Bootstrap imputation minimized misclassification bias when measuring Colles' fracture prevalence and its associations using health administrative data

Objectives: Misclassification bias can result from the incorrect assignment of disease status using inaccurate diagnostic codes in health administrative data. This study quantified misclassification bias in the study of Colles' fracture. Study Design and Setting: Colles' fracture status was determined in all patients >50 years old seen in the emergency room at a single teaching hospital between 2006 and 2014 by manually reviewing all forearm radiographs. This data set was linked to population-based data capturing all emergency room visits. Reference disease prevalence and its association with covariates were measured. A multivariate model using covariates derived from administrative data was used to impute Colles' fracture status and measure its prevalence and associations using bootstrapping methods. These values were compared with reference values to measure misclassification bias. This was repeated using diagnostic codes to determine Colles' fracture status. Results: Five hundred eighteen thousand, seven hundred forty-four emergency visits were included with 3,538 (0.7%) having a Colles' fracture. Determining disease status using the diagnostic code (sensitivity 69.4%, positive predictive value 79.9%) resulted in significant underestimate of Colles' fracture prevalence (relative difference -13.3%) and biased associations with covariates. The Colles' fracture model accurately determined disease probability (c-statistic 98.9 [95% confidence interval {CI} 98.7-99.1], calibration slope 1.009 [95% CI 1.004-1.013], Nagelkerke's R-2 0.71 [95% CI 0.70-0.72]). Using disease probability estimates from this model, bootstrap imputation (BI) resulted in minimal misclassification bias (relative difference in disease prevalence -0.01%). The statistical significance of the association between Colles' fracture and age was accurate in 32.4% and 70.4% of samples when using the code or BI, respectively. Conclusion: Misclassification bias in estimating disease prevalence and its associations can be minimized with BI using accurate disease probability estimates. (C) 2017 Elsevier Inc. All rights reserved.