Public Health and Preventive Medicine
Core concepts
Public health questions test epidemiology, screening, and prevention. Know measures of disease frequency (incidence - new cases; prevalence - all cases), measures of association (relative risk for cohort studies, odds ratio for case-control), and the interpretation of sensitivity, specificity, and predictive value. Understand study designs (randomized trial, cohort, case-control, cross-sectional) and their biases (selection, recall, confounding). Master screening principles (important disease, acceptable test, early detection improves outcome, cost-effective) and the levels of prevention: primary (immunization), secondary (screening), tertiary (rehabilitation). Know notifiable diseases, outbreak investigation steps, and vaccination schedules.
Traps
The most common trap is confusing incidence with prevalence and relative risk with odds ratio. Another is misreading predictive value: a positive test in a low-prevalence population has a low positive predictive value even with high sensitivity and specificity. Questions may test that screening is not appropriate when early detection does not change outcome, and that lead-time bias can make survival look better without changing mortality. Watch for confounding versus effect modification, and for the difference between statistical significance and clinical importance.
Worked rule of thumb
For any screening or test stem, build a two-by-two table mentally: disease present or absent against test positive or negative. Sensitivity is the proportion of diseased who test positive; specificity is the proportion of nondiseased who test negative; positive predictive value depends heavily on prevalence. Example: a test with 95% sensitivity and 95% specificity used in a population with 1% prevalence yields many false positives, so most positives are not true disease - a key reason screening programs target higher-prevalence groups or use confirmatory testing.