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	2005 - 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. 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.

variable_name	english_description
pnr	Pseudonymised social security number
koen	Sex
foed_dato	Date of birth

Simulated example of what the data looks like for the bef register.

koen	pnr	foed_dato
2	295879519622	20190425
1	052484763211	19600411
2	473656557183	19360804
1	539882494700	20081206

lmdb: Laegemiddelstatistikregisteret

Variables and their descriptions within the lmdb register.

variable_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

Simulated example of what the data looks like for the lmdb register.

volume	pnr	eksd	atc	apk	indo
4.533506	147984702032	20060924	N02AC54	1.574508	0042792
1.351237	083970684754	19980806	N05AF05	4.117670	0706765
5.844536	561044861491	20161023	G03EK01	2.079107	5280885
8.763184	914165936316	20160303	A10AE56	4.974808	8739373

lpr_adm: Landspatientregisterets administrationstabel (LPR2)

Variables and their descriptions within the lpr_adm register.

variable_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

Simulated example of what the data looks like for the lpr_adm register.

c_spec	pnr	recnum	d_inddto
81	036625008624	692088110968529476	20010625
90	998619262488	508879511776395750	19841018
87	034739382189	356292023160324193	19791106
03	966957471138	167936488877794449	20090926

lpr_diag: Landspatientregisterets diagnosetabel (LPR2)

Variables and their descriptions within the lpr_diag register.

variable_name	english_description
recnum	Record id number
c_diag	Diagnosis code
c_diagtype	Diagnosis type

Simulated example of what the data looks like for the lpr_diag register.

c_diagtype	recnum	c_diag
B	437823253484852508	DB958
B	050971173523403506	DR13
A	777378071916480689	DO700B
A	277017543702845196	DK754A

kontakter: Landspatientregisterets kontakttabel (LPR3)

Variables and their descriptions within the kontakter register.

variable_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

Simulated example of what the data looks like for the kontakter register.

cpr	dw_ek_kontakt	dato_start	hovedspeciale_ans
479497165788	701164834656651184	19910430	Dermato-venerologi
592500805436	184973744127885455	20020815	Børne- og ungdomspsykiatri
255670621038	718615486876865290	20121101	Hæmatologi
493041743629	974782023363670879	20061218	Samfundsmedicin

diagnoser: Landspatientregisterets diagnosetabel (LPR3)

Variables and their descriptions within the diagnoser register.

variable_name	english_description
dw_ek_kontakt	Record id number
diagnosekode	Diagnosis code
diagnosetype	Diagnosis type
senere_afkraeftet	Was the diagnosis retracted later?

Simulated example of what the data looks like for the diagnoser register.

dw_ek_kontakt	diagnosekode	diagnosetype	senere_afkraeftet
295967218205190103	DQ600	B	Nej
419136150597320977	DL022	B	Ja
733999403212878558	DF252	B	Ja
263706881734175834	DI252B	A	Nej

sysi: Sygesikringsregisteret

Variables and their descriptions within the sysi register.

variable_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

Simulated example of what the data looks like for the sysi register.

pnr	barnmak	speciale	honuge
558843081164	0	70200	0302
125417342293	1	07107	0402
101150906190	0	15874	9031
735054834392	0	75440	0109

sssy: Sygesikringsregisteret

Variables and their descriptions within the sssy register.

variable_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

Simulated example of what the data looks like for the sssy register.

pnr	barnmak	speciale	honuge
949048896915	0	76581	1534
917925900176	0	70652	1925
141858753104	0	65647	0538
010080256306	0	96171	2304

lab_forsker: Laboratoriedatabasens forskertabel

Variables and their descriptions within the lab_forsker register.

variable_name	english_description
patient_cpr	Pseudonymised social security number
samplingdate	Date of sampling
analysiscode	Npu code of analysis
value	Numerical result of analysis

Simulated example of what the data looks like for the lab_forsker register.

patient_cpr	samplingdate	analysiscode	value
215104581333	20201220	NPU27300	159.98672
995011179485	20160906	NPU70363	66.82853
430295258937	20220301	NPU60750	137.51173
217144925484	20140603	NPU01153	89.97764

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.