Epidemiology Study Guide
A standard undergraduate epidemiology course: history and principles of epidemiology, measures of disease frequency (incidence, prevalence, mortality), measures of association (relative risk, odds ratio, attributable risk), study designs (cohort, case-control, cross-sectional, experimental), bias and confounding, causal inference, screening and surveillance, outbreak investigation, and infectious and chronic disease epidemiology.
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12 Topics Covered
History and Foundations of Epidemiology
John Snow, Semmelweis, Goldberger; definition, uses, and population perspective essential for understanding epidemiologic thinking on exams.
Measures of Disease Frequency
Incidence, prevalence, mortality rates, and their relationships; fundamental calculations appearing in every exam's quantitative sections.
Measures of Association
Risk ratio, odds ratio, attributable risk, NNT calculations from 2×2 tables; core quantitative skills tested extensively.
Descriptive Epidemiology
Person-place-time analysis, epidemic curves, case reports; hypothesis generation skills tested through data interpretation questions.
Cohort Studies
Prospective and retrospective designs, person-time calculation, strengths and limitations; frequent exam questions on design identification.
Case-Control Studies
Case and control selection, odds ratio interpretation, matched analysis; exam staple for bias scenarios and calculations.
Cross-Sectional and Ecological Studies
Prevalence surveys, ecologic fallacy, study limitations; commonly tested for design critique and appropriate inference questions.
Experimental Studies and Clinical Trials
RCT design, randomization, blinding, intention-to-treat analysis; ethical considerations and CONSORT guidelines tested on exams.
Bias in Epidemiologic Studies
Selection and information bias types, misclassification effects; critical evaluation scenarios dominate exam study critique questions.
Confounding and Effect Modification
Identifying, assessing, and controlling confounding; distinguishing from interaction; stratified analysis interpretation essential for exams.
Causal Inference
Hill's criteria, component cause model, DAG basics; exam questions assess ability to evaluate causation claims.
Screening, Surveillance, and Outbreak Investigation
Sensitivity, specificity, predictive values, outbreak steps, attack rate tables; applied scenarios common on final exams.
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