Identifying and distinguishing cases of parkinsonism and Parkinson's disease using ICD-9 CM codes and pharmacy data

Kari Swarztrauber, Jane Anau, Dawn Peters

Research output: Contribution to journalReview articlepeer-review

56 Scopus citations


Administrative databases have the potential to assess quality and cost of care for parkinsonism and Parkinson's disease. However, the validity of findings is limited by our understanding of how cases are identified. Patient records listing International Classification of Diseases, Version 9, Clinical Modification (ICD-9 CM) codes for parkinsonism (n = 2,076) and dopaminergic medications (n = 2,798) were pulled from fiscal years 1999 to 2001 for patients in the Pacific Northwest Veterans Administration. Samples of these records (n = 397) and records without these ICD-9 CM codes (n = 500) were reviewed, and clinical data were extracted. The accuracy of administrative data to identify and distinguish between Parkinson's disease and parkinsonism was calculated. A total of 37.9% of parkinsonism cases were detected using pharmacy data and ICD-9 CM codes compared to 18.7% by using ICD-9 CM codes alone. The ICD-9 CM code for paralysis agitans (332.0) did not distinguish between probable Parkinson's disease and other causes of parkinsonism, whereas the ICD-9 CM code for degenerative basal ganglia disorder (333.0) predicted having secondary parkinsonism (odds ratio [OR] = 5.0) as well as dopa-responsiveness in patients without secondary parkinsonism (OR = 4.5). Administrative data are limited in the ability to identify parkinsonism. The ICD-9 CM code, 332.0, which is generally considered the code to identify Parkinson's disease, did not distinguish between parkinsonism and Parkinson's disease.

Original languageEnglish (US)
Pages (from-to)964-970
Number of pages7
JournalMovement Disorders
Issue number8
StatePublished - Aug 2005
Externally publishedYes


  • Administrative data
  • ICD-9 CM codes
  • Parkinson's disease
  • Prevalence
  • Sensitivity and specificity

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology


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