Validity of chronic disease diagnoses in Icelandic healthcare registries

Scand J Public Health. 2023 Mar;51(2):173-178. doi: 10.1177/14034948211059974. Epub 2021 Dec 13.

Abstract

Aims: To evaluate the validity of recorded chronic disease diagnoses in Icelandic healthcare registries.

Methods: Eight different chronic diseases from multiple sub-specialties of medicine were validated with respect to accuracy, but not to timeliness. For each disease, 30 patients with a recorded diagnosis and 30 patients without the same diagnosis were randomly selected from >80,000 participants in the iStopMM trial, which includes 54% of the Icelandic population born before 1976. Each case was validated by chart review by physicians using predefined criteria.

Results: The overall accuracy of the chronic disease diagnoses was 96% (95% CI 94-97%), ranging from 92 to 98% for individual diseases. After weighting for disease prevalence, the accuracy was estimated to be 98.5%. The overall positive predictive value (PPV) of chronic disease diagnosis was 93% (95% CI 89-96%) and the overall negative predictive value (NPV) was 99% (95% CI 96-100%). There were disease-specific differences in validity, most notably multiple sclerosis, where the PPV was 83%. Other disorders had PPVs between 93 and 97%. The NPV of most disorders was 100%, except for hypertension and heart failure, where it was 97 and 93%, respectively. Those who had the registered chronic disease had objective findings of disease in 96% of cases.

Conclusions: When determining the presence of chronic disease, diagnosis data from the Icelandic healthcare registries has a high PPV, NPV and accuracy. Furthermore, most diagnoses can be confirmed by objective findings such as imaging or blood testing. These findings can inform the interpretation of studies using diagnostic data from the Icelandic healthcare registries.

Keywords: Chronic diseases; Iceland; comorbidity; data accuracy; registries; validation study.

MeSH terms

  • Health Facilities*
  • Humans
  • Iceland
  • International Classification of Diseases*
  • Predictive Value of Tests
  • Registries