This won’t happen without, among other things, a healthy environment.  Gundersen Lutheran is doing its part to ensure that.  What more could we do?


Getting Away

November 28, 2014

CHW LogoWe just had the great fortune to spend 2 weeks vacationing in Argentina.  It represented a couple of first for us: first time in that country, and first time taking an entire two week vacation.  We thought it was pretty luxurious, and actually it was.  So we were struck by the number of travelers we met, from a range of other countries, who were in the midst of a 4, or 8, or 12 week trip.  Indeed, the only people we encountered who were on a shorter vacation than us was a couple from Philadelphia.  What were we doing wrong?

Some of the people were retired (although they indicated that they had done similarly long vacations when they were younger), but many were working age.  How do they pull this off, we wondered?  There seem to be a few factors:

  1. Paid leave. Most industrialized countries provide much more generous paid leave, either by mandate or due to union leverage, than the US.  One of our guides, when asked whether Argentine workers get much vacation time, seemed shocked at the question.  “Of course, it’s in our constitution!  I mean, we may be Argentina, but it’s not like we’re in Africa or someplace like that.”
  2. Culture. Americans work longer hours than most other industrialized countries (including all of Europe and Japan, though we are still outpaced by Singapore, South Korea, and Taiwan).  The difference in hours worked is greater than the difference in paid leave (i.e., we don’t even use the little we get), suggesting that some of it is due to a cultural reluctance to get away from our jobs.  In many industries, it’s almost a badge of pride to be “on” constantly.  We talked with several people who were taking extended leave without pay, something few Americans do (or are permitted to do).
  3. Health care. Even if an American wanted to take unpaid leave for an extended trip, she would in many cases have a pause in employer-provided benefits, especially health insurance.  Paying for coverage while on unpaid leave is too much of a financial burden for most people.  This is not an issue for people from countries with government-provided health care, like Canada, the UK, France, Germany, Australia, New Zealand, Belgium, Denmark, Norway, Switzerland, Spain, the Netherlands, etc., etc.

Which leads me to two other points.  First, among the folks I had the opportunity to talk with were a doctor from Argentina and a nurse from England.  Both had wonderful things to say about their national health systems, including that fact that while people do often carry private insurance for things like a nicer room in the hospital, or a shorter wait for an imaging test, people are enormously satisfied with the quality of care.  “Everyone knows that the best doctors are the ones that work for the National Health Service.”

Second, when they talk about their “free” health care, of course we know that it is not free.  People pay either through their insurance premiums, or out of pocket costs, or taxes.   But while we might gag at the tax rates in places like Scandinavia, a recent study shows that, when you add it all up, public and private sources, the US actually has the second highest total social expenditures (largely health care, but also unemployment, retirement, disability, etc.), after France but ahead of those tax-and-spend Nordic countries.

Oh, and the wine in Argentina is terrific and cheap!


Family Feast

November 21, 2014

CHW LogoI am still on vacation, so Happy Thanksgiving…

When I was growing up, family meals were not an everyday event.  My mother, a nurse, often worked evenings, so those days we obviously couldn’t all eat together.  But we did make an effort to eat as a family on the other days.  Similarly, when we were raising our kids, despite my working shifts and both my wife and I traveling a fair amount for our jobs, we also placed a premium on eating together whenever possible.  That often meant having dinner 5:30 some days and 8:30 on others, but it seemed worth it.  After all, the demise of the family dinner has been cited as one important factor in the obesity epidemic, along with a host of other societal ills.

A new study in Pediatrics suggests that when it comes to risk of obesity, at least, not all family meals are created equal.  Researchers at the University of Minnesota, using a mixed-methods study including direct observation of 120 primarily low-income families, identified aspects of mealtime that were associated with obesity in the children.  While non-overweight children tended to have somewhat longer meals, and were more likely to eat in the kitchen or dining room vs. family room, the differences were small, and most meals were short (< 20 minutes), and the majority of both groups ate in a dining area.  More important were the family dynamics.  After adjusting for demographic factors (including parental BMI), the most important factors associated with child overweight or obesity were presence of positive interactions among family members (for example, enjoyment of each others’ company, warm interactions, positive reinforcement), and the absence of negative ones (hostility, lack of discipline, etc.).  Interestingly, very few food-specific dynamics were relevant.  Only moralizing about food – for example, “Eat what I gave you – other children are hungry and would be happy to have it” – was associated with the child’s weight; children who were hectored were more likely to be overweight.

