Surveillance bias, also called detection bias, is a type of selection bias that results when one population is more likely to have the disease or condition detected than another because of increased testing, screening or surveillance in general. Basically, the data become skewed based on which cases are—or aren’t—identified. Or, more plainly: When you look for more, you find more.
Deeper dive
Here are a few examples:
- Postmenopausal women have a higher risk of bleeding when exposed to estrogen, and the bleeding might lead doctors to screen for endometrial cancer. Because doctors are looking for the cancer, they will be more likely to find it in women they screen — those with the bleeding. Surveillance bias can make it appear as though postmenopausal exposure to estrogen is a risk factor for endometrial cancer when it’s actually a result of this population getting screened more.
- Hospitals may appear to have a higher rate of deep vein thrombosis in patients not because their rate is actually higher but because they screen for it in high-risk populations more often.
- Cities might appear to have higher rates of confirmed COVID-19 cases compared to rural areas when, in reality, cities may have better access to tests and city residents may have better access to healthcare compared to rural areas.
The danger with surveillance bias is that risk factors might be ascribed to a condition when, in fact, those risk factors are conditions that lead to a higher rate of screening, testing or other surveillance for another condition. It’s important to consider who is not being tested or screened for conditions.