Press Release Sample | Who can predict their risk of disease?


Who can predict their risk of disease? Body mass index is linked to people’s predictions about their future illness risk.
16th April 2018

Overweight and obese adults frequently over-estimate or under-estimate their risk of developing diseases such as diabetes and breast cancer, according to research from Harvard Medical School and the Washington University School of Medicine.

The study involved 4703 adult primary care patients from Boston, Massachusetts, who provided information about their lifestyle and family history. Participants were asked whether, compared to an average person of the same age, they saw themselves as being at a lower, higher, or average risk of developing each of four illnesses: diabetes, breast cancer, heart disease, and colon cancer. Their answers were compared to individual risk reports calculated by the “Your Health Snapshot” service.

Overall, both “high-risk” and “low or average-risk” participants frequently misjudged their likelihood of developing the four diseases. 13% – 40% of low or average-risk participants over-estimated their likelihood of developing each disease, while 55% – 75% of high-risk participants under-estimated their likelihood.

Overweight and obese participants were more likely to misjudge their likelihood of future disease than normal weight participants. The overweight or obese participants who were at a low or average risk were more likely to believe they faced a higher risk. However, high-risk overweight or obese patients often saw themselves as being at a low or average risk. Over 4 in 5 (81%) of the obese patients with a high risk of developing breast cancer mistakenly assumed they had a low or average likelihood of developing the illness.

“These results really underscore the need to health education programs targeted to overweight and obese people, which stress weight as a modifiable cancer risk” say the authors.

The study aimed to investigate which factors affected how people make judgements about their disease risk, to understand which groups of people are likely to over- and under-estimate their risk. This is important as people who under-estimate their likelihood of future illness are less likely to make lifestyle changes that could lessen their risk. People who over-estimate their risk can experience anxiety about their future health, and so may seek out unnecessary tests and treatments.

Although BMI had the strongest effect on risk prediction, gender and race were also linked to misjudgements, especially for diabetes. Women at a low or average risk of developing diabetes were more likely to over-estimate their risk than men, while Black participants were more likely to over-estimate their likelihood of developing diabetes than White participants. In contrast, Hispanic patients who faced a high chance of developing diabetes were more likely to correctly predict their high risk.

The authors suggest that doctors could be able to reduce misperceptions by being aware of how patients perceive their own disease risks. Further research is needed to understand how public health education programs could counteract these potential misperceptions.

The article, titled “Accuracy of self-perceived risk for common conditions” can be found at

The Brigham and Women’s Primary Care Practice-based Research Network is a group of 15 medical practices located around Boston, Massachusetts.

Harvard Medical School, established in 1782, is the world’s third-oldest medical school. Based in Boston, Massachusetts, the school serves approximately 1,600 students and 11,000 staff.

The TH Chan School of Public Health was established in 1913, and is based next to Harvard Medical School. It serves 1130 students, and about 470 faculty members.

The Washington University School of Medicine, based in St Louis, Missouri, was established in 1891. It serves about 1400 students, and 2300 faculty members.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.