The Predictive Value of Screening Tests and Phone Calls from Guys You Want to Love You, Part 1

By William London

On my to-do list today is grading the final exam in my “Introduction to Epidemiology” class. About a quarter of the exam addresses issues in screening for latent (subclinical) disease. In his 1986 book, Medical Care Can Be Dangerous to Your Health: A Guide to the Risks and Benefits, Eugene D. Robin provided this apt description of what screening programs do: Make patients out of normal human beings. Screening involves applying a test or procedure to asymptomatic individuals–in other words, normal human beings–in order to detect indications of disease at an early stage so that treatment can be applied early enough in order to improve outcome. When normal human beings are screened, they take on the role of patient and they remain in the role of patient when they receive follow-up testing or treatment. It’s good to be a patient when medical care is much more likely to help than harm you. Otherwise, we are better off as normal human beings.

In order to justify turning normal human beings into patients through screening for latent disease, it’s important that there be a suitable disease, suitable screening test or procedure, and suitable screening program.

The disease must be progressive, serious, and have a detectable preclinical (asymptomatic) phase. The disease in its detectable preclinical phase also needs to have high prevalence in the population to be screened. If the proportion of the population screened who are in a detectable preclinical phase of the disease is too low, it turns out that positive test results will usually be shown in follow-up testing (which sometimes is invasive) to be false alarms. Screening when latent disease is uncommon leads much more to unnecessary medical care than to lives extended and saved.

The screening test or procedure should have high validity (accuracy) and reliability (repeatability in the results it gives). It should not place an unreasonable cost burden upon the health care system or on the normal human beings it transforms into patients. It should be acceptable to the screened population; it should not be very inconvenient, unpleasant, and uncomfortable to be screened.

The screening program should also be demonstrated to be feasible and efficacious in reducing mortality and complications from the disease. The benefits of medical care given to screened individuals should exceed the harms of medical care. It’s important to emphasize that people who receive false-positive results can be harmed, but cannot benefit from follow-up medical care.

Recommendations that various populations of human beings should be screened for various diseases should only be given after serious consideration of all the suitability issues. The US Preventive Services Task Force takes a conscientious approach to formulating recommendations for screening and other clinical preventive services. The Task Force recently began the process of updating its recommendation about screening for breast cancer. Its draft research plan addresses the various issues to consider when making patients out of normal human beings.

As an illustration of a feasibility consideration for the Task Force to consider, I gave my epidemiology students this practice problem (similar to one on their final exam):

In a hypothetical community, 100 out of every 10,000 women actually have breast cancer at a detectable preclinical phase of the disease and the rest do not have breast cancer. (Let’s assume that we know all this to be true.) All 10,000 women in this community receive screening mammography.

For women who actually have breast cancer, assume that mammography will correctly detect evidence of breast cancer 80% of the time (which means it will fail to detect evidence of breast cancer 20% of the time).

For women who don’t actually have breast cancer, assume that mammography will correctly identify them as not having breast cancer 90% of the time, but incorrectly identify them as having breast cancer 10% of the time.

A mammography result suggestive of cancer is called a positive result. A mammography result suggesting that that cancer is not present is called a negative result.

Given the information above, approximately, what percent of the women who receive a positive test result actually have breast cancer?

Given the information above, what percent of the women who receive a negative test result are truly cancer free?

If you know the answers, please don’t post them in a comment, but please feel free to mention whether you are already familiar with this type of problem, whether you think you know the answers, or how long you spent trying to figure out the answers.

This type of problem tends to stump lots of people, including many licensed health care practitioners and almost all of my students. I have struggled for years to effectively teach my students to understand and solve the problem. My latest teaching strategy has involved an analogous problem: the heteronormative concerns of the white women in the movie “He Just Not That Into You.” I refer students to the movie’s trailer and I make these points:

• The probability that guys will call you if they love you is not the same as the probability that guys love you if they call you.
• The probability that guys will not call you if they don’t love you is not the same as the probability that guys don’t love you if they don’t call you.

Hint: I just gave you a hint to solve the problem.

In Part 2 of this post, I’ll:

• explain how the hint is analogous to the mammography screening problem
• provide detailed instructions for solving the problem
• give you the answers and discuss their implications
• offer a follow-up problem (with answers provided in Part 3)

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