Overview of registers

Variable names for the most commonly used DST registers

Published

July 2, 2026

Warning

For DARTER: Check when cleaned-data was last updated before running the pipeline.

The registers in cleaned-data/ are not necessarily updated to today. Check the modification date on the DST server:

file.info("E:/workdata/[projectnumber]/cleaned-data/parquet-registers/")$mtime

Consider: when does your follow-up extend to, and are the registers updated up to that date? An outdated extraction cuts off censoring dates and outcomes too early - no error message, just silently wrong results. If the registers do not cover your study period, you need to order a new extraction from DST.

Important

The column names are checked on DARTER (project 708421) in June 2026 - not necessarily on your project. Names are shown after rename_with(tolower) has been called. In your own project folder, both which columns exist and their spelling - including upper/lower case - may look different. Always verify against your own files with colnames() (see Phase 7 - Inspect your data).

Key columns - the ones you actually use in the code - are marked in bold.

Note

Opening registers - two ways: The code examples on this page use open_dataset("path/to/register/") as a generic placeholder - replace with the path to your project’s parquet folder. With fastreg, use read_register("registername") instead (by name). DARTER users can use read_register("registername") - see DARTER - Register paths and datastores.

See Pitfalls for special quirks with each register. Column names apply after rename_with(tolower) - inspect your own register with colnames() if something does not match.

Tip

Looking for “which register contains X?” - start with the decision table in Phase 8 - Find your registers. This page is the deep reference with full column names, types and code examples.

Overview - all registers

Register	Register name	Join key	Period	Critical column
BEF	`"bef"`	`pnr`	All years	`koen`, `foed_dag`, `familie_id`
DODSAARS	`"dodsaars"`	`pnr`	See project guide	`d_dodsdto` (death date for censoring)
VNDS	`"vnds"`	`pnr`	All years	`indud_kode`, `haend_dato`
LPR2 contacts	`"lpr_adm"`	`recnum`	Up to March 2019	`d_inddto`, `c_pattype`
LPR2 diagnoses	`"lpr_diag"`	`recnum`	Up to March 2019	`c_diag`, `c_diagtype`
LPR2 SKS procedures	`"lpr_sksopr"`	`recnum`	Up to 2018	`c_opr`, `d_odto`
LPR2 psych contacts	`"t_psyk_adm"`	`k_recnum` → `recnum`	1995–March 2019	`v_cpr` → `pnr`
LPR2 psych diagnoses	`"t_psyk_diag"`	`v_recnum` → `recnum`	1995–March 2019	`c_diag`, `c_diagtype`
LPR3 contacts	`"lpr_a_kontakt"`	`dw_ek_kontakt`	March 2019+	`kont_starttidspunkt` (datetime)
LPR3 diagnoses	`"lpr_a_diagnose"`	`dw_ek_kontakt`	March 2019+	`diag_kode`, `diag_kode_type`, `senere_afkraeftet`
LPR3 SKS procedures	`"procedurer_kirurgi"`	`dw_ek_forloeb`	2019+	`procedurekode`, `dato_start`
LMDB	`"lmdb"`	`pnr`	Approx. 1994+	`atc`, `eksd`
UDDA	`"udda"`	`pnr`	All years	`hfaudd`, `aar`
FAIK	`"faik"`	`familie_id`	All years	`famaekvivadisp_13`
AKM	`"akm"`	`pnr`	All years	`socio13`, `aar`
The Cancer Register	see variable list	`pnr`	~1943+	incident cancer diagnoses - section 6
Primary sector	`"sysi"` / `"sssy"`	`pnr`	sysi older · sssy newer	GP/specialist contacts - section 6
Laboratory results	`"laboratorieproevesvar_"`	`pnr`	Varies	NPU codes, `samplevalue` (character) - section 6
LPR SKS examinations	`"lpr_sksube"`	`recnum`	As LPR	ZZ examination codes - section 5
Project-specific registers	See project guide	`pnr`	Varies	DARTER: see register paths →

1. Demographics and deaths

BEF - Population Register

Status register - one snapshot per person per reference time point (ultimo the period). Delivered quarterly since 2008 (March, June, September, December); before 2008 December only. Whether aar == 2020 corresponds to a particular reference time point depends on the project convention - confirm in your project guide. A person who dies during 2020 still appears in the 2020 snapshot - use DODSAARS to determine whether a person was alive on a specific date.

