Defining a risk-adjustment formula for the introduction of population-based payments for primary care in France. Issue 9 (September 2022)

• Record Type: Journal Article
• Authors: Constantinou, Panayotis; Tuppin, Philippe; Gastaldi-Ménager, Christelle; Pelletier-Fleury, Nathalie
• Is Part Of: Health policy. Volume 126:Issue 9(2022)
• Page Start: 915
• Page End: 924
• Publication Date: 2022-09
• Subjects: Risk adjustment -- Health expenditures -- Capitation fee -- Primary health care -- France; Medical education -- Periodicals; Medical policy -- Periodicals; Delivery of Health Care -- Periodicals; Education, Medical -- Periodicals; Health Education -- Periodicals; Health Planning -- Periodicals; Public Policy -- Periodicals; Enseignement médical -- Périodiques; Politique sanitaire -- Périodiques; Medical education; Medical policy; Periodicals; Electronic journals; Electronic journals; 362.1
• DOI: 10.1016/j.healthpol.2022.06.007
• Languages: English
• ISSNs: 0168-8510

Abstract

Highlights: Novel risk-adjusted schemes for financing primary care are being experimented in France. We assessed a set of individual and contextual factors for expenditure prediction. Predictive performance should be assessed at both individual and aggregated levels. Summary morbidity measures reduce risk of prediction error in multiplicative models. Method to account for contextual characteristics determines area-level allocations. Abstract: Novel risk-adjusted payment models for financing primary care are currently being experimented in France. In particular, pilot schemes including shared-savings contracts or prospectively allocated capitation payments are implemented for voluntary primary care structures. Such payment mechanisms require defining a risk-adjustment formula to accurately estimate expected expenditure while maintaining appropriate efficiency incentives. We used nationwide data from the French national health data system (SNDS) to compare the performance of different prospective models for total and outpatient expenditure prediction among more than 8 million individuals aged 65 or more and their application at an aggregate level. We focused on the characterization of morbidity status and on the contextual characteristics to include in the formula. We proposed a set of practical routinely available predictors with fair performance for patient-level expenditure prediction (explaining 32% of variance) that could be used to risk-adjust prospective payments in the French setting. Morbidity information was the strongest predictor but could lead to considerable error in predicted expenditures if introduced as independent binary variables in multiplicative models, underlining the importance of summary morbidity measures and of using the appropriate metric to assess model performance. Distribution of aggregate-level allocations was greatly modified according to the method to account for contextual characteristics. Our work informs the introduction of risk-adjusted models in France and underlines efficiency and fairness issues raised.