A researcher conducts a cross-sectional study to determine the association between obesity and hypertension. Subjects are recruited from outpatient clinics. More obese individuals attend the clinic because they also have diabetes requiring frequent follow-up. This is an example of:

• A Recall bias
• B Neyman bias
• C Berkson's bias ✓
• D Hawthorne effect

Explanation

Berkson's bias (Berkson's fallacy) arises in hospital-based studies when the exposure (obesity) and outcome (hypertension) both independently increase the probability of hospitalisation or clinic attendance, creating a spurious association or modifying the true one. Neyman bias (incidence-prevalence bias) occurs in cross-sectional studies when cases that rapidly die or recover are missed, altering prevalence estimates. Recall bias affects case-control studies where cases remember exposures better than controls.

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

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