Column	Type	Contents
`pnr`	character	Personal identifier
`koen`	numeric	Sex: 1 = male, 2 = female
`foed_dag`	Date	Date of birth
`aar`	integer	Register year (one record per year)
`familie_id`	character	Household key - join to FAIK
`reg`	character	Region
`civst`	character	Marital status

Note

BEF does not contain date of death. Use DODSAARS (d_dodsdto) for censoring. See DST’s official BEF documentation: statistikdokumentation/befolkningen →

DODSAARS - Death Register

One row per deceased person.

Note

Coverage period and availability depends on your project’s cleaned-data. Check the modification date and ask your data manager about current coverage.

Column	Type	Contents
`pnr`	character	Personal identifier
`d_dodsdto`	Date	Date of death - use this for censoring
`fdato`	Date	Date of birth
`c_sex`	character	Sex
`v_alder`	numeric	Age at death
`year`	integer	Year of death
`c_dod1`	character	Underlying cause of death (ICD-10)
`c_dod2`–`c_dod4`	character	Contributing causes of death
`c_dodskom`	character	Manner/place of death
`c_bopkom`	character	Municipality of residence at death

Warning

dodsaasg is the classification register for causes of death - it is not the source for individual death dates. Always use dodsaars with the column d_dodsdto.

VNDS - Migration Register

One row per migration event per person.

Column	Type	Contents
`pnr`	character	Personal identifier
`indud_kode`	character	`"U"` = emigration (use for censoring), `"I"` = immigration
`haend_dato`	Date	Event date

Use: filter(indud_kode == "U") → min(haend_dato) per pnr for first emigration date. Non-emigrants do not appear in VNDS with a “U” event and get emigration_date = NA.

2. LPR2 - Somatic (up to March 2019)

Join: lpr_adm LEFT JOIN lpr_diag ON recnum.

lpr_adm - Contacts

Column	Type	Contents
`recnum`	character	Contact key - join to lpr_diag
`pnr`	character	Personal identifier
`d_inddto`	Date	Admission date - use as contact date
`c_pattype`	character	Contact type: `"0"` = inpatient, `"1"` = outpatient, `"2"` = emergency
`d_uddto`	Date	Discharge date
`c_adiag`	character	Action diagnosis (copy - use lpr_diag via join instead)
`c_spec`	character	Specialty code - see specialty/department overview (in Danish)
`year`	integer	Year

lpr_diag - Diagnoses

Column	Type	Contents
`recnum`	character	Join key to lpr_adm
`c_diag`	character	ICD-10 code with D-prefix (e.g. `"DG30"`) - use `substr(c_diag, 2, 4)`
`c_diagtype`	character	`"A"` = action diagnosis, `"B"` = secondary diagnosis, `"G"` = underlying condition
`c_diagmod`	character	Diagnosis modifier
`year`	integer	Year

3. LPR2 - Psychiatric (1995 – March 2019)

Psychiatric contacts before March 2019 are in separate registers from somatic LPR2. From March 2019, LPR3 covers both in one table.

Note

Before 1995: inpatients only, and ICD-8. The Danish Psychiatric Central Register is electronic from 1969, but covers only inpatients until 1995, and diagnoses before 1994 are coded in ICD-8 (numeric codes, e.g. 290-315) - not ICD-10 F-codes. These older data are normally not part of the standard extract and are requested separately via Rigsarkivet. See also Understand LPR.

