Comparison of diagnoses of amyotrophic lateral sclerosis by use of death certificates and hospital discharge data in the Danish population

Amyotroph Lateral Scler Frontotemporal Degener. 2015 Jun;16(3-4):224-9. doi: 10.3109/21678421.2014.988161. Epub 2015 May 6.

Abstract

Because ALS is rare, large-scale studies are difficult. Hospital and death certificate data are valuable tools, but understanding of how well they capture cases is needed. We identified 3650 incident cases in the Danish National Patient Register (NPR) between 1982 and 2009, using ICD-8 (before 1994) or ICD-10 codes. Death certificates were obtained from the Danish Register of Causes of Death. We obtained medical records for 173 of the cases identified in the NPR and classified these according to the El Escorial criteria. We compared ALS identification from death certificates to hospital discharges, and both to medical records. Results showed that the sensitivity for use of death certificates was 84.2% (95% CI 82.9-85.5%) and was significantly higher for females, subjects younger than 77 years, and when coded with ICD-8. Using only the underlying cause of death resulted in significantly lower sensitivity. The estimated overall positive predictive value (PPV) was 82.0% (95% CI 80.0-83.8%). Sensitivity and PPV were similar compared with medical records. In conclusion, we found that use of hospital discharges and death certificates is highly reliable and, therefore, a valuable tool for ALS epidemiologic studies. The possible effects on findings of slight differences by age, gender, and ICD coding should be considered.

Keywords: Epidemiology; risk; survival.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Amyotrophic Lateral Sclerosis* / diagnosis
  • Amyotrophic Lateral Sclerosis* / epidemiology
  • Amyotrophic Lateral Sclerosis* / mortality
  • Cause of Death
  • Death Certificates*
  • Denmark / epidemiology
  • Female
  • Humans
  • Male
  • Medical Records / statistics & numerical data
  • Middle Aged
  • Patient Discharge / statistics & numerical data*
  • Registries
  • Retrospective Studies
  • Sensitivity and Specificity
  • Time Factors