Adjusting For Drug Wastage In Economic Evaluations Of New Therapies For Hematologic Malignancies: A Systematic Review Of The Literature and Implications
Bibliographic record
Abstract
Abstract The cost of cancer care is rising to levels that appear unsustainable, in large part driven by a rapid increase in expenditures for novel therapies. In 2005, the direct costs of cancer care in the United States were $74 billion, with a prediction that these figures will double this decade as the population ages and we enter an age of biologically-tailored drug development. Historically, the evaluation of the cost-effectiveness of new drug therapies presumes that every milligram of drug is effectively utilized. With manufacturers dictating the availability of vial sizes for parenteral drugs, however, this may not be the case. A recent single-institution study in Canada estimated that an annual loss of $150 000 ($CDN) could be attributed to drug wastage of a parenteral therapy for myelodysplasia (Walker et al., Can J Hosp Pharm 2012). Whether current economic evaluations routinely model the potential for drug wastage, or the strategies to mitigate wastage, has not previously been determined. We sought to identify the frequency of drug wastage modeling in economic evaluations of modern parenteral therapies for hematologic malignancies. All US Food and Drug Administration (FDA) approved parenteral therapies from 2006-2013 (n=14) were identified for review. A systematic review of published English-language economic evaluations for these therapies was conducted and data on the conduct of the cost-analyses were extracted in duplicate. The primary outcome was the proportion of studies that explicitly modeled drug wastage in the base-case analysis, with results presented descriptively. Where possible, we calculated the impact of wastage on incremental cost-effectiveness ratios (ICERs) and drug acquisition costs. Thirty-three publications were retrieved; five of these studies were excluded (3 were non-English language and 2 were review articles). Of the remaining 27 reports, 3 were publicly-funded health technology assessments (HTAs) to inform funding decisions by the UK National Institute for Health and Care Excellence (NICE) and the remainder (n=24) were peer-reviewed publications. Sixteen economic evaluations were of rituximab-based regimens and 6 were bortezomib-based. Drug wastage was considered in the primary or base-case analysis in one third of the publications (9 of 27; 33%); wastage was considered in 2 of the 3 HTAs (67%) and 7 of the 24 peer-reviewed reports (29%). Of the 9 models that considered wastage in the base-case analysis, 2 completed sensitivity analyses in which no drug wastage occurred (i.e. vial sharing between patients). In both studies, consideration of wastage changed the calculated ICER. In one study, the ICER increased by 32% when wastage was incorporated into drug acquisition costs. In the second study, azacitidine (AZA) was compared to decitabine (DEC) for myelodysplasia, with both drugs provided as single-use vials and subject to substantial wastage. When no drug wastage was modeled, AZA was associated with higher costs than DEC (+$2 655 USD) and yielded an ICER of $15 528/QALY. When wastage for both drugs was considered, AZA was associated with lower costs (-$15 890) and AZA dominated DEC. Of the 18 publications that did not model wastage in the primary analysis, no study went on to model wastage in sensitivity analyses. In 4 of these 18 studies, the potential effect of wastage on drug acquisition costs could be calculated from data provided in the publication. The calculated drug acquisition costs if wastage occurred would potentially be +5-16% higher (mean +9%) than the costs modeled in the base-case analyses of these 4 studies. The potential costs associated with drug wastage are considered in only one third of modern cost-effectiveness models. The impact of wastage on calculated ICERs and drug acquisition costs is potentially substantial. The modeling of wastage in base-case and sensitivity analyses is recommended for future economic evaluations of new intravenous therapies for hematologic malignancies. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".