A researcher conducts a cross-sectional survey to estimate prevalence of hypertension. She finds that individuals with hypertension are over-represented because they make more clinic visits and are thus more likely to be included in the sample. This is best described as:
- A Volunteer bias
- B Berkson's bias ✓
- C Prevalence-incidence (Neyman) bias
- D Length-biased sampling
Explanation
Berkson's bias occurs when study subjects are drawn from clinic/hospital attendees rather than the general population; frequent attenders (like hypertensives on treatment) are over-sampled, inflating apparent prevalence. Neyman bias specifically describes the under-counting of rapidly fatal or quickly resolving conditions in cross-sectional or case-control designs. Length-biased sampling refers to over-representation of slowly progressing diseases in screening programmes. Volunteer bias results from self-selection of healthier or more health-conscious individuals.
Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.
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