bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel elements of value in the context of oncology.
Authors: Kamal-Bahl Sachin , Puckett Justin , Singh Amitabh , Willke Richard 
Published: June 2021

e18823 Background: Over the past decade, perhaps no single area of the health sector has seen as much groundbreaking innovation as pharmaceuticals. This has been especially true in oncology, where promising curative treatments have been initially celebrated by the media only for access and cost challenges to limit their impact. A narrative has emerged that many of these breakthroughs are not priced according to the value they provide. This thinking has been reinforced by traditional approaches to cost-effectiveness analysis (CEA) that fail to incorporate critically relevant dimensions of value for innovative oncology therapies. What might a broader conception of value in oncology look like? To answer that question, this study reviews oncology-specific considerations when assessing elements of value. Methods: We first review the background on the nature and development of quality-adjusted life years (QALYs) as a part of cost-effectiveness analyses and some of their key limitations. This is followed by a review of “novel” elements of value, which capture specific aspects of patient and societal preferences not included in QALYs, and their applicability to oncology, including discussion on areas where further thought and research might be needed. We conclude with a potential checklist of novel elements of value that should be considered when designing value assessment strategies for products in oncology. Results: Key novel value elements not captured in QALYs under traditional CEA that can be relevant for oncology include value of hope (for a cure), value of insurance (reducing health and financial risk), value of treating very severe diseases, and, real option value (for treatments extending survival enough to allow the option of future cures). The novel value elements of reduced diagnostic uncertainty and the value of knowing are also directly relevant for oncology products with companion diagnostics. Other relevant novel value elements within the context of oncology include scientific spillovers and equity. Few studies have measured these value elements with applications in oncology. Conclusions: Our key takeaways include: (1) since cancer is often a severe disease that is expensive to treat, the potential for novel value elements to be important in economic evaluations of oncology products is high; (2) while some elements of value have been explored in specific economic evaluations, there has been limited adoption of novel elements of value in oncology; (3) the conceptual justification for use of novel value elements in oncology is strong, but standards for their measurement are evolving and further experience with their use would be beneficial; and (4) given the lower prioritization of many of them from the payer perspective, barriers to payer acceptance also seem significant, implying attention must also be paid to establishing the relevance of those elements to payer decision-making.
Factors associated with off-label (OL) drug use in oncology: The role of cost and financing in a universal healthcare system.
Authors: Gordon Noa , Goldstein Daniel A , Tadmor Boaz , Stemmer Salomon M. , Greenberg Dan 
Published: June 2021

e18825 Background: In recent years, OL use in oncology has become widespread, with estimates ranging up to 50-75% of all prescriptions. Most OL use is not publicly reimbursed within universal healthcare systems. We aimed to characterize the financing sources of OL use and to identify predictors for forgoing such treatment. Methods: We studied 708 oncology OL prescription requests submitted for approval to the Institutional Drug Committee in Rabin Medical Center, a large tertiary center in Israel, between January 2016 and December 2018. We included only requests for patients that were alive for more than 60 days following prescription approval. For each indication we extracted the level of supporting evidence at the time of prescription (sufficient, limited, inadequate). We also examined patients’ disease and sociodemographic properties, treatment costs, and financing sources. We used univariable logistic regression to ascertain these variables’ effects on actual OL drug initiation. We then used multivariable logistic regression to explore predicting factors for drug initiation. Results: The median monthly cost of a planned OL treatment was ILS39,928 (approximately US$ 11,500). Approximately one third (31%) of the treatments did not have a financing source at the time of request approval. The primary financing sources were patient access plans (30%) and private health insurance (25%). Of 708 approved OL requests, only 583 (82.3%) treatments were initiated. The proportion of requests that were eventually initiated was higher in the metastatic versus adjuvant setting (84.9% vs. 76.5%; p= .008). The estimated median OS for metastatic patients was 9.9 months, significantly higher in patients that did initiate treatment versus patients that did not (10.4 vs. 7.2 months; p= .048). Although not significant, median costs of initiated treatments were higher compared to treatments that were not (ILS41,686 vs. ILS24,670). Prescriptions for the metastatic setting and immunotherapy as well as ones with sufficient evidence had higher odds for treatment initiation (OR = 1.73; p= .007, OR = 1.69; p= .013 and OR = 1.62; p= .016). Prescriptions with limited supporting evidence and with no planned financing source had lower odds for treatment initiation (OR = 0.59; p= .012, OR = 0.37; p< .0005). A multivariable logistic regression showed that if no financing plan was in place at the time of request, there was a 2.5 times higher likelihood of not initiating the treatment (OR = 2.5; p< .0005). Conclusions: While OL recommendation is widespread and institutional approval is granted, a substantial proportion of treatments are not initiated. Although cost was not associated with treatment initiation, having a planned financing source was a strong predictor for OL treatment initiation. This study elucidates the financing sources of OL treatments in a universal healthcare system and identifies access barriers.
U.S. budget impact (BI) model for selinexor, bortezomib, and dexamethasone (XVd) for the treatment of patients with previously treated multiple myeloma (MM).
Authors: Dolph Mike , Tremblay Gabriel , Leong Hoyee 
Published: June 2021

e18839 Background: Despite advances in the treatment of MM, prognosis remains poor for most patients due to its refractory nature. In a phase 3 study, a once-per-week regimen of XVd showed a novel, effective, and convenient option for patients with previously treated MM. This study was conducted to estimate the projected budget impact (BI) of XVd in patients with previously treated MM from the perspective of a third-party payer and Medicare in the US. Methods: The introduction of XVd as a treatment option for patients with previously treated MM compared to the status quo (i.e. without the introduction of XVd) was considered from a private third-party US payer and a Medicare perspective in one-year increments for the first 3 years. The model assumed market uptake of 5.9%, 7.2%, and 7.7% at years 1, 2 and 3, respectively. Total annual treatment costs (2020 US dollars [USD]) were calculated as the sum of drug costs, the costs of treating serious treatment emergent adverse events (grade ≥3), ongoing best supportive care costs, and mortality costs. Results: In the base-case analysis for a private third-party payer plan (1,000,000 members), 3 to 4 patients were projected to receive XVd out of 47 to 49 eligible patients each year. The absolute BI (Millions, USD) of including XVd from a private third-party payer plan perspective was $0.06, $0.07, $0.08 and $0.22 for years 1, 2, 3, and overall, respectively. The relative BI of including XVd was 0.33%, 0.40%, 0.43%, and 0.38% for years 1, 2, 3, and overall, respectively. This translated to a per member per month (PMPM) budget impact of $0.005, $0.006, $0.007, and $0.006 (USD), for years 1, 2, 3, and overall, respectively. In the base-case analysis from the Medicare perspective (59,499,015 members), 1,361 to 1,808 patients were projected to receive XVd out of 22,892 to 23,425 eligible patients each year. From a Medicare perspective, the absolute BI (Millions, USD) of including XVd was $29.68, $36.62, $39.42 and $105.72 for years 1, 2, 3, and overall, respectively. The relative BI of including XVd was 0.33%, 0.40%, 0.43%, and 0.38% percent for years 1, 2, 3, and overall, respectively. This translated to a PMPM budget impact of $0.041, $0.051, $0.054, and $0.049 (USD), for years 1, 2, 3, and overall, respectively. One-way sensitivity analyses showed general consistency with the base-case findings. Conclusions: Understanding the potential BI of new therapies in MM is vital for decision makers to manage spending and assess the value of treatments. The introduction of XVd presents a small and manageable BI for a third-party US payer and Medicare.
Economic impact of compassionate use of medicines.
Authors: Jommi Claudio , Pantellini Federico , Stagi Lisa , Cavazza Marianna 
Published: June 2021

e18840 Background: The economic impact of clinical trials in the perspective of trial sites has been already investigated. Instead, there is no evidence on the economic net benefit of compassionate use programs for medicines (CUP). This research aims to fill the information gap, investigating the economic consequences of 8 CUP in Italy carried out from May 2015 to December 2020 in the hospitals’ perspective. These programs concern five cancer medicines (alectinib, atezolizumab, cobimetinib, polatuzumab vedotin, trastuzumab emtansine TDM-1), for a total of 8 programs. Methods: Economic net benefit includes avoided costs for standard of care (SoC) the patient would have received if he/she has not joined the CUP and costs not covered by the pharmaceutical industry and sustained by the hospital hosting CUP. The latter include costs of adverse event (only severe sides effects generating hospitalisation and ascribed to medicines used in CUP), combination therapies and diagnostic procedures not covered by the sponsor. SoC costing relied on publicly available estimation. Adverse events and diagnostic procedures were retrieved from the CUP and monetized using the relevant fee for episode. Results: 1635 cancer patients were treated in eight CUPs. The SoC mean cost per patient ranges from €13355 to €28098 for all cancer drugs. The total cost of the SoC ranges from €21.8 - €45.9 million. The mean cost per patient covered by hospitals hosting CUP was equal to €2732 for cancer drugs, with a total cost of €4.5 million. The net economic benefit ranges from €17.4 million to €41.5 million for cancer treatments. Conclusions: Despite its limitations this paper illustrates for the first time the net economic impact of CUP in oncology patients in the perspective of payers. Additional evaluations are ongoing to better understand the overall effects of CUP implementation, i.e. the economic value of the comparative benefit profile of medicines used in CUP versus the SoC, including potential effects on indirect costs.
Prospects of off-label (OL) drug use in oncology: Identifying predicting variables for registration and universal healthcare reimbursement.
Authors: Gordon Noa , Goldstein Daniel A , Tadmor Boaz , Stemmer Salomon M. , Greenberg Dan 
Published: June 2021

e18842 Background: Many cancer drugs used OL, eventually complete clinical development and are registered and reimbursed, while others are not. We aimed to describe OL indications' registration and reimbursement prospects in a universal healthcare system and identify groups of treatments that remain OL longer. Methods: We studied 612 OL oncology prescription requests for 115 unique indications and 29 drugs. All requests were approved by the Institutional Drug Committee in Rabin Medical Center, a tertiary center in Israel, between January 2016 and December 2018. OL indications were not registered by the Israeli Ministry of Health (MOH) and were not included in the Israeli National List of Health Services (NLHS) for reimbursement at the time of prescription. Additionally, 459 requests for 100 indications were not approved by the FDA at the time of prescription. We explored the subsequent regulatory and reimbursement milestones in the timeline following each prescription. We identified three temporal events: FDA indication approval, MOH indication registration, and NLHS indication inclusion of reimbursement. We then defined three time-to-event variables from the OL prescription to each of the three milestones. We applied Kaplan-Meier analysis and multivariable Cox regression to identify drug and indication properties, which are predictors for registration and reimbursement. Results: With a median follow-up of 34.5 months, 10.4% of indications were eventually registered in Israel, and 6% were included in the NLHS. 7% of non-FDA approved indications were subsequently approved. For MOH registration, positive predictors were immunotherapy, sufficient evidence (ESMO-MCBS A-B, 5-4), and a patient access program at the time of prescription (HR = 21.99; p< .0005, HR = 3.4; p= .02 and HR = 2.73; p< .0005, respectively). Negative predictors were metastatic setting and lung and gastrointestinal (GI) cancers (HR = 0.42; p= .002, HR = 0.17; p= .001, and HR = 0.24; p= .043, respectively). For NLHS inclusion, positive predictors were immunotherapy, sufficient evidence, and an access program (HR = 9.03, HR = 6.24, and HR = 2.56, respectively, all p< .0005). Negative predictors were lung and GI cancers and orphan disease designation (HR = 0.07; p= .002, HR = 0.14; p= .021, and HR = 0.13; p= .009, respectively). For FDA approval, positive predictors were immunotherapy, sufficient evidence, and a patient access program (HR = 12.11; p< .0005, HR = 4.34; p= .002, and HR = 2.25; p= .001, respectively). Negative predictors were metastatic setting, lung cancer, and orphan disease (HR = 0.43; p= .003, HR = 0.25; p= .006, and HR = 0.08; p= .018, respectively). Conclusions: Few OL indications are subsequently approved and reimbursed, usually due to clinical futility and insufficient evidence. However, we identified factors related to lack of initiative for further clinical development and registration.
Dispersion in total cost of care for Medicare fee-for-service (FFS) patients with metastatic pancreatic cancer receiving FDA-approved/NCCN Category 1 regimens at 340B verus non-340B institutions.
Authors: Latimer Helen , Tomicki Samantha , Dieguez Gabriela , Cockrum Paul , Kim George P. 
Published: June 2021

e18843 Background: The Department of Health and Human Services (HHS) designed the 340B drug pricing program to allow institutions that service specialty populations to acquire drugs at lower prices. Objective: To analyze the dispersion in total cost of care (TCOC) for Medicare FFS patients (pts) with metastatic pancreatic cancer (m-PANC) treated at 340B or non-340B institutions, by NCCN Category 1 regimen. Methods: We identified pts with m-PANC using ICD-10 diagnosis codes in the 2016-18 Medicare Parts A/B/D 100% Research Identifiable Files. Study pts had 2+ claims with a pancreatic cancer diagnosis and Medicare FFS coverage for 6 months pre- and 3 months post-metastasis diagnosis. Study pts were treated with NCCN Category 1 regimens: 1L gemcitabine monotherapy (gem-mono), 1L gemcitabine/nab-paclitaxel (gem-nab), 1L FOLFIRINOX (FFX), and 2L liposomal irinotecan-based regimen (nal-IRI). Pts were attributed to 340B or non-340B institutions based on plurality of chemotherapy claims. TCOC reflects insurer-paid services per line of therapy (LOT) for 3 categories: chemotherapy/supportive drugs (chemo/Rx), inpatient care (IP), and other outpatient care (OP). We grouped pts by quartile (qrt) and evaluated drivers of TCOC and mean rates of admissions (admits/pt). Results: We identified 2,697 (340B) and 3,839 (non-340B) pts taking NCCN Category 1 regimens. Gem-mono represented 1% and 4% of all pts in 340B and non-340B institutions, respectively. Gem-nab accounted for 72% of pts in both cohorts. For gem-nab, FFX, and nal-IRI pts, median TCOC was similar in both cohorts, although mean TCOC by qrt was lower at 340B institutions than non-340B institutions, except for gem-nab in the 1st qrt. The components of TCOC were similar between 340B and non-340B institutions in all qrts. In both cohorts, % IP costs increased between the 1st and 4th qrt (340B:15% to 23%, non-340B:14% to 25%). From the 1st to the 4th qrt, admits/pt increased in both cohorts. In the 340B cohort, nal-IRI pts had the lowest admits/pt while gem-nab pts had the highest in all qrts. In the non-340B cohort, nal-IRI pts had the lowest admits/pt except for in the 1st qrt. Conclusions: Median TCOC was lower at 340B institutions than non-340B institutions for all regimens, and the range of TCOC dispersion was also smaller at 340B institutions. Across qrts, chemotherapy accounted for approximately half the TCOC; however, IP costs were proportionally higher in the 4th qrt. Comparing regimens, despite 2L nal-IRI pts being more heavily pretreated, median costs in each cohort were similar to 1L gem-nab and 1L FFX, while admits/pt were generally lower than 1L gem-nab and 1L FFX across qrts and cohorts.
Cost-effectiveness of apixaban versus other direct oral anticoagulants and low-molecular-weight-heparins for cancer associated venous thromboembolism in Spain.
Authors: Muñoz Martín Andres J. , Gallardo Díaz Enrique , Crespo Carlos , Masana Domenech Roma , Soto Javier , Arumi Daniel , Fernandez Susana 
Published: June 2021

e18845 Background: Venous thromboembolism (VTE) causes substantial morbidity and mortality in patients with cancer. The guidelines for the treatment of VTE in cancer patients recommend low molecular weight heparins (LMWH) and direct oral anticoagulants (DOAC) for patients where major bleeding is a low risk factor. Several studies show that DOAC represent a convenient and effective treatment option in alternative to LMWH in patients with deep-vein thrombosis or pulmonary embolism. Even though some recent studies have compared the effectiveness of DOAC vs LMWH, there is no available a cost-effectiveness analysis (CEA) comparing the relative effectiveness and cost-effectiveness of apixaban, other DOAC and. LMWH. The study aim was to conduct a CEA of apixaban (API), edoxaban (EDO), rivaroxaban (RIVA) and LMWH for the treatment of cancer associated VTE in Spain. Methods: We developed a Markov model with 12 transition health states. The model has been face-validated by two oncologists from two different Spanish hospitals. The use of resources and costs were obtained from the 2021 Spanish Ministry of Health database, and the main references for obtaining the outcomes were derived from CARAVAGGIO, HOKUSAI-VTE, ADAM VTE and SELECT-D trials. Our model yielded the effectiveness score in terms of cost per life-year (LY) gained and cost per quality-adjusted for life-year (QALY) gained. The time horizon was 12 months. We performed a deterministic and probabilistic sensitivity analysis to validate the robustness. Results: API showed the lowest 12-month cost (1943 €), and the highest amount of life years (0.79) and highest amount of QALY (0.55) gained. RIVA and EDO were less effective in terms of LY (0.76 and 0.74, respectively) and QALY (0.53 and 0.52, respectively) gained than LMWH (LY of 0.76 and QALY of 0.53), and less costly. The Incremental Cost-Effectiveness Ratio (ICER) scores in terms both of €/LY and €/QALY gained show that API is dominant over LMWH, RIVA and EDO. Conclusions: Our results suggest that API is more effective and more cost-effective than LMWH, RIVA or EDO with the 2021 Spanish healthcare costs. For interpretation of the results, reader must consider that the costs of resources analyzed in this paper may vary from country to country, and dabigatran was not included in the analysis since there are not cancer associated VTE clinical trials with dabigatran data to calculate CEA from.[Table: see text]
Bundling cancer subtypes in value-based care: A pilot analysis of lymphoma episodes in the Oncology Care Model.
Authors: Yue Andrew , Chaudhry Basit Iqbal , Schleicher Stephen Matthew , Huntington Scott F. , Lyss Aaron J. , Connor Nora , Tran Lisa , Adelson Kerin B. 
Published: June 2021

e18850 Background: Value based models (VBMs) in which cancers are bundled are a growing alternative to fee for service, as in the Oncology Care Model (OCM). However, bundles in OCM may not capture the clinical granularity needed to predict resource utilization for cancer subtypes. One such bundle is lymphoma, which groups highly heterogeneous diseases with distinct treatments and differing intensity of care. Here, we compare OCM predicted episode costs (targets) to actual episode costs by lymphoma subtype. Methods: Our cohort study used OCM data from a large academic medical center (AMC) and large community oncology practice (COP). Six-month episodes of lymphoma beginning between July 2016 and June 2019 were categorized based on ICD-10 diagnoses on antineoplastic infusions and E&M visits, as well as disease and data modeling. Episodes were subdivided into follicular (FL), diffuse large B (DLBCL), small B (SBCL), mantle (MCL), Hodgkin (HL), Waldenstrom macroglobulinemia (WM), mature T/NK (T/NK), and Other. The distributional consistency of episode costs and targets for each subtype relative to the rest of the episodes was evaluated by Kolmogorov-Smirnov tests. We also compared the proportion of subtypes contributing to episodes in the AMC vs. COP. Results: A total of 1801 lymphoma episodes were identified (44% in AMC, 56% in COP). The most common subtypes (DLBCL and FL) contributed a larger proportion of episodes in the COP, while less frequent subtypes (T/NK, WM) were more prevalent at the AMC. Further, episode costs are significantly different across individual subtypes. Target variance was significantly lower than cost variance across subtypes. For example, the average target for WM was $50.4K, average costs were $40.2K, with 26% of episodes over target. In contrast, the average target for T/NK was $55.9K, average costs were $72.7K, with 64% of episodes over target. Conclusions: VBMs such as OCM currently aggregate cancer types and lack clinical granularity. Our evaluation of OCM episodes at an AMC and COP found considerable differences in lymphoma populations and in costs by subtype. Failure to account for clinical features (i.e. lymphoma history) could lead to inappropriate shifts of risk from payers to providers in VBMs.[Table: see text]