Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index

Background Interpretation of health-related quality of life (QOL) outcomes requires improved methods to control for the effects of multiple chronic conditions (MCC). This study systematically compared legacy and improved method effects of aggregating MCC on the accuracy of predictions of QOL outcomes. Methods Online surveys administered generic physical (PCS) and mental (MCS) QOL outcome measures, the Charlson Comorbidity Index (CCI), an expanded chronic condition checklist (CCC), and individualized QOL Disease-specific Impact Scale (QDIS) ratings in a developmental sample (N = 5490) of US adults. Controlling for sociodemographic variables, regression models compared 12- and 35-condition checklists, mortality vs. population QOL-weighting, and population vs. individualized QOL weighting methods. Analyses were cross-validated in an independent sample (N = 1220) representing the adult general population. Models compared estimates of variance explained (adjusted R 2 ) and model fit (AIC) for generic PCS and MCS across aggregation methods at baseline and nine-month follow-up. Results In comparison with sociodemographic-only regression models (MCS R 2 = 0.08, PCS = 0.09) and Charlson CCI models (MCS R 2 = 0.12, PCS = 0.16), increased variance was accounted for using the 35-item CCC (MCS R 2 = 0.22, PCS = 0.31), population MCS/PCS QOL weighting (R 2 = 0.31–0.38, respectively) and individualized QDIS weighting (R 2 = 0.33 & 0.42). Model R 2 and fit were replicated upon cross-validation. Conclusions Physical and mental outcomes were more accurately predicted using an expanded MCC checklist, population QOL rather than mortality CCI weighting, and individualized rather than population QOL weighting for each reported condition. The 3-min combination of CCC and QDIS ratings (QDIS-MCC) warrant further testing for purposes of predicting and interpreting QOL outcomes affected by MCC.