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Using Ontario Health Insurance Plan physician billing claims to ascertain individual influenza immunization status: an updated validation study


Background — Owing to the absence of a vaccination registry in Ontario, administrative data are currently the best available source to determine population-based individual-level influenza vaccination status. Our objective was to validate physician billing claims for influenza vaccination in the Ontario Health Insurance Plan database against the Canadian Community Health Survey.

Methods — We used self-reported seasonal influenza vaccination status of Ontario residents surveyed between 2007 and 2009 as the reference standard. The survey responses were linked to physician claims database records to validate billing codes for influenza vaccination. We calculated sensitivity, specificity, positive predictive value and negative predictive value with 95% confidence intervals (CIs). We stratified the data by several covariates and comorbidities to determine stratum-specific performance characteristics. We used these estimates to adjust an estimate of influenza vaccine effectiveness for the 2010/11 influenza season.

Results — For the 47 301 patients included in the analysis, the sensitivity for the billing codes was 49.8% (95% CI 49.0%-50.5%), specificity was 95.7% (95% CI 95.5%-96.0%), positive predictive value was 88.4% (95% CI 87.8%-89.0%) and negative predictive value was 74.5% (95% CI 74.0%-74.9%). Performance measures were optimized in patients aged 65 years and older, particularly those with comorbidities.

Interpretation — Although administrative data have limitations for determining influenza vaccination status, owing to the high positive predictive value, they are well suited for self-controlled study designs that are often used to assess vaccine safety. For studies of coverage and effectiveness, restricting the cohort to patients aged 65 years and older will minimize misclassification bias. Performance characteristics from this study can be used to mitigate misclassification bias.



Schwartz KL, Jembere N, Campitelli MA, Buchan SA, Chung H, Kwong JC. CMAJ Open. 2016; 4(3):E463-70.

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