Prevalence

What is prevalence?

Disease prevalence is the proportion of a defined population that has a disease at a specified point in time. The numerator is the number of existing cases and the denominator is the population in which those cases could occur. Existing cases may include both recently diagnosed people and those who have lived with the disease for some time.

This definition describes point prevalence. Studies may also report period prevalence, covering anyone who had the disease during a stated interval. The population, disease definition and reference date or period should always be stated.

Why does prevalence matter in market access?

Prevalence helps market access teams estimate the scale and distribution of a disease population. It informs disease-burden assessments, patient segmentation, service planning, budget-impact models and forecasts of how many people might be considered for treatment.

A prevalence estimate is not automatically the addressable market. Teams must account for diagnosis, treatment eligibility, contraindications, access restrictions and expected uptake. In a health technology assessment, unsupported assumptions at any of these stages can materially change the expected population and budget impact.

How is prevalence calculated or estimated?

Point prevalence is calculated by dividing the number of people who meet the case definition on the reference date by the total relevant population on that date. It may be presented as a proportion, percentage or number of cases per stated population size.

Data may come from disease registries, electronic health records, claims databases, surveys or published epidemiological studies. A usable estimate should specify:

• the disease and diagnostic criteria
• the geography and population covered
• the reference date or period
• age, sex and other relevant subgroup boundaries
• whether cases are diagnosed, recorded, self-reported or modelled
• how missing data, duplicate records and population changes were handled

Estimates may be crude or adjusted to a standard population. Any adjustment should be transparent, especially when transferring evidence between countries or applying published rates to a different population.

How is prevalence different from incidence?

Prevalence counts all existing cases at a point in time, whereas incidence concerns new cases arising over a defined period. Prevalence is therefore a measure of how common a disease is; incidence describes how frequently it is newly occurring.

The measures are related but not interchangeable. Prevalence is affected by incidence, disease duration, recovery, mortality and migration. A condition can have low incidence but substantial prevalence if people live with it for a long time. Conversely, a short-duration or rapidly fatal condition may have high incidence but lower point prevalence.

Which prevalence estimate should a pharma team use?

The right estimate depends on the decision. Total disease prevalence may suit burden-of-disease work, while diagnosed prevalence is often more relevant to current service demand. Treated prevalence describes patients receiving therapy, and eligible prevalence applies clinical and reimbursement criteria to identify those who could receive a particular intervention.

Epidemiology or HEOR teams usually lead the method and evidence review, with input from clinical, commercial and medical affairs colleagues. Teams should agree one documented case definition and population pathway rather than combining figures built for different purposes. Where evidence is uncertain, models should show the base estimate, plausible alternatives and the effect of key assumptions.

How does prevalence sit alongside related measures?

Prevalence describes the existing disease population. Incidence describes new cases, disease burden describes the wider health and economic consequences, and an eligible patient estimate applies clinical and access criteria to the prevalent population. Keeping these measures separate prevents the total number of people with a disease from being mistaken for the number expected to receive a treatment.