Warning

If you forget to query the psychiatric registers for the period 1995–2019, you miss all dementia diagnoses (F00–F03) recorded at geriatric psychiatry outpatient clinics and memory clinics. Those patients will appear dementia-free and remain in the cohort as false negatives.

t_psyk_adm - Psychiatric contacts

Column names differ from somatic LPR2 - rename at load:

psyk_adm <- open_dataset("path/to/t_psyk_adm/") %>%
  rename_with(tolower) %>%
  rename(pnr = v_cpr, recnum = k_recnum)

Raw column name	After rename	Type	Contents
`v_cpr`	→ `pnr`	character	Personal identifier
`k_recnum`	→ `recnum`	character	Contact key - join to t_psyk_diag
`d_inddto`	(unchanged)	Date	Contact date - same as lpr_adm
`c_pattype`	(unchanged)	character	Contact type

t_psyk_diag - Psychiatric diagnoses

psyk_diag <- open_dataset("path/to/t_psyk_diag/") %>%
  rename_with(tolower) %>%
  rename(recnum = v_recnum)

Raw column name	After rename	Type	Contents
`v_recnum`	→ `recnum`	character	Join key to t_psyk_adm
`c_diag`	(unchanged)	character	ICD-10 with D-prefix - use `substr(c_diag, 2, 4)`
`c_diagtype`	(unchanged)	character	`"A"` / `"B"` / `"G"` - same as lpr_diag

4. LPR3 (March 2019 and onwards)

LPR3 covers both somatic and psychiatric contacts in one table. Join: lpr_a_kontakt LEFT JOIN lpr_a_diagnose ON dw_ek_kontakt.

Note

The “a” in lpr_a_diagnose does not mean A-type diagnoses. It refers to the analysis model designation for the LPR3 series (LPR_A, introduced 2025). The table contains all types: A, B and G - you still need to filter on diag_kode_type.

lpr_a_kontakt - Contacts

Column	Type	Contents
`pnr`	character	Personal identifier
`dw_ek_kontakt`	character	Contact key - join to lpr_a_diagnose
`kont_starttidspunkt`	datetime	Contact start time - convert with `as.Date()`
`kont_type`	character	Contact type: `"ALCA00"` = inpatient
`kont_sluttidspunkt`	datetime	Contact end time
`kont_ans_hovedspec`	character	Specialty code
`borger_doedsdato`	Date	Date of death (copy from CPR)
`borger_foedselsdato`	Date	Date of birth (copy from CPR)
`borger_koen`	character	Sex (copy from CPR)
`year`	integer	Year

All confirmed columns in lpr_a_kontakt

pnr, dw_ek_kontakt, kont_starttidspunkt, kont_sluttidspunkt, kont_type, kont_type_tekst, kont_patient_type, kont_patient_type_tekst, kont_ans_hovedspec, kont_ans_hovedspec_shak, kont_ans_inst, kont_ans, kont_ans_geo_reg, kont_ans_geo_reg_tekst, kont_ans_org_reg, kont_ans_org_reg_tekst, borger_doedsdato, borger_foedselsdato, borger_koen, borger_alder_aar_ind, borger_alder_aar_ud, borger_bo_kom, borger_bo_kom_tekst, borger_bo_reg, borger_bo_reg_tekst, dw_sk_sygehusophold, dw_ek_helbredsforloeb, dw_ek_forloeb, dw_ek_borger, adiag, adiag_tekst, beh_starttidspunkt, flag_kont_afsluttet, kont_aarsag, kont_aarsag_tekst, kont_indb_tidspunkt, kont_fir_kode, kont_fir_tekst, kont_fritvalg, kont_fritvalg_tekst, kont_henv_aarsag, kont_henv_aarsag_tekst, kont_henv_instans, kont_henv_maade, kont_henv_maade_tekst, kont_henv_tidspunkt, kont_inst_ejertype, lprindberetningssystem, prioritet, prioritet_tekst, kont_lpr_entity_id, cprtjek, cprtype, year

DST’s variable list: LPR_A_KONTAKT → (in Danish). Look up the specialty code kont_ans_hovedspec in DST’s specialty/department overview → (in Danish).

lpr_a_diagnose - Diagnoses

Column	Type	Contents
`dw_ek_kontakt`	character	Join key to lpr_a_kontakt
`diag_kode`	character	ICD-10 with D-prefix (e.g. `"DG30"`) - use `substr(diag_kode, 2, 4)`
`diag_kode_type`	character	`"A"` = action diagnosis, `"B"` = secondary diagnosis, `"G"` = underlying condition
`senere_afkraeftet`	character	`"Ja"` = retracted (exclude), `"Nej"` = confirmed, `NA` = not recorded
`diag_kode_tekst`	character	ICD-10 code text
`diag_parent_kode`	character	Parent diagnosis code
`year`	integer	Year

