Objectives: To contribute to the policy discussion of equity and health financing in Chile by evaluating the improvement in the capitation formula for paying PHC providers by using selected diagnoses. The socio-economic status of the municipalities and the urban/rural location of providers are used to adjust current capitation payments to providers. Issues have been raised about the ability of the formula to predict the utilization of PHC. In this study we test if adding individual information, such as age, gender and selected diagnoses improves the formula's explanatory power. Age, gender and diagnosis are important variables because of their strong relationship to increased morbidity risk and to needs for preventive and reproductive health care respectively. Methods: A sample of 10,000 individuals was drawn and two years utilization-of- services data was collected from a region in Chile. Information on age, gender, socio-economic status and urban/rural residence, the number of preventive or curative visits and the presence of seven key diagnoses was collected. Regression analysis and two tests to identify the best model were performed: i) R-square, which measures the proportion of the variance in individual expenditures and ii) predictive-ratio which measures the accuracy of prediction at the group level (where 1.0 indicates perfect prediction). Conclusions: We recommend to add age, gender and two diagnoses; hypertension and diabetes to the current capitation formula. The explanatory power of this model at the individual level was 28.5% and at health centre level the predictive ratio was close to 1.0.
capitation risk adjustment Chile health care system primary health car