Measuring Health in Populations

Epidemiology is fundamentally concerned with measuring how much disease exists in a population and how quickly new disease is arising. Two broad categories of measures serve this purpose: prevalence and incidence.

Prevalence is the proportion of a population who have the disease at a given point in time. It is calculated by dividing the number of existing cases by the total population at that point; the denominator does not exclude people who were lost to follow-up or who died. Prevalence informs burden of disease estimates and resource allocation decisions. Its key limitation is that it captures no temporal information — we cannot infer when disease began, and it does not tell us the rate of new case development.

Incidence measures the occurrence of new cases. Two forms exist. Incidence proportion (IP), also called cumulative incidence, is the proportion of an outcome-free population that develops the outcome over a specified time period. It assumes a closed population and requires the time period to be explicitly stated. Incidence rate (IR) is the rate at which new cases occur, calculated by dividing new cases by total person-time at risk. Person-time accrues from each participant until they become a case, are lost to follow-up, die, or the study ends. IR is expressed per unit of person-time (e.g., per 100 person-years) and does not require a stated calendar time period.

Participants cease to be at risk when they develop the outcome, when they can no longer develop it (e.g., already affected), or when they leave the study. Including affected individuals in the denominator underestimates the true incidence rate.

The relationship P ≈ I × D (prevalence approximately equals incidence multiplied by disease duration) is a conceptual model, not a strict formula. It explains why adult-onset diabetes (low incidence, long duration) has high prevalence, while the common cold (high incidence, short duration) has low prevalence at any given point.

Age standardisation removes the distorting effect of different age distributions when comparing disease rates across populations. It is applied when age structures differ between populations and disease risk varies by age.

Results are interpreted using a standard template: state the measure of occurrence, the outcome, the population, the time point (omitted for IR), and the value. For IR, report per 100 person-years at risk.