Really? Cancer Screening Doesn't Save Lives?!
/A new study asks whether all that stress is for nothing.
Read MoreNo, Smoking Won't Save You From COVID-19
/A critically important study is being misinterpreted.
Read MoreInterpreting the "Population Attributable Fraction" or What Percent of Cancer Deaths are Due to Smoking?
/An article appearing in JAMA Internal Medicine quantifies the Population Attributable Fraction for smoking's relationship to various cancers on a state-by-state level. But what is the PAF? And how do we interpret it? For the video version, click here.
Read MoreChantix goes to bat against the nicotine patch for quitting smoking. The winner? No one.
/For the video version of this post, click here. Quitting smoking is really hard. It’s frustrating for smokers and for their doctors. And I need to come clean and admit that when varenicline (Chantix) came out I was excited to have one more weapon in my anti-smoking armamentarium. After all, the gums, lozenges, and patches didn’t seem to work very well, but here was this new drug – a pill – that initially boasted quit rates as high as 40%. Compare that to 8% with placebos.
Even compared to the nicotine patch, varenicline seemed better, with one study showing quit rates of 26% versus 20% at one year. Of course, patients had some interesting side-effects, but smoking must be worse than vivid nightmares, right?
Recent studies have suggested that combination nicotine replacement therapy, with a patch to give some basal nicotine and lozenges to curb cravings, might be the better strategy.
Now a study appearing in the Journal of the American Medical Association finally pits the drug against a fair competitor. Researchers randomized around 1000 smokers to treatment with 12 weeks of a nicotine patch alone, a patch plus lozenges, or varenicline. The big question was what percent of those would stay quit at 14 weeks after all interventions stopped.
And I’ll skip right to the punchline. Patch: 23%, Varenicline 24%, patch plus lozenge 27%. There were no statistically significant differences between any of these numbers. Out at 1 year? 20% stayed quit across the board. The winner appears to be… nobody.
To me, more interesting than the intervention results was the analysis of factors that would predispose to quitting. Some were no surprise – if someone else was smoking in the home, your chance of quitting was 22% instead of 27% in a smoke-free home. But there was a pretty large discrepancy in quit rates based on whether or not you smoked menthols. 30% of standard cigarette smokers stayed quit, compared to just 19% of minty cigarette smokers.
Now, we may be tempted to tell our patients – "use whatever you like". But maybe the guidepost should be their tolerance of adverse events – as these were significantly different between the treatment strategies. Hate itching, hives, and hiccups? Avoid the patch. Hate nausea, vomiting, and vivid dreams? Stay away from varenicline.
Actually, you know what's even easier? Stay away from cigarettes in the first place.
Should we reduce the dose of nicotine in cigarettes?
/For the video version of this post, click here. This is oversimplifying a bit, but cigarettes basically contain two things: tar, the products of combustion that give you cancer and heart disease, and nicotine, the drug that is the reason you smoke cigarettes. In 2009, the Tobacco Control Act empowered the FDA to reduce, but not eliminate, the nicotine in cigarettes if it would benefit the public health. They have yet to exercise this power. But a new randomized trial, appearing in the New England Journal of Medicine, gives us some insight into what we might expect should they take that path.
The nuts and bolts of the trial are as follows. Researchers, primarily at the University of Pittsburgh, randomized 839 individuals into one of 7 groups: your usual brand of cigarettes, a study cigarette containing the normal 16mg of nicotine, or 5 other study cigarettes containing reduced doses of nicotine down as low as 0.4mg, one fortieth of a standard dose.
The study participants, who were all smokers with no desire to quit, received free cigarettes and some money for their participation in the six-week study. The question is what would happen to those randomized to the lower-dose groups. Would they smoke more cigarettes to compensate? Would they just buy regular cigarettes down the street? Would they give up on smoking altogether?
The results were somewhat surprising. Those randomized to their usual brand actually smoked a bit more over the 6 week period, increasing their consumption on average from 15 to 20 cigarettes per day. Remember, the cigarettes were free. Those in the lowest dose group kept their intake at pretty much 15 cigarettes a day for the whole study. Urine nicotine metabolite levels were lower in the low-dose group, suggesting they werent cheating too much with store-bought cigarettes. Importantly, 34% of those in the low-dose group reported attempts at quitting, compared to just 17% in the regular strength group.
So, youre the FDA, do you exercise your power to lower the nicotine content of cigarettes? People kept smoking their usual amount, after all, getting all that tar, despite the lower nicotine content. Maybe it would be easier for them to quit, maybe new smokers wouldnt get addicted, but thats not what this study was designed to test.
In the end, separating tar and nicotine is a great idea, but maybe this study gets it backward. Instead of making cigarettes less appealing by reducing the nicotine content, perhaps we should find ways to deliver nicotine without all the tar? Such methods, it turns out, exist, and are increasingly popular if my hipster neighbor is any indication. Keeping people away from addictive substances is just really hard to do. Giving access to such substances in a safer way may seem like giving up, but perhaps it is simply giving deference to human nature.
Adjustment is a lie. Or how I've never seen a linear relationship in any aspect of biology.
/We researchers "adjust" our observational data all the time. And we should But we often apply a simple linear model when we adjust, which is almost always wrong. Read more:
The Methods Man: Devil in the (Adjusted) Data | Medpage Today.