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Data sources

This document describes the sources of data needed by the OSDC algorithm and gives a brief overview of each of these sources and how they might look like. In addition, the final section contains information on how to gain access to these data.

The algorithm uses these Danish registers as input data sources:

Danish registers used in the OSDC algorithm.
Register Abbreviation Years
CPR-registerets befolkningstabel bef 1968 - present
Laegemiddelstatistikregisteret lmdb 1995 - present
Landspatientregisterets administrationstabel (LPR2) lpr_adm 1977 - 2018
Landspatientregisterets diagnosetabel (LPR2) lpr_diag 1977 - 2018
Landspatientregisterets kontakttabel (LPR3) kontakter 2019 - present
Landspatientregisterets diagnosetabel (LPR3) diagnoser 2019 - present
Sygesikringsregisteret sysi 1990 - 2005
Sygesikringsregisteret sssy 2006 - present
Laboratoriedatabasens forskertabel lab_forsker 2011 - present

In a future revision, the algorithm can also use the Danish Medical Birth Register to extend the period of time of valid inclusions further back in time compared to what is possible using obstetric codes from the National Patient Register.

Data required from registers

The following is a list of the variables required from specific registers in order for the package to classify diabetes status:

Register Variable
CPR-registerets befolkningstabel (bef) pnr
CPR-registerets befolkningstabel (bef) koen
CPR-registerets befolkningstabel (bef) foed_dato
Laegemiddelstatistikregisteret (lmdb) pnr
Laegemiddelstatistikregisteret (lmdb) eksd
Laegemiddelstatistikregisteret (lmdb) atc
Laegemiddelstatistikregisteret (lmdb) volume
Laegemiddelstatistikregisteret (lmdb) apk
Laegemiddelstatistikregisteret (lmdb) indo
Landspatientregisterets administrationstabel (LPR2) (lpr_adm) pnr
Landspatientregisterets administrationstabel (LPR2) (lpr_adm) recnum
Landspatientregisterets administrationstabel (LPR2) (lpr_adm) d_inddto
Landspatientregisterets administrationstabel (LPR2) (lpr_adm) c_spec
Landspatientregisterets diagnosetabel (LPR2) (lpr_diag) recnum
Landspatientregisterets diagnosetabel (LPR2) (lpr_diag) c_diag
Landspatientregisterets diagnosetabel (LPR2) (lpr_diag) c_diagtype
Landspatientregisterets kontakttabel (LPR3) (kontakter) cpr
Landspatientregisterets kontakttabel (LPR3) (kontakter) dw_ek_kontakt
Landspatientregisterets kontakttabel (LPR3) (kontakter) dato_start
Landspatientregisterets kontakttabel (LPR3) (kontakter) hovedspeciale_ans
Landspatientregisterets diagnosetabel (LPR3) (diagnoser) dw_ek_kontakt
Landspatientregisterets diagnosetabel (LPR3) (diagnoser) diagnosekode
Landspatientregisterets diagnosetabel (LPR3) (diagnoser) diagnosetype
Landspatientregisterets diagnosetabel (LPR3) (diagnoser) senere_afkraeftet
Sygesikringsregisteret (sysi) pnr
Sygesikringsregisteret (sysi) barnmak
Sygesikringsregisteret (sysi) speciale
Sygesikringsregisteret (sysi) honuge
Sygesikringsregisteret (sssy) pnr
Sygesikringsregisteret (sssy) barnmak
Sygesikringsregisteret (sssy) speciale
Sygesikringsregisteret (sssy) honuge
Laboratoriedatabasens forskertabel (lab_forsker) patient_cpr
Laboratoriedatabasens forskertabel (lab_forsker) samplingdate
Laboratoriedatabasens forskertabel (lab_forsker) analysiscode
Laboratoriedatabasens forskertabel (lab_forsker) value

Expected data structure

This section describes how the data sources listed from the above table are expected to look like when they are input into the OSDC algorithm. This should mimic the structure of the raw data as it appears within the virtual machines of Statistics Denmark. The package does not support inputs in their raw .sas7bdat format, and these must be converted to .parquet files first, which can then be loaded in as DuckDB objects. The registers2parquet package provides functions to conveniently handle this.

