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.
SUMMARYObjective: Previous validation studies assessing the use of administrative data to identify patients with epilepsy have used targeted sampling or have used a reference standard of patients in the neurologist, hospital, or emergency room setting. Therefore, the validity of using administrative data to identify patients with epilepsy in the general population has not been previously assessed. The purpose of this study was to determine the validity of using administrative data to identify patients with epilepsy in the general population. Methods: A retrospective chart abstraction study was performed using primary care physician records from 83 physicians distributed throughout Ontario and contributing data to the Electronic Medical Record Administrative data Linked Database (EMRALD) A random sample of 7,500 adult patients, from a possible 73,014 eligible, was manually chart abstracted to identify patients who had ever had epilepsy. These patients were used as a reference standard to test a variety of administrative data algorithms. Results: An algorithm of three physician billing codes (separated by at least 30 days) in 2 years or one hospitalization had a sensitivity of 73.7% (95% confidence interval [CI] 64.8-82.5%), specificity of 99.8% (95% CI 99.6-99.9%), positive predictive value (PPV) of 79.5% (95% CI 71.1-88.0%), and negative predictive value (NPV) of 99.7% (95% CI 99.5-99.8%) for identifying patients who had ever had epilepsy. Significance: The results of our study showed that administrative data can reasonably accurately identify patients who have ever had epilepsy, allowing for a "lifetime" population prevalence determination of epilepsy in Ontario and the rest of Canada with similar administrative databases. This will facilitate future studies on population level patterns and outcomes of care for patients living with epilepsy.
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