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 cancer screening data that are being tracked breast cancer, cervical cancer, colorectal cancer and laboratory diagnostic data.
Cervical Cancer:
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get_cervical_screened(): Downloads data on women screened for cervical cancer. -
get_cervical_positive(): Retrieves data on women who tested positive for cervical pre-cancer or cancer. -
get_cervical_treated(): Accesses data on women who received treatment for cervical pre-cancer or cancer.
Breast Cancer:
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get_breast_cbe(): Downloads data on women who received CBE for breast cancer screening. -
get_breast_ultrasound(): Downloads data on women who received ultrasound for breast cancer screening. -
get_breast_mammogram(): Downloads data on women who received mammograms for breast cancer screening.
Colorectal Cancer:
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get_colorectal_fobt(): Retrieves data on individuals who receive FOBT for colorectal cancer screening tests. -
get_colorectal_colonoscopy(): Retrieves data on individuals who receive colonoscopy for colorectal cancer screening tests.
Laboratory Data
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get_lab_fluid_cytology(): Retrieve data on fluid cytology done in the lab. They include Ascitic fluid, cerebral spinal fluid, pleural fluid and urine. -
get_lab_tissue_histology(): Retrieve data on tissue histology done in the lab. They include Breast, colorectal, oesphageal, head & neck, hepatobiliary, lymph nodes, oral, ovary, prostate, skin and uterine tissues; -
get_lab_bone_marrow(): Retrieve data on bone marrow done in the lab. -
get_lab_fna(): Retrieve data on fine-needle aspiration done in the lab. They include breast, liver, lymph node and thyroid. -
get_lab_smears()Retrieve data on smears done in the lab. They include pap smear, tissue impressions, and touch preparation.
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')
cervical_screened
#> # A tibble: 192 × 11
#> value country element category period month year quarter fiscal_year
#> <dbl> <chr> <fct> <fct> <date> <ord> <dbl> <fct> <fct>
#> 1 99 Kenya Pap Smear Initial S… 2022-01-01 Janu… 2022 Q3 2021/2022
#> 2 4 Kenya HPV Routine S… 2022-01-01 Janu… 2022 Q3 2021/2022
#> 3 133 Kenya Pap Smear Initial S… 2022-04-01 April 2022 Q4 2021/2022
#> 4 19 Kenya VIA Post-trea… 2022-06-01 June 2022 Q4 2021/2022
#> 5 127 Kenya HPV NA 2022-03-01 March 2022 Q3 2021/2022
#> 6 231 Kenya Pap Smear Initial S… 2022-05-01 May 2022 Q4 2021/2022
#> 7 1 Kenya HPV Routine S… 2022-05-01 May 2022 Q4 2021/2022
#> 8 27 Kenya HPV Initial S… 2022-03-01 March 2022 Q3 2021/2022
#> 9 274 Kenya Pap Smear Initial S… 2022-06-01 June 2022 Q4 2021/2022
#> 10 444 Kenya HPV NA 2022-01-01 Janu… 2022 Q3 2021/2022
#> # ℹ 182 more rows
#> # ℹ 2 more variables: age_group <fct>, source <fct>
# Get data for those screening for colorectal cancer using FOBT
colorectal_screened <- get_colorectal_fobt('2022-01-01', end_date = '2022-06-30')
colorectal_screened
#> # A tibble: 29 × 10
#> value country element age_group period month year quarter fiscal_year
