Quick questions on Epidemiology: incidence, prevalence, mortality and study designs: HSC Biology Module 8

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.

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.

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

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.

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

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

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

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.

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.

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).

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

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

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

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.

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.