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Quantifying outcome misclassification in multi-database studies: The case study of pertussis in the ADVANCE project
Rosa Gini1, Caitlin N Dodd2, Kaatje Bollaerts3
1Agenzia regionale di sanità della Toscana, Osservatorio di epidemiologia, Florence, Italy.
Vaccine
|November 4, 2019
Summary
This study developed a method to quantify misclassification in vaccine safety data. Comparing algorithms for Bordetella pertussis (BorPer) cases in European databases revealed potential biases, aiding accurate vaccine benefit-risk monitoring.
Area of Science:
- Pharmacovigilance and Pharmacoepidemiology
- Vaccine Safety Monitoring
- Health Informatics and Database Analysis
Background:
- The Accelerated Development of Vaccine Benefit-risk Collaboration in Europe (ADVANCE) project aims to enhance vaccine safety monitoring using European healthcare databases.
- Accurate estimation of vaccine benefit-risk relies on precise identification of adverse events, which can be challenging due to event misclassification in large databases.
- Manual chart review is often infeasible for rapid monitoring, necessitating automated strategies to quantify misclassification.
Purpose of the Study:
- To develop and test a strategy for quantifying event misclassification in vaccine safety data when manual review is not feasible.
- To evaluate different algorithms for identifying Bordetella pertussis (BorPer) cases across multiple European healthcare databases as a case study.
- To estimate the validity of these algorithms, including positive predictive value (PPV) and sensitivity, by comparing them with external surveillance data.
Main Methods:
- Utilized four primary care (PC) databases (BIFAP, THIN, RCGP RSC, PEDIANET) and one PC/hospital database (SIDIAP) across Europe.
- Defined BorPer algorithms based on healthcare setting, data domain (diagnoses, drugs, lab tests), and concept sets (specific/unspecified pertussis).
- Estimated BorPer incidence rates (IRs) in children (0-14 years) and approximated validity indices (PPV, sensitivity) using novel formulas in SIDIAP.
Main Results:
- Incidence rates varied significantly across databases, with higher rates than ECDC TESSy suggesting false positives when including unspecified pertussis concepts.
- In SIDIAP, including discharge diagnoses increased the estimated IR to 45.0 per 100,000 person-years.
- The estimated sensitivity and PPV for BorPer cases using combined PC diagnoses in SIDIAP were approximately 85% and 72%, respectively.
Conclusions:
- Using only specific BorPer concepts in PC databases leads to low sensitivity, while including unspecified concepts introduces false positives (estimated ~28%).
- An estimated 15% of cases may not be captured in PC databases if they are exclusively diagnosed in hospitals.
- Quantifying algorithm impact across databases and benchmarking with surveillance data provides valuable approximate estimates of algorithm validity for vaccine safety.
Keywords:
Event misclassificationEvent-finding algorithmsIncidence of pertussisPositive predictive valueMore Related Videos
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