Screening screening

Mammograms continue to be an emotional and controversial topic. I blogged about it some time ago. (link) Felix Salmon, whose blog should be daily reading, praises an article by Peggy Orenstein called "Our Feel Good War on Breast Cancer", NYT Magazine (link). Salmon's blog provides a quick summary; Orenstein's article is very long.

Orenstein's point of view has particular weight because she was diagnosed at an early age, and was an early advocate of breast cancer screening and now has a change of heart. She's worried about false positives, overdiagnosis, and over treatment.

You may recall that two or three years ago, there was a furor about new recommendations by experts that women reduce the frequency of screening. I used this example in a talk to illustrate the failure of these experts to communicate their statistical results, leading to a prompt, forced retreat. Immediately after the talk, several women raised objections--they believed this is such an emotional issue that numbers mean nothing. It was illuminating since I was at an analytics conference, and the women work with numbers on a daily basis. In the meantime, I was on shaky ground because of my gender. I'm glad that someone of Orenstein's stature has come out to try to explain the other side of this story. (See also my post on the Paradox of Screening here.)

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Instead of rehashing the arguments around screening tests, today I just want to highlight some subtle aspects of measuring health outcomes.

One of these is described in Orenstein's article. One of the metrics used to measure health outcomes is the five-year survival rate... what proportion of patients are still alive five years after they are diagnosed with breast cancer? Said differently, we are interested in the length of time between the time of diagnosis and the time of death. There are two ways to improve this metric, by delaying the time of death, or by moving earlier the time of diagnosis. The former is difficult to achieve, and requires genuine progress in medication. The latter is very easy! If we screen younger people, we will move the time of diagnosis earlier. Without any medical progress, we will make the five-year survival rate fall. This phenomenon is known as lead-time bias.

Expanding screening to younger and less vulnerable populations has another effect, which I discussed in Chapter 4 of Numbers Rule Your World. The marginal person who gets diagnosed will be less ill than the average person - Orenstein tells us that the form of disease most commonly diagnosed in younger women (DCIS) ought not even to be called "cancer". Add less ill people to the population of ill people will automatically improve the survival rate, again without any medical progress. This phenomenon is known as length bias.