Age standardization, sometimes referred to as age adjustment, is a method that applies observed age-specific rates to a standard age distribution. It is used when comparing two or more populations at one point in time, or one population at two or more points in time.
This method allows you to take away the confounding effect of age which can distort comparisons between groups with different age distributions when age is related to the outcome of interest. While many factors affect health outcomes, age is generally the strongest, since the chance of developing or dying from chronic health conditions typically increases with age; also, different age groups might have differential exposure to behavioral or environmental risks. For example, imagine you were interested in comparing the burden of hypertension among non-Hispanic Whites and Mexican-Americans. Say you found that hypertension was much less common among Mexican-Americans. You need to be very careful before drawing any conclusions since the prevalence of hypertension increases with age, and the Mexican American population is substantially younger than the non-Hispanic White population.
There are a number of ways to solve this problem. One way would be to compare the prevalence of the condition within similar age groups. For example, compare prevalence of hypertension among non-Hispanic Whites and Mexican-Americans age 20-24 years, age 25-29 years, age 30-34 years, and so on. The problem with this approach is that it is tedious; also it makes it hard to draw an overall conclusion. Age standardized estimates let you compare the prevalence of the condition overall in the groups after removing any differences in age. It lets you say what the prevalence would be if the groups under consideration had exactly the same age structure.
There are two widely used methods of age standardization: direct and indirect. In both cases, the general idea is to construct an estimate based on what would be seen if the age distributions in the comparison groups were the same. This tutorial will use the direct method. There are two basics steps:
Where pi is the prevalence of the condition in the study population, and wi is the proportion of people in that age group in the standard population.
It should be clear that the age adjusted estimate is a fiction. The observed (or unadjusted, or crude) prevalence in the study population is real. But the age standardized estimates are extremely useful because they are not confounded by age. That is, as long as you use the same standard population, you can now safely make comparisons across groups even if their underlying age structures vary substantially.
When comparing health outcomes between subgroups, age-adjusted rates can be considerably different from crude rates - when there is a lot of confounding by age. This usually occurs because the population distribution of the subgroups is different from the distribution of the standard population, and because the health conditions and risk factors used in an analysis are associated with the confounding variable of age. It is generally good practice to use age-adjusted estimates when comparing health outcomes among subgroups, or at least compare the age-adjusted estimates with the crude rates to make sure there are no substantial differences, before using the crude estimates.
Klein RJ, Schoenborn, CA. Age Adjustment using the 2000 projected U.S. population. Healthy People Statistical Notes, no. 20. Hyattsville, Maryland: National Center for Health Statistics. January 2001.
Buescher PA. Age-adjusted death rates. Statistical Primer. State Center for Health Statistics. Raleigh, NC