4340.0: Tuesday, October 23, 2001 - 9:10 PM

Abstract #30146

Modeling risk-adjusted capitation rates in Italy

Elaine J. Yuen, PhD1, Daniel Z. Louis, MS1, Paolo DiLoreto2, and Joseph S. Gonnella, MD1. (1) Jefferson Medical College, Thomas Jefferson University, Suite 119, 1025 Walnut Street, Center for Research in Medical Education and Health Care, Philadelphia, PA 19128, 215-955-9405, elaine.yuen@mail.tju.edu, (2) Regione Umbria, Italy

This project with the Regione Umbria in Italy studied the effect of refining per capita financing by risk adjustment. Age-sex adjustment, as well as adjustment using severity of illness, was studied. Hospitalizations in 1997 were used to identify clinical cohorts who were at risk for higher health services costs in 1998. Age-sex adjustment was based upon 11 age strata commonly used by the U.S. National Center for Health Statistics. Severity-of-illness risk adjustment was based upon U.S. Medicare PIP-DCG models as well as Disease Staging models. Regression models using estimated the contribution of age-sex cohort and clinical categories to the variation in costs in 1998. The population of the Umbria region of Italy (N=823,266) was studied. Final Disease Staging models identified 155 clinical risk adjustment categories. Although these categories identified 5.3% of the Umbria population in 1997, these people used 21.6% of the tariffs in the next year. In prediction models, R square values for Disease Staging models were .16, compared to values of .10 for Medicare PIP-DCG models. Financing models based upon Disease Staging may be used to adjust population-based tariffs, taking into account the severity of illness of population sub-groups. These models also identify groups within the overall population who are more severely ill (and therefore who use more resources). Identification of these groups would not only help to project tariff amounts, but would also help health care planners to estimate health care resources such as facilities, manpower, and programs, that are necessary to care for these populations effectively.

Learning Objectives: 1. Understand the importance of risk adjustment based upon severity of illness 2. Articulate the procedure for identifying those in a population who are more severely ill, and therefore at risk for higher capitation costs. 3. Understand implications of risk adjustment for planners and policy makers

Keywords: Public Health Policy, International

Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

Handout (.ppt format, 255.0 kb)

The 129th Annual Meeting of APHA