This function generates a list of tibbles representing the Danish health registers and the data necessary to run the algorithm. The dataset contains individuals who should not be included in the final classified cohort.
Value
A named list of 9 tibble::tibble() objects, each representing a
different health register: bef, lmdb, lpr_adm, lpr_diag,
lpr3a_kontakt, lpr3a_diagnose, lpr3f_kontakter, lpr3f_diagnoser, sysi, sssy, and lab_forsker.
Details
The generated data is used in testthat tests to ensure the algorithm
behaves as expected under a wide range of conditions, but it is also intended
to be explored by users to better understand how the algorithm logic works
and to be shown in the documentation.
Examples
non_cases()
#> $bef
#> # A tibble: 7 × 3
#> pnr koen foed_dato
#> <chr> <int> <date>
#> 1 nc_pcos_1 2 1980-01-01
#> 2 nc_pcos_2 2 1980-01-01
#> 3 nc_pcos_3 2 1980-01-01
#> 4 nc_preg_1 2 1980-01-01
#> 5 nc_preg_2 2 1980-01-01
#> 6 nc_preg_3 2 1980-01-01
#> 7 nc_preg_4 2 1980-01-01
#>
#> $lmdb
#> # A tibble: 7 × 6
#> pnr volume eksd atc apk indo
#> <chr> <dbl> <date> <chr> <dbl> <chr>
#> 1 nc_pcos_1 10 2021-01-01 A10BA02 5 0000276
#> 2 nc_pcos_2 10 2019-01-01 A10BA02 5 0000276
#> 3 nc_pcos_3 10 2019-01-01 A10BA02 5 0000276
#> 4 nc_preg_1 10 2018-01-01 A10 5 0000000
#> 5 nc_preg_2 10 2018-01-01 A10 5 0000000
#> 6 nc_preg_3 10 2020-01-01 A10 5 0000000
#> 7 nc_preg_4 10 2020-01-01 A10 5 0000000
#>
#> $lpr_adm
#> # A tibble: 7 × 4
#> pnr c_spec recnum d_inddto
#> <chr> <chr> <chr> <date>
#> 1 nc_pcos_1 08 1 2018-01-01
#> 2 nc_pcos_2 08 1 2017-01-01
#> 3 nc_pcos_3 08 1 2017-01-01
#> 4 nc_preg_1 08 1 2018-01-01
#> 5 nc_preg_2 08 1 2018-01-01
#> 6 nc_preg_1 08 2 2018-01-01
#> 7 nc_preg_2 08 3 2018-01-01
#>
#> $lpr_diag
#> # A tibble: 3 × 3
#> recnum c_diag c_diagtype
#> <chr> <chr> <chr>
#> 1 1 149 A
#> 2 2 DO00 A
#> 3 3 DZ33 A
#>
#> $lpr3a_kontakt
#> # A tibble: 7 × 4
#> pnr dw_ek_kontakt kont_ans_hovedspec kont_starttidspunkt
#> <chr> <chr> <chr> <date>
#> 1 nc_pcos_1 1 medicinsk endokrinologi 2021-01-01
#> 2 nc_pcos_2 1 medicinsk endokrinologi 2019-01-01
#> 3 nc_pcos_3 1 medicinsk endokrinologi 2019-01-01
#> 4 nc_preg_3 1 abc 2020-01-01
#> 5 nc_preg_4 1 abc 2020-01-01
#> 6 nc_preg_3 2 abc 2020-01-01
#> 7 nc_preg_4 3 abc 2020-01-01
#>
#> $lpr3a_diagnose
#> # A tibble: 3 × 4
#> dw_ek_kontakt diag_kode diag_type senere_afkraeftet
#> <chr> <chr> <chr> <chr>
#> 1 1 DI10 A Nej
#> 2 2 DO00 A Nej
#> 3 3 DZ33 A Nej
#>
#> $lpr3f_kontakter
#> # A tibble: 7 × 4
#> pnr dw_ek_kontakt hovedspeciale_ans dato_start
#> <chr> <chr> <chr> <date>
#> 1 nc_pcos_1 1 medicinsk endokrinologi 2021-01-01
#> 2 nc_pcos_2 1 medicinsk endokrinologi 2019-01-01
#> 3 nc_pcos_3 1 medicinsk endokrinologi 2019-01-01
#> 4 nc_preg_3 1 abc 2020-01-01
#> 5 nc_preg_4 1 abc 2020-01-01
#> 6 nc_preg_3 2 abc 2020-01-01
#> 7 nc_preg_4 3 abc 2020-01-01
#>
#> $lpr3f_diagnoser
#> # A tibble: 3 × 4
#> dw_ek_kontakt diagnosekode diagnosetype senere_afkraeftet
#> <chr> <chr> <chr> <chr>
#> 1 1 DI10 A Nej
#> 2 2 DO00 A Nej
#> 3 3 DZ33 A Nej
#>
#> $sysi
#> # A tibble: 7 × 4
#> pnr barnmak speciale honuge
#> <chr> <int> <chr> <chr>
#> 1 nc_pcos_1 0 53 2101
#> 2 nc_pcos_2 0 53 1901
#> 3 nc_pcos_3 0 53 1901
#> 4 nc_preg_1 0 53 2001
#> 5 nc_preg_2 0 53 2001
#> 6 nc_preg_3 0 53 2001
#> 7 nc_preg_4 0 53 2001
#>
#> $sssy
#> # A tibble: 7 × 4
#> pnr barnmak speciale honuge
#> <chr> <int> <chr> <chr>
#> 1 nc_pcos_1 0 53 2101
#> 2 nc_pcos_2 0 53 1901
#> 3 nc_pcos_3 0 53 1901
#> 4 nc_preg_1 0 53 2001
#> 5 nc_preg_2 0 53 2001
#> 6 nc_preg_3 0 53 2001
#> 7 nc_preg_4 0 53 2001
#>
#> $lab_forsker
#> # A tibble: 7 × 4
#> pnr samplingdate analysiscode value
#> <chr> <date> <chr> <dbl>
#> 1 nc_pcos_1 2021-01-01 NPU27300 48
#> 2 nc_pcos_2 2019-01-01 NPU03835 6.5
#> 3 nc_pcos_3 2019-01-01 NPU03835 6.5
#> 4 nc_preg_1 2017-03-01 NPU27300 48
#> 5 nc_preg_2 2018-03-01 NPU03835 6.5
#> 6 nc_preg_3 2019-03-01 NPU03835 6.5
#> 7 nc_preg_4 2020-03-01 NPU27300 48
#>