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.
| 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:
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. We try to mimic as much as possible how the raw data looks
like within Denmark Statistics. So since registers are often stored on a
per year basis, we don’t expect a year variable in the data itself. If
you’ve processed the data so that it has a year variable, you will
likely need to do a split-apply-combine approach when using the osdc
package. We internally convert all variable names to lower case, and so
we present them here in lower case, but case may vary between data
sources (and even between years in the same data source) in real
data.
A small note about the National Patient Register. It contains several
tables and types of data. The algorithm uses only hospital diagnosis
data that contained in four registers, which are a pair of two related
registers used before (LPR2) and after (LPR3) 2019. So the LPR2 to LPR3
equivalents are lpr_adm to kontakter and
lpr_diag to diagnoser. Most of the variables
have equivalents as well, except that while c_spec is the
LPR2 equivalent of hovedspeciale_ans in LPR3, the specialty
values in hovedspeciale_ans are coded as literal specialty
names and are different from the padded integer codes that
c_spec contains.
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().
| 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().
| 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().
| 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().
| 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().
| 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().
| 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().
| 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().
| 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().
| 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 https://www.dst.dk/en/TilSalg/Forskningsservice.
