The Budget Impact of Including Necitumumab on the Formulary for First-Line Treatment of Metastatic Squamous Non-Small Cell Lung Cancer: U.S. Commercial Payer and Medicare Perspectives

Budget impact models for lung cancer interventions: A systematic literature review

BACKGROUND

Budget impact models (BIMs) forecast the financial implications of adopting new technologies and the potential need for budget reallocation, thus playing a crucial role in reimbursement decisions. Despite the importance of accurate forecasts, studies indicate large discrepancies between estimates and reality. We are developing an artificial intelligence-based clinical decision tool to identify patients with non-small cell lung cancer who are most likely to benefit from immunotherapy.

OBJECTIVE

To evaluate the budgetary implications and describe a systematic literature review of published lung cancer BIMs.

METHODS

We searched PubMed and EMBASE for studies published between 2010 and 2023 that include BIMs that describe lung cancer interventions. Forward and backward reference searches were performed for all qualifying studies. We extracted author and publication year, country, interventions, disease stages, time horizon, analytical perspective, modeling methods used, types of costs included, sensitivity analyses conducted, and data sources used. We then evaluated adherence to the Professional Society for Health Economics and Pharmacoeconomics Research best-practice guidelines.

RESULTS

A total of 25 BIMs were identified, spanning 14 different countries. Model structure could not be ascertained definitively for nearly half of the models. The cost calculator approach was most common among the others. Time horizons ranged from 1 to 5 years, in line with recommendations. Most models compared drugs, 4 compared nondrug interventions, and 7 compared diagnostic technologies. Assumptions about market uptake were poorly documented and poorly motivated. Inclusion of cancer-related costs was rare. Adherence to best practices was variable and did not appear to improve over time.

CONCLUSIONS

The number of published BIMs for lung cancer exceeded expectations. There were modest trends toward publication frequency and model quality over time. Our analysis revealed variability across the models, as well as their adherence to best practices, indicating substantial room for improvement. Although none of the models were individually suitable for the purpose of evaluating an artificial intelligence-based treatment selection tool, some models provided valuable insights.

The budget impact of introducing mobocertinib for the postplatinum treatment of advanced non-small cell lung cancer harboring epidermal growth factor receptor exon 20 insertion mutations

BACKGROUND: Lung cancer is a leading cause of cancer morbidity and death in the United States. Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer cases, and oncogenic mutations in the gene encoding the epidermal growth factor receptor (EGFR) are among its most common genetic causes. Although NSCLC tumors harboring more common oncogenic EGFR mutations can be effectively treated with EGFR tyrosine kinase inhibitors (TKIs), those harboring EGFR exon 20 insertion mutations respond poorly to treatment with therapies approved for advanced NSCLC, including TKIs. Mobocertinib, a first-in-class potent, oral, irreversible TKI, is effective in this population. OBJECTIVE: To estimate the budget impact, for a US health plan with 10 million members, of introducing mobocertinib for the treatment of patients with locally advanced or metastatic NSCLC harboring EGFR exon 20 insertion mutations who have been previously treated with platinum-based chemotherapy. METHODS: A budget impact model was developed to compare 2 scenarios: a reference scenario in which 50% of patients received amivantamab and 50% received physician's choice/usual care therapy and an alternative scenario in which mobocertinib replaced the physician's choice/usual care option. The model had a 5-year time horizon in the base case. The model included epidemiologic inputs to estimate the size of the treatment-eligible population; clinical inputs to estimate treatment duration and efficacy, as well as adverse event frequency; and cost inputs for treatment acquisition and administration, management of adverse events, monitoring, and terminal care. The duration and cost of subsequent therapies were also considered. Budget impact was reported as a total cost, as per-member per-year costs, and as per-member per-month (PMPM) costs. To assess the robustness of model estimates and identify cost drivers, one-way sensitivity analyses and a range of scenario analyses were conducted. RESULTS: The model estimated an eligible treatment population of 55 patients (11 per year) over a 5-year time horizon. In the base case, the estimated budget impact of introducing mobocertinib was $5,615,808, or $0.01 PMPM. Model findings were robust to one-way sensitivity analyses and a range of sensitivity analyses; none of these analyses led to a PMPM budget impact of more than $0.06. Cost drivers included the percentage of eligible patients, the median duration of physician's choice/usual care therapy, patient weight, and the percentage of patients who undergo molecular testing. CONCLUSIONS: The estimated budget impact of mobocertinib is low, primarily because NSCLC harboring EGFR exon 20 insertion mutations is rare. DISCLOSURES: Dr Hernandez is an employee of Takeda Pharmaceuticals America, Inc. Dr Young was an employee of Takeda Pharmaceuticals America, Inc., at the time this study was conducted. This study and the editorial assistance were funded by Takeda Pharmaceuticals America, Inc.

