Community Medicine and Public Health: Populations, Measures and Prevention
Core concepts
Community medicine tests measurement and decision-making at population level. Master the definitions: incidence versus prevalence, and when each is appropriate; sensitivity, specificity, positive and negative predictive values, and how prevalence changes predictive value. Understand study designs - cohort, case–control, cross-sectional, randomised trial - and the measures of association each produces. Learn the levels of prevention and give examples at each level. Know the principles of screening and the criteria a disease must satisfy before screening is worthwhile.
Public health practice includes outbreak investigation, immunisation schedules, and the control of communicable and non-communicable disease. Understand health systems concepts: equity, access, and the social determinants of health. Biostatistics questions test the choice of test by data type and the interpretation of confidence intervals and p-values. Be comfortable with standardisation of rates and the difference between crude and adjusted measures.
Traps
Examiners frequently test the confusion between sensitivity and positive predictive value, particularly when prevalence changes. A highly sensitive test in a low-prevalence population has many false positives. Watch for questions that ask for the measure most appropriate to a study design, and for distractors that describe secondary prevention as primary.
Rule of thumb
Sensitivity and specificity belong to the test; predictive values belong to the population. If prevalence changes, predictive values change while sensitivity and specificity do not.
Worked example
A screening test with 95% sensitivity and 90% specificity is applied in a population with 1% prevalence. Of 10,000 people, 95 true positives arise but about 990 false positives - so a positive result has a predictive value near 9%, not 95%.