by Carl V Phillips
There is always some interest in quantifying how many people could be saved by THR, and it flared up this week thanks to estimates by Robert West (his paper; his press comments). As a result, Brad Rodu and I were discussing the challenge of correctly accounting for smokers who could not be saved by quitting (in any manner) because the disease by which smoking is going to kill them is already established. (Recall that CDC is apparently planning to try to trick people into believing that such diseases among vapers were caused by vaping itself, rather than because their learning about THR came a year or two too late — thanks in part to the CDC.) Quitting is not the same as never having smoked, and THR switching is not the same as having always used the low-risk alternative.
The required adjustment for the number of people who cannot be saved is not huge, but it is also not zero, and it is a mistake to pretend that it is. The following back-of-the-envelope that I came up with is obviously not exact, but it is better than the (woefully common) approach of saying “we don’t know where the number is really 5 or 10, so let’s just call it 0.” I am putting it here for comment and discussion, and because experience gives me the sinking feeling that my quick-cut will remain the best available calculation of the figure for a long time.
[Update: Brief very simple summary for those who do not want to read this whole thing, and that might prime the mind to understand what follows: For some people who currently smoke, it is too late to save themselves by quitting, so we cannot claim they can be saved by THR. I estimate how many. I then point out that any simple statement along the lines of “N people per year would be saved if…” is probably wrong.]
The text in bullet points is part of the calculation itself. The plain paragraphs are further analysis or explanation.
- Take as the population in question those smokers who are fated to die from smoking if something does not intervene. For verbal parsimony, label these people “fated”.
Conveniently, the present calculation — done as a percentage of that total — does not depend on agreeing about how large that population is, or what portion of total smokers they constitute.
- Ignore deaths that are hastened by smoking by hours or days. More generally, ignore YPLL and just count deaths as the events.
Anyone who thinks seriously about “dying from smoking” statistics quickly realizes that serious analysis of them forces you to make all kinds of simplifications, and make guesses about what the “official” statistics really mean. Most such calculations demand these particular simplifications because it is probably the case that almost everyone with a long smoking history who eventually dies from a deteriorating “natural cause” dies a bit earlier because of the lung and CV weakening from smoking. This illustrates that the usual statistics are almost impossible to use in serious analysis because it is never clear how many years (or days) of potential life lost are necessary before someone makes the dichotomous move from “did not die of smoking” to “died of smoking”. Indeed, the people putting out the statistics probably have no clue about the answer, and most would not even understand the question. So for present purposes, I stick with the (perhaps false) simplification that each of the N people who are counted as dying from smoking do so many years before they otherwise would have.
- Of the fated, some have already smoked the cigarette that pushed them over the edge so that smoking will kill them even if they quit now. Call this group “doomed”.
- It is possible to estimate how many of the fated are doomed, and thus adjust the potential deaths-from-smoking saved by quitting/switching. Note, however, that the result of this calculation is based on the average smoker (or equivalently: randomly selected smokers or all smokers) — if you want to calculate the effects of those who have actually switched, it becomes a lot more complicated (discussed bel0w).
Note that these points are the ones at the core of the calculations I used in this paper.
- Assume the fated will smoke for an average of 50 years without intervention.
- Half of the fated will die from CVD.
- Half of the elevated CVD risk disappears almost immediately upon quitting smoking, and the other half returns to (almost) baseline over two years.
- This averages to one-quarter of the fated having one year of being doomed, yielding .25*(1/50) or about one half of one percent of the fated already being doomed from CVD.
This is the good news — there is almost always still time to escape fatal CVD if you quit now.
- The other half of the fated will die mostly from cancer and long-term lung disease.
- These risks take an average of ten years, or a bit more, to return to baseline after quitting. (Lung cancer is longer than that, and COPD never fully reverses, but other of these risks disappear more quickly.)
- That yields .5*(5/50), or about 5% (probably a bit more) of those the fated who are already doomed from those diseases.
Bad news for smokers, but good news for the aforementioned CDC propaganda efforts. (Protip: quit sooner rather than waiting!)
- When considering switching rather than quitting to abstinence, any synergistic effects from using the alternative product and being a recent ex-smoker need to be considered. These are probably quite small (down in the rounding error range) but presumably do push up the total a bit. That is, a few more are already doomed if the alternative is switching as compared to abstinence.