Is the Scope of Costs Considered in Budget Impact Analyses for Anticancer Drugs Rational? A Systematic Review and Comparative Study

Background: With the increasing disease burden of cancer worldwide, more and more anticancer drugs have been approved in many countries, and the results of budget impact analyses (BIAs) have become important evidence for related reimbursement decisions.

Objectives: We systematically reviewed whether BIAs for anticancer drugs consider the scope of costs rationally and compared the results of different cost scopes to provide suggestions for future analyses and decision-making.

Methods: Eligible BIAs published in PubMed, Embase, Web of Science, and the Cochrane Library from 2016 to 2021 were identified based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We extracted 15 terms from the included studies and analyzed how they considered the scope of costs. In addition, a budget impact model was developed for the introduction of geptanolimab to China's National Reimbursement Drug List to enable a comparison of two cost-scope scenarios.

Results: A total of 29 studies were included in the systematic review. All 29 studies considered the costs of anticancer drugs, and 25 (86%) also considered condition-related costs, but only 11 (38%) considered subsequent treatment costs. In the comparative study, the predicted budget impacts from 2022 to 2024 were significantly impacted by subsequent treatment costs, with annual differences between the two cost-scope scenarios of $39,546,664, $65,866,161, and $86,577,386, respectively.

Conclusions: The scope of costs considered in some existing BIAs for anticancer drugs are not rational. The variations between different cost scopes in terms of budget impact were significant. Thus, BIAs for anticancer drugs should consider a rational scope of costs that adheres to BIA guidelines. Researchers and decision-makers should pay more attention to the scope of costs to achieve better-quality BIAs for anticancer drugs and enhance reimbursement decision-making.

A budget impact analysis of gilteritinib for the treatment of relapsed or refractory FLT3 mut+ acute myeloid leukemia in a US health plan

AIMS

To estimate the economic impact of the introduction of gilteritinib for the treatment of relapsed/refractory (R/R) FLT3 mutation-positive (FLT3 ^mut+) acute myeloid leukemia (AML) from a US payer's perspective.

METHODS

A budget impact model (BIM) was developed to evaluate the 3-year total budgetary impact of treating adults with R/R FLT3 ^mut+ AML eligible for gilteritinib in a hypothetical US health plan. Total costs (drugs/administration, hospitalization, monitoring, adverse events, transfusions, subsequent hematopoietic stem cell transplantation, post-progression, and FLT3 testing) were estimated before and after gilteritinib entry. The budget impact was the total cost difference between the two scenarios. The target population size and cost inputs were based on public data or published literature, drug market share was informed by market research data, and the model included recommended treatments for R/R FLT3 ^mut+ AML per clinical guidelines. Deterministic sensitivity analyses (DSAs) and scenario analyses varying key model inputs and assumptions were conducted to test for robustness.

