PHILADELPHIA –
It is difficult to communicate medical risk to a large audience, especially
when official recommendations conflict with emotional narratives. That is why,
when the United States Preventive Services Task Force (USPSTF) in 2009
presented its guidelines for breast cancer screening, which recommended against
routine screenings for asymptomatic women in their 40’s and biennial, rather
than annual, mammograms for women over 50, the public responded with confused
fury.And, if so, why not, reductio ad absurdum, begin monthly mammograms at age 15?
The answer, of course, is that such
intensive screening would cause more harm than good. But striking the proper
balance is challenging. Unfortunately, it is not easy to weigh breast cancer’s
dangers against the cumulative effects of radiation from dozens of mammograms over
the years, the invasiveness of biopsies, and the debilitating impact of
treating slow-growing tumors that would never have proven fatal.
The USPSTF recently issued an even
sharper warning about the prostate-specific antigen test for prostate cancer,
after concluding that the test’s harms outweigh its benefits. Chest X-rays for
lung cancer and Pap tests for cervical cancer have received similar, albeit
less definitive, criticism.
The next step in the reevaluation of
cancer screening was taken last year, when researchers at the Dartmouth
Institute for Health Policy announced that the costs of screening for breast
cancer were often minimized, and that the benefits were much exaggerated.
Indeed, even a mammogram (almost 40 million are given annually in the US)
that detects a cancer does not necessarily save a life.
The Dartmouth
researchers found that, of the estimated 138,000 breast cancers detected
annually in the US,
the test did not help 120,000-134,000 of the afflicted women. The cancers either
were growing so slowly that they did not pose a problem, or they would have
been treated successfully if discovered clinically later (or they were so
aggressive that little could be done).
A related concern is measurement.
Since the patient’s duration of survival is calculated from the time of
diagnosis, more sensitive screening starts the clock sooner. Survival times can
thus appear longer, even if the earlier diagnosis had no real effect on
survival.
Naturally, individual cases dictate
which tests and treatments are best, but an additional concern about frequent
screenings is the problem of false positives. When one is looking for something
relatively rare (whether cancer or terrorists), it is wise to remember that a
positive result is often false. Either the “detected” pathology is not there,
or it is not the sort that will kill you.
Consider the following hypothetical
example. Assume that the screening test for a certain cancer is 95% accurate,
meaning that if someone has the cancer, the test will be positive 95% of the
time. Next, assume that if someone does not have the cancer, the test will be
positive only 1% of the time. Finally, assume further that 0.5% of people – one
of every 200 – actually have this type of cancer. If your doctor tells you that
you have tested positive, does this mean that you are likely to have the
cancer? Surprisingly, the answer is no.
A little arithmetic shows why. Suppose
that 100,000 screenings are conducted. On average, 500 people will have the
cancer. Since 95% of them will test positive, there will be, on average, 475
positive tests. Of the 99,500 people without cancer, 1% will test positive,
yielding 995 false positives out of 1,470 positive tests. In other words, even
if you tested positive for the cancer, the probability that you actually have
it is only about 32%.
That answer is decidedly
counterintuitive and hence easy to reject. Most people do not think in terms of
probabilities other than “50-50” and “one in a million.” But, whatever the
probabilities, the fact remains that there will generally be a high percentage
of false positives when screening for rare conditions. Moreover, the patients
who receive these faulty diagnoses will usually receive further treatments,
which often will have harmful consequences.
The “availability heuristic” – a
pervasive cognitive bias caused by people’s tendency to estimate the likelihood
of a phenomenon by how easily an example of it comes to mind – routinely clouds
the issue. People relate much more readily to a friend dying of cancer than
they do to statistics about strangers suffering from the consequences of
testing.
But the bottom line is that the
ongoing reevaluation of cancer screening is evidence-based. When it comes to
policymaking, decisions must be based on facts and argument, not anecdotes and
stories, however compelling those narratives may be.
John Allen Paulos is Professor of Mathematics at Temple University and
the author of Innumeracy and A Mathematician Reads the Newspaper.
