Monthly Archives: November 2011

Unhealthful News 191 – Absurd claims about the effects of smoking place restrictions, North Carolina edition (Part 3)








Give up without even bothering to try.










Unhealthful News 190 – Absurd claims about the effects of smoking place restrictions, North Carolina edition (Part 2)

In my previous post, I started commenting on the absurdity of a state of North Carolina claim that their recent ban on smoking in restaurants and bars caused a 21% reduction in heart attacks.  I presented part of a generic analysis, from an unpublished paper I wrote a few years ago, that basically says “if you are claiming that a such a ban caused such a large reduction in a disease rate then you are obviously wrong, regardless of what your statistical model says, and anyone who gives it a few minutes’ thought should understand this”.  In this post I make basically the same point, looking at it from a different angle, in case that might be clearer for some readers.

Start by considering the epidemiologic estimates for the increase in risk from second-hand smoke.  The evidence, when assessed by someone who is not intent on promoting smoking bans, puts the risk at so close to zero that it is impossible to say from the data that risk definitively exists.  (There are decent theoretical reasons to surmise that there is some risk, so it seems safe to assume that the risk is not zero.  But it is small.)  A few studies of people who have experienced the most extreme long-term exposure get numbers like a 20% increase in risk.  As is inevitable with random sampling and publication bias, there are a few that go tens of percentage points higher.

So let us consider the possibility that the risk is a bit higher that 20% — that is, a nonsmoker who is exposed to second-hand smoke is over 20% likely to have a heart attack, at all or much sooner, compared to that person not being exposed.  (Re the “much sooner” point, see the observation from Part 1 that a very-short-term harvesting effect would wash out of the annual statistics.)  This number is unrealistically high and at most might be considered a worst-case estimate of the risk for those with the highest accumulated lifetime exposure.  But even if it were the average effect for those with passing exposure at smoking-allowed restaurants and bars, it would obviously be far higher than the effect of that exposure averaged across the whole population.  Only people who were exposed in the first place would have that risk, and only those who go from exposed to unexposed as a result of an intervention can benefit from it.

How many people go from being exposed to restaurant/bar smoke to unexposed as a result of the ban?  It is a bit fuzzy to define this since there will be a lot of people whose exposure is reduced, and a spectrum of how much it is reduced.  But we can start with the observation that roughly half of everyone had approximately zero such exposure before the ban, never or almost never going out to eat and drink, or avoiding smoking-allowed venues when they did.  (To really get this right, we would need to figure out the portion not of people but of total risk — a 20% risk increase for an exposed 70-year-old would cause a lot more absolute risk than the same percentage would for the 25-year-olds who pack some bars — but it seems likely this would strengthen the point I am making, since the older highest-risk people tend to go out to party less.)  Thus, even if you believed that exposure at the level of visiting restaurants and bars causes somewhat more than 20% increase in risk, which is an absurd belief in itself, there is no possible way the effect of the smoking ban could be more than about half of the claimed 21%.

Not only are there a lot of people who were not exposed in the first place, but many of those who are exposed are smokers (where do you think the smoke comes from?).  No one seriously claims that the minor increase in exposure from second-hand smoke dramatically increases the risk for a smoker, on top of the already substantial risk increase from smoking.  Perhaps it does somewhat, but it is going to be a lot smaller than the effect on a nonsmoker.  Many others who are no longer exposed in bars after the ban are still exposed at home — perhaps more since their smoking spouses do more of their smoking at home or in the car before arriving at a venue.  Furthermore, most of the people who experience a substantial reduction in their total exposure — all but the nonsmoking workers and hardcore nonsmoking barflies, rather tiny percentages of the population — experience a reduction of an exposure that was far less than the extreme exposures that sometimes generate measurable effects in epidemiologic studies.

This is enough to show that the 21% estimate is utterly implausible.  Taking it further, what does this way of looking at it suggest would be a plausible maximum effect of a bar/restaurant smoking ban?

To start, even a 5% increase in risk from the bar/restaurant exposure would be a high estimate of the effect for everyone except the aforementioned workers and barflies.  We can figure that half of the population was not exposed in the first place, that easily a third of those exposed were smokers, that many of those exposed had very minor and occasional exposure, and that many others that were exposed had only a minor reduction in exposure since most of their exposure was elsewhere.  So it seems unlikely that even one-fifth of the population experienced a substantial reduction in exposure, getting the effect down below 1% of the total.  Even if we allow for a greater effect for the small highly-exposed minority, as well as some small effect for those with a very small reduction in their total exposure, it is difficult to come up with any at-all-plausible scenario that results in a reduction of more than about 2%.  (And keep in mind that this still depends on assuming the 5% increase in risk in the first place, something that is largely speculative.  Thus the real figure could be much lower than even this.)

