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.

Worked example

Australia's National Bowel Cancer Screening Programme.

Background. Bowel cancer incidence in Australia was approximately 60 per 100 000 in 2006, with mortality around 25 per 100 000.

Intervention. Free immunochemical faecal occult blood test (iFOBT) mailed every two years to all adults aged 50 to 74.

Evaluation method. Cohort comparison of screened versus unscreened individuals over 10 years, plus surveillance of population-level incidence and mortality.

Outcome. Mortality reduced by approximately 15 to 20 percent in screened populations; early-stage detection rates rose substantially.

Interpretation. Population-level intervention is effective and cost-saving.

Common traps

Confusing incidence and prevalence. Incidence is new cases (a rate); prevalence is existing cases (a proportion). A chronic disease with long survival has high prevalence but moderate incidence; an acute fatal disease can have high incidence but low prevalence.

Claiming causation from a cross-sectional or case-control study. These designs show associations; causation requires triangulating with cohort data, RCTs and biological mechanisms (Bradford Hill).

Ignoring confounding. "Coffee drinkers have higher lung cancer rates" may be confounded by smoking, which correlates with coffee.

Forgetting units. Rates need a denominator (per 100 000) and a time frame.

In one sentence

Epidemiology measures disease in populations using incidence, prevalence and mortality, and applies cohort, case-control and randomised study designs to identify causes and evaluate interventions, as classically demonstrated by Doll and Hill's case-control plus cohort studies linking smoking to lung cancer.