Skip to main content
v2026.1714 entries · CC-BY 4.0
CASRAI

Epidemiology · Reference

What is screening?

Screening, as a public-health concept, is the application of a test to apparently healthy people to identify those more likely to have a disease, so it can be addressed earlier. This page explains the methodology — sensitivity, specificity, lead-time and over-diagnosis — and not any individual screening decision.

What screening is

Screening applies a test to apparently healthy people to identify those who are more likely to have a disease or condition, so that further investigation or action can follow. A screening test is not diagnostic: a positive result indicates higher likelihood and is normally followed by a confirmatory diagnostic test. Screening is classed as secondary prevention, because it aims to detect disease early in its course rather than to stop it arising. This page treats screening as a population-level methodology and a research-standards topic; it does not advise on whether any individual should be screened.

Measuring test performance

A screening test is judged partly by how well it classifies people. Sensitivity is the proportion of those with the disease whom the test correctly identifies (true positives); specificity is the proportion without the disease whom it correctly clears (true negatives). There is usually a trade-off between them set by the test threshold.

The positive predictive value — the chance that a positive result is a true case — depends not only on sensitivity and specificity but on how common the disease is. When a condition is rare, even a good test yields many false positives, which is a central reason screening can cause harm as well as benefit.

Lead-time, length and over-diagnosis

Evaluating whether screening helps must allow for several biases. Lead-time bias arises because detecting disease earlier moves the diagnosis date forward, lengthening apparent survival even if the time of death is unchanged. Length(-time) bias arises because slower-progressing cases are more likely to be caught by periodic screening, making screen-detected disease look more survivable. Over-diagnosis is the detection of disease that would never have caused symptoms or harm in a person’s lifetime, leading to needless labelling and intervention. Because of these biases, improved survival among screen-detected cases does not by itself prove screening works; randomised evidence on mortality is the stronger standard.

Principles for screening programmes

Whether a population screening programme is worthwhile is assessed against established criteria, classically those set out by Wilson and Jungner for the WHO (1968) and since updated. These ask, among other things, that the condition be an important health problem with a recognisable early stage, that a suitable and acceptable test exist, that effective action follow a positive result, and that the benefits outweigh the harms and costs. The framing makes clear that screening is a programme-level decision balancing benefit against harm across a population — a standards and policy question, addressed here conceptually rather than as personal guidance.

Key facts

At a glance

  • Definition: Testing apparently healthy people to detect disease early
  • Not: Diagnostic — a positive needs confirmatory testing
  • Prevention: Classed as secondary prevention
  • Performance: Sensitivity, specificity, positive predictive value
  • Biases: Lead-time, length-time, over-diagnosis

Common questions

FAQ

What is the difference between screening and diagnosis?+

Screening applies a test to apparently healthy people to identify those more likely to have a disease, sorting them for further assessment. Diagnosis establishes whether disease is actually present, usually with a more definitive test. A positive screen indicates higher likelihood, not a confirmed diagnosis.

What are sensitivity and specificity?+

Sensitivity is the proportion of people who have the disease that a test correctly identifies as positive, and specificity is the proportion without the disease that it correctly identifies as negative. There is usually a trade-off between the two, set by where the test threshold is placed.

What is lead-time bias?+

Lead-time bias is an apparent increase in survival caused simply by detecting a disease earlier through screening. Because the diagnosis is moved forward in time, survival measured from diagnosis looks longer even if screening does not change when the person dies, so it can make screening seem more effective than it is.

The step most authors miss

Doing CRediT right? Don’t stop at the statement.

A CRediT statement credits you inside one paper. The recognition CRediT was built for happens when those roles are tied to you, persistently. Sign in with your ORCID — free — and claim your CRediT contributions on casrai.org, the home of the standard. They become a verified, portable part of your identity, not a line that disappears into one PDF.

Free: claim your contributions, then export a journal-ready CRediT statement, schema.org structured data, JATS XML, CSV or BibTeX — and preview your public profile. A membership publishes that profile publicly and verifies the journals you serve.

Referenced across the research world

University of Cambridge logoColumbia University logoUniversity of Edinburgh logoHarvard University logoUniversity of Oxford logoPrinceton University logoStanford School of Medicine logoUniversity College London logoORCID logoCrossref logoUniversity of Cambridge logoColumbia University logoUniversity of Edinburgh logoHarvard University logoUniversity of Oxford logoPrinceton University logoStanford School of Medicine logoUniversity College London logoORCID logoCrossref logo
  • University of Cambridge logo
  • Columbia University logo
  • University of Edinburgh logo
  • Harvard University logo
  • University of Oxford logo
  • Princeton University logo
  • Stanford School of Medicine logo
  • University College London logo
  • ORCID logo
  • Crossref logo

View CASRAI adoption →