Good information for those of us with families, or who are providing advice to families.  Michael Pollan, author of The Omnivore’s Dilemma and a proponent of better eating (for the sake of our own health as well as that of the planet, sums his advice up succinctly into 3 rules: “Eat food.  Not too much.  Mostly vegetables.”  To which we might add a fourth “Eat with people you like and get along with..”


Back to Normal

November 14, 2014

CHW LogoI am on vacation this week and next, so I am re-running this blog, on the anniversary of a tragic event that may be on people’s minds this week.

I now know that the five most disquieting words in the English language are “This is not a drill.”

As some of you undoubtedly know from national news coverage, we had a shooting at Children’s Hospital of Wisconsin yesterday. Police, responding to a report of a visitor who was armed and dangerous, shot the suspect (not fatally) and gained control of the situation. From around noon until 2 pm, the hospital was in a lockdown situation. During that time, the other leaders and I were in a command center; much of our time since then has been spent in analyzing what happened and our response, and most important, in supporting all of our patient families and staff that were affected.

Thanks to our planning and procedures, and the outstanding work of our staff and law enforcement, no patients, families, or hospital staff were injured. In retrospect, things went as well as one could reasonably expect, maybe even better. I mean let’s face it, education and drills notwithstanding, there is no way to really rehearse for the real thing. Adrenaline and neurotransmitters are running rampant, time becomes completely elastic, people get hungry.

You might think an actual situation like this would be less choreographed, more chaotic than the drills. (We actually had an active shooter drill within the last couple of months. It was kind of boring.) Although I was never in danger myself, it was certainly nerve-wracking. And going around to all the care areas after, behind the modest words I could sense that many people had been frankly frightened and concerned for others. But what I saw everywhere was not chaos, but calm. Even when communications were spotty, or procedures unclear, there was no panic. It was almost surreal. At the time, I was mostly relieved and appreciative (and a bit hungry). I chalked it up to the supreme professionalism of the people I work with.

But reflecting now after 24 hours, that wasn’t quite it. Not that there wasn’t extraordinary professionalism, it’s just that that isn’t enough. What I saw was skilled professionals living out our values of being At Our Best:

1. Purpose – We act in the service of patients and their families.

The nurses who shepherded families to safe locations in the clinics, and the nurses who stayed with the patients who couldn’t be moved.

The code team that despite the lockdown responded to not one, but four different emergency (“code”) situations, including to assist the man who was shot.

2. IntegrityWe build confidence and trust in all interactions.

Altheia, the administrator on call who took charge as the incident commander and calmly directed activities.

The CHW security staff who worked with four different law enforcement agencies to control access, provide escort to personnel who needed to move about, and provide a sense of confidence that all was under control.

3. CollaborationWe work together to care for children and families.

The administrative team in the command center who during the incident and in the hours after worked together to return the hospital to normal.

The off duty security officer who happened to be in the hospital with his child for an appointment, who stepped in to help. And the clinic staff who watched his child in the meantime.

4. Innovation – We commit to breakthrough solutions with continuous learning.

The many people who made creative suggestions of ways we can make our response even better should we ever need to in the future.

The communications team who use various means to get information out via email, Intranet, Twitter, etc. to try to keep people informed.

5. Health – We are at our best.

The behavioral health providers who canceled clinics to be available as a resource for staff, along with social workers, human resources, etc.

The environmental staff who within minutes of the “all clear” were out making sure our facility was clean and ready.

Every single person who stopped to ask someone else if they were OK and if they needed anything.

As the swarm of media vans and news helicopters attests, this is the kind of incident that draws a lot of attention. News is, by definition, what doesn’t happen every day – it’s what’s not normal. Our values, though, are a constant. Not terribly newsworthy. But as the attention fades, as we get back to our routine, I’m reflecting on how grateful I am to be part of an organization that lists and lives those values. That’s our normal.


