The contempt laden Op-Ed is here. Below is the first paragraph:
In his inaugural address, Barack Obama promised to restore science to its”rightful place.”This has partly occurred, as evidenced by this month’s release of 13 new human embryonic stem-cell lines. The recent brouhaha over the guidelines put forth by the government task force on breast-cancer screening, however, illustrates how tricky it can be to deliver on this promise. One big reason is that people may not like or even understand what scientists say, especially when what they say is complex, counterintuitive or ambiguous.
So right out of the box author John Allen Paulos rhetorically links the G.W. Bush era right wing “Christianity” driven opposition to stem cell research with concerns about the new mammogram guidelines. But that noxious opener is completely unfair. Women did not start scheduling mammograms for dubious religious reasons. We did it because SCIENTISTS CONVINCED US IT WOULD BE BENEFICIAL – scientists at places like the Centers For Disease Control. So did organizations like the American Cancer Society, which last time I checked was not “faith based.”
In the next sentence Paulos moves from despicable analogy to outright lies. He writes:
As we now know, the panel of scientists advised that routine screening for asymptomatic women in their 40s was not warranted and that mammograms for women 50 or over should be given biennially rather than annually.
How often are women getting mammograms? According to this American Cancer Society report: “Mammography usage has not increased since 2000. In 2005, 51.2% of women aged 40 and older reported getting a mammogram in the past year. Women who lack health insurance have the lowest use of mammograms (24.1%).” Have breast cancer deaths decreased since breast cancer screening has increased? Yes they have. Did Paulos actually read the report? Because I did, and as I blogged previously, here is what it said:
There is convincing evidence that screening with film mammography reduces breast cancer mortality, with a greater absolute reduction for women aged 50 to 74 years than for women aged 40 to 49 years. The strongest evidence for the greatest benefit is among women aged 60 to 69 years. …
How will reduced screening change these numbers? We don’t know. Here is another excerpt from the Report:
A series of randomized clinical trials that would compare the results of stopping breast cancer screening at different ages (by first comparing stopping screening at age 75 years with continued screening, and then further comparing stopping screening at earlier ages, depending on the results of the first study) would be ethical and informative.
Extended follow-up of this type of study might also provide useful information about overdiagnosis in this age group. In general, more studies of overdiagnosis, including comparisons of lifetime breast cancer incidence among similar screened and unscreened women, would be helpful. Studies on overdiagnosis might also include long-term follow-up of women with probable missed cases of DCIS on the basis of microcalcifications that were missed in an earlier mammogram. Such studies could provide the percentage of these women who develop invasive breast cancer over the next 10 or more years.
Randomized clinical trials of film versus digital mammography among women with dense breast tissue, with sufficient follow-up to detect stage shifts (reductions of late-stage cancer) or decreases in clinical interval cases, would also be ethical and helpful.
Better understanding of certain facets of tumor biology is needed, particularly how age, race, breast density, and other factors may predispose certain women toward tumors with faster growth rates and greater lethality. This would improve the ability to determine at diagnosis which patients can be treated minimally.
In short, the Report is pretty equivocal about whether reducing breast cancer screening will have net benefits beyond cost savings, reducing”psychological harms, unnecessary imaging tests and biopsies in women without cancer, and inconvenience due to false-positive screening results,”and overdiagnosis. How much overdiagnosis? We don’t know. The Report admits:”Methods for estimating overdiagnosis at a population level are not well established, and thus the proportion of all detected DCIS lesions that constitute overdiagnosis is uncertain.”
But let’s get back to Paulos:
Fortunately, both the panel’s concerns and the public’s reaction to its recommendations may be better understood by delving into the murky area between mathematics and psychology.
Now we get to learn in more detail how stupid he thinks women are, as a matter of psychology and poor math skills:
Much of our discomfort with the panel’s findings stems from a basic intuition: since earlier and more frequent screening increases the likelihood of detecting a possibly fatal cancer, it is always desirable. But is this really so? Consider the technique mathematicians call a reductio ad absurdum, taking a statement to an extreme in order to refute it. Applying it to the contention that more screening is always better leads us to note that if screening catches the breast cancers of some asymptomatic women in their 40s, then it would also catch those of some asymptomatic women in their 30s. But why stop there? Why not monthly mammograms beginning at age 15?
Yes, and why stop with monthly mammograms at age 15? You broads are so dumb, when the doctor tells you to take one pill per day for a month, you probably swallow all thirty at once, thinking this will cure you faster. Just because medical professionals advised you for years that a yearly mammogram would help protect your health, doesn’t mean you should have believed them. But now that a brainy mathematician like Paulos says you shouldn’t have them as frequently, you should absolutely listen, because clearly he’s right and every shred of available evidence supports his position. Or does it? More Paulos:
…Alas, it’s not easy to weigh the dangers of breast cancer against the cumulative effects of radiation from dozens of mammograms, the invasiveness of biopsies (some of them minor operations) and the aggressive and debilitating treatment of slow-growing tumors that would never prove fatal.
