Personal Breast Cancer Risk Communication

Why do some women feel that their personal breast cancer risk estimate is not accurate for them, and how can we communicate personal risk better?

What do we know already?

Personalized risk calculators are available for many diseases, from estimating a person's future risk of Alzheimer's disease to predicting their likelihood of developing cancer. Yet research shows that people often do not believe these estimates are accurate for them. For example, a person might feel that the calculator fails to reflect their individual circumstances.

What have we found so far?

We found that women are often skeptical of their personal breast cancer risk estimate because they feel it does not adequately account for their lifestyle — healthy and unhealthy alike — their extended family history of breast cancer and other cancers (or their lack of family history), and their environmental exposures, such as living near a pollution or toxic waste.

Some women are certain they will develop cancer, potentially reflecting fatalism, while others are certain they will not, potentially reflecting an overemphasis on the absence of family history.

A breast cancer risk model that takes into account a more extensive family history and additional health factors — weight, exercise, breast density, and others — might seem more accurate to patients. It may also be important to convey that risk is not entirely determined by family history, and that there is no such thing as zero risk for breast cancer.

What’s next?

We have consistently found that women believe their risk is lower than the estimate the calculator provides. That is surprising, and it points to the possibility that the way risk is communicated affects both comprehension and skepticism. We are currently examining how the risk estimate's denominator (N in 100 versus N in 1,000) and its duration (5-year, 10-year, or lifetime risk) affect how people interpret their estimate.

We have also identified a number of frequently asked questions, and developed an FAQ answering the most common questions women have about their risk estimates. We plan to test whether this FAQ helps women interpret their risk estimate.

Funding

Funded by the National Cancer Institute, R01 CA279953.

Further Reading

Publications

Waters EA, Taber JM, Ackermann N, Maki J, McQueen AM, Scherer LD. (2023). Testing explanations of skepticism of personalized risk information. Medical Decision Making, 1–15.

Waters E, Foust J, Scherer LD, McQueen A, Taber JM. (2021). To what extent do internet-based cancer risk assessment tools adhere to best practices in risk communication: a content analysis. Journal of Medical Internet Research, 23(1), e23318.

Waters EA, Taber JM, McQueen A, Housten AJ, Studts J, Scherer LD. (2020). Translating cancer risk prediction models into personalized cancer risk assessment tools: stumbling blocks and strategies for success. Cancer Epidemiology, Biomarkers & Prevention, 29(12), 2389–2394.

Scherer LD, Ubel PA, McClure J, Greene SM, Hensley-Alford S, Holtzman L, Exe N, Fagerlin A. (2013). Belief in numbers: when and why women disbelieve tailored breast cancer risk statistics. Patient Education and Counseling, 92, 253–259.