# Direct standardization

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A researcher is studying the annual mortality rate in two Midwestern towns, Town A and Town B. The mortality rates by age bracket for the two towns are shown in Table 1:

The age distribution of Town A is used as the reference (standardized) population and shown below:
Direct standardization is performed so that the mortality rates of the two towns can be better compared. Which of the following best approximates the age-adjusted mortality rate of Town B?

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#### Content Reviewers:

Rishi Desai, MD, MPH

#### Contributors:

In epidemiology, we often want to compare the mortality rates, or the frequency of deaths, and morbidity rates, or the frequency of a certain disease, in different populations.

Typically, we do this by calculating the crude mortality rate for each population, which is the number of deaths that occur within a certain timespan, like a year, divided by the total number of people in the population.

For example, let’s say we want to compare the crude mortality rates in two cities - City 1, which has a population of 23,000 people, and City 2, which has a population of 26,000 people.

In one year, there were 68 deaths in City 1, and 122 deaths in City 2.

So the crude mortality rate in City 1 is 68 deaths divided by 23,000 people, or 0.003.

This means that there were 3 deaths for every 1,000 people that year in City 1.

The crude mortality rate for City 2 is 122 divided by 26,000, which equals 0.005, or 5 deaths per 1,000 people.

We can use a mortality ratio, or a ratio of two mortality rates, to compare the crude mortality rate of City 1 to the mortality rate of City 2, and we get a ratio of 3 to 5.

And if we divide both sides by the bigger number, 5, we get a mortality ratio of 0.6 to 1, which means that, in one year, City 1 had a mortality rate 40% lower than City 2.

That may convince some folks to pack their bags and move to City 1!

Sometimes though, calculating the crude mortality ratio doesn’t provide an accurate picture of the two populations, and this is usually because the populations have different distributions of certain characteristics, like age, sex, or race.

For example, let’s say City 1 and City 2 have different age distributions, so City 1 has an older population with a large percentage of people over the age of 40, whereas City 2 has a younger population with only a small percentage of people over the age of 40.

Typically, mortality rates tend to be higher in older populations and lower in younger populations.

So, we can speculate that if City 2 has a smaller percentage of older people, then the crude mortality rate for City 2 might be lower.

Perhaps it’s time to put down those bags and pick up a calculator instead.

Standardization is a method that’s used to adjust for differences in characteristics between two populations, and when standardization is used to adjust for age, the result is called an age- adjusted rate.

Oftentimes, standardization is used to adjust mortality rates, but it can also be used to adjust incidence rates - the frequency of new diseases - or prevalence rates - the frequency of currently existing diseases.

There are two ways to calculate standardized rates, direct standardization and indirect standardization.

Direct standardization is used when the number of events or the mortality rates in each age group within the population, is known, whereas indirect standardization is used when the number of events or the mortality rates in each age group within the population is not known.

So let’s say we know the age- specific mortality rates in City 1 and City 2.

In that situation, we can use direct standardization.

In City 1, let’s say that within the total population of 23, 000 and 68 deaths, there are 18,000 people and 18 deaths in the over 40 age group, and 5,000 people and 50 deaths in the under 40 age group.

Summary

Direct standardization is a technique used to compare groups that have different characteristics. This technique adjusts for the differences between the groups so they can be compared more accurately. For example, direct standardization can be used to compare the health outcomes of two different groups of people. By adjusting for factors such as age, gender, and lifestyle, researchers can get a more accurate picture of the health outcomes of the two groups.