The exact weight the panel gave to these considerations is unclear …
Alas? Not easy to weigh? Unclear? Huh, suddenly it appears maybe the new recommendations aren’t so definitive after all. So on to abstract mathematical modeling with made-up numbers:
… A little vignette with made-up numbers may shed some light. Assume there is a screening test for a certain cancer that is 95 percent accurate; that is, if someone has the cancer, the test will be positive 95 percent of the time. Let’s also assume that if someone doesn’t have the cancer, the test will be positive just 1 percent of the time. Assume further that 0.5 percent : one out of 200 people : actually have this type of cancer. Now imagine that you’ve taken the test and that your doctor somberly intones that you’ve tested positive. Does this mean you’re likely to have the cancer? Surprisingly, the answer is no.
To see why, let’s suppose 100,000 screenings for this cancer are conducted. Of these, how many are positive? On average, 500 of these 100,000 people (0.5 percent of 100,000) will have cancer, and so, since 95 percent of these 500 people will test positive, we will have, on average, 475 positive tests (.95 x 500). Of the 99,500 people without cancer, 1 percent will test positive for a total of 995 false-positive tests (.01 x 99,500 = 995). Thus of the total of 1,470 positive tests (995 + 475 = 1,470), most of them (995) will be false positives, and so the probability of having this cancer given that you tested positive for it is only 475/1,470, or about 32 percent! This is to be contrasted with the probability that you will test positive given that you have the cancer, which by assumption is 95 percent.
That clear things up for you? No? It’s because you are stupid, obviously:
… Most people don’t naturally think probabilistically, nor do they respond appropriately to very large or very small numbers. For many, the only probability values they know are”50-50″and”one in a million.”Whatever the probabilities associated with a medical test, the fact remains that there will commonly be a high percentage of false positives when screening for rare conditions. Moreover, these false positives will receive further treatments, a good percentage of which will have harmful consequences. This is especially likely with repeated testing over decades.
Note Paulos doesn’t actually know the probabilities associated with mammograms. He just generalizes that “there will commonly be a high percentage of false positives” when screening for “rare conditions.” Is breast cancer truly a rare condition? Scientists at the American Cancer Society do not think so. But let’s get back to how stupid women are, that’s a lot more fun than data, apparently:
… Cognitive biases also make it difficult to see the competing desiderata the panel was charged with balancing. One such bias is the availability heuristic, the tendency to estimate the frequency of a phenomenon by how easily it comes to mind. People can much more readily picture a friend dying of cancer than they can call up images of anonymous people suffering from the consequences of testing. Another bias is the anchoring effect, the tendency to be overly influenced by any initially proposed number. People quickly become anchored to such a number, whether it makes sense or not (â€œwe use only 10 percent of our brains”), and they’re reluctant to abandon it. If accustomed to an annual mammography, they’re likely for that reason alone to resist biennial (or even semiannual) ones. …
Women are so easily “anchored” to an “initially proposed number” that we would resist decreasing or increasing the number of mammograms we undergo no matter what our doctors recommend, that’s how dumb we are. Nice. And you just knew there would be a truly reprehensible conclusion, didn’t you? Paulos wraps up by saying:
Whatever the role of these biases, the bottom line is that the new recommendations are evidence-based. This doesn’t mean other right-thinking people would necessarily come to the same judgments. To oppose the recommendations, however, requires facts and argument, not invective.
Has the New York Times stopped requiring logic or coherence altogether? Let’s unpack this sentence by sentence:
Whatever the role of these biases, the bottom line is that the new recommendations are evidence-based.
Paulos doesn’t know what the role of these biases may be, but damn it felt good to accuse women of having them. And he says the new recommendations are “evidence based,” completely ignoring the fact that one key recommendation is that more research should be conducted, because so much important evidence is lacking.
This doesn’t mean other right-thinking people would necessarily come to the same judgments.
And in fact, they haven’t, as noted above. Which might be a good reason not to cast aspersions on women who are asking hard questions about the new guidelines.
To oppose the recommendations, however, requires facts and argument, not invective.
Maybe if people like Paulos were less condescending the invective would flow less freely. Women have good reasons to be cautious about “guideline changes” that give insurance companies cover to reduce the kind or quantity of health care that woman are able to receive. I’m keeping an open mind about the new guidelines, it would be nice if people like Paulos would do the same.