Get started with 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:

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:

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:

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

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.