title: Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records.
abstract: OBJECTIVE
  The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada's first national chronic disease surveillance system based on electronic health record (EHR) data. The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy.
  
  
  METHODS
  Using a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms. A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism. Charts were reviewed by trained research assistants and residents who were blinded to the algorithmic diagnosis. Sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated.
  
  
  RESULTS
  We obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite). For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism).
  
  
  CONCLUSIONS
  The CPCSSN diagnostic algorithms showed excellent sensitivity and specificity for hypertension, diabetes, epilepsy, and parkinsonism and acceptable values for the other conditions. CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases.
authors.affiliation: Department of Family Medicine, Queen's University, Kingston, Ontario, Canada Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada tylerw@cpcssn.org.
authors.forename: Tyler
authors.initials: T
authors.lastname: Williamson
authors.affiliation: Department of Family Medicine, Queen's University, Kingston, Ontario, Canada Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Green
authors.affiliation: Department of Family Medicine, Queen's University, Kingston, Ontario, Canada Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada.
authors.forename: Richard
authors.initials: R
authors.lastname: Birtwhistle
authors.affiliation: Department of Family Medicine, Queen's University, Kingston, Ontario, Canada.
authors.forename: Shahriar
authors.initials: S
authors.lastname: Khan
authors.affiliation: Department of Family Medicine, University of Calgary, Alberta, Canada.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Garies
authors.affiliation: School of Nursing, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Sabrina T
authors.initials: ST
authors.lastname: Wong
authors.affiliation: Department of Family Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
authors.forename: Nandini
authors.initials: N
authors.lastname: Natarajan
authors.affiliation: Department of Family Medicine, University of Alberta, Alberta, Canada.
authors.forename: Donna
authors.initials: D
authors.lastname: Manca
authors.affiliation: Department of Family Medicine, University of Alberta, Alberta, Canada.
authors.forename: Neil
authors.initials: N
authors.lastname: Drummond
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002170:Canada / Q000453:epidemiology; D002908:Chronic Disease / Q000453:epidemiology*; D003430:Cross-Sectional Studies; D016208:Databases, Factual / Q000706:statistics & numerical data*; D003704:Dementia / Q000453:epidemiology; D003863:Depression / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000453:epidemiology; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000453:epidemiology; D005260:Female; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D010003:Osteoarthritis / Q000453:epidemiology; D020734:Parkinsonian Disorders / Q000453:epidemiology; D011320:Primary Health Care / Q000706:statistics & numerical data*; D029424:Pulmonary Disease, Chronic Obstructive / Q000453:epidemiology; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance
keywords: chronic disease; electronic health records; primary health care; sensitivity and specificity; validation studies