Luk Arbuckle

Misleading Americans about public health care

In news on 22 February 2009 at 7:45 pm

Canadians often wait months or even years for necessary care. For some, the status quo has become so dire that they have turned to the courts for recourse. Several cases currently before provincial courts provide studies in what Americans could expect from government-run health insurance.

At least that’s story told by the Fraser Institute in an op-ed in the Wall Street Journal. “As we inch towards nationalized health care,” reads the subtitle, ” important lessons from north of the border.”  With a couple of dire tales, and a couple of national averages, Americans are led to believe that introducing government-run public health insurance will drastically increase wait times in U.S. health care.

Where problems lie
Making an appropriate comparison between wait times in the U.S. and Canada is not trivial. How do you deal with those people that can’t get treatment in the U.S. because of inadequate or nonexistent medical insurance (infinite wait times)? Even comparing specific treatments is tricky because disease coding between the U.S. and Canada differs (ICD-9-CM is currently used in the U.S., and ICD-10-CA in Canada). And then you have to consider subgroups to see how population trends change for socioeconomic classes, say, and to ensure they aren’t reversed entirely (Simpson’s paradox).

Take, for example, a study that found that “socioeconomic status and breast cancer survival were directly associated in the U.S. cohort, but not in the Canadian cohort.”  Also note that “this study replicated the finding of advantaged Canadian cancer survival in smaller metropolitan areas that had been consistently observed in larger metropolitan areas.”  Although it’s possible there are other (confounding) factors influencing these results, it shows that socioeconomic status needs to be considered when comparing medical treatment and outcomes in the U.S. and Canada.  And, therefore, it is likely to affect wait times as well.

Instead of dealing with technical details, however, the article in the WSJ uses stories in which Canadians wait months for treatment.  There’s nothing inherently wrong with this—it is, after all, an op-ed piece and not a journal article—but you have to ask yourself about the choice of stories.  Are they representative of public health care in Canada, or extreme cases?  Also, we don’t know whether the  individual that “paid for surgery that may have saved his life”, rather than wait for treatment in Canada, was in immediate need of treatment.  These are, nonetheless, compelling stories that should not be disregarded—but they don’t prove a trend.

The basic argument put forward is that Canadians wait a long time for treatment under the public health care system.  But what’s considered a “long” wait time, and how does it depend on the condition and severity?  Notice that there’s no mention of wait times in the U.S., even for those that have appropriate health coverage.  Instead we’re given some specific average wait times, but why cataract surgery or hip and knee replacements, and not others?  How much do these wait times vary based on treatment, location, socioeconomic class, and how do they compare with U.S. figures?  We’re left with more questions than answers.

The real confounder
Ultimately, to consider how wait times would increase in the U.S. with the introduction of publicly run, universal health coverage—that is, health coverage for all, as in Canada—there is one factor that would need to be disassociated from wait times in Canada. This factor, not unique to Canada but certainly rare, is not stressed enough in the article.

The Supreme Court of Canada found that Canadians suffer physically and psychologically while waiting for treatment in the public health-care system, and that the government monopoly on essential health services imposes a risk of death and irreparable harm.

Disregarding the inflamed rhetoric, the important point here is that there’s a “government monopoly on essential health services” in Canada.  In other words, there’s no competing private system for health services deemed medically necessary, and the government funds and regulates the public health care system (although the government doesn’t operate it).  You could probably argue that this monopoly is equivalent to price fixing for those services the government decides it’ll pay for.  This is likely the main reason “care is rationed by waiting”—there is, after all, no alternative (besides paying for treatment in the U.S.).

It’s probably only a matter of time before Canada allows for a parallel private system for most, if not all, health services. Private spending currently represents about 30% of the average provinces total health care spending (mostly for medications and services not covered by the public system, such as dentists, optometrists, and physiotherapists).  But until a parallel private system exists for all services in Canada, or the monopoly in essential services is taken into account, it’s disingenuous to suggest that wait times are simply because ”individuals bear no direct responsibility for paying for their care.”