“Obscure Diagnoses” for $30,000, Please

November 7, 2014

CHW LogoAsk your doctor if you might be suffering from “restless legs syndrome.” Or “low testosterone,” or “social anxiety disorder.”  We’ve all seen the ads suggesting that our legs cramps or aging or shyness might instead represent a disorder with a name.  One that, not coincidentally, could be helped by a medication manufactured by the sponsor of the ad.  A medication for which you can ask your doctor for a prescription.  But while doctors like to complain about Big Pharma’s “diagnosis mongering,” what if we are also part of the problem?

Overdiagnosis: How Our Compulsion for Diagnosis May Be Harming Children,” in the November issue of Pediatrics, raises this question.  The authors here are not referring to the kind of pseudo-disorders pedaled by industry.  By overdiagnosis they mean the discovery of a true abnormality, where the diagnosis does not benefit the patient.  This may include minor forms of a condition that would neither benefit from treatment nor be expected to progress to something more severe, or conditions for which treatment has been shown not to affect outcomes.  Think of low levels of elevated bilirubin in a newborn, asymptomatic skull fracture due to minor accidental trauma, or positive IgE blood test results indicating a response to food allergens in the absence of clinical symptoms.  None of these is treatable, and even knowing the diagnosis isn’t helpful in any way.  Yet physicians often perform – and sometimes parents request – tests for these and other diagnoses.

What is behind this drive for a diagnosis that doesn’t matter?  The article cites a few groups of factors.  One is industry influence.  There is no doubt that advertising does drive some demand.  (There is a reason pharma spent $4.5 billion on direct-to-consumer advertising in 2009, in addition to support for various disease advocacy groups, with varying degrees of legitimacy.)  Another is incentives in the current health care system.  For one thing, providers are often financially rewarded for unnecessary testing and care.  A review of pediatric quality measures also shows a marked bias toward indicators focused on underuse of resources rather than overuse.  Public perception that diagnosis is more precise than it really is, coupled with an intuitive sense that it must be better to detect disease, as another factor.  But the largest influence, according to the authors, is physicians themselves.  We have a culture of intolerance of uncertainty.  We hate not having an answer, something that is ingrained from the earliest days of medical education where students are encouraged to develop a lengthy list of potential diagnoses and then exhaustively eliminate them one by one until finally arriving at the right one.  “Defensive medicine” is frequently cited, but most of the research suggests that this plays at most a minor role.

Cost is the obvious downside.  But there are others.  There are potential adverse physical effects, if having a diagnosis leads to treatment that will not benefit and might harm the patient.  Sometimes the tests ordered in search of a diagnosis are themselves risky (procedures requiring anesthesia, for example, or radiation exposure).  There is also real psychological harm in carrying a diagnosis.  The newborn who is a little yellow and has a mildly elevated bilirubin gets a diagnosis of  “hyperbilirubinemia.”  A child with nonspecific symptoms who tests positive for antibodies to shellfish and eggs is now labeled as “food allergic.”  Numerous studies have documented the “vulnerable child syndrome” in such children.  It results in increased utilization of health care, overprotective parenting, and bullying, among other consequences.

There are several efforts – from professional societies and academic medical centers – targeted at both providers and lay people to increase awareness of the presence and problems of overdiagnosis.  Traditionally, academic medicine has probably been more of a cause, but is now trying to be part of the solution.

Ask your doctor if you might be suffering from “adiagnosticophobia.”


Bayes Watch

October 31, 2014

CHW LogoSay a coin is tossed 10 times, and each time it comes up heads.  What is the probability of heads on the next toss?  It might be tempting to say that the probability is low, since surely 11 heads in a row is extremely unlikely.  But the correct answer is 50%.

Or is it?  It turns out, it depends on what kind of statistics you rely on.

Mark Twain talked about three kinds of falsehood: lies, damned lies, and statistics.  What he didn’t point out is that there are actually different kinds of statistics, and they sometimes give different answers!  The traditional school, known as “frequentist” statistics, is based on the independence of events.  The chance of heads on any toss is completely unrelated to the prior results.  While the probability of 11 heads in a row is indeed extremely unlikely (about 1 in 2000), the chance of any one of those tosses being heads – even the last one after a string of other heads – is still 1 in 2.  Yet it still feels counterintuitive.  After all, I just saw 10 heads some up – how could there possibly be another?

