Prevalence: Meaning and calculation

What prevalence means

Prevalence measures the existing burden of a condition, meaning how many people in a group are affected at a point in time or during a period. Noordzij and colleagues briefly described these frequency measures in 2010. A prevalence figure has little meaning without context. You need to know which population, region, age group, time period, and case definition it refers to. In Germany, the Robert Koch Institute publishes such data from health surveys, among others.

Point, period, and lifetime prevalence

There is no single type of prevalence. Point prevalence refers to people affected at a specific point in time. Period prevalence refers to everyone affected during a defined period, such as over the course of a year. Lifetime prevalence refers to the proportion who have ever been affected during their lifetime. These three figures can differ substantially even for the same condition. You therefore always need to know which type is being reported.

Prevalence is not incidence and not your risk

Two types of confusion are especially common. First, prevalence is confused with incidence. Prevalence describes existing cases, while incidence describes new cases during a period. Second, a prevalence figure is treated as personal risk. That does not follow either. The frequency of a condition in a group does not tell you your individual probability. That depends on many additional factors.

Why frequency changes what a test result means

Here is a simplified calculation example using made-up numbers chosen only for illustration. Suppose a test has both sensitivity and specificity of 90 percent. In a group of 1,000 people where the condition is relatively common, suppose 200 are affected. The test identifies 180 of the 200 affected people (true positives) and incorrectly flags 80 of the 800 unaffected people as positive (false positives). Of the 260 positive results, 180 are true positives, which is about 69 percent.

Now consider the same test quality with a rare condition. Of 1,000 people, suppose only 20 are affected. The test identifies 18 of them (true positives) and incorrectly flags 98 of the 980 unaffected people as positive (false positives). Of the 116 positive results, only 18 are true positives, or about 16 percent. The test is the same, but a positive result means much less when the condition is rarer. This relationship between prevalence and how informative a positive result is is well established. You can read more about the underlying measures under Sensitivity and specificity.

What this means for self-tests on medtests.net

This relationship is why we never equate a positive screening result with having a condition. In the Autism Test with the AQ-10 and the ADHD Test with the ASRS, we treat a positive result as a reason for further evaluation, not as proof. The same applies to the Depression Test . In the Heart Attack Risk Test , the purpose is explicitly to estimate risk in a population group, not to make an individual prediction. We provide specific frequency figures only when we also state the population and source.

What the term does not mean

A prevalence figure does not tell you your personal risk and is not a result about you. It only describes how common something is in a group and helps you assess what a test result may mean. A self-test provides orientation and does not replace medical, psychotherapeutic, or diagnostic evaluation.