Understand LPR

Structure, history and nuances - before you write code

Published

July 2, 2026

The National Patient Register (LPR) is the source of diagnoses and hospital contacts. It covers all public hospital admissions and outpatient contacts in Denmark.

LPR is more complex than most registers, because it changed format in 2019 and is split into somatic and psychiatric tables. This page explains the structure - the periods, the ICD codes, the diagnosis types and the pitfalls you need to know before you extract data. The concrete extraction recipes are in Extract from LPR.

Tip

In short: LPR changed format in 2019 - use LPR2 (up to March 2019) and LPR3 (after) and combine them. All ICD codes carry a D prefix you usually strip, and you choose diagnosis types (A/B for outcomes, +G for baseline comorbidity).

Tip

Reading order: Read this page first to understand the data. Then go to Extract from LPR for the runnable code examples. You build the cohort itself in Phase 10.

LPR is split into two periods

In March 2019 LPR changed format. Studies covering the period across 2019 must query both systems and combine them.

LPR2 somatic LPR2 psychiatric LPR3
Period up to March 2019 up to March 2019 March 2019 and onwards
Contact register lpr_adm t_psyk_adm lpr_a_kontakt
Diagnosis register lpr_diag t_psyk_diag lpr_a_diagnose
Covers psychiatry No Yes Yes (both combined)
Join key recnum k_recnum / v_recnum¹ dw_ek_kontakt
Date column d_inddto (Date) d_inddto (Date) kont_starttidspunkt (datetime)²
pnr column pnr v_cpr³ pnr
Diagnosis code c_diag c_diag diag_kode
Diagnosis type c_diagtype c_diagtype diag_kode_type
Contact type c_pattype ("0" = inpatient) c_pattype kont_type ("ALCA00" = inpatient)

¹ t_psyk_adm has k_recnum; t_psyk_diag has v_recnum - rename both to recnum before joining. ² datetime format - convert with as.Date(). ³ Rename: rename(pnr = v_cpr).

Why two registers - contact and diagnosis? LPR splits each hospital contact into two tables: the contact register (e.g. lpr_adm) has one row per contact with pnr, dates and hospital, but not the diagnoses; the diagnosis register (e.g. lpr_diag) has one row per diagnosis with the ICD code, but not pnr or date. One contact can have several diagnoses. You join the two on the contact key (recnum in LPR2, dw_ek_kontakt in LPR3) to get pnr + date + diagnosis in one table. That is the join the extraction recipes in Extract from LPR are built on. The same principle applies to operations and procedures: lpr_sksopr (LPR2) has the SKS code + recnum, but not pnr or date, so it is joined to lpr_adm in exactly the same way when you want to find who had an operation and when.

Note

Psychiatry: separate in LPR2, combined in LPR3 Before 2019, psychiatric diagnoses (F-codes: dementia, depression etc.) were stored in separate registers (t_psyk_adm, t_psyk_diag). The structure resembles somatic LPR2, but column names differ - see the table footnotes above. From March 2019, LPR3 combines both: somatic and psychiatric contacts and diagnoses are in the same tables, and no separate psychiatric query is needed.

Even older psychiatry (1969-1994). The Danish Psychiatric Central Register is electronic from 1969, but covers only inpatients until 1995 (outpatient visits were added only from 1995). Diagnoses before 1994 are coded in ICD-8 (numeric codes, e.g. 290-315, where 290 covers dementia), not ICD-10 F-codes. These older data are normally not part of the standard LPR extract and are requested separately (see Rigsarkivet and NCRR, Aarhus University). If your study covers that period, remember to map ICD-8 to your F-code groups.

gantt
    title LPR registers
    dateFormat  YYYY
    axisFormat  %Y
    section LPR2 somatic
    lpr_adm + lpr_diag               :done,   1977, 2019
    section LPR2 psychiatric
    t_psyk_adm + t_psyk_diag         :done,   1995, 2019
    section LPR3 combined
    lpr_a_kontakt + lpr_a_diagnose   :active, 2019, 2027

LPR3 covers hospital contacts from March 2019 onwards, and you access them through the LPR_A files (lpr_a_kontakt, lpr_a_diagnose). Note that LPR_A also contains some outpatient data going back to around 2017, part of which is already in LPR2 - so it must be de-duplicated (see the pitfall below).

Important

New researcher / new project? Work with the LPR_A files only. LPR3 has been delivered in two formats: the older LPR_F (kontakter, diagnoser, forloeb) and the current LPR_A (lpr_a_kontakt, lpr_a_diagnose). For new projects LPR_F is effectively dead - use LPR_A. Both may sit in your folder and cover the same years, so loading LPR_F as well (or mixing the two) gives you duplicated rows. Every example on this site uses lpr_a_*.

