Hospitalization data underestimate the occurrence of transient ischemic attack (TIA). As TIA is frequently diagnosed in primary care, methodologies for the accurate ascertainment of a TIA from physician claims data are required for surveillance and health systems planning in this population. The present study evaluated the diagnostic accuracy of multiple algorithms for TIA from a longitudinal population-based physician billing database.
Population-based administrative data from the province of British Columbia were used to identify the base population (1992–2007; N=102,492). Using discharge records for hospital admissions for acute ischemic stroke with a recent (<90 days) TIA as the reference standard, we performed receiver-operating characteristic analyses to calculate sensitivity, specificity, positive and negative predictive values and overall accuracy, and to compare area under the curve for each physician billing algorithm. To evaluate the impact of different case definitions on population-based TIA burden, we also estimated the annual TIA occurrence associated with each algorithm.
Physician billing algorithms showed low to moderate sensitivity, with the algorithm for two consecutive physician visits within 90 days showing the highest sensitivity at 37.7% (CI
95%=37.4–38.1). All algorithms demonstrated high specificity and moderate to high overall accuracy, resulting in low positive predictive values (≤5%), low discriminability (0.53–0.57) and high false positive rates (1 – specificity). Population-based estimates of TIA occurrence were comparable to prior studies and declined over time.
Physician billing data have insufficient sensitivity to identify TIAs but may be used in combination with hospital discharge data to improve the accuracy of estimating the population-based occurrence of TIAs.