Collect and represent data from secondary sources to evaluate the method used in an example of an epidemiological study, including incidence, prevalence, mortality, and the methods and benefits of epidemiology

What this dot point is asking

NESA wants you to define the core epidemiological measures, describe the main study designs, evaluate a real epidemiological study, and explain how epidemiology informs public health.

The answer

Epidemiology is the study of the distribution, causes and control of disease in populations. It uses observational and experimental study designs to identify risk factors, estimate disease burden, and evaluate interventions.

Core measures

Incidence

New cases per population per time. Formula: $\text{incidence rate} = \frac{\text{new cases}}{\text{population at risk} \times \text{time}}$. Reported per 100 000 per year. Tracks how fast a disease is emerging.

Prevalence

Existing cases at a point in time. Formula: $\text{prevalence} = \frac{\text{existing cases}}{\text{total population}}$. Reported as a percentage. Tracks total disease burden.

Mortality

Deaths per population per time. Crude mortality counts all deaths; cause-specific mortality counts deaths from a specific disease. Reported per 100 000 per year. Tracks lethality.

Case fatality rate

Deaths divided by diagnosed cases. Measures how deadly a disease is once contracted.

Morbidity

Total illness in a population, including non-fatal disease burden (often measured as DALYs, disability-adjusted life years).

Study designs

Cross-sectional study

Measures prevalence and risk factors in a population at a single point in time. Useful for snapshots but cannot establish temporal sequence.

Cohort study (prospective)

Follows a group of healthy people forward in time, recording exposures and waiting for disease to develop. Strong for establishing temporal sequence and calculating incidence and relative risk. Example: the Framingham Heart Study (1948 onwards) identified cholesterol, smoking and hypertension as cardiovascular risk factors.

Case-control study (retrospective)

Compares people with the disease (cases) to matched people without (controls), looking backward at exposures. Efficient for rare diseases. Vulnerable to recall and selection bias.

Randomised controlled trial (RCT)

Participants are randomly assigned to intervention or control groups. The gold standard for testing whether an intervention causes an outcome. Used for treatment trials, less often for risk factor studies (cannot ethically assign people to smoke).

Ecological study

Compares disease rates across populations (e.g. fluoride in water versus dental caries). Cannot make individual-level claims (ecological fallacy).

Worked example: Doll and Hill and lung cancer

In 1950, Richard Doll and Austin Bradford Hill published a case-control study of 1298 patients in London hospitals. Cases were lung cancer patients; controls were matched patients without lung cancer. Smoking history was recorded by interview.

Result. Smokers had a much higher rate of lung cancer than non-smokers, with a dose-response gradient: more cigarettes per day, higher cancer risk.

Follow-up. The British Doctors Study (1951 onwards) followed 40 000 male doctors prospectively for over 50 years. It confirmed:

• Lung cancer mortality 25 times higher in heavy smokers than non-smokers.
• Half of long-term smokers die from a smoking-related disease.
• Quitting at any age reduces risk.

Bradford Hill criteria. Hill later proposed nine criteria for inferring causation from observation: strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment and analogy. Smoking and lung cancer satisfied all nine.

Impact. The studies led to public health warnings, advertising restrictions, taxation, plain-packaging laws (in Australia from 2012), and a roughly two-thirds reduction in adult smoking rates in developed countries.

Benefits of epidemiology

1. Identifies causes. Smoking and lung cancer, asbestos and mesothelioma, HPV and cervical cancer.
2. Targets prevention. Identifies high-risk groups for screening (e.g. women over 50 for breast cancer).
3. Evaluates interventions. Did the cervical cancer vaccine reduce incidence? (Yes, by over 50 percent in vaccinated cohorts.)
4. Tracks emerging disease. Surveillance systems detect new outbreaks early (COVID-19, HIV).
5. Allocates resources. Prevalence data informs hospital capacity, drug stockpiles and staffing.

Limitations of epidemiology

• Cannot prove causation in observational studies. Only RCTs can do that directly; observational studies use Bradford Hill criteria.
• Confounding. Hidden variables may explain associations.
• Bias. Selection bias, recall bias, reporting bias.
• Generalisability. A study in one population may not apply elsewhere.

Examples in context

Example 1. NSW BreastScreen and population-level mortality reduction. BreastScreen NSW invites women aged 50 to 74 for free biennial mammographic screening, with participation reaching about 55 percent of eligible women. Epidemiological evaluation by the Cancer Institute NSW compares age-adjusted breast cancer mortality between regular screeners and non-screeners, controlling for confounders such as socioeconomic status. Modelled data show a roughly 25 percent reduction in breast cancer mortality among regularly screened women. This is a population-level intervention evaluation using cohort study design, and it illustrates how prevalence (women living with breast cancer), incidence (new diagnoses) and mortality (deaths) move differently when screening is introduced: incidence rises initially due to detection, then mortality falls.

Example 2. Mater Mothers' Hospital and the COVID-19 maternal cohort study. During 2020-2022, the Mater Mothers' Hospital in Brisbane led an Australian prospective cohort study following 8500 pregnant women, tracking SARS-CoV-2 infection, vaccination and obstetric outcomes. Results published in The Lancet Regional Health showed vaccinated women had no increase in adverse pregnancy outcomes, while unvaccinated infected women had a 4.5-fold higher risk of preterm birth. The cohort design (following exposed and unexposed groups forward in time) was essential to establish temporal causation, which a cross-sectional snapshot could not have shown. The findings directly informed Royal Australian and New Zealand College of Obstetricians and Gynaecologists guidelines for COVID-19 vaccination in pregnancy.

Try this

Q1. Define incidence, prevalence and mortality, and explain how each is calculated for a population. [3 marks]

• Cue. Incidence = new cases per population per time. Prevalence = total cases at one point in time per population. Mortality = deaths from the disease per population per time.

Q2. In a NSW town of 50 000 people, 200 new cases of type 2 diabetes were diagnosed in 2025 and 3200 people were living with diabetes at year end. Calculate (a) the annual incidence rate per 100 000, (b) the point prevalence per 1000. [3 marks]

• Cue. (a) 200 / 50000 = 400 per 100 000 per year. (b) 3200 / 50000 = 64 per 1000 (6.4 percent).

Q3. Compare cohort and case-control study designs in epidemiology. (a) Describe each design. (b) Identify one advantage and one disadvantage of each. (c) Justify which is more suitable for studying a rare disease such as mesothelioma. [2+3+2 marks]

• Cue. (a) Cohort: follow exposed/unexposed forward; case-control: compare diseased and non-diseased retrospectively. (b) Cohort allows incidence calculation but is slow and expensive; case-control is fast but susceptible to recall bias. (c) Case-control better for rare disease due to efficiency.