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

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.**背景**：既往用于检出帕金森病（Parkinson's Disease, PD）或帕金森综合征（Parkinsonism, PKM）患者的流行病学研究，受限于非随机抽样策略且研究队列多为退伍军人人群，这大幅限制了其在卫生服务规划中的应用潜力。本研究旨在以初级保健患者为金标准，验证从行政数据库中检出帕金森综合征病例的算法有效性。 **方法**：本研究采用回顾性病历数据提取法，从加拿大安大略省的初级保健电子病历行政数据链接数据库（Electronic Medical Record Administrative data Linked Database, EMRALD）中随机抽取73003名年龄≥20岁的成年人作为研究样本。以电子病历中的医师诊断作为金标准，基于人群的行政数据库用于推导并识别帕金森综合征患者。我们通过灵敏度、特异度及预测值计算算法性能，并借助最优算法分析人群水平的患病率与发病趋势。 **结果**：针对成人及老年（≥65岁）人群，我们筛选出“1年内出现2项医师计费代码”作为最优行政数据算法，其灵敏度为70.6%~72.3%、特异度为99.9%~99.8%、阳性预测值为79.5%~82.8%、阴性预测值为99.9%~99.7%，对应患病率为0.28%~1.20%。 **结论**：利用行政数据库构建的算法可高精度、可靠地检出帕金森综合征患者。