Validating pertussis data measures using electronic medical record data in Ontario, Canada 1986–2016
Shilo H. McBurney,
Jeffrey C. Kwong,
Kevin A. Brown,
Frank Rudzicz,
Branson Chen,
Elisa Candido,
Natasha S. Crowcroft
Affiliations
Shilo H. McBurney
Dalla Lana School of Public Health, University of Toronto, 155 College Street, 6th Floor, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Brown University School of Public Health, 121 South Main Street, Box G-S121-2, Providence, RI 02912, United States of America; Corresponding author at: Department of Epidemiology, 121 South Main Street, 2nd Floor Box G-S121-2, Providence, RI 02912, United States of America.
Jeffrey C. Kwong
Dalla Lana School of Public Health, University of Toronto, 155 College Street, 6th Floor, Toronto, ON M5T 3M7, Canada; Public Health Ontario, 661 University Avenue, Suite 1701, Toronto, ON M5G 1M1, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, 1 King’s College Circle, 6th Floor, Toronto, ON M5S 1A8, Canada; ICES, G1 06, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Department of Family and Community Medicine, University of Toronto, 500 University Avenue, 5th Floor, Toronto, ON M5G 1V7, Canada
Kevin A. Brown
Dalla Lana School of Public Health, University of Toronto, 155 College Street, 6th Floor, Toronto, ON M5T 3M7, Canada; Public Health Ontario, 661 University Avenue, Suite 1701, Toronto, ON M5G 1M1, Canada; ICES, G1 06, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
Frank Rudzicz
Department of Computer Science, University of Toronto, 40 St. George Street, Room 4283, Toronto, ON M5S 2E4, Canada; Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada; Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, ON M5G 1M1, Canada
Dalla Lana School of Public Health, University of Toronto, 155 College Street, 6th Floor, Toronto, ON M5T 3M7, Canada; Immunization, Vaccines and Biologicals, World Health Organization, Avenue Appia 20, 1211 Geneva 27, Switzerland
Background: Pertussis is a reportable disease in many countries, but ascertainment bias has limited data accuracy. This study aims to validate pertussis data measures using a reference standard that incorporates different suspected case severities, allowing for the impact of case severity on accuracy and detection to be explored. Methods: We evaluated 25 pertussis detection algorithms in a primary care electronic medical record database between January 1, 1986 and December 30, 2016. We estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We used sensitivity analyses to explore areas of uncertainty and evaluated reasons for lack of detection. Results: The algorithm including all data measures achieved the highest sensitivity at 20.6%. Sensitivity increased to 100% after reclassifying symptom-only cases as non-cases, but the PPV remained low. Age at first episode was significantly associated with detection in half of the tested scenarios, and false negatives often had some history of immunization. Conclusions: Sensitivity improved by reclassifying symptom-only cases but remained low unless multiple data sources were used. Results demonstrate a trade-off between PPV and sensitivity. EMRs can enhance detection through patient history and clinical note data. It is essential to improve case identification of older individuals with vaccination history to reduce ascertainment bias.
