JSM 2004 - Toronto

Abstract #302141

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Activity Number: 409
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Health Policy Statistics
Abstract - #302141
Title: Imputation of Financial Fields in Integrated Anonymous Patient-level Databases
Author(s): Evguenia I. Jilinskaia*+ and Teri Condon Goodwin and Cathy Johnson and Stanley Norton
Companies: Pharmetrics Inc. and Pharmetrics Inc. and Pharmetrics Inc. and Pharmetrics Inc
Address: 311 Arsenal St., Watertown, MA, 02472,
Keywords: imputation ; factor analysis ; cluster analysis ; generalized linear models
Abstract:

Anonymous patient-level databases of integrated medical and pharmacy claims, provide the most complete representation of a patient experience with the health care system, including diagnoses, retail and mail order pharmacy prescriptions , medical procedures, laboratory tests, and corresponding financial fields: paid, allowed, and charged amounts. In some cases, the process of aggregating data from different sources to create Regional and National Normative studies and reports involves adjustments for differences of participating health plans ("Plan Effect") together with imputation of missing financial fields (due to capitation, etc.). To impute financial fields, claim records are classified by record type, type of product, place of service, provider type, and inpatient/outpatient settings. Further stratification of claims data is performed, based on factor scores and cluster analysis, to form homogeneous subsets of data. Mean values for dependent cost variables are estimated using generalized loglinear regression modeling (SAS Proc GenMod ); based on the exponential family of distributions, which constitute a direct extension of traditional regression and analysis of variance. Predictor variables involve discrete categories, as well as continuous variables such as year, region, size of health plan, type of product.

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