Perhaps the full details of this analysis might call for more than common sense, but I have to assume that most people who thought about it would realize that claims of 10% reduction, let alone 20%, are completely incompatible with reality.  This brings us back to the question I asked in Part 1:

So, who would be stupid enough to believe this claim?

I suppose I phrased that too harshly to be a general statement:  The average casual reader of the news does not have time to think through most of the claims that they hear — about the benefits of wars, the causes of unemployment, or health claims — so their failure to question the claim should not be attributed to poor judgment on their part.  They just do not have time to judge.  But I will not back off on the harsh accusation when talking about news reporters and other opinion leaders who spend more than a few minutes on the topic.

Several North Carolina local news outlets reported the story without a hint of questioning the result.  Once again, it becomes apparent that the journalism curriculum for health reporters no longer includes the classes that teach governments lie habitually and that perhaps when someone (anyone) puts out a press release that claims “hey, everyone, look!  statistics show that the decision we made was a good one and did everything we said it would” it is perhaps not best to just assume they are correct and transcribe their claims.  Can you imagine if these guys were teachers?  “Class, now that you have finished your quiz, I am putting the correct answers up on the screen.  Please grade yourself and write your score on the grade sheet that I am passing around.”

The good news might be that the national press was so bored of these claims (not critical, just bored) that it does not appear to have been picked up by any national news outlet.  But that did not stop Stanton Glantz and his junk science shop at UCSF from posting about it (h/t to Snowdon for reporting that post), and you can count on it showing up in future national news stories where these hacks are quoted.  We would not expect the thoughtful analysis like the above from these people; we can count on them to repeat (repeatedly) any absurd claim like the one from NC as if it were correct.  Indeed, we could count on them to conveniently ignore any result that was down in the realistic range.

(Q: How do you know if Stanton Glantz is spouting junk science in support of his personal political goals, damaging both science and public policy?  A: They have not announced his funeral yet.  Interestingly, it is not entirely clear whether he spouts junk because he has not acquired a modicum of understanding about the science in the field where he has worked for decades, or because he is a sociopath-level liar; I am not entirely sure which is the more charitable interpretation.)

So that is the easy side of this analysis, wherein reporters transcribe claims that are obviously wrong and extremist activists embrace them because they are wrong.  In Part 3 I will go into some details of the modeling that are beyond the abilities of reporters and junk-science activists, but that emphasize that those who reported the results are lying and/or are attempting analyses that are way beyond their abilities, and presumably know it.

Unhealthful News 189 – Absurd claims about the effects of smoking place restrictions, North Carolina edition (Part 1)

I was asked to comment on a report from the state of North Carolina which claims that their early 2010 implementation of a rule that prohibited smoking in a few public places where it was previously allowed (bars and restaurants) reduced emergency hospital admission for MI (heart attack) by 21%.  This story broke a few days ago, and Snowdon has already written about it, as has Michael Siegel.  Both of them offered the observation that this claim is just an artifact of complex modeling that can generate any result you might want.  Snowdon already pointed out that the downward time-trend in heart attacks actually flattened out after the ban (i.e., it was dropping over time, but dropped less after the ban than before).  That is pretty much all you need to know.  The time trend is by far the dominant statistic, and anything else has to be measured against it.  It is, of course, possible that the ban saved some would-be MIs, but since the time trend lessened, there is no possible way anyone can claim to see the result in the data.

(Siegel added the observation that for women there was actually an increase in MIs after the ban (it was less than the decrease for men, so the net was the continuing decline), though it is not actually clear that this means much — after all, if men were the ones primarily “saved” from second-hand smoke, this is what we would expect to see.  I am not inclined to make much of this observation, since the report authors did not pull the obvious junk science trick of reporting just the result for men, trying to gloss over the result for women.  Just as it is always possible to find a subgroup that exaggerates an observed/claimed population effect, it is always possible to find one that runs counter.)

