Morbidity and Mortality Rates

Research updated on August 13, 2025
Cite: Biopharma Foundry. (2026, Month Day). Article title in italics. Article link
Author: Santhosh Ramaraj

In public health studies, rates are one of the most important ways to describe how likely an event is to occur in a given population. These events can be diseases, deaths, or other significant health outcomes. When you know how to calculate and interpret these rates, you can compare risks, identify health trends, and make informed decisions.

What Exactly Is a Rate in Public Health?

A rate tells you how frequently an event happens in a certain group of people over a specific time period, often one year.

The general formula for a rate is:

$\text{Rate} = \frac{x}{y} \times k$

Where:

x is the number of people who experienced the event
y is the total number of people at risk of experiencing the event
k is a scaling factor, usually 1,000 or 100,000, to make the number easier to read

In practice:

Mortality rate refers to deaths
Morbidity rate refers to diseases
Fertility rate refers to births

Most of the time in population health, k is set to 1,000.

Example: Crude Mortality Rate

Imagine you are looking at a country’s population for a given year.

Deaths: 2,416,000
Population: 285,318,000

Using the formula:

$\text{Mortality Rate} = \frac{2,416,000}{285,318,000} \times 1,000$

That equals 8.4677, which we round to 8.5 deaths per 1,000 people.

This means for every thousand people in that country, about eight and a half died during that year.

Example: Infant Mortality Rate

Now, let’s look at infant deaths.

Live births: 4,026,000
Infant deaths (under 1 year old): 27,500

Using the formula:

$\text{Infant Mortality Rate} = \frac{27,500}{4,026,000} \times 1,000$

That equals 6.8306, which rounds to 6.8 deaths per 1,000 live births.

For perspective, some countries have rates over 35 per 1,000, showing significant differences in public health outcomes.

Why Use Rates Instead of Raw Numbers?

Raw numbers can be misleading because larger populations naturally have more events. By using rates, you compare events in proportion to the population size. This allows fair comparisons between different regions, years, or demographic groups.

For example:

100 deaths in a village of 1,000 people is very high
100 deaths in a city of 1 million is very low

The rate adjusts for population size so you can see the real impact.

Comparing Risks Between Two Groups

Sometimes you need to compare the likelihood of an event between two groups. This is common in clinical trials or epidemiological studies.

One of the most famous examples is the Salk Polio Vaccine Study.

The Salk Polio Vaccine Study

Researchers wanted to know if the Salk vaccine reduced the risk of paralytic poliomyelitis in children.

They had two groups:

Vaccine group: 200,745 children received the Salk vaccine
Placebo group: 200,229 children received a placebo

Polio cases in each group:

Vaccine group: 33 cases
Placebo group: 115 cases

Step 1: Calculate Rates for Each Group

For the vaccine group:

$p_1 = \frac{33}{200,745} = 0.0001639$

For the placebo group:

$p_2 = \frac{115}{200,229} = 0.0005743$

Step 2: Calculate Relative Risk

Relative Risk (RR) compares the rate in one group to the rate in another:

$RR = \frac{p_1}{p_2} = \frac{0.0001639}{0.0005743} = 0.2854$

An RR of 0.2854 means the vaccine group had about 28.5 percent of the risk seen in the placebo group.

Step 3: Interpret the Reciprocal

Sometimes it is more intuitive to calculate the reciprocal:

$\text{Reciprocal} = \frac{1}{0.2854} = 3.504$

This means the placebo group was about 3.5 times more likely to develop polio compared to the vaccinated group.

Why This Matters

When you only look at one group’s rate, you cannot tell how much better or worse they are compared to others.
Relative measures like RR give context.

You can think of it like comparing two sports teams: knowing one team scored 50 points tells you nothing about who won until you know the other team’s score.

Key Takeaways

Always define the population at risk clearly
Use an appropriate scaling factor k to make rates readable
Compare rates between groups to see relative differences
Interpret reciprocal risk when it adds clarity

Practice Problems

Here are some simple exercises to apply these concepts:

A city has 500 flu cases in a population of 50,000. What is the flu morbidity rate per 1,000?
In a trial, the treatment group has 4 cases out of 2,000 and the control group has 12 cases out of 2,000. Calculate RR and the reciprocal.
Country A has 1,200 infant deaths from 240,000 live births. Country B has 900 infant deaths from 300,000 live births. Compare the infant mortality rates.

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