Background: The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adjust for comorbidities, using as an example the association between insurance and mortality. Materials and Methods: Using all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 ICD-9-derived comorbidities. We estimated ORs for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights (IPTW) for CCI, LPCA, and separate covariates. Results: In 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD=0.086, OR=1.17), which was almost entirely eliminated by IPTW on separate covariates (mean SMD=0.012, OR=1.36). The CCI performed similarly to the unweighted sample (mean SMD=0.080, OR=1.25), while 2 LPCA axes were better able to control for confounding (mean SMD=0.04, OR=1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas 2 LPCA axes accounted for 91.3%.
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