Making Sense of Your Medical Math
In their new book, Your Medical Mind: How To Decide What Is Right For You, oncologist Jerome Groopman and his wife, endocrinologist Pamela Hartzband, offer a roadmap to help people make the best medical decisions they can.
Knowing your medical mind depends in large part on understanding your own personality and how that affects your decisions. The authors describe how someone who's a "believer" in medical intervention, for example, will be more likely to embrace treatment than a "doubter" who worries the treatment may be worse that the illness.
There are other distinctions they describe, such as people who favor the use of technology versus those who prefer natural remedies. But no matter your personality type, sooner or later most people who are trying to make a medical decision have to decipher statistics that describe how successful a particular treatment is likely to be, among other things.
And that's when it gets dicey for many people, because although numbers don't necessarily lie, they can be deceptive. Groopman and Hartzband suggest some concepts to discuss with your physician to help make sense of medical statistics.
First, Groopman says, patients should always ask their doctors what the likelihood is of something happening — having a heart attack, for example — if they do nothing. That's their baseline risk.
If your baseline risk is very low to start with, then numbers showing that you can reduce it even further with a particular medical treatment may not be as impressive as they appear.
The authors cite the example of a patient, Susan Powell, whose doctor told her she could reduce her risk of a heart attack by 30 percent if she took a statin medication to lower her cholesterol level. That figure was correct. But Powell learned that based on her age, current cholesterol levels, non-smoking status and other factors, her baseline risk of a heart attack over the next 10 years was only 1 in 100, or 1 percent.
She decided not to take the statin.
Once you understand your baseline risk, the authors encourage you to consider another figure: "the number needed to treat." That number tells you how many people would need to receive the treatment you're considering in order for one person to benefit.
In the case of an antibiotic to treat a bacterial infection, maybe that number is very low. If the number needed to treat is one, then nearly every person who takes the antibiotic will benefit, says Hartzband.
But frequently, the benefit isn't as clear. In Susan Powell's case, if 300 women like Susan didn't take a statin, three of them would have a heart attack over the course of 10 years (1 percent). Taking a statin, however, would reduce the risk of heart attack by 30 percent. In practical terms, that means that one woman out of the three who could expect to have a heart attack would avoid it.
The number needed to treat, therefore, in this case is thus 300, a large enough number that some patients might not wish to go forward, Groopman suggests. "It's a very powerful concept," says Groopman. "If I were in Las Vegas and the odds were 1 in 300 and I was betting, would I do that?"
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