Which factor primarily affects the proportion of cases captured in a study when the disease has a short duration relative to the study period?

• A. Selection bias.
• B. Information bias.
• C. Neyman bias (prevalence-incidence bias).
• D. Lead-time bias.

Answer: (C)

Neyman bias, or prevalence-incidence bias, is the factor at play when disease duration is short relative to the study period. In a cross‑sectional look at prevalence, you only capture cases that are present at the moment of the survey. If many new cases arise and resolve quickly, they may already have recovered or died by the time you measure, so they are missed. This makes the observed set of cases skew toward those with longer-lasting disease, reducing the proportion of cases you actually observe even though incidence during the period may have been higher. In other words, the snapshot preferentially captures longer-duration cases, biasing the observed prevalence.

This isn’t lead-time bias, which relates to earlier detection changing apparent survival time, nor is it just general information or selection bias—the specific phenomenon here is that short-duration cases are underrepresented in a prevalence snapshot due to the timing of the study.