Enhancing case ascertainment of Parkinson's disease using Medicare claims data in a population-based cohort: the Cardiovascular Health Study.


Journal Article

PURPOSE: We sought to improve a previous algorithm to ascertain Parkinson's disease (PD) in the Cardiovascular Health Study by incorporating additional data from Medicare outpatient claims. We compared our results to the previous algorithm in terms of baseline prevalence and incidence of PD, as well as associations with baseline smoking characteristics. METHODS: Our original case ascertainment used self-reported diagnosis, antiparkinsonian medication, and hospitalization discharge International Classification of Diseases-Ninth version code. In this study, we incorporated additional data from fee-for-service Medicare claims, extended follow-up time, review of hospitalization records, and adjudicated cause of death. Two movement disorders specialists adjudicated final PD status. We used logistic regression models and controlled for age, sex, African American race, and education. RESULTS: We identified 75 additional cases but reclassified 80 previously identified cases as not having PD. We observed significant inverse association with smoking status (odds ratio = 0.42; 95% confidence interval (CI) = 0.22, 0.79), and inverse linear trends with pack-years (p = 0.005), and cigarettes per day (p = 0.019) with incident PD. All estimates were stronger than those from the previous algorithm. CONCLUSIONS: Our enhanced method did not alter prevalence and incidence estimates compared with our previous algorithm. However, our enhanced method provided stronger estimates of association, potentially due to reduced level of disease misclassification.

Full Text

Duke Authors

Cited Authors

  • Ton, TGN; Biggs, ML; Comer, D; Curtis, L; Hu, S-C; Thacker, EL; Searles Nielsen, S; Delaney, JA; Landsittel, D; Longstreth, WT; Checkoway, H; Jain, S

Published Date

  • February 2014

Published In

Volume / Issue

  • 23 / 2

Start / End Page

  • 119 - 127

PubMed ID

  • 24357102

Pubmed Central ID

  • 24357102

Electronic International Standard Serial Number (EISSN)

  • 1099-1557

Digital Object Identifier (DOI)

  • 10.1002/pds.3552


  • eng

Conference Location

  • England