In this document, we present the variable names in lower case, but case may vary between data sources (and even between years in the same data source) in real data. However, the package should be robust to inconsistent casing, as we internally convert all variable names to lower case.

A small note about the National Patient Register: It contains several tables and types of data. The algorithm uses only hospital diagnosis data contained in four registers, which are pairs of two related registers used before 2019 (LPR2) and from 2019 onward (LPR3). So the LPR2 to LPR3 equivalents are lpr_adm to kontakter and lpr_diag to diagnoser. Most variables have direct equivalents between LPR2 and LPR3, but it should be noted that while c_spec is the LPR2 equivalent of hovedspeciale_ans in LPR3, the specialty values in hovedspeciale_ans are coded as strings of the literal specialty names unlike the padded integer codes that c_spec contains (in our experience).

On Statistics Denmark, these tables are provided as a mix of separate files for each calendar year prior to 2019 (in LPR2 format) and a single file containing all the data from 2019 onward (LPR3 format). The two tables can be joined with either the recnum variable (LPR2 data) or the dw_ek_kontakt variable (LPR3 data).

bef: CPR-registerets befolkningstabel

Variables and their descriptions within the bef register. If you want to see what the data should look like, see simulate_registers().
name english_description
pnr Pseudonymised social security number
koen Gender/sex
foed_dato Date of birth

lmdb: Laegemiddelstatistikregisteret

Variables and their descriptions within the lmdb register. If you want to see what the data should look like, see simulate_registers().
name english_description
pnr Pseudonymised social security number
eksd Date of purchase
atc Atc code (fully specified)
volume Number of daily standard doses (ddd) in package
apk Number of packages purchased
indo Indication code

lpr_adm: Landspatientregisterets administrationstabel (LPR2)

Variables and their descriptions within the lpr_adm register. If you want to see what the data should look like, see simulate_registers().
name english_description
pnr Pseudonymised social security number
recnum Record id number
d_inddto Date of admission or initial contact
c_spec Specialty code of department

lpr_diag: Landspatientregisterets diagnosetabel (LPR2)

Variables and their descriptions within the lpr_diag register. If you want to see what the data should look like, see simulate_registers().
name english_description
recnum Record id number
c_diag Diagnosis code
c_diagtype Diagnosis type

kontakter: Landspatientregisterets kontakttabel (LPR3)

Variables and their descriptions within the kontakter register. If you want to see what the data should look like, see simulate_registers().
name english_description
cpr Pseudonymised social security number
dw_ek_kontakt Record id number
dato_start Date of admission or initial contact
hovedspeciale_ans Specialty of department

diagnoser: Landspatientregisterets diagnosetabel (LPR3)

Variables and their descriptions within the diagnoser register. If you want to see what the data should look like, see simulate_registers().
name english_description
dw_ek_kontakt Record id number
diagnosekode Diagnosis code
diagnosetype Diagnosis type
senere_afkraeftet Was the diagnosis retracted later?

sysi: Sygesikringsregisteret

Variables and their descriptions within the sysi register. If you want to see what the data should look like, see simulate_registers().
name english_description
pnr Pseudonymised social security number
barnmak Was the service provided to the patient’s child?
speciale Billing code of the service (fully specified)
honuge Year and week of service

sssy: Sygesikringsregisteret

Variables and their descriptions within the sssy register. If you want to see what the data should look like, see simulate_registers().
name english_description
pnr Pseudonymised social security number
barnmak Was the service provided to the patient’s child?
speciale Billing code of the service (fully specified)
honuge Year and week of service

lab_forsker: Laboratoriedatabasens forskertabel

Variables and their descriptions within the lab_forsker register. If you want to see what the data should look like, see simulate_registers().
name english_description
patient_cpr Pseudonymised social security number
samplingdate Date of sampling
analysiscode Npu code of analysis
value Numerical result of analysis

Getting access to data

The above data is available through Statistics Denmark and the Danish Health Data Authority. Researchers must be affiliated with an approved research institute in Denmark and fees apply. Information on how to gain access to data can be found at www.dst.dk/en/TilSalg/data-til-forskning (URL often changes, so we can’t link directly).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.