To compare the association of two comorbidity indices, Charlson Comorbidity Index (CCI), and HRQL Comorbidity Index (HRQL-CI), with measures of healthcare resource utilization (HCRU) and work limitations among a large sample of working adults. These indices are calculated based on the number and severity of comorbid conditions. The CCI was developed to predict mortality. The HRQL-CI uses a bi-dimensional algorithm (HRQL-CI Physical; HRQL-CI Mental) to predict SF-12v2 Health Survey physical and mental component summary scores (PCS; MCS), while a revised unidimensional algorithm was developed using the SF-6D. Data [N=17,773] came from the Medical Expenditure Panel Survey (MEPS), a study representative of U.S. households with 5-rounds of interviews across 2 years. HCRU variables were total medical expenditures (MEx), number of medical events (MEv), number of lost work/housework days, and cognitive limitations (Yes/No), reported over round 5 (in year 2) of the study. Comorbidity indices were captured through conditions from year 2. Spearman correlations (rs) were used to assess the association between CCI, HRQL-CI, HRQL-CI Physical, HRQL-CI Mental and each outcome, except for cognitive limitations, for which the generalized coefficient of determination (was used. MEx and MEv were both most strongly associated with HRQL-CI-Physical (rs = 0.40, 0.49, respectively), followed by the unidimensional HRQL-CI (rs = 0.38). Number of lost work days and cognitive limitations were both most strongly associated with the unidimensional HRQL-CI (rs = 0.18, = 0.12), followed by the HRQL-CI Mental (rs = 0.16, = 0.11). All four outcomes were more strongly associated with indices based on HRQL than a commonly-used index based on mortality. While the HRQL-CI Physical was more strongly associated with HCRU, the HRQL-CI Mental was more strongly associated with work limitations. Indices that take into account the effect of comorbid conditions on HRQL should be considered when predicting patient outcomes.