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1.
Cancer ; 123(18): 3583-3590, 2017 Sep 15.
Article in English | MEDLINE | ID: mdl-28640362

ABSTRACT

BACKGROUND: The objective of this study was to determine the impact of modeling cancer drug wastage in economic evaluations because wastage can result from single-dose vials on account of body surface area- or weight-based dosing. METHODS: Intravenous chemotherapy drugs were identified from the pan-Canadian Oncology Drug Review (pCODR) program as of January 2015. Economic evaluations performed by drug manufacturers and pCODR were reviewed. Cost-effectiveness analyses and budget impact analyses were conducted for no-wastage and maximum-wastage scenarios (ie, the entire unused portion of the vial was discarded at each infusion). Sensitivity analyses were performed for a range of body surface areas and weights. RESULTS: Twelve drugs used for 17 indications were analyzed. Wastage was reported (ie, assumptions were explicit) in 71% of the models and was incorporated into 53% by manufacturers; this resulted in a mean incremental cost-effectiveness ratio increase of 6.1% (range, 1.3%-14.6%). pCODR reported and incorporated wastage for 59% of the models, and this resulted in a mean incremental cost-effectiveness ratio increase of 15.0% (range, 2.6%-48.2%). In the maximum-wastage scenario, there was a mean increase in the incremental cost-effectiveness ratio of 24.0% (range, 0.0%-97.2%), a mean increase in the 3-year total incremental budget costs of 26.0% (range, 0.0%-83.1%), and an increase in the 3-year total incremental drug budget cost of approximately CaD $102 million nationally. Changing the mean body surface area or body weight caused 45% of the drugs to have a change in the vial size and/or quantity, and this resulted in increased drug costs. CONCLUSIONS: Cancer drug wastage can increase drug costs but is not uniformly modeled in economic evaluations. Cancer 2017;123:3583-90. © 2017 American Cancer Society.


Subject(s)
Antineoplastic Agents/economics , Cost-Benefit Analysis , Drug Costs , Neoplasms/drug therapy , Antineoplastic Agents/administration & dosage , Canada , Humans , Infusions, Intravenous/economics , Models, Economic , Neoplasms/pathology , Prescription Drug Misuse/economics
2.
CBE Life Sci Educ ; 16(2)2017.
Article in English | MEDLINE | ID: mdl-28495937

ABSTRACT

Large class learning is a reality that is not exclusive to the first-year experience at midsized, comprehensive universities; upper-year courses have similarly high enrollment, with many class sizes greater than 200 students. Research into the efficacy and deficiencies of large undergraduate classes has been ongoing for more than 100 years, with most research associating large classes with weak student engagement, decreased depth of learning, and ineffective interactions. This study used a multidimensional research approach to survey student and instructor perceptions of large biology classes and to characterize the courses offered by a department according to resources and course structure using a categorical principal components analysis. Both student and instructor survey results indicated that a large class begins around 240 students. Large classes were identified as impersonal and classified using extrinsic qualifiers; however, students did identify techniques that made the classes feel smaller. In addition to the qualitative survey, we also attempted to quantify courses by collecting data from course outlines and analyzed the data using categorical principal component analysis. The analysis maps institutional change in resource allocation and teaching structure from 2010 through 2014 and validates the use of categorical principal components analysis in educational research. We examine what perceptions and factors are involved in a large class that is perceived to feel small. Our analysis suggests that it is not the addition of resources or difference in the lecturing method, but it is the instructor that determines whether a large class can feel small.


Subject(s)
Biology/education , Perception , Students/statistics & numerical data , Universities , Humans , Learning
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