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

TitleEnhancing case ascertainment of Parkinson's disease using Medicare claims data in a population-based cohort: the Cardiovascular Health Study.
Publication TypeJournal Article
Year of Publication2014
AuthorsTon, TGN, Biggs, MLou, Comer, D, Curtis, L, Hu, S-C, Thacker, EL, Nielsen, SSearles, Delaney, JA, Landsittel, D, Longstreth, WT, Checkoway, H, Jain, S
JournalPharmacoepidemiol Drug Saf
Volume23
Issue2
Pagination119-27
Date Published2014 Feb
ISSN1099-1557
KeywordsAged, Aged, 80 and over, Algorithms, Antiparkinson Agents, Cohort Studies, Databases, Factual, Female, Hospitalization, Humans, Incidence, Logistic Models, Male, Medicare, Parkinson Disease, Prevalence, Prospective Studies, Smoking, Time Factors, United States
Abstract<p><b>PURPOSE: </b>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.</p><p><b>METHODS: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>CONCLUSIONS: </b>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.</p>
DOI10.1002/pds.3552
Alternate JournalPharmacoepidemiol Drug Saf
PubMed ID24357102
PubMed Central IDPMC3923620
Grant ListU01 HL080295 / HL / NHLBI NIH HHS / United States
HHSN268200800007C / HL / NHLBI NIH HHS / United States
N01HC55222 / HL / NHLBI NIH HHS / United States
N01HC85086 / HL / NHLBI NIH HHS / United States
K23 NS070867 / NS / NINDS NIH HHS / United States
HHSN268201200036C / HL / NHLBI NIH HHS / United States
R01 HL080295 / HL / NHLBI NIH HHS / United States
N01HC85082 / HL / NHLBI NIH HHS / United States
N01HC85083 / HL / NHLBI NIH HHS / United States
L30 NS052978 / NS / NINDS NIH HHS / United States
HL080295 / HL / NHLBI NIH HHS / United States
N01HC85079 / HL / NHLBI NIH HHS / United States
R01 AG023629 / AG / NIA NIH HHS / United States
N01HC85080 / HL / NHLBI NIH HHS / United States
AG023629 / AG / NIA NIH HHS / United States
R56 AG023629 / AG / NIA NIH HHS / United States
N01HC85081 / HL / NHLBI NIH HHS / United States