OBJECTIVE: To identify the effect of insurance coverage on prescription utilization by Medicare beneficiaries. DATA SOURCES/STUDY SETTING: Secondary data from the 1999 Medicare Current Beneficiary Survey (MCBS) Cost and Use files, a nationally representative survey of Medicare enrollees. STUDY DESIGN: The paper uses a cross-sectional design with (1) a standard regression framework to estimate the impact of prescription coverage on utilization controlling for potential selection bias with covariate control based on the Diagnostic Cost Group/Hierarchical Condition Category (DCG/HCC) risk adjuster, and (2) a multistage residual inclusion method using instrumental variables to control for selection bias and identify the insurance coverage effect. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from the 1999 MCBS. Study inclusion criteria are community-dwelling MCBS respondents with full-year Medicare enrollment and supplemental medical insurance with or without full-year drug benefits. The final sample totaled 5,270 Medicare beneficiaries. PRINCIPAL FINDINGS: Both the model using the DCG/HCC risk adjuster and the model using the residual inclusion method produced similar results. The estimated price elasticity of demand for prescription drugs for the Medicare beneficiaries in our sample was -0.54. CONCLUSIONS: Our results confirm that selection into prescription coverage is predictable based on observable health. Our results further confirm prior estimates of price sensitivity of prescription drug demand for Medicare beneficiaries, though our estimate is slightly above prior results.
OBJECTIVE: To identify the effect of insurance coverage on prescription utilization by Medicare beneficiaries. DATA SOURCES/STUDY SETTING: Secondary data from the 1999 Medicare Current Beneficiary Survey (MCBS) Cost and Use files, a nationally representative survey of Medicare enrollees. STUDY DESIGN: The paper uses a cross-sectional design with (1) a standard regression framework to estimate the impact of prescription coverage on utilization controlling for potential selection bias with covariate control based on the Diagnostic Cost Group/Hierarchical Condition Category (DCG/HCC) risk adjuster, and (2) a multistage residual inclusion method using instrumental variables to control for selection bias and identify the insurance coverage effect. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from the 1999 MCBS. Study inclusion criteria are community-dwelling MCBS respondents with full-year Medicare enrollment and supplemental medical insurance with or without full-year drug benefits. The final sample totaled 5,270 Medicare beneficiaries. PRINCIPAL FINDINGS: Both the model using the DCG/HCC risk adjuster and the model using the residual inclusion method produced similar results. The estimated price elasticity of demand for prescription drugs for the Medicare beneficiaries in our sample was -0.54. CONCLUSIONS: Our results confirm that selection into prescription coverage is predictable based on observable health. Our results further confirm prior estimates of price sensitivity of prescription drug demand for Medicare beneficiaries, though our estimate is slightly above prior results.
Authors: Gregory C Pope; John Kautter; Randall P Ellis; Arlene S Ash; John Z Ayanian; Lisa I Lezzoni; Melvin J Ingber; Jesse M Levy; John Robst Journal: Health Care Financ Rev Date: 2004