9.4.6 Prevalence and Positive/Negative Predictive Value

For Studies Evaluating Diagnostic Accuracy, Estimates the Characteristics of a Sample: Prevalence, Positive Predictive Value and Negative Predictive Value

When evaluating the diagnostic accuracy of a test, it is important to not only consider the test’s sensitivity and specificity but also the prevalence of the disease in the population being tested.

Prevalence is a fundamental concept in medical statistics that refers to the proportion of individuals in a specified population who have a certain disease or condition at a given time. It is a measure of the overall burden of a disease in a population, providing valuable insight into the distribution and extent of a health issue. Prevalence can be expressed as a percentage or a proportion, and it is commonly used in epidemiological research and public health planning to identify the most pressing health problems and allocate resources effectively. It is important to note that prevalence is not an indicator of the risk of developing a disease, as it reflects the current disease status rather than new occurrences. To better understand the dynamics of a disease, prevalence should be analyzed in conjunction with other epidemiological measures, such as incidence, which denotes the number of new cases within a specified period.

The positive predictive value (PPV) and negative predictive value (NPV) are also important measures of diagnostic accuracy. PPV is the proportion of individuals who test positive and actually have the disease, while NPV is the proportion of individuals who test negative and do not have the disease.

The formula for calculating PPV:

The formula for calculating NPV:

It is important to note that the PPV and NPV are affected by both the sensitivity and specificity of the test as well as the prevalence of the disease in the population being tested. As the prevalence of the disease increases, the PPV also increases and the NPV decreases, and vice versa.

Therefore, when interpreting the results of a diagnostic test, it is important to consider the test’s sensitivity and specificity, as well as the prevalence of the disease in the population being tested, in order to accurately assess the PPV and NPV.

Understanding these measures can help clinicians and researchers interpret the accuracy of diagnostic tests in the context of the population being tested.

References:

1. Deeks, J. J., & Altman, D. G. (2004). Diagnostic tests 4: likelihood ratios. BMJ (Clinical research ed.), 329(7458), 168-9. https://doi.org/10.1136/bmj.329.7458.168