LAUSR

Introduction Administrative data is increasingly used for chronic disease surveillance; however, its validity to define cases of chronic kidney disease (CKD) in children is unknown. We sought to evaluate the performance of case definitions for CKD in children. Methods We utilized population-based administrative data from the Manitoba Center for Health Policy to evaluate the validity of algorithms based on a combination of hospital claims, outpatient physician visits, and pharmaceutical use over 1–3 years in children <18 years of age. Algorithms were compared with a laboratory-based definition (estimated glomerular filtration rate < 90 ml/min/1.73 m 2 and/or presence of proteinuria). Results All algorithms evaluated had very low sensitivity (0.20–0.39) and moderate positive predictive value (0.52–0.68). Algorithms had excellent specificity (0.98–0.99) and negative predictive value (0.96–0.97). Receiver operating characteristic (ROC) curves indicate fair accuracy (0.60–0.68). Sensitivity improved with increasing years of data. One or more physician claims and one or more prescriptions over 3 years had the highest sensitivity and ROC. Conclusions The sensitivity of administrative data algorithms for CKD is unacceptably low for a screening test. Specificity is excellent; therefore, children without CKD are correctly identified. Alternate data sources are required for population-based surveillance of this important chronic disease.