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

OBJECTIVE: 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 healthcare expenditure.

STUDY DESIGN AND SETTING: 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 healthcare expenditure in separate models. Overall performance and calibration of the MRMI and ERMI were measured and compared to various versions of the Charlson index (CCI).

RESULTS: The MRMI included 16 conditions, was more discriminant than the age-adjusted CCI (c-statistic: 0.825 [95% CI: 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.

CONCLUSION: The proposed MRMI and ERMI indices are performant tools to account for health-state severity according to outcomes of interest.