PUBH5010 · Epidemiology Methods And Uses
The Complete Exam Bible · S1 2026Epidemiology Methods and Uses
Epidemiology Methods and Uses is the University of Sydney's core Master of Public Health methods subject — it teaches how to read, build and judge a study: the design tree, measures of frequency and association, the 2×2 table that the whole paper radiates from, selection bias, confounding and standardisation, measurement error, critical appraisal, randomised controlled trials, screening and diagnostic tests, and systematic reviews and causation. The final exam is open book with a supplied “Helpful Formulae” sheet, so the equations are handed to you and the marks live in judgement — which measure, which bias, which direction, and the one plain-English sentence a layperson would understand. It is also a hurdle: you must score at least 50% on the exam itself to pass the subject. This guide drills exactly that recurring answer shape: name the design → build the 2×2 → pick & compute the measure → interpret in plain words.
What PUBH5010 covers
Ten teaching blocks → one open-book exam-ready map. Each links to its free chapter guide.
How PUBH5010 is assessed
| Component | Weight | Format |
|---|---|---|
| Final exam · hurdle | 60–85% | Exam period · in-person, open book with a supplied formula sheet · 2 h + 10 min reading · three sections (A short / B scenario / C long study) · non-programmable calculator, no AI · HURDLE: must score ≥50% on the exam to pass |
| Mid-semester assignment | 25% / 0% | Critically appraise a paper — hypothesis type, study design, calculate & interpret the measures, identify bias (counts under one of two mark schemes, whichever is greater) |
| Online quizzes | 10% | Weekly Canvas quizzes — best 10 of 12 counted |
| Tutorial tasks | 5% | Short in-class tasks across the semester — attemptable only if you attend; confirm this year's exact split on Canvas |
Build the 2×2 and pick the right measure — the Section-B centrepiece, mark by mark
- +1Build the 2×2. Exposed: a=120 with the outcome, b=880 without (1,000 total). Unexposed: c=60, d=940. Cells a/b/c/d are the engine the measures radiate from.
- +1Risks in each arm. Exposed risk R₁ = a/(a+b) = 120/1000 = 0.12; unexposed risk R₀ = c/(c+d) = 60/1000 = 0.06. (Cumulative incidence, because the follow-up is complete.)
- +1Risk ratio. RR = R₁/R₀ = 0.12/0.06 = 2.0. Use the RR (not the OR) because this is a cohort — you can measure risk directly.
- +1Risk difference and NNH. RD = R₁ − R₀ = 0.12 − 0.06 = 0.06; number needed to harm = 1/RD = 1/0.06 ≈ 17.
- +2Interpret in plain words. The exposed group had twice the risk (a 100% higher risk) of the outcome — not “two times higher”, which would imply three times the risk.
Key terms
- Risk ratio (RR)
- The risk of the outcome in the exposed group divided by the risk in the unexposed group, R₁/R₀, computed straight from the 2×2 a/(a+b) over c/(c+d). It is the right measure for a cohort or RCT, where risk can be observed directly; RR = 1 means no association. Interpret it as “twice the risk”, never “two times higher”.
- Odds ratio (OR)
- The odds of exposure (or outcome) in one group over the other, ad/bc on the 2×2. It is the measure a case-control study licenses, because cases and controls are sampled on the outcome so risk cannot be read off directly. For a rare outcome the OR approximates the RR; for a common one it overstates it.
- Confounding
- A third factor associated with both the exposure and the outcome, and not on the causal pathway between them, that distorts the observed association. It must meet three criteria, and the examiner wants the direction of the distortion — whether it pulls the estimate away from or toward the null — not just the label.
- Non-differential misclassification
- Measurement error that is the same in the groups being compared (e.g. exposure mis-measured equally in cases and controls). For a binary exposure it characteristically biases the estimate toward the null — it dilutes a real association — which is why it is the safer direction to predict in Section C.
- Sensitivity and specificity
- Properties of a screening or diagnostic test read down the columns of the diagnostic 2×2: sensitivity = the proportion of true cases the test catches; specificity = the proportion of true non-cases it correctly clears. They are intrinsic to the test; predictive values (PPV/NPV), read across the rows, also depend on the prevalence of disease.
PUBH5010 FAQ
Is PUBH5010 hard?
It is concept-dense rather than maths-heavy: the calculations are short (risks, RR, OR, RD, NNT, sensitivity/specificity), and because the exam is open book with a supplied formula sheet, the difficulty is judgement under time — choosing the right measure, naming the right bias, calling its direction, and writing the clean plain-English sentence. The exam is also a 50% hurdle, so the stakes are concentrated on one open-book paper.
How is PUBH5010 assessed?
By a final exam (worth 60–85% under whichever of two mark schemes is greater), a mid-semester critical-appraisal assignment (25% or 0%), weekly online quizzes (10%, best 10 of 12), and short in-class tutorial tasks (5%). The exam carries a hurdle: you must score at least 50% on it to pass the subject. Confirm this year's exact weights, dates and conditions on Canvas.
Is the PUBH5010 exam open or closed book?
Open book. It is sat in person over 2 hours of writing plus 10 minutes of reading time, in three sections (A short answers, B a scenario, C a long study to appraise), with a supplied “Helpful Formulae” sheet and a non-programmable calculator allowed. No AI or other electronic aids are permitted. Because the formulas are handed to you, the marks are in judgement and speed, not recall.
What is on the PUBH5010 final exam?
Naming study designs from a cue; computing and interpreting measures of frequency and association from a 2×2 (RR, RD, OR, NNT); identifying and giving the direction of selection bias, confounding and measurement error; the critical-appraisal pipeline on a long study; RCT concepts (intention-to-treat, blinding); and screening and diagnostic-test evaluation (sensitivity, specificity, PPV). The plain-words interpretation of every number carries marks.
Is using AskSia for PUBH5010 cheating?
No. AskSia is a study reference written in our own words — we host none of your lecturer's files, every worked example uses our own invented numbers, and we never reproduce a past paper's stem or data. Sia teaches you the method to earn the marks; it does not complete or sit your assessments. (The exam itself bans AI in the room — this guide is for your preparation beforehand.)
How to study for the exam
Make the 2×2 table your reflex: nearly every Section-A and Section-B prompt reduces to “name the design, build the 2×2, pick the measure it licenses, then say what the number means in plain words.” Because the exam is open book with a supplied formula sheet, do not waste revision copying equations — instead drill the decisions: cohort/RCT → RR & RD; case-control → OR; a third factor linked to both exposure and outcome → confounding (check the three criteria and call the direction); error the same in both groups → non-differential → bias toward the null. Tab the book so the right page opens in seconds — open-book speed is a skill you practise. And rehearse the lay-sentence stems (“twice the risk”, never “two times higher”), because the interpretation marks are the ones most students drop. Since the exam is a 50% hurdle, banked interpretation and measure-choice marks are the safest marks in the subject.
