ArticleAnnals of family medicine2014
Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records.
Article in Annals of family medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03689049 (SPIDER), which is not on this map. Cited by 151 papers, 5 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
SPIDER: A Structured Process Informed by Data, Evidence and Research - A Research and Quality Improvement Collaboration Supporting Practices in Improving Care for Complex Elderly Patients
Who cites it
151 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Case Definitions and Data Sources for NNCSS Parkinson Disease Surveillance: A Systematic Review.JAMA network open · 2026Pooled it
- Use of Electronic Medical Record Data to Create a Dashboard on Access to Primary Care.Healthcare policy = Politiques de sante · 2023Pooled it
- Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature.BMC medical informatics and decision making · 2017Pooled it
- Diagnostic accuracy of administrative data algorithms in the diagnosis of osteoarthritis: a systematic review.BMC medical informatics and decision making · 2016Pooled it
- Advancing Family Medicine Through Dyadic Electronic Medical Record Linkage in Dementia Care.Annals of family medicine · 2026Article
- Primary care for depression before and during the COVID-19 pandemic: a retrospective pre-post study.BMJ open · 2026Article
- Implementation of a surveillance and monitoring system for chronic disease: A scoping review: Running title: Chronic disease surveillance and monitoring system.AIMS public health · 2026Review
- Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach.International journal of population data science · 2026Article
- Performance of clinical prediction models for chronic kidney disease among people with diabetes: external validation using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).Diagnostic and prognostic research · 2025Article
- Validation of an Algorithm to Identify End-Stage Kidney Disease in Electronic Health Records Data.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2025Article
- Trends and Disparities in Technology Use and Glycemic Control in Type 1 Diabetes.JAMA network open · 2025Article
- Beyond the pandemic: rising administrative demands and changing disease profiles in primary care.Israel journal of health policy research · 2025Article
- Comparing the Quality of Primary Care Electronic Health Record Data in Australia and Canada: Case Study in Osteoarthritis.Journal of medical Internet research · 2025Article
- Primary care insights on the management of diabetes: results from a mixed method study of care changes and impacts during the COVID-19 pandemic.Family practice · 2025Article
- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- Trends of paediatric hypertension screening and management in primary care before and during the coronavirus disease 2019 pandemic: A retrospective cohort study.Paediatrics & child health · 2025Article
- Continuous-Time Causal Inference With Marked Point Process Weights: An Example on Sodium-Glucose Co-Transporters 2 Inhibitor Medications and Urinary Tract Infection.Statistics in medicine · 2025Article
- Article
- Using electronic medical records to analyze outpatient visits of persons with epilepsy during the pandemic-experience from a low middle income country.Acta epileptologica · 2025Article
91 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe 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.
methodsUsing 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.
resultsWe 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).
conclusionsThe 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.
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