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

Objective: 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.

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 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).

Results: 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.

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