Negative Predictive Value of a Test

Understanding negative predictive value (NPV) can be confusing. However, it is an important part of understanding the quality and accuracy of medical tests. The negative predictive value tells you what it means if you test negative for a disease. It is a marker of how accurate that negative test result is. In other words, it tells you how likely it is that you actually don't have the disease.

A chlamydia screening smear test
Peter Dazeley / Getty Images

The negative predictive value is defined as the number of true negatives (people who test negative who are not infected) divided by the total number of people who test negative. It varies with test sensitivity, test specificity, and disease prevalence as you can see in the example below. Because of the dependence on disease prevalence in the community where they work, figuring out the negative predictive value is complicated. Most doctors cannot simply give you a number for the negative predictive value when you go in for any given test even if they know the sensitivity and specificity.


If a chlamydia test has 80% sensitivity and 80% specificity in a population of 100 with a chlamydia prevalence of 10%:

  • 8 out of 10 true positives test positive
  • 72 out of 90 true negatives test negative

Out of 74 negative tests, 82 are true negatives and 2 are false negatives. Therefore, the NPV would be 97% (72/74). 97% of people who test negative would actually be negative for chlamydia. In contrast, if the same test is given in a population with a chlamydia prevalence of 40: 32 out of 40 true positives test positive
40 out of 60 true negatives test negative Out of 48 negative tests, 8 are false negatives. That means the negative predictive value is 83% (40/48).

How Various Factors Affect Negative Predictive Value

Negative predictive value goes down as a disease becomes more common in a population. In contrast, positive predictive value goes up.

Similarly, high sensitivity tests make the negative predictive value increase. That's because there are fewer false negatives. (More people who are positive test positive on a high sensitivity test.) In contrast, high specificity tests are more important for positive predictive value. With those tests, fewer false positives. The higher the specificity, the more people who are negative test negative. 

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