What is the difference between sensitivity and positive predictive value?

Sensitivity is the share of true cases that the test or surveillance definition detects: TP ÷ (TP + FN). Positive predictive value is the share of positive results that are true cases: TP ÷ (TP + FP). Sensitivity is a property of the test; PPV also depends on how common the condition is.

Why does PPV fall when prevalence is low?

When few people have the condition, even a small false-positive rate produces more false positives than true positives. With the same 90% sensitivity and 95% specificity, PPV is 26.9% at 2% prevalence but 81.8% at 20% prevalence.

How do quality teams use sensitivity and PPV?

To validate a measure or trigger tool. Sensitivity tells you how many true events a screen catches, for example how many adverse events a trigger tool finds compared with full chart review. PPV tells you how many flagged cases are real events, which drives the review workload. CDC describes the same ideas for surveillance: sensitivity is the ability to detect the cases it is meant to detect, and predictive value positive is the share of reported cases that truly are cases.

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Sensitivity & specificity calculator

From true and false positives and negatives, get sensitivity, specificity, PPV, NPV and likelihood ratios. Or see how prevalence changes PPV.

Screen or test result vs. true status
	True case	Not a case
Test positive	Test positive, True case (tp)	Test positive, Not a case (fp)
Test negative	Test negative, True case (fn)	Test negative, Not a case (tn)

tp = true positives, fp = false positives, fn = false negatives, tn = true negatives. Example: a trigger tool checked against full review of 200 charts.

Result

Sensitivity: 90.0%
Specificity: 90.0%
Positive predictive value (PPV): 75.0%
Negative predictive value (NPV): 96.4%
Prevalence in this sample: 25.0%
Accuracy: 90.0%
Positive likelihood ratio: 9.00
Negative likelihood ratio: 0.11

Of every 100 true cases, the test or measure catches 90. Of every 100 flagged as positive, 75 are true cases.

PPV and NPV from prevalence

Know a test’s sensitivity and specificity? See how predictive values change with how common the condition is.

Result

PPV: 26.9%
NPV: 99.8%

At 2.0% prevalence, 27 of every 100 positive results are true positives.

Formulas

CDC lays out the 2×2 table with the test or screening result in rows and true status in columns:

Sensitivity = TP ÷ (TP + FN): the share of true cases detected.
Specificity = TN ÷ (TN + FP): the share of non-cases correctly excluded.
Positive predictive value = TP ÷ (TP + FP): the share of positives that are true cases.
Negative predictive value = TN ÷ (TN + FN): the share of negatives that are truly negative.
Likelihood ratios: LR+ = sensitivity ÷ (1 − specificity); LR− = (1 − sensitivity) ÷ specificity.

Worked example

An adverse-event trigger tool is checked against full chart review for 200 patients: 45 true positives, 15 false positives, 5 false negatives and 135 true negatives. Sensitivity is 45 ÷ 50 = 90.0%, specificity 135 ÷ 150 = 90.0%, PPV 45 ÷ 60 = 75.0% and NPV 135 ÷ 140 = 96.4%.

Why prevalence matters

Predictive values depend on prevalence; sensitivity and specificity do not. A test with 90% sensitivity and 95% specificity has a PPV of 26.9% when 2% of people have the condition, but 81.8% when 20% do. That is why screening low-risk groups produces many false positives.

Test yourself: a CPHQ-style question

The 'sensitivity' of a clinical screening test refers to:

The test's ability to correctly identify patients who do NOT have the condition
The test's ability to correctly identify patients who DO have the condition (true positive rate)
The probability that a positive result means the patient truly has the condition
The consistency of results when the test is repeated

Show answer

B. The test's ability to correctly identify patients who DO have the condition (true positive rate)

Sensitivity (true positive rate) measures how well a test detects true cases. A highly sensitive test misses few cases, which matters for screening, where missing a case is the greater harm. Option C describes positive predictive value.

Questions like this come up in the Health Data Analytics domain of the CPHQ® exam. The 249 questions in the app each come with a written explanation.

FAQ

What is the difference between sensitivity and positive predictive value?: Sensitivity is the share of true cases that the test or surveillance definition detects: TP ÷ (TP + FN). Positive predictive value is the share of positive results that are true cases: TP ÷ (TP + FP). Sensitivity is a property of the test; PPV also depends on how common the condition is.
Why does PPV fall when prevalence is low?: When few people have the condition, even a small false-positive rate produces more false positives than true positives. With the same 90% sensitivity and 95% specificity, PPV is 26.9% at 2% prevalence but 81.8% at 20% prevalence.
How do quality teams use sensitivity and PPV?: To validate a measure or trigger tool. Sensitivity tells you how many true events a screen catches, for example how many adverse events a trigger tool finds compared with full chart review. PPV tells you how many flagged cases are real events, which drives the review workload. CDC describes the same ideas for surveillance: sensitivity is the ability to detect the cases it is meant to detect, and predictive value positive is the share of reported cases that truly are cases.

Sources

CDC. Principles of Epidemiology (SS1978), Lesson 5, Appendix A: Sensitivity, Specificity and Predictive Value Positive

Checked against these sources on October 29, 2026. For education and CPHQ® exam preparation only, not a substitute for your organization’s policies, official measure specifications or professional judgment. Calculations run in your browser; nothing you enter is sent anywhere.