These functions subsets the Kenyan population to the desirable screening population.
Usage
get_cervical_target_population(
year,
level = c("country", "county", "subcounty")
)
get_breast_cbe_target_population(
year,
level = c("country", "county", "subcounty")
)
get_breast_mammogram_target_population(
year,
level = c("country", "county", "subcounty")
)
get_colorectal_target_population(
year,
level = c("country", "county", "subcounty")
)Arguments
- year
Year for which to estimate population.
- level
The desired level of the organization unit hierarchy to retrieve data for:
"country"(default) ,"county"or"subcounty".
Value
A tibble containing the target screening population
county - name of the county. Optional if the level is county or subcounty
subcounty - name of the county. Optional if the level if subcounty
target - number to be screened
A tibble containing the target screening population
A tibble containing the target screening population
A tibble containing the target screening population
Details
get_cervical_target_population() subsets the target population for cervical
cancer screening: females aged between 25 years and 50 years
get_breast_cbe_target_population() subsets the target population for clinical
breast examination: females aged between 25 years and 74 years
get_breast_mammogram_target_population() subsets the target population for
breast cancer screening through mammography: females aged between 40 years to 74 years
get_colorectal_target_population() subsets the target population for
colorectal cancer screening: males and females aged between 45 years to 75 years
These target populations are guided by the Kenya National Cancer Screening Guidelines 2018. The population projection for counties and the national level are calculated based on population growth 2.2% obtained from the Kenya National Bureau of Statistics. The annual targets follows the guidance of screening guidelines and for cervical cancer it is also guided by the WHO publication 'Planning and implementing cervical cancer prevention programs: A manual for managers.'
Examples
# Get the country projection for cervical cancer screening for the year 2024
target_population <- get_cervical_target_population(2024)
target_population
#> # A tibble: 1 × 2
#> country target
#> <chr> <dbl>
#> 1 Kenya 1112735.
# Get the projection for cervical cancer screening for 2022 by county
target_population <- get_cervical_target_population(2022, level = 'county')
target_population
#> # A tibble: 47 × 3
#> # Groups: country [1]
#> country county target
#> <chr> <fct> <dbl>
#> 1 Kenya Baringo 12705.
#> 2 Kenya Bomet 18680.
#> 3 Kenya Bungoma 33151.
#> 4 Kenya Busia 18221.
#> 5 Kenya Elgeyo Marakwet 9093.
#> 6 Kenya Embu 15342.
#> 7 Kenya Garissa 15238.
#> 8 Kenya Homa Bay 23316.
#> 9 Kenya Isiolo 5062.
#> 10 Kenya Kajiado 29055.
#> # ℹ 37 more rows
# Get the projection for CBE for 2022 by county
target_population <- get_breast_cbe_target_population(2022, level = 'county')
target_population
#> # A tibble: 47 × 3
#> # Groups: country [1]
#> country county target
#> <chr> <fct> <dbl>
#> 1 Kenya Baringo 9206.
#> 2 Kenya Bomet 13021.
#> 3 Kenya Bungoma 24429.
#> 4 Kenya Busia 13914.
#> 5 Kenya Elgeyo Marakwet 6599.
#> 6 Kenya Embu 11919.
#> 7 Kenya Garissa 9730.
#> 8 Kenya Homa Bay 17383.
#> 9 Kenya Isiolo 3476.
#> 10 Kenya Kajiado 18656.
#> # ℹ 37 more rows
# Get the country projection of women to perform mammogram for the year 2024
target_population <- get_breast_mammogram_target_population(2024)
target_population
#> # A tibble: 1 × 2
#> country target
#> <chr> <dbl>
#> 1 Kenya 697245.
# Get the country projection colorectal cancer screening for the year 2024
target_population <- get_colorectal_target_population(2024)
target_population
#> # A tibble: 1 × 2
#> country target
#> <chr> <dbl>
#> 1 Kenya 1022777.