Detection Bias

Detection bias is a type of bias that occurs when the detection of a disease or health outcome is influenced by factors other than the disease itself. This can occur in a number of ways, such as differences in screening or testing practices, changes in diagnostic criteria, or differences in access to healthcare.

Detection bias is particularly relevant in field epidemiology, where researchers and public health officials are working to identify and respond to outbreaks of infectious diseases. In these situations, it is important to accurately identify cases of the disease in order to track the spread of the outbreak and develop appropriate interventions. However, the presence of detection bias can make it difficult to accurately identify all cases of the disease, leading to incomplete or inaccurate data.

One common example of detection bias is differences in access to healthcare. In some communities, access to healthcare may be limited, which can result in fewer cases of the disease being detected. This can lead to an underestimation of the true extent of the outbreak and can make it difficult for public health officials to respond effectively.

Another example is differences in diagnostic criteria. If diagnostic criteria are changed during the course of an outbreak, it can lead to differences in the number of cases identified before and after the change. This can make it difficult to accurately track the progress of the outbreak and may lead to inaccurate estimates of the disease burden.

To minimize the impact of detection bias, it is important to ensure that testing and screening practices are standardized and consistent across all populations. In addition, it is important to monitor and report any changes in diagnostic criteria or testing practices that may impact the detection of the disease.

Overall, detection bias is an important consideration in field epidemiology and public health. By understanding and addressing this type of bias, public health officials can work to accurately identify cases of infectious diseases and respond effectively to outbreaks.

References

• This text was originally generated on 30 March 2023 by ChatGPT4.0 and reviewed by Arnold Bosman.
• Gjini A, Burazeri G. Detection bias in epidemiologic studies. Eur J Epidemiol. 2018 May;33(5):453-455. doi: 10.1007/s10654-018-0409-3. Epub 2018 Apr 10. PMID: 29637207.