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 are sensitivity and specificity used in infection surveillance?

CDC describes the sensitivity of surveillance as its ability to detect the cases it is meant to detect, and predictive value positive as the proportion of reported cases that truly are cases. A sensitive definition misses few HAIs but may flag non-cases that need review.

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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.

Test or surveillance 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: 200 charts reviewed against a surveillance definition.

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 definition 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 surveillance 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

A surveillance definition 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 of26.9% 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 CIC-style question

The positive predictive value (PPV) of a surveillance definition increases when:

Disease prevalence decreases
Disease prevalence increases
Sensitivity decreases
The population is healthier

Show answer

B. Disease prevalence increases

PPV — the probability that a flagged case is a true case — rises as the prevalence of the condition rises, even when sensitivity and specificity stay the same. In low-prevalence settings, more flagged cases are false positives.

Questions like this come up in the Surveillance and Epidemiologic Investigation domain of the CIC® exam. The 654 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 are sensitivity and specificity used in infection surveillance?: CDC describes the sensitivity of surveillance as its ability to detect the cases it is meant to detect, and predictive value positive as the proportion of reported cases that truly are cases. A sensitive definition misses few HAIs but may flag non-cases that need review.

Sources

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

Checked against these sources on October 21, 2026. For education and CIC® exam preparation only — not a substitute for your facility’s policies, NHSN’s official reports, or clinical judgment. Calculations run in your browser; nothing you enter is sent anywhere.