Luk Arbuckle

Posts Tagged ‘lies’

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.

Using stats is racist, sexist, and generally bad

In lies on 13 October 2008 at 10:32 pm

I heard someone get upset because researchers were using statistics to study differences between racial and cultural groups, sexes, and economic classes.  And stated that way it sounds worse than it actually is—this was their interpretation of what the researchers were doing.  It’s not that the researchers were looking for differences between subgroups, but that subgroups emerged when differences were found.  So really, properly phrased, the researchers were using statistics to study social issues and found differences between subgroups of a population.  

The comments were made with reference to the book Freakonomics, which raises questions which deserve to be debated, but they underlie a deeper distrust with numbers and statistics.  Yes, it’s true that statistics can be used to “lie”, but that does not imply that all of what comes out of statistics is a lie.  Statistics is a tool, and the only way to defend yourself against an attack involving statistics is to educate yourself about statistical methodology.  Or take it for what it’s worth, evidence of a possible explanation to a question, and run with the idea of what is being argued.  Think critically, but don’t disregard ideas just because they use statistics.

As one friend pointed out, this “discrimination” between subgroups is needed to account for differences that are not the result of bias or preconditioned ideas on the part of the person doing the statistical analysis.  Otherwise we wouldn’t be able to identify discrimination based on race, sex, or economic class.  And there’s the irony.  Another point my friend raised was something known as Simpson’s Paradox, wherein an effect can be reversed when considered at the aggregate level compared to the subgroup level.  A great example is sex bias in graduate admissions to Berkeley in 1973—admission rates at the aggregate level showed bias against women, but at the departmental level the bias was actually (slightly) against men.  It turns out women were applying to more competitive departments that didn’t have the same degree of “prior screening” (e.g., they were applying to English and not Engineering, which requires math).

I also spoke to a couple of people that studied in the social sciences.  An economics grad noted that it was dangerous to not break data into subgroups, as demonstrated by Simpson’s paradox, and a psychologist grad said they were trained in their program that it was unethical to exclude subgroups from social science research.  Regarding the latter point, imagine norms established using caucasians then applied to First Nations groups.  We are not all the same.  And although we are all unique individuals, we correlate with subgroups.  There’s nothing inherently wrong with that observation.

I think the important point to note is that individuals may discriminate, but numbers don’t know race or sex.  This is what makes statistical analysis so appealing, as we can get around bias and preconceptions to evaluate data.  But it is also why we have to be careful when doing statistical analysis, not to inject such errors into our results.  Ultimately, however, I think the comment that studying subgroups is discriminatory was born out of the most innocent form of ignorance.  A basic understanding of statistics is needed by everyone given the importance numbers play in our lives, or at the very least tolerance of something that is not well understood.

How to lie with statistics

In lies on 22 June 2008 at 11:15 pm

How to lie with statistics—I couldn’t ask for a better title to a post on my blog. It is, however, the title of a book that came long before me or my blog. And it is one of the few books about statistics that does not use equations. At a slim 142 pages it goes a long way towards educating the reader about the tricks that are used to “sensationalize, inflate, confuse, and oversimplify”, as the author writes in the introduction.

A few years ago professor J. Michael Steele published a short commemorative article, for an introduction to a special section of the journal Statistical Science, to Darrell Huff and Fifty Years of How to Lie with Statistics.

Many statisticians are uncomfortable with Huff ’s title. We spend much of our lives trying to persuade others of the importance and integrity of statistical analysis, and we are naturally uncomfortable with the suggestion that statistics can be used to craft an intentional lie. Nevertheless, the suggestion is valid.

Steele gives a short biography of Huff, including some of his published works, before jumping into a detailed discussion of the book. He describes the reasons he believes the book has been successful all these years—although he never mentions the obvious lack of equations—and the contents:

  • The first four chapters cover introductory stats (that no one remembers a year after their first stats course),
  • the next three cover graphs (“the most original in the book”, says Steele),
  • a chapter on cause-and-effect,
  • another that argues that if a persons seems to by lying with stats then they probably are,
  • and lastly a chapter on critical thinking.

For me the important discoveries are: that there exists a book about stats without equations and with a provocative name (sounds like a fun read), and a bunch of related articles that I can use to write about in future posts (also fun reads, but more technical).