Bayesian Correction of Misclassification of Pertussis in Vaccine Effectiveness Studies: How Much Does Underreporting Matter?

Diagnosis of pertussis remains a challenge, and consequently research on the risk of disease might be biased because of misclassification. We quantified this misclassification and corrected for it in a case-control study of children in Philadelphia, Pennsylvania, who were 3 months to 6 years of age and diagnosed with pertussis between 2011 and 2013. Vaccine effectiveness (VE; calculated as (1 - odds ratio) × 100) was used to describe the average reduction in reported pertussis incidence resulting from persons being up to date on pertussis-antigen containing vaccines. Bayesian techniques were used to correct for purported nondifferential misclassification by reclassifying the cases per the 2014 Council of State and Territorial Epidemiologists pertussis case definition. Naïve VE was 50% (95% confidence interval: 16%, 69%). After correcting for misclassification, VE ranged from 57% (95% credible interval: 30, 73) to 82% (95% credible interval: 43, 95), depending on the amount of underreporting of pertussis that was assumed to have occurred in the study period. Meaningful misclassification was observed in terms of false negatives detected after the incorporation of infant apnea to the 2014 case definition. Although specificity was nearly perfect, sensitivity of the case definition varied from 90% to 20%, depending on the assumption about missed cases. Knowing the degree of the underreporting is essential to the accurate evaluation of VE.