Community psychiatry, epidemiology, prevention, and health system/administrative topics
This domain tests quantitative reasoning about tests and study designs, plus the service-delivery models that shape psychiatric care outside the hospital.
Core rules
- Incidence counts new cases over time; prevalence counts existing cases. A screening test's positive predictive value depends on prevalence, so a good test in a low-prevalence population yields many false positives.
- Sensitivity and specificity are properties of the test; PPV and NPV depend on the population tested.
- Study designs: randomized controlled trial for causality; cohort for incidence and relative risk; case-control for rare outcomes and odds ratios. Bias names: lead-time, length-time, selection, recall and observer bias; confounding can be handled by randomization, matching or adjustment.
- Prevention levels: universal, selective and indicated prevention (or primary, secondary and tertiary prevention at the population level).
- Service models: deinstitutionalization, community mental health centres, assertive community treatment for high-utilizer patients, case management, supported employment (individual placement and support), clubhouse models and the recovery orientation.
- Integration of mental health into primary care is the dominant strategy in Iran, where the national programme has been delivered through rural health houses and urban health posts, relying on trained primary-care staff and referral pathways, mirroring the WHO mhGAP approach to task-sharing.
Worked example
A screening test has 90% sensitivity and 90% specificity; prevalence is 1% in 10,000 people. Cases = 100, true positives = 90; non-cases = 9,900, false positives = 990. PPV = 90/(90+990) = 8.3%. Most positives are false, which is why screening is followed by a diagnostic assessment and why screening is reserved for high-prevalence or high-risk groups.
Traps
Reading PPV as a fixed test property; confusing relative risk with attributable risk; calling a cohort study good for rare diseases; treating prevalence as incidence when duration changes.