Note that this factor should not be confused with the risk, if any, of just using the alternative product instead of ever smoking. It is not unreasonable to assume that the wash-out period for CVD risk is increased, at least by a bit, by continuing to use nicotine. A bit of such delay is not completely trivial; recall that the good news about CVD risk was all about how rapidly it disappeared. Also, if the alternative has lung involvement (i.e., e-cigarettes or other inhalers), it could push incipient lung-based dooming over the edge. Again, we do not know this to be the case, but we cannot rule it out.
Important practical note: this does not mean that switching is an inferior alternative to quitting to abstinence. If the switching could happen today whereas the quitting to abstinence would not happen until next week (or more likely, long after that), the chance of smoking that dooming cigarette during that week is almost certainly greater than the synergy effect. See my above-linked paper for more on that theme.
- Adding up these risks yields something in the mid-single-digit percentage range for fated smokers who are already doomed.
- Any estimate of how many current smokers could be saved by THR — or by quitting via any other means — needs to be adjusted downward by this factor.
Notice the overtly stated lack of precision in that final estimate. Roughly speaking, we can say “5% or a bit more”, but experience shows that when you put out a number like that, people mistake it for being precise. I would guess that it is probably good within a factor of two. That is, if someone insisted that the real number was 11% or that it was 3%, I would not say I was sure they were wrong. If they said 20% or 1% I would say that was unsupportable.
So, circling back to what West said, he basically claimed that 90% of fated smokers would be saved if they all switched to e-cigarettes. This has been criticized because it was interpreted as suggesting that e-cigarettes are 10% as harmful as smoking, which is a huge overestimate based on all we know. However, if he were quietly accounting for 5% or more being already doomed, then the implicit estimated risk from e-cigarettes is down at 5% or a bit less. Still high, but not outside the plausible range. Of course, I don’t know if that was what he was doing.
[Note: I am going to ask West for his comments about this as soon as I get it up for review.]
Also, it gets rather more complicated. Saying it would save 90% of the currently fated if they all switched is fine (modulo disagreement about the exact number). But consider West’s formulation, “For every million smokers who switched to an e-cigarette we could expect a reduction of more than 6000 premature deaths in the UK each year”. There are two problems with this.
First, the millions smokers who actually switch will not be average (i.e., random) smokers. The data on this is pretty lousy, but it appears that in the USA (he was focused on the UK), the first wave of switchers were predominantly in their 30s and 40s, and many believed themselves unable to quit smoking after many other failed quit attempts. This put them at higher than average risk of being fated (if they really never would have quit otherwise) but a lower than average risk of being doomed (they were still young). This would mean that a calculation based on the average smoker quitting would substantially underestimate the benefits for this population (even apart from disagreements about the number for the average smoker).
After that first wave, switching appears to have skewed young; younger switchers were more likely to quit via other means before they were doomed, but are almost certainly not already doomed. These have opposite effects, so the net direction of bias compared to average smoker is ambiguous. Obviously some older smokers switched too, so there was a doomed subpopulation among switchers, but they appear to be significantly underrepresented compared to random selection.
Second, the “each year” is clearly not right. If a random million smokers switched right now, there would be some lives saved next year, but the number saved per year would increase over time, as the dates passed when more of the non-doomed fated reached the age they would have died from smoking. It would then pass a peak after most of that population passed the age at which they would have died from smoking, and drop down to zero late in the century. You could convert this to a scalar with something like “the average saved each year over the next 30 [or 20 or 40] years”, but it is clearly not an annual constant and there is no obvious right way to annualize it because of thin right tail makes it sensitive to the arbitrary choice of the annualization period.
[Update: It occurred to me that this was a classic “the units don’t match” problem — at least in spirit if not quite literally. For a change that is measured in one-off units of “X people change from state Y to state Z, once (and forevermore)”, with no time unit in the denominator, it is very unlikely that the outcome can be properly measured in a unit that has years in the denominator (i.e., lives saved per year). Measuring the absolute change in total people who would ever die from smoking would work.]
The problem here, of course, is that the easiest way for any of us to think about this is to start with what will happen under the status quo, pretending that it is in demographic equilibrium, even though we know it is not: Smoking currently skews older, increasing the doomed portion as a portion of current smokers, and the rate of uptake is steadily dropping. This is then compared to a steady state in which a portion of the current population switches and (roughly) the same proportion of would-be smokers takes up the alternative in the future to maintain the new relative rates. And finally, the statistics reported are what would occur after the initial washing-out period, when a new equilibrium emerged in this fictional population equilibrium scenario.
It is complicated. I realize that people want a simple scalar. But there must be some way to present a simplified scalar without embedding it in phrasing that is out-and-out wrong.