Two morbidity indices developed in a nationwide population permitted performant outcome-specific severity adjustment

The objective of the study was to develop and validate two outcome-specific morbidity indices in a population-based setting: the Mortality-Related Morbidity Index (MRMI) predictive of all-cause mortality and the Expenditure-Related Morbidity Index (ERMI) predictive of health care expenditure. A cohort including all beneficiaries of the main French health insurance scheme aged 65 years or older on December 31, 2013 (N = 7,672,111), was randomly split into a development population for index elaboration and a validation population for predictive performance assessment. Age, gender, and selected lists of conditions identified through standard algorithms available in the French health insurance database (SNDS) were used as predictors for 2-year mortality and 2-year health care expenditure in separate models. Overall performance and calibration of the MRMI and ERMI were measured and compared to various versions of the Charlson Comorbidity Index (CCI). The MRMI included 16 conditions, was more discriminant than the age-adjusted CCI (c-statistic: 0.825 [95% confidence interval: 0.824–0.826] vs. 0.800 [0.799–0.801]), and better calibrated. The ERMI included 19 conditions, explained more variance than the cost-adapted CCI (21.8% vs. 13.0%), and was better calibrated. The proposed MRMI and ERMI indices are performant tools to account for health-state severity according to outcomes of interest.