# 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 how much you can rest assured 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.

The negative predictive value is defined as the number of true negatives (people who test negative who don't have a condition) divided by the total number of people who test negative. It varies with test sensitivity, test specificity, and disease prevalence.

Because of variable disease prevalence in different communities, the negative predictive value of a test is not always straightforward. 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.

## Example

If a chlamydia test has 80% sensitivity and 80% specificity in a population of 100 with a chlamydia prevalence of 10%, you can expect the following:

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

Out of 74 negative tests, 72 are true negatives (they don't have the infection) and 2 are false negatives (they tested negative, but they actually have the infection).

Therefore, the NPV would be 97% (72/74). You can expect that 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, the NPV would be different. That's because NPV takes into account more than simply the sensitivity and specificity of a diagnostic test. In this case:

• 32 out of the 40 true positives test positive
• 48 out of the 60 true negatives test negative

Out of 56 negative tests, 8 are false negatives. That means the negative predictive value is 85% (48/56).

## How Various Factors Affect Negative Predictive Value

High sensitivity tests make the negative predictive value increase. That's because more people who are actually positive have a positive test result on a high sensitivity test and there are fewer false negatives.

Similarly, the negative predictive value goes down as a disease becomes more common in a population.

In contrast, the positive predictive value goes up as the disease is more common in a population. And, high specificity tests improve the positive predictive value. With high specificity tests, there are fewer false positives. The higher the specificity, the more people who are negative test negative.