Panayotis Constantinou1, Philippe Tuppin2, Anne Fagot-Campagna2, Christelle Gastaldi-Ménager2, François G Schellevis3, Nathalie Pelletier-Fleury4. 1. French National Health Insurance (Cnam), 50, Avenue du Professeur André Lemierre, 75986 Paris Cedex 20, France; Centre for Research in Epidemiology and Population Health, French National Institute of Health and Medical Research (INSERM U1018), Université Paris-Saclay, Université Paris-Sud, UVSQ, 16, Avenue Paul Vaillant Couturier, 94807 Villejuif Cedex, France. Electronic address: panayotis.constantinou@inserm.fr. 2. French National Health Insurance (Cnam), 50, Avenue du Professeur André Lemierre, 75986 Paris Cedex 20, France. 3. NIVEL (Netherlands Institute for Health Services Research), PO Box 1568, 3500 BN Utrecht, The Netherlands; Department of General Practice & Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Center, 1081BT, Amsterdam, The Netherlands. 4. Centre for Research in Epidemiology and Population Health, French National Institute of Health and Medical Research (INSERM U1018), Université Paris-Saclay, Université Paris-Sud, UVSQ, 16, Avenue Paul Vaillant Couturier, 94807 Villejuif Cedex, France.
Abstract
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.
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.
Authors: Søren T Skou; Frances S Mair; Martin Fortin; Bruce Guthrie; Bruno P Nunes; J Jaime Miranda; Cynthia M Boyd; Sanghamitra Pati; Sally Mtenga; Susan M Smith Journal: Nat Rev Dis Primers Date: 2022-07-14 Impact factor: 65.038
Authors: Philippe Tuppin; Thomas Lesuffleur; Panayotis Constantinou; Alice Atramont; Carole Coatsaliou; Emilie Ferrat; Florence Canouï-Poitrine; Gonzague Debeugny; Antoine Rachas Journal: BMC Prim Care Date: 2022-08-09
Authors: Charles Ouazana-Vedrines; Thomas Lesuffleur; Anne Cuerq; Anne Fagot-Campagna; Antoine Rachas; Chrystelle Gastaldi-Ménager; Nicolas Hoertel; Frédéric Limosin; Cédric Lemogne; Philippe Tuppin Journal: Front Psychiatry Date: 2022-09-08 Impact factor: 5.435