Standard filter for senere_afkraeftet:

filter(is.na(senere_afkraeftet) | senere_afkraeftet != "Ja")

5. LPR - SKS procedure codes

SKS (Sundhedsvæsenets Klassifikations System - the Danish Health Classification System) is the Danish classification system for operations and procedures - equivalent to the NOMESCO codes used in the other Nordic countries. Bariatric surgery has e.g. codes KJDF10 (RYGB) and KJDF40 (sleeve gastrectomy). Look up codes in the SKS browser → (in Danish).

SKS codes are split across two registers depending on period. For full coverage both must be queried and the results bound together.

Note

No pnr in the procedure tables. pnr is fetched via join to lpr_adm (LPR2) or lpr_a_kontakt (LPR3) respectively.

lpr_sksopr - LPR2 SKS procedures (up to 2018)

Location (DARTER): parquet-registers/lpr_sksopr

lpr_sksopr <- open_dataset("path/to/lpr_sksopr/") %>%
  rename_with(tolower)

Column	Type	Contents
`recnum`	character	Join key to `lpr_adm`
`c_opr`	character	SKS procedure code - use this for matching (e.g. `"KJDF10"`)
`d_odto`	Date	Surgery date
`c_oprart`	character	Procedure type code
`c_osgh`	character	Operating hospital
`c_tilopr`	character	Supplementary procedure code
`year`	integer	Year (partition column)

procedurer_kirurgi - LPR3 SKS procedures (2019 and onwards)

Location (DARTER): parquet-external/procedurer_kirurgi

proc_kirurgi <- open_dataset("path/to/procedurer_kirurgi/") %>%
  rename_with(tolower)

Column (after tolower)	Type	Contents
`dw_ek_forloeb`	character	Intended join key to `lpr_a_kontakt` - but many NA on DARTER, see note below
`dw_ek_kontakt`	character	NA for all rows in this parquet file on DARTER (see note below)
`procedurekode`	character	SKS procedure code - use this for matching (e.g. `"KJDF10"`)
`dato_start`	Date	Procedure date
`proceduretype`	character	`"P"` = procedure, `"+"` = add-on code
`procedurekode_parent`	character	Parent procedure code
`proceduretype_parent`	character	Parent procedure type
`tidspunkt_start`	datetime	Procedure start time
`dato_slut`	Date	Procedure end date
`tidspunkt_slut`	datetime	Procedure end time
`lprindberetningssystem`	character	LPR reporting system
`sorenhed_pro`	character	SOR unit for the procedure
`procedureregistrering_id`	character	Internal registration ID

Warning

The join from procedurer_kirurgi to lpr_a_kontakt is not yet resolved on DARTER (under investigation). dw_ek_kontakt is NA for all rows in DARTER’s parquet version (confirmed 2026-06-02), so the obvious join key can’t be used. dw_ek_forloeb looks like the alternative but also has many NA, so the pnr lookup isn’t solved yet. Applies to DARTER/project 708421 - check on your own project. We’ll update the page with a solution once one is found. Column names are mixed case in raw data - call rename_with(tolower) immediately after loading.

lpr_sksube - SKS examinations and treatments (ZZ codes)

Examination and treatment codes (ZZ codes) live in lpr_sksube (LPR2), separate from the operation codes in lpr_sksopr above. Join like the other SKS tables - via recnum. Column names are not verified here - see DST’s variable list for LPR_SKSUBE and confirm with colnames().