#> <dbl> <chr> <fct> <fct> <date> <ord> <dbl> <fct> <fct>
#> 1 4 Kenya Positive 65-75 2022-02-01 Februa… 2022 Q3 2021/2022
#> 2 5 Kenya Positive 65-75 2022-04-01 April 2022 Q4 2021/2022
#> 3 9 Kenya Positive 65-75 2022-03-01 March 2022 Q3 2021/2022
#> 4 13 Kenya Negative 45-54 2022-03-01 March 2022 Q3 2021/2022
#> 5 2 Kenya Positive 45-54 2022-02-01 Februa… 2022 Q3 2021/2022
#> 6 24 Kenya Negative 55-64 2022-03-01 March 2022 Q3 2021/2022
#> 7 5 Kenya Negative 55-64 2022-04-01 April 2022 Q4 2021/2022
#> 8 5 Kenya Negative 45-54 2022-02-01 Februa… 2022 Q3 2021/2022
#> 9 44 Kenya Negative 45-54 2022-04-01 April 2022 Q4 2021/2022
#> 10 39 Kenya Positive 55-64 2022-04-01 April 2022 Q4 2021/2022
#> # ℹ 19 more rows
#> # ℹ 1 more variable: source <chr>
# Get data for those screening for breast cancer using mammogram
breast_screened <- get_breast_mammogram('2022-01-01', end_date = '2022-06-30')
breast_screened
#> # A tibble: 19 × 11
#> value country element age_group period month year quarter fiscal_year
#> <dbl> <chr> <fct> <fct> <date> <ord> <dbl> <fct> <fct>
#> 1 11 Kenya BIRADS 0-3 35-39 2022-04-01 April 2022 Q4 2021/2022
#> 2 7 Kenya BIRADS 0-3 56-74 2022-02-01 Febr… 2022 Q3 2021/2022
#> 3 3 Kenya BIRADS 0-3 35-39 2022-03-01 March 2022 Q3 2021/2022
#> 4 1 Kenya BIRADS 5 56-74 2022-04-01 April 2022 Q4 2021/2022
#> 5 16 Kenya BIRADS 0-3 56-74 2022-03-01 March 2022 Q3 2021/2022
#> 6 1 Kenya BIRADS 0-3 25-34 2022-05-01 May 2022 Q4 2021/2022
#> 7 3 Kenya BIRADS 0-3 40-55 2022-05-01 May 2022 Q4 2021/2022
#> 8 6 Kenya BIRADS 6 35-39 2022-06-01 June 2022 Q4 2021/2022
#> 9 2 Kenya BIRADS 0-3 40-55 2022-06-01 June 2022 Q4 2021/2022
#> 10 1 Kenya BIRADS 0-3 40-55 2022-04-01 April 2022 Q4 2021/2022
#> 11 2 Kenya BIRADS 0-3 25-34 2022-03-01 March 2022 Q3 2021/2022
#> 12 1 Kenya BIRADS 4 40-55 2022-03-01 March 2022 Q3 2021/2022
#> 13 11 Kenya BIRADS 0-3 40-55 2022-02-01 Febr… 2022 Q3 2021/2022
#> 14 2 Kenya BIRADS 0-3 35-39 2022-02-01 Febr… 2022 Q3 2021/2022
#> 15 1 Kenya BIRADS 4 40-55 2022-04-01 April 2022 Q4 2021/2022
#> 16 1 Kenya BIRADS 0-3 56-74 2022-06-01 June 2022 Q4 2021/2022
#> 17 11 Kenya BIRADS 0-3 40-55 2022-03-01 March 2022 Q3 2021/2022
#> 18 6 Kenya BIRADS 5 35-39 2022-06-01 June 2022 Q4 2021/2022
#> 19 2 Kenya BIRADS 0-3 56-74 2022-05-01 May 2022 Q4 2021/2022
#> # ℹ 2 more variables: source <chr>, category <fct>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)
cervical_target_population
#> # A tibble: 1 × 2
#> country target
#> <chr> <dbl>
#> 1 Kenya 1112735.
# Get the colorectal cancer screening target population for 20223 by county
colorectal_target_population <- get_colorectal_target_population(2023, level = 'county')
colorectal_target_population
#> # A tibble: 47 × 3
#> # Groups: country [1]
#> country county target
#> <chr> <fct> <dbl>
#> 1 Kenya Baringo 8277.
#> 2 Kenya Bomet 10887.
#> 3 Kenya Bungoma 21427.
#> 4 Kenya Busia 12263.
#> 5 Kenya Elgeyo Marakwet 6050.
#> 6 Kenya Embu 12288.
#> 7 Kenya Garissa 6872.
#> 8 Kenya Homa Bay 14163.
#> 9 Kenya Isiolo 2848.
#> 10 Kenya Kajiado 12606.
#> # ℹ 37 more rows
# 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')
wra_pop
#> # A tibble: 1 × 2
#> country target
#> <chr> <dbl>
#> 1 Kenya 13023053.