Direct standardization

Definitions & Key takeaways

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.

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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 in 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 tends 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.
In City 2, of the total population of 26, 000 and 122 deaths, let’s say there are 3,000 people and 7 deaths in the over 40 age group and 23,000 people and 115 deaths in the under 40 age group.
The age- specific mortality rates can be calculated by dividing the number of deaths in each age category by the number of people in each age category.
So, for City 1, the age- specific mortality rate would be 18 divided by 18,000, or 0.001 in the over 40 group, and 50 divided by 5,000, or 0.01, in the under 40 group.
For City 2, the age- specific mortality rate would be 7 divided by 3,000, or 0.0024 in the over 40 group, and 115 divided by 23,000, or 0.005, in the under 40 group.
The first step in direct standardization is to choose a reference or standard population, which is the population whose age distribution will be used for both populations of interest.
The reference population can be a completely separate population, like if you’re comparing two cities within the same country, you might use national- level information as the reference.
On the other hand, you can also use one of the two populations of interest as the reference, since you already have information on the age- distribution.
In our example, let’s say we choose City 1 to be the reference population. Next up is step 2.
Going back to our two tables, we can multiply the age- specific mortality rates from the population of interest - or City 2 - by the number of people in each age group from the reference population - or City 1 - to get the expected number of deaths in City 2, or the number of deaths you would expect to see in City 2 if it had the same age distribution as City 1.
So, in the over 40 group, 18,000 people times the 0.0024 gives us 43 expected deaths; and similarly in the under 40 group, 5,000 people times 0.005 gives us 25 expected deaths, for a total of 68 expected deaths.
We can use this number to calculate the age- adjusted mortality rate for City 2, which would be the total number of expected deaths, 68, divided by the total reference population, 23,000, which equals 0.003, or 3 deaths for every 1,000 people in City 2.
Finally, we can compare the crude mortality rate from City 1 with the age- adjusted mortality rate from City 2 to get a mortality ratio.
So, there 3 deaths per 1,000 people in City 1, and 3 deaths per 1,000 people in City 2. When we divide both sides by 3, we get a ratio of 1 to 1, which means for every 1 deaths per 1,000 people in City 1, there is 1 death per 1,000 people in City 2.
Now you may notice that this is different than the crude mortality ratio we got of 0.6 to 1, and - after using direct standardization - we can see that the mortality rate is actually the exact same in City 1 compared to City 2!
And, because the crude and the adjusted mortality rates were different, it’s clear that the age distribution was in fact confounding the true mortality rate in City 2.
Alright, as a quick recap, standardization is a method used to adjust for sex- specific or age- specific differences in mortality or morbidity rates between two populations.
Standardization can be direct, when the rates in each group is known, or indirect, when the rates in each group is unknown.
In direct age- standardization of mortality rates, the number of people in each age group from the reference population is multiplied by the age- specific mortality rates in the population of interest to get an expected number of deaths for each age group.
The total expected deaths is then divided by the total reference population to get the age- adjusted mortality rate, which can then be compared to another mortality rate using a mortality ratio.