Combination across the full period

See the code example: combination across the full period

# Replace [projectnumber] with your own project number
# DARTER: use read_register("registername") instead of open_dataset("path")

# SKS from LPR2 (up to 2018)
surg_lpr2 <- open_dataset("path/to/lpr_sksopr/") %>%
  rename_with(tolower) %>%
  filter(toupper(c_opr) %in% !!SKS_CODES) %>%   # !! sends the local R vector to DuckDB
  left_join(
    open_dataset("path/to/lpr_adm/") %>%
      rename_with(tolower) %>%
      select(recnum, pnr, d_inddto),
    by = "recnum"
  ) %>%
  select(pnr, surgery_date = d_odto, surgery_code = c_opr) %>%
  collect()

# SKS from LPR3 (2019 and onwards) - join via dw_ek_forloeb
# NOTE: the join is unresolved on DARTER (many NA) - see the warning above
surg_lpr3 <- open_dataset("path/to/procedurer_kirurgi/") %>%
  rename_with(tolower) %>%
  filter(toupper(procedurekode) %in% !!SKS_CODES) %>%   # !! sends the local R vector to DuckDB
  left_join(
    open_dataset("path/to/lpr_a_kontakt/") %>%
      rename_with(tolower) %>%
      select(dw_ek_forloeb, pnr),
    by = "dw_ek_forloeb"
  ) %>%
  select(pnr, surgery_date = dato_start, surgery_code = procedurekode) %>%
  collect()

# Combined
surg_all <- bind_rows(surg_lpr2, surg_lpr3)

6. Other clinical registers

These registers are used less often than those above and are not column-verified here - the descriptions give an overview, but look up the exact variable names in DST’s overview of registers and variable lists and confirm against your own files with colnames().

The Cancer Register

The gold standard for incident cancer diagnoses - more complete and precise for cancer than LPR diagnoses. One row per tumour; join to your population via pnr. Covers many decades back (modern coding with ICD-10 + ICD-O morphology/topography). Use it for cancer as outcome or exclusion. Variable names: see DST’s variable list.

Primary sector - sysi and sssy (Health Insurance Register)

Contacts and services in the primary sector (general practice, practising specialists, physiotherapy etc.). As with LPR, the register is split over time: sysi covers the older years, sssy the newer. Join via pnr. Use it for e.g. GP/specialist contacts, screening or vaccinations billed in primary care. Key fields (specialty, service code, date) are named differently in sysi and sssy - verify with colnames().

Laboratory results - laboratorieproevesvar_

Laboratory/blood test results (e.g. HbA1c, lipids). A very large register - over 2.2 billion rows, so filter with arrow/duckplyr before you collect() into R. Central fields: pnr, NPU code (analyte type) and sample date. samplevalue is character (not numeric), because some results are coded in natural language (e.g. "ikke påvist", "negativ") - handle that before computing on the values. Name and availability are project-/DARTER-specific.

Tip

DARTER: a dedicated extraction guide for laboratorieproevesvar_ is in progress - see DARTER - Register paths and datastores.

Healthcare costs (under development)

Warning

Under development - confirm everything in your own delivery. This section describes cost sources at the register level. Exact table and column names and the available years change from year to year (the DRG rate system is updated annually) and are not verified here. Use it as a pointer, and clarify the specific files with your data manager.

To compute healthcare use/costs per person, they are typically assembled from several sources:

Somatic hospital contacts: DRG-grouped rates from the Danish Health Data Authority (DRG = inpatient, DAG = outpatient). Rates express average operating expenses per DRG group and are computed annually - see SDS DRG rates.
Psychiatric hospital contacts: are not billed by DRG. The main principle is a bed-day rate for inpatients and a visit rate for outpatients - keep somatic and psychiatric separate.
Primary sector (general practice, specialists etc.): fees in sysi/sssy (see the section above).
Medication: patient co-payment vs. reimbursement in LMDB (section 7).

Availability of cost data varies (some years/sources are missing, e.g. more recent DRG years). The pattern is inspired by the Plana-Ripoll group’s code on OSF, but the variable names there are from a 2022 delivery and should not be assumed current.

7. LMDB - Prescription Register

One row per dispensed prescription. Covers approximately 1994 onwards.