An increasingly popular approach to statistics attempts to answer this.  Bayesian statistics, named for the Reverend Thomas Bayes1, does not assume that the probability of an event is completely independent of prior events.  Instead, the expected probability of an event incorporates other known information, including other results up to that point.  In this case, if I am asked to estimate the chance of an 11th head, I would look at the string of 10 heads in a row and reasonably wonder if perhaps this is not a fair coin.  The answer to that question, in turn, would be based on other information.  How well do I know the person tossing the coin?  Did I have a chance to examine it beforehand?  If there is good reason to believe that the coin may in fact be biased, then I would have to conclude that the probability of a head coming up on the next toss is indeed higher than 50%.  Which is what it feels like intuitively.

While frequentist statistics is what is most commonly taught, most of us in reality behave like Bayesians.  We don’t simply ignore the string of 10 heads as being irrelevant.  While a frequentist would assume the coin is fair, the Bayesian at least asks the question when confronted with evidence that it might not be.

This shouldn’t be an excuse for complete subjectivity.  A true Bayesian approach is just as analytic and quantitative as a frequentist one, and in practice can be more complicated.  But it does more closely mirror the way our minds actually work.  Most physicians are Bayesians when it comes to diagnostic decision-making.  Here’s an example.  I see a child with abdominal pain, and am concerned about appendicitis.  At first, all I know is the age and gender – say, an 11 year old boy.  I know that approximately 10% of all 11 year old boys who come to the ER for belly pain will have appendicitis.  That seems high enough to worry about, but not high enough to go ahead and remove his appendix just yet.  So I go ahead and examine him.  He has a Pediatric Appendicitis Score of 3.  According to the research, half of patients with appendicitis would have a score of at least 3, and only 17% of the patients without appendicitis have a score that high.  Using Bayes’ theorem (look it up if you want, but trust me on the math), I can revise my estimate for the probability of this patient of having appendicitis, knowing not only that he is an 11 year old boy, but an 11 year old boy with a PAS of 4.  His chance of appendicitis is no longer 10%, it is 25%, high enough that I should probably not just ignore it but do further tests.  Based on the results of those tests, I would again update my estimate of probability upward or downward.  On the other hand, with a score of 1, this boy’s chance goes from 10% to less than 2%, and I can reassure the family that we do not need to worry about it unless something changes.

This example illustrates something that may be surprising to non-medical professionals, who may think that tests can tell you whether someone does or does not have a disease.  This is almost never the case.  Every test can have false positive or false negative results.  They are simply one more piece of information that must be interpreted in light of what else we know.  A positive test generally increases the likelihood that someone has the condition we are checking for.  A good test will indicate a high enough probability to take action, while a not-so-good test leaves us sufficiently uncertain that we need more information.  And there are a lot of not-so-good tests out there.

Perhaps the biggest challenge to Bayesian analyses is the need for prior information.  Sometimes this might be based on good research or our own prior experience.  However, we must often make an educated guess.  In those cases, our judgment may be biased by many factors, including what I have previously referred to as availability bias (the tendency to be overly influenced by recent experience or information.)  A child comes to the emergency department with a fever.  His mother recently returned from Africa.  What is the chance the child has Ebola?  Your first reaction might be a small but measurable number, say, a 1% or even a 5% chance.  In reality, we know very little, for example, whether or not the mother has been in a part of Africa affected by Ebola, when she was there, and whether she has had any symptoms and could therefore have transmitted the disease to the child.  Based on what we do know, our highest possible estimate (assuming she had fever and that her travel was in the past 21 days) would be the number of known Ebola patients in Africa (about 10,000) divided by total population of Africa (a little over a billion), or 0.001%.  If we found out, for example, she had been in Guinea, we would change our estimate to a higher chance, while if she had been in South Africa it would be far lower.

And Mark Twain never heard of Ebola.

 

[1] An 18th century Presbyterian minister in England.  The apochryphal story is that he developed his theorem in an effort to prove the existence of God.

 


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