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

Abstract

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.

Validation of Secondary Data Sources to Identify Parkinson Disease Against Clinical Diagnostic Criteria

“…ICD-9 codes (332.0, 332.1, and 333.0) had a sensitivity of 18.7%, a specificity of 99.9%, and PPV of 81%. Use of antiparkinsonian medications (dopamine agonist or levodopa/carbidopa) had a sensitivity of 34.6%, a specificity of 99.6%, and a PPV of 60% (11). In another study in the United Kingdom, Meara et al (12) assessed the accuracy of medication inventories in identifying PD cases using a prescription database from general practices.…”

“…The etiology for most sporadic cases remains unknown, as genetic variants account for only approximately 10% of cases (2). PD has a low incidence (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) per 100,000 person-years), and it is impractical to conduct a prospective, longitudinal epidemiologic study on a condition that relies on a clinical examination for a diagnosis because there is no biomarker or simple laboratory test that can confirm a PD diagnosis except autopsy. Registries for PD that cover large populations do not exist.…”

A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

“…Furthermore, one study involved only seniors [10] and the other three were limited to veterans [8,9,11]. In this study, we performed a healthcare administrative data validation for identifying patients with PKM using a primary care electronic medical record (EMR), a more general population, as the reference standard.…”

“…Four previous studies validated algorithms for PD/PKM using administrative databases [8,9,10,11] but each used nonrandom sampling methods to select PD/PKM cases and non-cases for validation [8,9,10,11]. Furthermore, one study involved only seniors [10] and the other three were limited to veterans [8,9,11].…”

Advancing veterans' healthcare using electronic data: Lessons learned from researchers in the field

“…VA investigators responded-many more worthy abstracts were submitted than could be included in one JRRD issue. Reflecting strong research-community support for VA research, 54 scholars readily accepted invitations to peer review and almost 100 individuals participated as authors, reviewers, or editors.VA researchers have long been major contributors to the methodological literature in the area of administrative data use [6][7][8][9][10][11][12][13][14][15][16][17]. This issue advances that literature by focusing specifically on the quality and research utility of databases used in veterans research.…”