A researcher compares smoking rates between cases (lung cancer patients) and controls recruited from the same hospital's orthopedic ward. The odds ratio for smoking is found to be 1.2 (95% CI 0.9–1.6), lower than the known true association. The most likely explanation is:

• A Neyman bias (incidence–prevalence bias)
• B Recall bias
• C Hawthorne effect
• D Berkson's bias (hospital admission bias) ✓

Explanation

Berkson's bias arises in hospital-based case-control studies when controls are drawn from the same hospital. Hospitalized patients have higher rates of exposures (like smoking) compared with the general population, inflating the exposure prevalence in controls and therefore diluting the odds ratio toward the null. Recall bias would spuriously increase rather than decrease the OR. Neyman bias occurs when cases die or recover before selection. Hawthorne effect pertains to behavioral changes due to observation.

Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.

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Written and medically reviewed by the StethoPrep medical team.