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Quality of cancer-related data from the Danish National patient registry (1994-2025) and the Danish cancer registry (2004-2025): a systematic review

Research output: Contribution to journal › Review article › peer-review

Abstract

The Danish National Patient Registry (DNPR) and the Danish Cancer Registry (DCR) are central to registry-based cancer research. This systematic review evaluates studies assessing the quality of cancer-related data in these registries under their current data structures. PubMed and Embase were systematically searched on January 24, 2025 (PROSPERO: CRD420251005952). Studies validating cancer-related data in the DNPR or DCR against a gold standard were included. Findings were synthesized narratively and categorized by DNPR data, DCR data, or multi-source algorithms. The literature search generated 915 records, of which 50 were included: 23 validated DNPR data, 9 DCR data, and 18 algorithm performance. The quality of DNPR cancer diagnoses and treatment showed positive predictive values (PPVs) of 57-100%, highest for common malignancies and treatments. The quality of DNPR comorbidities and complications varied substantially (PPVs 0-98%). The PPV of a melanoma diagnosis in the DCR was 97%. The DCR staging completeness varied considerably (34-95%). Algorithms presented PPVs of 60-96% for recurrence, active cancer, and recognized metastases, and 28% for unrecognized metastases. The DNPR and DCR provide high-quality data for many cancer diagnoses, treatments, and outcomes, supporting their use in register-based research. While some data elements, including data on complications, exhibit lower quality, algorithmic approaches can enhance utility for less robust data. However, several aspects of cancer-related data remain unvalidated.

Original languageEnglish
JournalEuropean Journal of Epidemiology
Volume41
Issue number3
Pages (from-to)273-296
Number of pages24
ISSN0393-2990
DOIs
Publication statusPublished - Mar 2026

Bibliographical note

© 2025. Springer Nature B.V.

Keywords

  • Epidemiology
  • Neoplasms
  • Predictive value of tests
  • Sensitivity and specificity
  • Validation study

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