Column	Type	Contents
`pnr`	character	Personal identifier
`atc`	character	Full ATC code (e.g. `"N06D01"`)
`eksd`	Date	Dispensing date - use as prescription date
`atc1`–`atc4`	character	ATC levels 1–4
`indo`	character	Indication code
`vnr`	character	Item number
`apk`	numeric	Number of packages
`aldr`	numeric	Age at dispensing
`year`	integer	Dispensing year

All confirmed columns in LMDB

pnr, eksd, ekst, atc, atc1, atc2, atc3, atc4, indo, vnr, apk, aldr, bald, eksp, korr, rinr, name, streng, packtext, volume, voltypecode, voltypetxt, dosform, strnum, strunit, packsize, cprtjek, cprtype, year, etid, ovnr, patt, doso, reca, abc

Note

Data quality of indo and doso. indo (indication code) is recorded only when the doctor picks an indication from the drop-down menu in the electronic prescription; typed as free text, it is not carried over. It is therefore missing on about 12-18% of prescriptions (more before 1 October 2017) and is often nonspecific. The dosage field doso is effectively empty (recorded for ~0.06% of prescriptions). See Medication (ATC) for how to handle this.

8. Socioeconomic registers

All three registers are used for SEP extraction following SEPLINE guidelines (Hjorth et al. 2025). No single combined SEP variable is calculated - three separate dimensions.

UDDA - Education Register

One record per person per year - updated when the education level changes.

Column	Type	Contents
`pnr`	character	Personal identifier
`hfaudd`	character	ISCED education code (e.g. `"35"` = vocational education)
`aar`	integer	Register year

Categorisation (SEPLINE): substr(as.character(hfaudd), 1, 2) → "10"/"15" = short, "20"–"35" = medium, "40"–"80" = long, "90" = unknown.

FAIK - Family Income

Household-equivalised disposable income per year. Link: join BEF (pnr, familie_id, aar) with FAIK (familie_id, aar).

Column	Type	Contents
`familie_id`	character	Household key - join to BEF
`famaekvivadisp_13`	numeric	Household-equivalised disposable income
`aar`	integer	Register year

Income quintiles are calculated as 3-year averages compared against Q20/Q40/Q60/Q80 cut-points from the full BEF population stratified by sex × 5-year age group × reference year.

AKM - Labour Classification Module

Labour market status per person per year.

Column	Type	Contents
`pnr`	character	Personal identifier
`socio13`	numeric	Employment code
`aar`	integer	Register year

SEPLINE categorisation of socio13: - Employed: 110–114, 120, 131–135, 139 - Student: 310 - Unemployed: 210, 410 - Outside labour market: 220, 321, 330 - Retired: 322, 323 - Unknown: 0, 420 or missing

9. Project-specific registers

Many projects have access to registers beyond the standard list above - e.g. quality registers from clinical databases or pre-computed classification files.

These are project-specific and not available in all projects on DST.

Examples are private hospitals (priv_adm, priv_diag, priv_skspor - structured in parallel with LPR) and various clinical quality registers. Availability varies from project to project.

Tip

Working on DARTER / project 708421? The project uses among others DBSO (the Danish Obesity Treatment Database) and OSDC (Open Source Diabetes Classifier).

All applied-for variables and registers in a searchable table: steno-aarhus.github.io/darter-project →
Confirmed paths and column names: DARTER - Register paths and datastores →

Data resource profiles and reporting

Data resource profiles are the papers you cite when you describe a register in your methods section: they document the register’s content, coverage and validity. Cite the profile for each register you use.

Register	Data resource profile
The Danish health-care system and epidemiological research (overview)	Schmidt et al. 2019, Clin Epidemiol - doi:10.2147/CLEP.S179083
CPR (Civil Registration System)	Schmidt, Pedersen & Sørensen 2014, Eur J Epidemiol - doi:10.1007/s10654-014-9930-3
LPR (National Patient Registry)	Schmidt et al. 2015, Clin Epidemiol - doi:10.2147/CLEP.S91125
LMDB (Prescription Registry)	Pottegård et al. 2017, Int J Epidemiol - doi:10.1093/ije/dyw213
Cause of Death Register	Helweg-Larsen 2011, Scand J Public Health - doi:10.1177/1403494811399958

Reporting: report observational studies following STROBE. For register-based / routinely-collected-data studies, RECORD extends STROBE, and RECORD-PE covers pharmacoepidemiology specifically - see RECORD-PE (EQUATOR Network).