So, the main message is already out there, but I think I can add two things to it to bracket it:  (1) At a level that most anyone can understand, the NC claim is utterly implausible, regardless of what the statistical analysis says.  This is in keeping with my goal of showing how thoughtful people can often analyze science — and call bullshit on it where appropriate — without needing to understand all of the arcane details.  (2) I can also provide some additional insight into the statistical modeling, from the perspective of someone who can do statistics like that, and more important, has observed the behavior of other people who do it.  This is kind of a big topic, one that I wrote a paper about once, though never got around to publishing, so I will start with this post and then continue it.

What does it mean to claim that a particular intervention reduced a disease by 21%?  It sounds impressive.  Indeed it is.  It means that whatever it was that the intervention brought about — in this case, the removal of second-hand smoke — was causing one-fifth of the outcomes in question.  (Rounding to “one fifth” is a much more accurate way to describe the statistic — reporting down to the last decimal place is good evidence that someone does not understand the limits of their statistics.)  So, second-hand smoke was causing one-fifth of all heart attacks?  Really?  That would make its impact roughly as great as that of smoking itself.  This is not even remotely plausible.  Right there is evidence that this result is wrong, and you do not need to know anything about how they did the calculation.

But, wait, it gets worse.  The claim is not that the totality of second-hand smoke exposure causes as many heart attacks as smoking, but that the fraction of exposure that is eliminated by the bar and restaurant ban was causing that much.  The more common and constant exposure in the home would not be eliminated; indeed it would probably increase as smokers gathered to drink somewhere they can smoke.  So the claim must actually be that second-hand smoke causes a lot more heart attacks than does smoking, up around half of all heart attacks, and this intervention eliminated roughly half of those.

But, wait, there’s more.  The claim must be that exposure to second-hand smoke in restaurants and bars over a medium time period (roughly: measured in months) causes one-fifth of all heart attacks.  Epidemiologic studies, even those by anti-tobacco activists, have only been able to sometimes find an elevated risk in life-long nonsmoking spouses of smokers, or long-term workers in smoky environments.  But the smoking ban obviously did not eliminate lifetime exposure during its first year, the one year of data that was available.

Those who wish to defend this absurdity would undoubtedly reply with their “one puff” hypothesis, the claim that even a brief exposure to second-hand smoke can cause acute physiological effects that can trigger a heart attack in someone who is vulnerable.  But even setting aside whether that claim is plausible at all, it does not work in this scenario.  The claimed phenomenon is what is known, morbidly, as a “harvesting effect”, triggering an event that is on the verge of happening a few weeks sooner.  Someone who was close enough to a heart attack in March that being in a smoking-permitted restaurant would have triggered it, but who avoids that event due to the new ban, is still on the verge and will likely encounter a similar trigger by April, or undoubtedly in June when hot North Carolina weather kills many of the vulnerable.  So, according to the story, some people are being saved from this trigger by a week or two or maybe ten.

If that were really the case, there would be a slight drop during the year after the ban, but it would be very slight since basically only a few weeks of heart attacks would be eliminated from the year:  The ones from the first week after the ban would just be shifted to later in the year; those that would have happened at those times would be pushed later, and so there would be close to a wash; and only those that would have happened at the end of the year would be pushed beyond the range of the data and thus represent a reduction.  It might be interesting to see if there was a drop in that first week, because that would be a good test of the “one puff” harvesting claim.  But that would be only a matter of scientific inerest, not a substantial effect on public health.

So, for there to be a major reduction due to this intervention, it needs to be the case that many heart attacks are not caused by accumulated lifetime exposure (which is not changed much) or a immediate-term trigger (which is only delayed), but that exposure accumulated over the last few weeks or months causes heart attacks that never would have happened or would have happened much later.  This story suffers from both the fact that there are no models or epidemiologic results to support it and because of the enormous portion of all heart attacks that would then have to be caused by second-hand smoke.  The claim would be that medium-term exposure in restaurants and bars causes one-fifth of all heart attacks, and so trigger-term and long-term exposure in those venues cause more still, and exposure in the home and other venues must cause at least that many again, which totals to a substantial majority of all heart attacks.  So if we can just eliminate the smoke, it looks like we can stop worrying about obesity and lack of exercise.  Even smoking itself is looking pretty good, as long as you ventilate the ambient smoke.

So, who would be stupid enough to believe this claim?

Since this is Unhealthful News, you can assume it includes the press.  More on that, and on the analysis, in Part 2.