A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends | Zendy

Debra A. Butt | Zendy; Karen Tu | Zendy; Jacqueline Young | Zendy; Diane Green | Zendy; Myra Wang | Zendy; Noah Ivers | Zendy; Liisa Jaakkimainen | Zendy; Robert Lam | Zendy; Mark Guttman | Zendy

Open Access

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

Author(s) -

Debra A. Butt,

Karen Tu,

Jacqueline Young,

Diane Green,

Myra Wang,

Noah Ivers,

Liisa Jaakkimainen,

Robert Lam,

Mark Guttman

Publication year - 2014

Publication title -

neuroepidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.217

H-Index - 87

eISSN - 1423-0208

pISSN - 0251-5350

DOI - 10.1159/000365590

Subject(s) - medicine , parkinsonism , incidence (geometry) , epidemiology , algorithm , pathology , disease , physics , computer science , optics

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.

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