期刊
NEUROEPIDEMIOLOGY
卷 43, 期 1, 页码 28-37出版社
KARGER
DOI: 10.1159/000365590
关键词
Parkinsonism; Administrative data; Validation; Sensitivity; Predictive values; Prevalence; Incidence
资金
- Public Health Agency of Canada
- Institute of Clinical Evaluative Sciences (ICES) - Ontario Ministry of Health and Long-Term Care (MOHLTC)
- Department of Family and Community Medicine (DFCM) by the Research Department of the University of Toronto
- Canadian Institutes of Health Research (CIHR) Fellowship Award in Primary Care
- CIHR Fellowship Award in Clinical Research
- DFCM Fellowship Award from the University of Toronto
Background: Epidemiological studies for identifying patients with Parkinson's disease (PD) or Parkinsonism (PKM) have been limited by their nonrandom sampling techniques and mainly veteran populations. This reduces their use for health services planning. The purpose of this study was to validate algorithms for the case ascertainment of PKM from administrative databases using primary care patients as the reference standard. Methods: We conducted a retrospective chart abstraction using a random sample of 73,003 adults aged 20 years from a primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Physician diagnosis in the EMR was used as the reference standard and population-based administrative databases were used to identify patients with PKM from the derivation of algorithms. We calculated algorithm performance using sensitivity, specificity, and predictive values and then determined the population-level prevalence and incidence trends with the most accurate algorithms. Results: We selected, '2 physician billing codes in 1 year' as the optimal administrative data algorithm in adults and seniors (65 years) due to its sensitivity (70.6-72.3%), specificity (99.9-99.8%), positive predictive value (79.5-82.8%), negative predictive value (99.9-99.7%), and prevalence (0.28-1.20%), respectively. Conclusions: Algorithms using administrative databases can reliably identify patients with PKM with a high degree of accuracy. (C) 2014 S. Karger AG, Basel
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