Popularized but not written by Mark Twain is the phrase “There are three kinds of lies: lies, damned lies, and statistics.”
The unfortunate truth about health statistics is that they can mislead you into taking or not taking action, which can have a negative impact on your health and mortality.
In their book “Know Your Chances: Understanding Health Statistics,” physicians Steven Woloshin, Lisa Schwartz and Gilbert Welch breakdown how health statistics are and should be calculated and presented while also describing the many risks of using statistics inappropriately.
Not all health statistics are bad or poorly presented, however there are enough examples out there and the stakes can be very high.
We need to be on the lookout for exaggerated messages of fear or hope used to motivate us to take a drug, follow a certain course of treatment, buy a specific product, or any number of other actions not in our best interest.
By understanding what is real and what is hype, you can recognize the true advantages and potential dangers. It takes some effort, but as the stakes get higher the need to be a critical thinker, even in light of expert recommendations (i.e. by your healthcare professional), becomes more important.
The authors use many examples to highlight the issues. One common strategy used to attract attention and probably funding is to exaggerate statistics while providing minimum supporting information.
One such example used is “Colon cancer will strike about 150,000 Americans.” This attention grabbing statement leaves many important questions unanswered.
Out of how many people? What age group was the research focused on? Over what time period: one, 5, 10 years, or perhaps a lifetime? What are the chances of dying from this diagnosis?
Are my chances of surviving any better with early detection or the same as not knowing until symptoms develop? How frequently do health professionals screen for colon cancer and what are the side effects?
The authors suggest medical information would be more accurate and honest if broader perspectives were communicated, rather than focusing only on shock-and-awe statistics.
Using the cancer example, they suggest that a more robust presentation would be “over 10 years, the average person’s chance of dying from colon cancer is two out of 1,000.”
Another way of saying this is “their chance of not dying from colon cancer is 998 out of 1000.” Now how do you feel after reading this vs the original “Colon cancer will strike about 150,000 Americans?”
Both statements are based on the same underlying data, but convey very different information to the reader.
Another example in the book on how framing results can mislead the public shows an ad for Zocor, a drug used to lower cholesterol levels.
The ad in large font says “A clinical study among people with high cholesterol and heart disease found 42% fewer deaths from heart attacks among those taking Zocor.”
A powerful and true statement. However, if you read the fine print and do a few calculations, what you also get is “For men with heart disease and high cholesterol, Zocor reduces the five-year chance of dying of a heart attack from 8.5% to 5.0%.” Not quite as impressive as the ad would like us to believe.
These are two of many examples offered in the book, along with guidance on how the average health information consumer can seek out accurate information upon which to build strong decisions.
In the end, the authors advise us to ask questions when we are given statistics such as, what is the real risk? How big is it? Does the risk information apply to me? How does this risk compare with other risks? Is the risk reduction worth the downsides? What kind of science is behind the numbers and are the people creating or communicating the numbers looking after my best interests or theirs?
When it comes to your health, don’t gamble — know your chances and make an informed decision. No one cares more about you than you.