In a cross-sectional seroprevalence survey of dengue IgG antibodies, a significantly higher prevalence is found in elderly subjects than in young adults. A virologist suggests this reflects higher past exposure in the elderly cohort, not aging per se. This difference between age-effect and cohort-effect is best untangled by:

• A Increasing sample size in cross-sectional study
• B Standardization of rates to a reference population
• C Stratifying by sex in the current dataset
• D Age-period-cohort (APC) analysis using repeated cross-sectional surveys ✓

Explanation

Age-period-cohort (APC) analysis decomposes disease patterns into three components: age effects (biological aging), period effects (calendar time, e.g., new vaccine introduction), and cohort effects (shared exposures of a birth cohort). A single cross-sectional study cannot separate these effects; repeated cross-sectional surveys or longitudinal follow-up are needed. Standardization adjusts for age distribution differences but does not disentangle age from cohort effects. Stratification by sex addresses a different dimension.

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

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