Warning

Pitfall: overlapping data. Some projects have older contacts (already covered by LPR2) sitting inside the LPR3 tables (lpr_a_kontakt). Remove them by filtering lprindberetningssystem == "LPR3", so the same contact is not counted twice across LPR2 and LPR3. This is project-specific - on DARTER it is pitfall 5; otherwise check with your data manager.

Background: LPR_F vs LPR_A (safe to skip)

When LPR3 launched in March 2019, the research data was first distributed in the LPR_F model: a course-oriented (forloeb) format with separate kontakter, diagnoser and forloeb tables. Forskerservice later moved to the contact-based LPR_A model (lpr_a_kontakt, lpr_a_diagnose), which is what projects receive today. The two represent the same underlying contacts differently, so they are not meant to be combined - pick LPR_A.

The official LPR_F data model is documented in Vejledning til LPR3_F (and the other links at the bottom of this page). Documentation for LPR_A is still incomplete.

ICD codes and the D-prefix

ICD-10 (International Classification of Diseases, 10th revision) is the WHO’s international system for classifying diseases and conditions. All hospital diagnoses in Denmark are coded with ICD-10, e.g. G30 for Alzheimer’s disease and F00 for dementia in Alzheimer’s.

All ICD-10 codes in DST have a prepended "D": "DG30" (Alzheimer’s), "DF00" (dementia), "DI21" (acute myocardial infarction).

Strip the D-prefix before comparison - it makes code more readable and easier to reuse:

mutate(icd3 = substr(c_diag, 2, 4))   # "DG30" → "G30" (3-digit code)
mutate(icd4 = substr(c_diag, 2, 5))   # "DI219" → "I219" (4-digit code)

substr(x, start, stop) keeps characters from position start up to and including stop (counted from 1). substr(c_diag, 2, 4) skips position 1 (the D-prefix) and keeps characters 2, 3 and 4: "DG30" → "G30". Use 2–5 for 4-digit codes: "DI219" → "I219".

Diagnosis types: A, B and G

Code Meaning When to include
A Action diagnosis - primary reason for the contact Always for outcomes
B Secondary diagnosis - additional condition present Always for outcomes
G Underlying condition - background comorbidity Only for baseline comorbidity 
# For outcomes and exclusion diagnoses:
filter(c_diagtype %in% c("A", "B"))

# For baseline comorbidity (NMI):
filter(c_diagtype %in% c("A", "B", "G"))
Tip

Keep the type column in your extract. Carry c_diagtype (in LPR3: diag_kode_type) into your output, not just the diagnosis code. It costs one column and lets you vary the case definition later - e.g. main analysis on A + B, sensitivity analysis on A only (primary diagnosis) or including G - without re-querying LPR.

Retracted diagnoses in LPR3 (senere_afkraeftet)

LPR3 flags diagnoses that have been retracted. The standard filter:

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

The is.na() part is deliberate. R’s default behaviour is: NA != "Ja" returns NA - not TRUE. A filter treats NA as FALSE and drops the row. filter(senere_afkraeftet != "Ja") alone would therefore remove all diagnoses that have no retraction marker at all (i.e. NA fields) - even though they are definitely not retracted. is.na(...) fixes this: “keep the row if the field is NA OR if it is not "Ja"”. The filter thus retains uncategorised diagnoses, which is the safest assumption.

Challenge with LPR_A: diagnosis spike around 2019-2020

The move to LPR3’s contact-based model also changed how outpatient diagnoses are registered, and this can distort diagnosis counts across the transition.

In LPR2, a course of outpatient visits was typically summarised with a single (action) diagnosis for the whole course. In LPR3, each contact can carry its own diagnoses. So a patient with 10 outpatient visits for depression that produced one diagnosis row in LPR2 can produce ten rows in LPR3 - the same illness, many more registrations.

The visible effect is a spike in the number of diagnoses around 2019-2020, present for most diagnoses, and visible even if you restrict to the primary (action) diagnosis. It is further complicated by an overlap with COVID-19 in 2020, which makes the period even harder to interpret.

Warning

There is no single agreed fix. This is a real problem in register research, especially for analyses that rely only on hospital diagnoses.

We therefore encourage you to visualise, across calendar years, the counts of the diagnoses used in your study.

library(lubridate)
alle_dx %>%
  mutate(year = year(date_contact)) %>%   # date_contact from your LPR extract
  count(year) %>%
  arrange(year)
# plot n against year and look for a jump around 2019-2020

Most diagnoses show the spike. A few are stable across the transition (type 1 diabetes, for example) and make a useful sanity check: if even a stable diagnosis jumps in your data, something else is wrong.

For inspiration and for evaluating diagnostic stability across the transition, see Aarhus-Psychiatry-Research/diagnostic-stability-lpr2-lpr3 (methodological inspiration, not code for reuse).

Next steps

You now know LPR’s structure and the most important pitfalls. The next step is to extract the diagnoses with code:

→ Extract from LPR

See also

External resources on LPR3 and the 2019 transition

Code look-ups

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