RESULTS

In a hypothetical health plan with 1 million members, 20.9 adults with R/R FLT3 ^mut+ AML were estimated to be eligible for gilteritinib. Of these, it was assumed 30.0% would be treated with gilteritinib in Year 1 following gilteritinib entry, increasing the total plan budget by $663,795 and the per-member-per-month (PMPM) cost by $0.055. In Years 2-3, the market share of gilteritinib increased to 45.0%, increasing the total plan budget impact by $1,078,371 and $1,087,230, and the PMPM cost by $0.090 and $0.091, respectively. The model results remained robust in DSAs and scenario analyses, with the largest impact observed when the projected uptake of gilteritinib was changed.

LIMITATIONS

The results of this BIM are contingent upon the model's assumptions and inputs.

CONCLUSIONS

Adding gilteritinib to the formulary for the treatment of adults with R/R FLT3 ^mut+ AML had a minimal budget impact from a US payer's perspective.

A systematic review of the budget impact analyses for antitumor drugs of lung cancer

BACKGROUND

Budget impact analyses (BIAs) are used for reimbursement decisions and drug access medical insurance, as a supplement to cost-effectiveness analyses (CEAs).

OBJECTIVES

We systematically reviewed BIAs for antitumor drugs of lung cancer to provide reference for high-value drug budget impact analyses and decision making.

METHODS

We conducted a literature search on PubMed, EMbase, The Cochrane Library, China National Knowledge Infrastructure and Wanfang Data Knowledge Service Platform from 2010 to 2019. The methodological indicators and result information of the budget impact analyses were extracted and evaluated for quality.

RESULTS

A total of 14 studies on the budget impact for antitumor drugs of lung cancer were included, and the overall quality was good. Half of studies were from developed countries. Nine of the studies were designed using the BIA cost calculation model, and two were simulated using the Markov model Monte Carlo model. From all studies, only 14.3% reported model validation. The budget impact results of the same drug in different countries were inconsistent.

CONCLUSIONS

Included studies evaluating budget impact analyses for anti-tumor drugs of lung cancer showed variability in the methodological framework for BIAs. The budget impact analyses of high-value drugs need to be more stringent to ensure the accuracy of the parameters, and should provide reliable results based on real data to decision-making departments, which should carefully consider access to lung cancer drugs.

Budget Impact of Dabrafenib and Trametinib in Combination as Adjuvant Treatment of BRAF V600E/K Mutation-Positive Melanoma from a U.S. Commercial Payer Perspective

BACKGROUND

Before the approval of dabrafenib and trametinib in combination, there were no approved therapies in the adjuvant setting that target the RAS/RAF/MEK/ERK pathway.

OBJECTIVE

To evaluate the budget impact of dabrafenib and trametinib in combination for adjuvant treatment of patients with BRAF V600 mutation-positive resected Stage IIIA, IIIB, or IIIC melanoma from a U.S. commercial payer perspective using data from the COMBI-AD trial, as well as other sources.

METHODS

The budget impact of dabrafenib and trametinib in combination for patients with BRAF V600E/K mutation-positive, resected Stage IIIA, IIIB, or IIIC melanoma was evaluated from the perspective of a hypothetical population of 1 million members with demographic characteristics consistent with those of a commercially insured U.S. insurance plan (i.e., adults aged less than 65 years) using an economic model developed in Microsoft Excel. The model compared melanoma-related health care costs over a 3-year projection period under 2 scenarios: (1) a reference scenario in which dabrafenib and trametinib are assumed to be unavailable for adjuvant therapy and (2) a new scenario in which the combination is assumed to be available. Treatments potentially displaced by dabrafenib and trametinib were assumed to include observation, high-dose interferon alpha-2b, ipilimumab, and nivolumab. Costs considered in the model include those of adjuvant therapies and treatment of locoregional and distant recurrences. The numbers of patients eligible for treatment with dabrafenib and trametinib were based on data from cancer registries, published sources, and assumptions. Treatment mixes under the reference and new scenarios were based on market research data, clinical expert opinion, and assumptions. Probabilities of recurrence and death were based on data from the COMBI-AD trial and an indirect treatment comparison. Medication costs were based on wholesale acquisition cost prices. Costs of distant recurrence were from a health insurance claims study.

