Bruce C Stuart1, Jalpa A Doshi, Joseph V Terza. 1. Peter Lamy Center on Drug Therapy and Aging, University of Maryland Baltimore, 220 Arch Street, Room 01-212, Baltimore, MD 21201, USA. bstuart@rx.umaryland.edu
Abstract
OBJECTIVE: To assess whether outpatient prescription drug utilization produces offsets in the cost of hospitalization for Medicare beneficiaries. DATA SOURCES/STUDY SETTING: The study analyzed a sample (N=3,101) of community-dwelling fee-for-service U.S. Medicare beneficiaries drawn from the 1999 and 2000 Medicare Current Beneficiary Surveys. STUDY DESIGN: Using a two-part model specification, we regressed any hospital admission (part 1: probit) and hospital spending by those with one or more admissions (part 2: nonlinear least squares regression) on drug use in a standard model with strong covariate controls and a residual inclusion instrumental variable (IV) model using an exogenous measure of drug coverage as the instrument. PRINCIPAL FINDINGS: The covariate control model predicted that each additional prescription drug used (mean=30) raised hospital spending by $16 (p<.001). The residual inclusion IV model prediction was that each additional prescription fill reduced hospital spending by $104 (p<.001). CONCLUSIONS: The findings indicate that drug use is associated with cost offsets in hospitalization among Medicare beneficiaries, once omitted variable bias is corrected using an IV technique appropriate for nonlinear applications.
OBJECTIVE: To assess whether outpatient prescription drug utilization produces offsets in the cost of hospitalization for Medicare beneficiaries. DATA SOURCES/STUDY SETTING: The study analyzed a sample (N=3,101) of community-dwelling fee-for-service U.S. Medicare beneficiaries drawn from the 1999 and 2000 Medicare Current Beneficiary Surveys. STUDY DESIGN: Using a two-part model specification, we regressed any hospital admission (part 1: probit) and hospital spending by those with one or more admissions (part 2: nonlinear least squares regression) on drug use in a standard model with strong covariate controls and a residual inclusion instrumental variable (IV) model using an exogenous measure of drug coverage as the instrument. PRINCIPAL FINDINGS: The covariate control model predicted that each additional prescription drug used (mean=30) raised hospital spending by $16 (p<.001). The residual inclusion IV model prediction was that each additional prescription fill reduced hospital spending by $104 (p<.001). CONCLUSIONS: The findings indicate that drug use is associated with cost offsets in hospitalization among Medicare beneficiaries, once omitted variable bias is corrected using an IV technique appropriate for nonlinear applications.
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