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This vignette illustrates how EpiStandard can be used to
age standardise results.
Directly standardised rates are used to adjust rates of outcomes, such as disease or death, within a population to account for age. They work by assigning a weight to each age group, taken from a standard population, and applying these weights to the group-specific rates. This produces adjusted rates that remove the influence of age, making longitudinal trends easier to interpret.
For example, let’s look at incidence rates of brain cancer in Wales.
To standardise this by age group, we can use the function
directlyStandardiseRates. To do this we will need data on
the outcome of interest and the number of people at risk of developing
the outcome (denominator), which is our data, and a
standard population, which is our refdata.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(EpiStandard)
brain_cancer_2022 <- data.frame(
age_group = c("0 to 4","5 to 9", "10 to 14", "15 to 19","20 to 24","25 to 29","30 to 34","35 to 39","40 to 44","45 to 49","50 to 54","55 to 59",
"60 to 64", "65 to 69", "70 to 74", "75 to 79", "80 to 84", "85 to 89", "90 to 150"),
cases = c(5,4,4,4,2,6,6,14,8,9,17,19,34,30,39,41,23,13,7),
denominator = c(152519,174409,186628,177708,184964,187669,199578,192117,181871,176614,213390,224095,206338,180033,173220,144499,90861,54621,30506))
standard <- standardPopulation(region = "Europe")Data on brain cancer in Wales was obtained from Public
Health Wales. Note, the age groups used in data and
refdata must match.
directlyStandardiseRates(data = brain_cancer_2022,
event = "cases",
denominator = "denominator",
age = "age_group",
pop = "pop",
refdata = standard) |>
glimpse()
#> Rows: 1
#> Columns: 8
#> $ cases <dbl> 285
#> $ denominator <dbl> 3131640
#> $ crude_rate <dbl> 9.1007
#> $ crude_rate_95CI_lower <dbl> 8.0441
#> $ crude_rate_95CI_upper <dbl> 10.1572
#> $ standardised_rate <dbl> 8.6525
#> $ standardised_rate_95CI_lower <dbl> 7.6426
#> $ standardised_rate_95CI_upper <dbl> 9.6624By default, the Rather than using one of the standard populations
provided by the function standardPopulation, it is also
possible to use a bespoke standard population.
Let’s repeat the above example, but this time using a standard population specifically for Wales. This was obtained from the 2021 Welsh Census.
wales_standard <- data.frame(
age_group = c("0 to 4","5 to 9", "10 to 14", "15 to 19","20 to 24","25 to 29","30 to 34","35 to 39","40 to 44","45 to 49","50 to 54","55 to 59",
"60 to 64", "65 to 69", "70 to 74", "75 to 79", "80 to 84", "85 to 89", "90 to 150"),
pop = c(162528,165436,175568,182829,194959,186466,187941,188607,198164,207383,
213638,209169,206018,176291,163639,129008,95844,63537,39003)
)
directlyStandardiseRates(data = brain_cancer_2022,
event = "cases",
denominator = "denominator",
age = "age_group",
pop = "pop",
refdata = wales_standard) |>
glimpse()
#> Rows: 1
#> Columns: 8
#> $ cases <dbl> 285
#> $ denominator <dbl> 3131640
#> $ crude_rate <dbl> 9.1007
#> $ crude_rate_95CI_lower <dbl> 8.0441
#> $ crude_rate_95CI_upper <dbl> 10.1572
#> $ standardised_rate <dbl> 9.0148
#> $ standardised_rate_95CI_lower <dbl> 7.9644
#> $ standardised_rate_95CI_upper <dbl> 10.0652Sometimes additional stratifications are required, for example sex,
region, ethnicity etc. This can be added to the
directlyStandardiseRates function using the argument
strata. For example, if we want to look at longitudinal
trends, we’d want to calculate the standardised rates for each year
separately.
brain_cancer_2122 <- data.frame(
age_group = rep(c("0 to 4","5 to 9", "10 to 14", "15 to 19","20 to 24","25 to 29","30 to 34","35 to 39","40 to 44","45 to 49","50 to 54","55 to 59",
"60 to 64", "65 to 69", "70 to 74", "75 to 79", "80 to 84", "85 to 89", "90 to 150"),2),
cases = c(5,4,4,4,2,6,6,14,8,9,17,19,34,30,39,41,23,13,7,
5,6,5,5,1,3,3,6,12,14,24,18,21,22,36,38,28,17,2),
denominator = c(152519,174409,186628,177708,184964,187669,199578,192117,181871,176614,213390,224095,206338,180033,173220,144499,90861,54621,30506,
153564,175351,183177,174911,185152,185530,196157,186861,176206,183537,215783,222933,200666,178021,181109,133820,89018,53636,30201),
year = c(rep("2022",19), rep("2021", 19))
)
directlyStandardiseRates(data = brain_cancer_2122,
event = "cases",
denominator = "denominator",
age = "age_group",
pop = "pop",
strata = "year",
refdata = wales_standard) |>
glimpse()
#> Rows: 2
#> Columns: 9
#> $ year <chr> "2022", "2021"
#> $ cases <dbl> 285, 266
#> $ denominator <dbl> 3131640, 3105633
#> $ crude_rate <dbl> 9.1007, 8.5651
#> $ crude_rate_95CI_lower <dbl> 8.0441, 7.5358
#> $ crude_rate_95CI_upper <dbl> 10.1572, 9.5944
#> $ standardised_rate <dbl> 9.0148, 8.5603
#> $ standardised_rate_95CI_lower <dbl> 7.9644, 7.5281
#> $ standardised_rate_95CI_upper <dbl> 10.0652, 9.5925These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.