Screening Yield Calculator
Enter population size, disease prevalence, test sensitivity, and specificity to see how many cases a screening program will detect and at what cost in false positives.
Screening Parameters
1.0%
Percent of the screened population with the disease
90.0%
True positive rate — % of diseased correctly identified
95.0%
True negative rate — % of healthy correctly cleared
Cases Detected
—
true positives
NNS
—
number needed to screen
False Positives
—
per screening round
PPV
—
positive predictive value
NPV
—
negative predictive value
False Positive Rate
—
1 − specificity
2×2 Contingency Table
| Disease Present | Disease Absent | Row Total | |
|---|---|---|---|
| Test Positive | — | — | — |
| Test Negative | — | — | — |
| Column Total | — | — | — |
Correct classification
Misclassification
Summary
Enter population size, disease prevalence, test sensitivity, and specificity to see how many cases a screening program will detect and at what cost in false positives.
How it works
- Enter the total population size to be screened.
- Input the known or estimated disease prevalence (% of the population who have the disease).
- Enter the test sensitivity — the probability the test is positive given disease is present.
- Enter the test specificity — the probability the test is negative given disease is absent.
- The calculator shows true positives, false positives, true negatives, false negatives, NNS, PPV, and NPV.
Use cases
- Plan a cancer screening campaign and estimate how many positive results will be true cases.
- Compare two screening tests by their PPV under a given prevalence.
- Justify screening program resources by calculating the number needed to screen.
- Evaluate the burden of false positives before rolling out a population-level screen.
- Teach students how low-prevalence diseases produce poor PPV even with accurate tests.
- Estimate downstream workload (confirmatory tests, referrals) from a screening round.
Frequently Asked Questions
Last updated: 2026-07-24 ·
Reviewed by Nham Vu