Validating pertussis data measures using electronic medical record data in Ontario, Canada 1986-2016.

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.