RESULTS

In a hypothetical population of 1 million commercially insured members, 48 patients were estimated to become eligible for treatment with dabrafenib and trametinib in combination over the 3-year projection period; in the new scenario, 10 patients were projected to receive such treatment. Cumulative costs of melanoma-related care were estimated to be $6.3 million in the reference scenario and $6.9 million in the new scenario. The budget impact of dabrafenib and trametinib in combination was an increase of $549 thousand overall and 1.5 cents per member per month.

CONCLUSIONS

For a hypothetical U.S. commercial health plan of 1 million members, the budget impact of dabrafenib and trametinib in combination as adjuvant treatment for melanoma is likely to be relatively modest and within the range of published estimates for oncology therapies. These results may assist payers in making coverage decisions regarding the use of adjuvant dabrafenib and trametinib in melanoma.

DISCLOSURES

Funding for this research was provided to Policy Analysis Inc. (PAI) by Novartis Pharmaceuticals. Stellato, Moynahan, and Delea are employed by PAI. Ndife, Koruth, Mishra, and Gunda are employed by Novartis. Ghate was employed by Novartis at the time of this study and is shareholder in Novartis, Provectus Biopharmaceuticals, and Mannkind Corporation. Gerbasi was employed by PAI at the time of this study and is currently an employee, and stockholder, of Sage Therapeutics. Delea reports grant funding from Merck and research funding from Amgen, Novartis, Sanofi, Seattle Genetics, Takeda, Jazz, EMD Serono, and 21st Century Oncology, unrelated to this work.

Using a Budget Impact Model Framework to Evaluate Antidiabetic Formulary Changes and Utilization Management Tools

BACKGROUND

Traditional budget impact models predict the financial consequences of a new drug entering the market. This study provides an example of applying the budget impact framework to a new research question of interest to managed care organizations-what is the budget impact of our formulary and utilization management (UM) policy changes?

OBJECTIVE

To predict the 3-year annual budgetary impact of TRICARE's antidiabetic formulary and UM policy changes using TRICARE claims data.

METHODS

A budget impact model was built in Microsoft Excel using health plan claims data for a 3-year time horizon. Model outcomes included spending on antidiabetic medications and medications used for side effect treatment. In sensitivity analyses, medical costs from inpatient, outpatient, and emergency room visits were also estimated. Model inputs included health plan antidiabetic medication utilization, as well as publicly available drug cost, rebate, dispensing fee, and patient cost-sharing estimates. Type of enrollee and pharmacy were also incorporated into the model. Sensitivity analyses varied estimates for utilization switch rates between preferred and nonpreferred agents, drug costs, rebates, and dispensing fees, as well as predicted impact from implementation delays.

RESULTS

For the 623,827 affected by the formulary and UM policy changes, the model predicted annual savings that increased from $24 million in the first year to $43 million in the third year after the changes. The majority of savings came from drug acquisition costs, as opposed to rebates, copays, and dispensing fees. Sensitivity analyses found savings across all varied parameters and scenarios except an unlikely scenario when 0% of utilization switched from nonpreferred to preferred agents. The model also predicted that the formulary and UM policy changes would lead to $529,439 in savings from medical visit costs in Year 3.

CONCLUSIONS

This budget impact model predicted cost savings from the payer's formulary and UM policy changes.

DISCLOSURES

This project was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ), which contracted with the University of Maryland School of Pharmacy to conduct this study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports a grant from the AHRQ, during the conduct of the study, and personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Mullins reports grants and personal fees from Bayer and Pfizer and personal fees from Boehringer-Ingelheim, Janssen/J&J, Regeneron, and Sanofi, outside the submitted work. Mullins, Slejko, and Shaya are employed by the University of Maryland School of Pharmacy. Haines and Lugo have nothing to disclose. Part of this content was previously presented as a poster at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. Part of this content was published as Hung's PhD dissertation.