Accuracy of Administrative Health Data for Surveillance of Traumatic Brain Injury: A Bayesian Latent Class Analysis.

BACKGROUND: Traumatic brain injury surveillance provides information for allocating resources to prevention efforts. Administrative data are widely available and inexpensive but may underestimate traumatic brain injury burden by misclassifying cases. Moreover, previous studies evaluating the accuracy of administrative data surveillance case definitions were at risk of bias by using imperfect diagnostic definitions as reference standards. We assessed the accuracy (sensitivity/specificity) of traumatic brain injury surveillance case definitions in administrative data, without using a reference standard, to estimate incidence accurately.
METHODS: We used administrative data from a 25% random sample of Montreal residents from 2000 to 2014. We used hierarchical Bayesian latent class models to estimate the accuracy of widely used traumatic brain injury case definitions based on the International Classification of Diseases, or on head radiologic examinations, covering the full injury spectrum in children, adults, and the elderly. We estimated measurement error-adjusted age- and severity-specific incidence.
RESULTS: The adjusted traumatic brain injury incidence was 76 (95% CrI = 68, 85) per 10,000 person-years (underestimated as 54 [95% CrI = 54, 55] per 10,000 without adjustment). The most sensitive case definitions were radiologic examination claims in adults/elderly (0.48; 95% CrI = 0.43, 0.55 and 0.66; 95% CrI = 0.54, 0.79) and emergency department claims in children (0.45; 95% CrI = 0.39, 0.52). The most specific case definitions were inpatient claims and discharge abstracts (0.99; 95% CrI = 0.99, 1.00). We noted strong secular trends in case definition accuracy.
CONCLUSIONS: Administrative data remain a useful tool for conducting traumatic brain injury surveillance and epidemiologic research when measurement error is adjusted for.