Using a test with higher specificity while prevalence stays constant will primarily affect which predictive value, and how?

• A. Decreases negative predictive value
• B. Increases both PPV and NPV equally
• C. Increases positive predictive value by reducing false positives
• D. Has no effect on predictive values

Answer: (C)

When prevalence is constant, predictive values hinge on how well the test distinguishes disease status, especially how many false positives you get. Increasing specificity reduces false positives among people without the disease. Positive predictive value is the proportion of positive test results that are true positives; if you cut down the false positives, a larger share of positives are true disease cases, so PPV goes up.

For example, with a fixed prevalence, boosting specificity lowers the number of non-diseased people who test positive. That makes positive results more trustworthy overall, boosting PPV. Negative predictive value is more tied to identifying negatives (and to sensitivity), so the primary and most noticeable change with higher specificity is the rise in PPV.