Modeling Risk Adjusted Capitation Rates in Regione Umbria Elaine Yuen, PhD; Daniel Z. Louis, MS; Paolo DiLoreto; Joseph S. Gonnella, MD American Public Health Association Meeting October 22, 2001 ThomasJeffersonCenter for Research JeffersonMedicalin Medical Education UniversityCollegeand Health Care

Project Overview Purpose: to risk adjust per capita reimbursement rates Age sex adjustment Severity of illness adjustment Three major tasks: Collection and compilation of data from Regione Umbria Risk adjustment using US Medicare PIP-DCGs Risk adjustment using Disease Staging

Description of Study Database Data from Regione Umbria, Hospital data day and ordinary admissions DRGs and DRG based tariffs clinical and demographic information Umbria residents hospitalized in Umbria and other regions Pharmacy data individual prescription level captured drug codes, tariffs and co-pays Demographic file age, sex, USL

Mean Tariffs per Year

1998 Tariffs by Age and Sex Entire Umbria Population

Disease Staging Clinically-based patient classification system Over 400 disease categories Based upon disease etiology, organ involvement, and severity of comorbidity. Computerized algorithm uses ICD-9-CM codes Severity of illness stages: Stage 1, conditions with no complications or problems of minimal severity Stage 2, problems limited to an organ or system, with significantly increased risk of complications Stage 3, diseases with multiple site involvement, generalized systemic involvement, and/or poor prognosis

Use of Staging for Severity Adjustment All admissions were aggregated by Disease Staging category and severity stage Reviewed by clinicians for propensity of affecting future year resource use Excluded clinical categories Acute illnesses that can be cured, e.g. Stage 1 Appendicitis Vague signs/symptoms with no etiology at Stage 1 or 2 Chronic diseases that were cured, e.g. Stage 1 Cholecystitis after cholecystectomy

Use of Staging for Severity Adjustment (continued) Included clinical categories All Cancers (except basal cell) All stages of Central Nervous System, Cardiovascular, and Respiratory Diseases Stage 2 and 3 of Gastrointestinal, Hemapoetic, Renal, and Endocrine HIV/AIDs Impact on future year tariffs of included cases were Minimum Moderate Severe

Worksheet for Clinical Categories

Descriptive Statistics Used clinical and demographic information, 1997 Test database Aggregated admissions if there were less than 50 cases in any one category Considered 155 unique clinical categories within 5 larger categories Cancer, HIV, Minimum, Moderate, Severe Collapsed admissions into person-level file and merged with demographic data Test database (N=411,539 persons) 87.21% (N=358,893) were not hospitalized in % (N=30,908) were excluded from our severity categories 5.28% (N=21,738) persons were considered in the models

Included and Excluded Severity Categories Test Database, Regione Umbria

Risk Adjustment Models Predicting 1998 Tariffs Models were built at the individual person level Used a split sample One part of the data was used for modeling The other part for the testing of model TOTAL COSTS in 1998 = f (clinical categories in age/gender cohorts + error) 22 age-sex cohorts Disease Staging clinical categories in 1997 PIP-DCGs - 15 PIP-DCGs in 1997

Predicted VS Observed Tariffs Age-Sex Adjustment Only

Predicted VS Observed Tariffs Disease Staging Groups

Predicted VS Observed Tariffs PIP-DCG Groups

Limitations Case finding: Uses hospitalization data to identify a person’s severity of illness Persons who are ill but may not be hospitalized are not captured (for example, someone with diabetes who uses only outpatient care) Uses only hospitalization and pharmaceutical data to calculate tariffs Ideally would calculate all costs of medical care Use of GP and/or outpatient services may vary by condition

Where do we go from here? Refine model Outpatient or GP data included in year 2 costs Separate models for hospital and pharmacy tariffs Re-run with more recent data Re-calibrate Disease Staging groupings Improve case finding, possibly using pharmaceutical data Estimate impact On USL or distretto within the region For different demographic cohorts