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# Evaluating a screening programme

- Measure

A screening programme is judged by how many people it reaches, what it finds and, above all, whether it cuts deaths from the disease.

Updated Nov 13, 2026

## Key points

- Coverage (participation) = people screened / target population; without enough coverage there is no population effect.
- Detection rate (yield) = cases detected / people screened; it depends on prevalence and on the round (the first finds more).
- Positive predictive value = confirmed cases / positives fully worked up; it falls when prevalence is low.
- Follow-up of positives is the weak link: a positive without a confirmatory diagnosis is a programme failure.
- The outcome that matters is disease-specific mortality; survival of detected cases is inflated by lead-time and length bias and by overdiagnosis.
- Non-randomised evaluation (case-control, before-after trends) suffers from healthy volunteer bias.

On this page

- The basics Key points
- What it is
- Formula and reading
- Worked example
- Operational versus outcome measures
- Markers
- How to read it
- Common pitfalls
- Memory aid

- Study Test yourself

- Connections Connections

## What it is

Evaluating a screening programme means measuring whether it works in the population, not only whether the test is good. Operational measures (coverage, positivity rate, follow-up of positives, detection rate and positive predictive value) show whether the programme runs well. Outcome measures (disease-specific mortality, stage at diagnosis, harms) show whether it helps, and the strongest evidence is a randomised trial with disease-specific mortality as the end point.

## Formula and reading

- Coverage = screened / target population × 100.
- Positivity rate = positive results / screened × 100.
- Follow-up = positives with a complete diagnostic work-up / positives × 100.
- Detection rate = confirmed cases / screened (per 1 000).
- Programme PPV = confirmed cases / positives worked up × 100.
- Interval cases: cases diagnosed between two rounds after a negative result; many suggest low sensitivity or too long an interval.

## Worked example

- Data (made up): a cervical cancer screening programme; target population 20 000 women; 13 000 are screened.
- Coverage: 13 000 / 20 000 = 0.65, that is, 65 % (the made-up local target was 70 %).
- Positives: 650; positivity rate 650 / 13 000 = 0.05, that is, 5 %.
- Follow-up: 520 of the 650 complete the diagnostic work-up: 520 / 650 = 0.80 (80 %); 130 positives are left unexplored.
- High-grade lesions confirmed: 104; PPV = 104 / 520 = 0.20 (20 %).
- Detection rate: 104 / 13 000 = 0.008, that is, 8 per 1 000 screened.
- Reading: the programme misses its coverage target and loses 1 in 5 positives; improving follow-up pays more than changing the test.

## Operational versus outcome measures

| Type | Measure | What it tells | Limitation |
| --- | --- | --- | --- |
| Operational | Coverage | Whether the programme reaches the population | Says nothing about whether the test works |
| Operational | Follow-up of positives | Whether positives are confirmed and treated | Needs linked records |
| Operational | Detection rate and PPV | Yield and burden of false positives | Change with prevalence and round |
| Outcome | Stage at diagnosis | Whether diagnosis comes earlier | Can improve without changing mortality |
| Outcome | Disease-specific mortality | Whether the programme saves lives | Takes years and ideally a randomised trial |

## Markers

- Coverage
- Detection rate
- PPV
- Interval cases
- Disease-specific mortality

## How to read it

- A programme is audited by rounds: coverage by age and territory, time from positive to diagnosis, and interval cases.
- Longer survival of detected cases does not prove benefit: mortality must be compared across the whole invited population.
- Harms are measured too: false positives, unnecessary procedures and overdiagnosis.

## Common pitfalls

- Judging the programme by 5-year survival of detected cases (lead-time and length bias).
- Comparing screened and unscreened people as if they were alike: those who attend are usually healthier.
- Comparing the first round's detection rate with later rounds without noting that the first one picks up accumulated prevalence.
- Celebrating coverage without measuring follow-up of positives.

## Memory aid

‘Reach, find, confirm, save’: coverage, detection, follow-up with PPV and, in the end, fewer deaths.

## Test yourself

Think of the answer, then tap the question to see it.

## How is the coverage of a screening programme calculated?

People screened divided by the target population; for example, 13 000 / 20 000 = 65 %.

## Why is survival of detected cases not enough to evaluate a programme?

Because lead time, length bias and overdiagnosis lengthen apparent survival even if nobody lives longer; disease-specific mortality must be measured.

## What is an interval case?

A case diagnosed between two screening rounds in a person whose result was negative; its frequency reflects the test's sensitivity and whether the interval is right.

## Which design gives the strongest evidence that screening helps?

A randomised trial comparing disease-specific mortality between a population invited to screening and one not invited.

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## Connections

## Related 6

- [Principles of screening](https://durgical.com/en/epidemiology/principles-of-screening/)
- [Lead-time bias](https://durgical.com/en/epidemiology/lead-time-bias/)
- [Length-time bias](https://durgical.com/en/epidemiology/length-time-bias/)
- [Overdiagnosis](https://durgical.com/en/epidemiology/overdiagnosis/)
- [Positive and negative predictive values](https://durgical.com/en/epidemiology/positive-and-negative-predictive-values/)
- [Randomised controlled trial](https://durgical.com/en/epidemiology/randomised-controlled-trial/)

## Related conditions 1

- [Cervical cancer](https://durgical.com/en/conditions/cervical-cancer/)

[← All of epidemiology](https://durgical.com/en/epidemiology/)

Educational content on epidemiological and statistical methods; the examples use made-up numbers to teach the calculation. Not a clinical or legal guide: current Colombian rules are in the official sources.

Sources: [Cancer Screening Overview (PDQ®)–Health Professional Version](https://www.cancer.gov/about-cancer/screening/hp-screening-overview-pdq) (National Cancer Institute, 2023); [Screening programmes: a short guide. Increase effectiveness, maximize benefits and minimize harm](https://www.who.int/europe/publications/i/item/9789289054782) (World Health Organization, Regional Office for Europe, 2020).

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License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Cite as: DurgiPedia (durgical.com), https://durgical.com/en/epidemiology/evaluating-a-screening-programme/