Surveillance bias, also called detection bias, is a type of selection bias that results when one population is more likely to have the disease 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:
- 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.
- Low-income countries might appear to have fewer cases of an infectious disease because they have poorer surveillance mechanisms.
- During the COVID-19 pandemic, it appeared that more variants were occurring in the United Kingdom or in South Africa when, in reality, those countries had far more advanced and sophisticated surveillance systems for detecting new variants and were actively looking for them whereas most other countries either lacked those resources or were not actively looking for variants.
- Rates of Lyme disease may be underestimated if researchers rely only on positive tests for Lyme disease instead of including highly suspected cases based on symptoms.
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.
As noted in a recent study on spillover viruses, “Surveillance bias poses a central challenge to assessing the rate of disease spillover from one period of time to another: an increasing trend may simply reflect stronger capacity to identify and report spillover events. There are no robust, direct measures of global reporting capacity and effort which can be taken into account, and only limited proxy measures.”