I love counterintuitive data that challenges commonly held beliefs. And there is a lot of counterintuitive health data out there (For example, data questioning the health benefits associated with taking vitamins or data that lead to a revolution in how we put our babies to sleep AND cut incidents of SIDS in half).
This story by Aschwanden for fivethirtyeight.com discusses efficacy data for various kinds of cancer screening. Short version of this article: Early cancer screening detects non-cancerous lumps and abnormalities in the human body, which in turn leads to additional and evasive tests and procedures in order to ensure that an individual really is cancer-free or to remove growths that are not life-threatening (but expose an individual to all the risks associated with surgery).
Specific Examples:
1) Diagnosis of thyroid cancer in South Korea has increased. Because it is being tested more often. However, death due to thyroid cancer has NOT increased (see figure below). As such, all of the extra detection hasn't actually decreased mortality, but it probably has increased more evasive screening measures and surgery on people who will not die of cancer.
2) The number of false positives for cancer resulting from breast cancer screenings. While women are told to do monthly breast exams at home/annual screenings with the gynecologist, most of the lumps detected are innocuous, but women are exposed to radiation and biopsies in order to confirm this.
This article is a good example for:
1) Type I Errors
2) Absolute versus relative risk
3) How should data be used to prioritize health spending?
4) Would your students refuse preventive screenings after seeing data like this? Why or why not? What happens when intuition battles data?
This story by Aschwanden for fivethirtyeight.com discusses efficacy data for various kinds of cancer screening. Short version of this article: Early cancer screening detects non-cancerous lumps and abnormalities in the human body, which in turn leads to additional and evasive tests and procedures in order to ensure that an individual really is cancer-free or to remove growths that are not life-threatening (but expose an individual to all the risks associated with surgery).
Specific Examples:
1) Diagnosis of thyroid cancer in South Korea has increased. Because it is being tested more often. However, death due to thyroid cancer has NOT increased (see figure below). As such, all of the extra detection hasn't actually decreased mortality, but it probably has increased more evasive screening measures and surgery on people who will not die of cancer.
2) The number of false positives for cancer resulting from breast cancer screenings. While women are told to do monthly breast exams at home/annual screenings with the gynecologist, most of the lumps detected are innocuous, but women are exposed to radiation and biopsies in order to confirm this.
This article is a good example for:
1) Type I Errors
2) Absolute versus relative risk
3) How should data be used to prioritize health spending?
4) Would your students refuse preventive screenings after seeing data like this? Why or why not? What happens when intuition battles data?
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