Prevalence: Simple Explanation + Examples

Prevalence is the proportion of individuals who have the disease at a given time. It is used to quantify the burden of disease in a population.

Understanding what is going on in society at a certain point in time can help us with planning a policy change or creating the right health service.

How to calculate prevalence?

prevalence formula

Numerator: This is the count of individuals who have the disease regardless of when they encountered it. These individuals are sometimes referred to as “prevalent cases”.

Denominator: Total population — includes those who have the disease and those who don’t. Note that this only includes individuals who COULD have the disease. For example, in calculating the prevalence of miscarriage, the denominator should only include pregnant women who are within the first 20 weeks of gestation.

Prevalence is a number between 0 and 1 (because the numerator cannot be bigger than the denominator).

Whenever you report prevalence don’t forget to specify the time over which it was measured, which can be:

  • A single point (point prevalence).
  • A specific period (period prevalence).

Types of prevalence

Point prevalence

Point prevalence answers the question: What is the proportion of people who currently have the disease? (or had the disease last year).

For instance:

In 2018, in the WHO African region, 3.9% of adults had HIV

World Health Organization

3.9% is the point prevalence.

Prevalence is not always reported as a percentage, because 3.9% is almost equal to 1/25, we can report the example above as follows:

In 2018, in the WHO African region, 1 in every 25 adults had HIV

Period prevalence

Period prevalence answers the question: What is the proportion of people who had the disease over this specific time period? The flu is a perfect example where knowing the period prevalence is of high importance:

The “Spanish” influenza pandemic of 1918–19 caused acute illness in 25–30% of the world’s population

The Origin and Virulence of the 1918 “Spanish” Influenza Virus

In this example, the numerator is the number of flu cases that existed in the period between 1918 and 1919, and the denominator is the total population during that same period.

Limitations of prevalence

Prevalence is not useful when researching causality.

Here’s why:

Consider the increasing prevalence of diabetes in the past few years.

This can be explained in 2 very different ways:

  • Good scenario: People with diabetes are living longer because of advancements in the treatment, this is why the proportion of diabetics (prevalence) is increasing over time
  • Bad scenario: More people are becoming overweight and less active, therefore more people are having diabetes and this is why the prevalence is increasing over time

We can learn from this example that an increase in prevalence can be attributed to 2 very different causes.

Also, a decrease in prevalence can be either a good or a bad thing (more people can be either dying or curing from the disease).

The problem with prevalence is that it DOES NOT measure new cases.

Measuring new cases is important to examine the effect of a risk factor or an exposure or a policy change, therefore prevalence is not useful when researching causality.

A more useful measure to study the cause of a disease is incidence — which estimates the risk that an individual will have the disease during a period of time, this will help us examine the relationship between exposure and disease.

If you’re interested in a detailed discussion of this, I recommend my other article on Prevalence-Incidence Bias.

Further reading