Predictive modeling of total healthcare costs using pharmacy claims data: a comparison of alternative econometric cost modeling techniques

Published: November 1, 2005
Category: Bibliography > Papers
Authors: Meyer CM, Powers CA, Roebuck MC, Vaziri B
Countries: United States
Language: null
Types: Finance/Budgeting
Settings: Hospital

Med Care 43:1065-1072.

Caremark, Hunt Valley, MD, USA

OBJECTIVE: We sought to evaluate several statistical modeling approaches in predicting prospective total annual health costs (medical plus pharmacy) of health plan participants using Pharmacy Health Dimensions (PHD), a pharmacy claims-based risk index.

METHODS: We undertook a 2-year (baseline year/follow-up year) longitudinal analysis of integrated medical and pharmacy claims. Included were plan participants younger than 65 years of age with continuous medical and pharmacy coverage (n = 344,832). PHD drug categories, age, gender, and pharmacy costs were derived across the baseline year. Annual total health costs were calculated for each plan participant in follow-up year. Models examined included ordinary least squares (OLS) regression, log-transformed OLS regression with smearing estimator, and 3 two-part models using OLS regression, log-OLS regression with smearing estimator, and generalized linear modeling (GLM), respectively. A 10% random sample was withheld for model validation, which was assessed via adjusted r, mean absolute prediction error, specificity, and positive predictive value.

RESULTS: Most PHD drug categories were significant independent predictors of total costs. Among models tested, the OLS model had the lowest mean absolute prediction error and highest adjusted r. The log-OLS and 2-part log-OLS models did not predict costs accurately as the result of issues of log-scale heteroscedasticity. The 2-part model using GLM had lower adjusted r but similar performance in other assessment measures compared with the OLS or 2-part OLS models.

CONCLUSION: The PHD system derived solely from pharmacy claims data can be used to predict future total health costs. Using PHD with a simple OLS model may provide similar predictive accuracy in comparison to more advanced econometric models.

PMID: 16224298

Cost Burden Evaluation,Predictive Risk Modeling,Prescription Drug Use and Expenditures,United States,Adolescent,Adult,Child,Preschool,Clinical Pharmacy Information Systems,Gender,Forecasting,Health Expenditures,Health Maintenance Organizations/economics,Infant,Newborn,Insurance Claim Review,Longitudinal Studies,Middle Aged,Preferred Provider Organization/economics

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