Can we identify allergic rhinitis from administrative data: A validation study.

Background Important insights on, for example, prevalence, disease progression, and treatment of allergic rhinitis can be obtained from large-scale database studies if researchers are able to identify allergic individuals. We aimed to assess the validity of 13 different algorithms based on Danish nationwide prescription and/or hospital data to identify adults with allergic rhinitis. Methods Our primary gold standard of allergic rhinitis was a positive serum specific IgE (>= 0.35) and self-reported nasal symptoms retrieved from two general health examination studies conducted in Danish adults (18-69 years) during 2006 to 2008 (n = 3416) and 2012 to 2015 (n = 7237). The secondary gold standard of allergic rhinitis was self-reported physician diagnosis. We calculated sensitivity, specificity, positive predictive value (PPV), negative predictive value, and corresponding 95% confidence intervals (95% CI) for each register-based algorithm in the two time periods. Results Sensitivity (<= 0.40) was low for all algorithms irrespective of definition of allergic rhinitis (gold standard) or time period. The highest PPVs were obtained for algorithms requiring both antihistamines and intranasal corticosteroids; yielding a PPV of 0.69 (0.62-0.75) and a corresponding sensitivity of 0.10 (0.09-0.12) for the primary gold standard of allergic rhinitis in 2012 to 2015. Conclusion Algorithms based on both antihistamines and intranasal corticosteroids yielded the highest PPVs. However, the PPVs were still moderate and came at the expense of low sensitivity when applying the strict primary gold standard (sIgE and nasal symptom).