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Assessing the impact of drug use on hospital costs.

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.

SUBMITTER: Stuart BC

PROVIDER: S-EPMC2669633 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Assessing the impact of drug use on hospital costs.

Stuart Bruce C BC Doshi Jalpa A JA Terza Joseph V JV

Health services research 20080908 1

<h4>Objective</h4>To assess whether outpatient prescription drug utilization produces offsets in the cost of hospitalization for Medicare beneficiaries.<h4>Data sources/study setting</h4>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.<h4>Study design</h4>Using a two-part model specification, we regressed any hospital admission (part 1: probit) and hospital spending by those ...[more]

PMID: 18783453

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Project description:ObjectivesIncreasing age is a well-recognized risk factor for in-hospital mortality in patients receiving extracorporeal membrane oxygenation for cardiogenic shock, but the shape of this relationship is unknown. In addition, the impact of age on hospital length of stay, patterns of patient disposition, and costs has been incompletely characterized.DesignRetrospective analysis of the National Inpatient Sample.SettingU.S. nonfederal hospitals, years 2004-2016.PatientsAdults with cardiogenic shock treated with extracorporeal membrane oxygenation (3,094; weighted national estimate: 15,415).InterventionsNone.Measurements and main resultsThe mean age of extracorporeal membrane oxygenation recipients was 54.8 ± 15.4 years (range, 18-90 yr). Crude in-hospital mortality was 57.7%. Median time-to-death was 8 days (interquartile range, 3-17 d). A linear relationship between age and in-hospital mortality was observed with a 14% increase in the adjusted odds of in-hospital mortality for every 10-year increase in age (adjusted odds ratio, 1.14; 95% CI, 1.08-1.21; p < 0.0001). Thirty-four percent of patients were discharged alive at a median time of 30 days (interquartile range, 19-48 d). The median length of stay and total hospitalization costs were 14 days (interquartile range, [5-29 d]) and $134,573 ($71,782-$239,439), respectively, both of which differed significantly by age group (length of stay range from 17 d [18-49 yr] to 9 d [80-90 yr]; p < 0.0001 and cost range $147,548 [18-49 yr] to $105,350 [80-90 yr]; p < 0.0001).ConclusionsAge is linearly associated with increasing in-hospital mortality in individuals receiving extracorporeal membrane oxygenation for cardiogenic shock without evidence of a threshold effect. Median time-to-death is approximately 1 week. One third of patients are discharged from the hospital alive, but the median time-to-discharge is 1 month. Median length of stay ranges from 9 to 17 days depending on age. Hospitalization costs exceed $100,000 in all age groups.

Project description:BackgroundSeveral Canadian provinces have introduced reimbursement policies mandating substitution of innovator biologics with lower-cost biosimilars. We estimated the number of patients affected and cost implications if such policy changes were to be implemented in Ontario, Canada.MethodsWe conducted a cross-sectional time series analysis of Ontarians dispensed publicly funded biologics indicated for inflammatory diseases (rheumatic conditions, inflammatory bowel disease: infliximab, etanercept, adalimumab) between January 2018 and December 2019, and forecasted trends to Dec. 31, 2020. The primary source of data was pharmacy claims data for all biologics reimbursed by the public drug program. We modelled the number of patients affected and government expenditures (in nominal Canadian dollars) of several biosimilar policy options, including mandatory nonmedical biosimilar substitution, substitution in new users, introduction of a biosimilar for adalimumab, and price negotiations. In a secondary analysis, we included insulin glargine.ResultsIn 2018, 14 089 individuals were prescribed a publicly funded biologic for inflammatory diseases. A mandatory nonmedical biosimilar substitution would potentially have affected 7209 patients and saved $238.6 million from 2018 to 2020. A new-user substitution would have affected 757 patients and saved $34.2 million. If an adalimumab biosimilar were to become available, 12 928 patients would be affected by a mandatory nonmedical substitution and the 3-year savings would increase to $645.9 million (all biosimilars priced at 25% of innovator biologics). Finally, an expanded nonmedical substitution policy including insulin glargine would affect 115 895 patients and save $288.7 million (not including adalimumab).InterpretationPolicies designed to curb rising costs of biologics can have substantially different effects on patients and government expenditures. Such analyses warrant careful consideration of the balance between cost savings and effects on patients.

Dataset Information

Assessing the impact of drug use on hospital costs.

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Assessing the impact of drug use on hospital costs.

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