Bottom line
Many factors impact health care and wait times.  You can’t look at just one aspect or descriptive statistic and know whether the system works as intended.  It would be like judging a person’s health based on blood pressure alone.  I agree with the author regarding comments he made in the past about improving Canada’s health care system.  But making inferences into a public health care system in the U.S. based on the results from a couple of average wait times in Canada, where other factors confuse these results and make them unreliable to begin with, is inappropriate and misleading at best.

Statistical concepts in presenting data

In data display on 18 February 2009 at 8:29 pm

Finally someone has written a text something like Tufte’s Visual Display of Quantitative Information but specifically for statistics.  Rafe M. J. Donahue, of  Biomimetic Therapeutics and Vanderbilt University Medical Center,  gave a seminar course on presenting statistical data at a meeting of the American Statistical Associtation (ASA) in June 2008, and will be giving a similar course  in April 2009 (as part of a continuing education program of the ASA).  I learned of his course in a recent blog post at Statistical Modeling, Causal Inference, and Social Science.

The current version of Donahue’s text is a 100 pages [PDF], but well worth a casual read (it’s not as bad as it sounds, as a lot of those pages are dedicated to visual displays of the ideas he is describing).  If you enjoy reading Tufte’s opinions on the topic of displaying data, and you have to create charts and diagrams of statistical data, then you should enjoy Donahue’s writing as well.  Reading Tufte a couple of years ago had a tremendous impact on my view of visual displays.  But the focus here is in on statistical data.

The two fundamental acts of science, description and comparison, are facilitated via models. By models, we refer to ideas and explanations that do two things: describe past observations and predict future outcomes. [...] Statistical models, then, allow us to describe past observation and predict future within the confines of our understanding of probability and randomness. Statistical models become tools for understanding sources of variation.

Show the atoms; show the data.

Show the atoms; show the data.

A summary of some principles presented by Donahue:

  • The exposition of the distribution is paramount.
  • Show the atoms; show the data.
  • Each datum gets one glob of ink.
  • Erase non-data ink; eliminate redundant ink.
  • Take time to document and explain.
  • The data display is the model.
  • Avoid arbitrary summarization, particularly across sources of variation.
  • Reward the viewer’s investment in the data display.
  • In viewing CDFs, steepness equals dataness.
  • Plot cause versus effect.
  • Typically, color ought be used for response variables, not design variables—but not always.
  • We understand the individual responses by comparing them to a distribution of like individuals.
  • Data presentation layouts and designs should be driven by intended use.
  • Time series make fine accounting but poor scientific models.
One glob of ink.

Each datum gets one glob of ink.

Naturally Donahue was also influenced by Tufte.  As he says, “the idea of analysis  is to understand the whole by decomposing into component parts.”  And he therefore reminds the reader of Tufte’s principles of analytical design: 

  • Show comparisons, contrasts, differences.
  • Show causality, mechanism, structure, explanation.
  • Show multivariate data; that is, show more than 1 or 2 variables.
  • Completely integrate words, numbers, images, diagrams.
  • Thoroughly describe the evidence. 
  • Analytical presentations ultimately stand or fall depending on the quality, relevance, and integrity of their content.
Take time to document and explain.

Take time to document and explain.

The sexy job in the next ten years

In news on 1 February 2009 at 6:10 pm

Hal VarianGoogle’s chief economist and author of arguably the two most popular textbooks in microeconomics (one at the undergraduate level and the other intro graduate), shared the following during an interviewed for The McKinsey Quarterly:

I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves.

You always have this problem of being surrounded by “yes men” and people who want to predigest everything for you. In the old organization, you had to have this whole army of people digesting information to be able to feed it to the decision maker at the top. But that’s not the way it works anymore: the information can be available across the ranks, to everyone in the organization. And what you need to ensure is that people have access to the data they need to make their day-to-day decisions. And this can be done much more easily than it could be done in the past. And it really empowers the knowledge workers to work more effectively.

It’s nice to hear that your skills may become a hot commodity.  I came across the article from Gelman’s post on What should an introduction to statistics be like?  What I enjoyed most was the discussion that followed.  Like how “the time of playing with integrals, density functions and demonstrations is over”, and that we should “focus on coding and implementation”.