Cost-Effectiveness Analysis in Public Health

Cost-effectiveness analysis compares the resources required by a health intervention with the health benefits it produces. For screening, which tests people without apparent symptoms to identify possible disease, it asks whether the additional health benefit justifies the additional cost compared with an alternative, such as no organised screening.

Comparing costs and health gains

Evaluation follows the entire pathway from screening to diagnostic confirmation and treatment, rather than considering the screening test alone.

• Relevant costs include testing, follow-up, treatment and programme delivery; a societal perspective also considers costs such as patients’ travel and time.
• Benefits may be expressed as deaths prevented, life-years gained, quality-adjusted life-years (QALYs) gained or disability-adjusted life-years (DALYs) averted.
• The incremental cost-effectiveness ratio (ICER) divides the additional cost by the additional health benefit relative to the comparator.

Establishing whether screening improves health

Earlier diagnosis is valuable only when it leads to better health outcomes. Evaluation must account for both benefits and harms.

• Disease prevalence, test accuracy, participation and access to effective treatment influence a programme’s cost-effectiveness.
• False positives can cause anxiety and unnecessary investigations, while false negatives can provide false reassurance.
• Overdiagnosis identifies disease that would never have caused symptoms or death, potentially leading to unnecessary treatment.
• Longer survival measured from diagnosis does not alone prove benefit because lead-time bias can increase recorded survival without postponing death.

Using the findings for public decisions

Cost-effectiveness informs priority-setting but does not settle it. Opportunity cost matters because funding screening can displace other beneficial services.

• Budget impact, affordability, equitable access and implementation capacity must also guide decisions.
• Sensitivity analysis tests whether conclusions change when uncertain assumptions about costs, participation or benefits vary.