Get started with cancerscreening

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This article takes a quick tour of the main features of cancerscreening.

Remember to see the articles for more detailed treatment of all these topics and more.

Key Functions for Data Download:

These functions directly call the KHIS server to download the data require the setting of KHIS credentials. See setting the credentials for more information. The get analytics call is supported by three metadata functions that help make the data complete and these include the get_organisation_units_metadata() to show where the data is from and get_data_elements_metadata() to show what the data is about and group the data into various categories.

The cancer screening data that are being tracked breast cancer, cervical cancer, colorectal cancer and laboratory diagnostic data.

Cervical Cancer:

Breast Cancer:

Colorectal Cancer:

Laboratory Data

To get the data the following calls can be made

# Get data for those screening for cervical cancer
cervical_screened <- get_cervical_screened('2022-01-01', end_date = '2022-06-30')

# Get data for those screening for colorectal cancer using FOBT
colorectal_screened <- get_colorectal_fobt('2022-01-01', end_date = '2022-06-30')

# Get data for those screening for breast cancer using mammogram
breast_screened <- get_breast_mammogram('2022-01-01', end_date = '2022-06-30')

Target Population Functions:

These functions do not require to access the KHIS server the project and calculate the target population guided the Kenya housing and population census 2019 and the Kenya National Cancer Screening guidelines 2019.

The functions include: get_cervical_target_population(), get_colorectal_target_population(), get_breast_cbe_target_population() and get_breast_mammogram_target_population().

If these function do not meet your criteria you can make your target population using the get_filtered_population().

# Get the cervical screening target population for 2022
cervical_target_population <- get_cervical_target_population(2022)

# Get the colorectal cancer screening target population for 20223 by county
colorectal_target_population <- get_colorectal_target_population(2023, level = 'county')

# Get the population of women 15-49 year for the year 2024
wra_pop <- get_filtered_population(year = 2024, min_age = 15, max_age = 49, pop_sex = 'female')