Comorbidity scores are widely used to help address confounding bias in nonrandomized studies conducted within health-care databases, but existing scores were developed to predict all-cause mortality in adults and might not be appropriate for use in pediatric studies. We developed and validated a pediatric comorbidity index, using health-care utilization data from the tenth revision of the International Classification of Diseases. Within the MarketScan database of US commercial claims data, pediatric patients (aged ≤18 years) continuously enrolled between October 1, 2015, and September 30, 2017, were identified. Logistic regression was used to predict the 1-year risk of hospitalization based on 27 predefined conditions and empirically identified conditions derived from the most prevalent diagnoses among patients with the outcome. A single numerical index was created by assigning weights to each condition based on its β coefficient. We conducted internal validation of the index and compared its performance with existing adult scores. The pediatric comorbidity index consisted of 24 conditions and achieved a C statistic of 0.718 (95% confidence interval (CI): 0.714, 0.723). The index outperformed existing adult scores in a pediatric population (C statistics ranging from 0.522 to 0.640). The pediatric comorbidity index provides a summary measure of disease burden and can be used for risk adjustment in epidemiologic studies of pediatric patients.
Keywords: claims data; comorbidity; confounding; health services research; pediatrics; pharmacoepidemiology; risk adjustment.
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