This function requires that each source of register data is represented as a single DuckDB object in R (e.g. a connection to Parquet files). Each DuckDB object must contain a single table covering all years of that data source, or at least the years you have and are interested in.
Usage
classify_diabetes(
kontakter,
diagnoser,
lpr_diag,
lpr_adm,
sysi,
sssy,
lab_forsker,
bef,
lmdb,
stable_inclusion_start_date = "1998-01-01"
)Arguments
- kontakter
The contacts information table from the LPR3 patient register
- diagnoser
The diagnoses information table from the LPR3 patient register
- lpr_diag
The diagnoses information table from the LPR2 patient register
- lpr_adm
The administrative information table from the LPR2 patient register
- sysi
The SYSI table from the health service register
- sssy
The SSSY table from the health service register
- lab_forsker
The register for laboratory results for research
- bef
The BEF table from the civil register
- lmdb
The LMDB table from the prescription register
- stable_inclusion_start_date
Cutoff date after which inclusion events are considered true incident diabetes cases. Defaults to "1998-01-01", which is one year after the data on pregnancy events from the Patient Register are considered valid for dropping gestational diabetes-related purchases of glucose-lowering drugs. This default assumes that the user is using LPR and LMDB data from at least Jan 1 1997 onward. If the user only has access to LPR and LMDB data from a later date, this parameter should be set to one year after the beginning of the user's data coverage.
Value
The same object type as the input data, which would be a
duckplyr::duckdb_tibble() type object.
See also
See the osdc vignette for a detailed description of the internal implementation of this classification function.
Examples
register_data <- simulate_registers(
c(
"kontakter",
"diagnoser",
"lpr_diag",
"lpr_adm",
"sysi",
"sssy",
"lab_forsker",
"bef",
"lmdb"
),
n = 10000
)
classify_diabetes(
kontakter = register_data$kontakter,
diagnoser = register_data$diagnoser,
lpr_diag = register_data$lpr_diag,
lpr_adm = register_data$lpr_adm,
sysi = register_data$sysi,
sssy = register_data$sssy,
lab_forsker = register_data$lab_forsker,
bef = register_data$bef,
lmdb = register_data$lmdb
)
#> # A duckplyr data frame: 5 variables
#> pnr stable_inclusion_date raw_inclusion_date has_t1d has_t2d
#> <chr> <date> <date> <lgl> <lgl>
#> 1 734096773097 2021-06-03 2021-06-03 FALSE TRUE
#> 2 461545659514 2018-12-03 2018-12-03 FALSE TRUE
#> 3 724751539892 2020-01-21 2020-01-21 FALSE TRUE
#> 4 278805655411 2021-09-02 2021-09-02 FALSE TRUE
#> 5 443484078922 2014-01-17 2014-01-17 FALSE TRUE
#> 6 035748926726 2017-07-22 2017-07-22 FALSE TRUE
#> 7 980089718120 2023-05-29 2023-05-29 FALSE TRUE
#> 8 694379686251 2019-01-24 2019-01-24 FALSE TRUE
#> 9 300913020651 2022-03-29 2022-03-29 FALSE TRUE
#> 10 732779718856 2022-12-05 2022-12-05 FALSE TRUE
#> # ℹ more rows