Prevalence-incidence bias is a type of selection bias. It is also known as “Neyman bias”.
Prevalence-incidence bias occurs when individuals with severe or mild disease are excluded, resulting in an error in the estimated association between an exposure and an outcome. There are a number of different ways that this bias can arise in research.
If, when studying cardiac arrest, one only collects data on arrival the the emergency department, you would miss all the patients who were declared dead on scene, who may be systematically different from those who make it to hospital.
If you were studying risk factors for myocardial infarction among patients admitted to a cardiac ward, you could get a skewed result if you failed to include healthier patients (perhaps those who has “silent MIs”) or sicker patients, such as those who have already died from a cardiac arrest.
This post is part of a series of posts on bias in medical research. You can find the whole bias catalogue here.
Sackett DL. Bias in analytic research. Journal of chronic diseases. 1979; 32(1-2):51-63. PMID: 447779
Justin Morgenstern. Prevalence-incidence bias, First10EM, 2018. Available at: