Stuart Bruce C, Doshi Jalpa A, Terza Joseph V
Peter Lamy Center on Drug Therapy and Aging, University of Maryland Baltimore, 220 Arch Street, Room 01-212, Baltimore, MD 21201, USA.
Health Serv Res. 2009 Feb;44(1):128-44. doi: 10.1111/j.1475-6773.2008.00897.x. Epub 2008 Sep 8.
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.
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.
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).
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.
评估门诊处方药的使用是否会抵消医疗保险受益人的住院费用。
数据来源/研究背景:该研究分析了从1999年和2000年医疗保险当前受益人调查中抽取的一个样本(N = 3101),样本为居住在社区的按服务收费的美国医疗保险受益人。
我们采用两部分模型设定,在一个具有强协变量控制的标准模型中,对任何住院情况(第1部分:概率单位模型)以及有一次或多次住院的人的住院支出(第2部分:非线性最小二乘回归)与药物使用情况进行回归分析,并使用一个残差纳入工具变量(IV)模型,该模型使用药物覆盖范围的外生测量作为工具。
协变量控制模型预测,每多使用一种处方药(平均 = 30种)会使住院支出增加16美元(p <.001)。残差纳入IV模型预测,每次额外的处方配药会使住院支出减少104美元(p <.001)。
研究结果表明,一旦使用适用于非线性应用的IV技术校正遗漏变量偏差,药物使用与医疗保险受益人住院费用的抵消相关。