Drug Expenditure, Price, and Utilization in US Medicaid: A Trend Analysis for New Multiple Myeloma Medications from 2016 to 2022
Abstract
:1. Introduction
2. Materials and Methods
2.1. Statistical Analyses
- Utilization: The number of prescriptions for each MM medication was recorded and analyzed. Utilization was calculated by aggregating the number of prescriptions for each drug per quarter, and yearly utilization was determined by summing the four quarters of each year. This metric reflects the frequency of drug utilization and provides insights into prescribing patterns and trends.
- Reimbursement: The total spending by Medicaid on each MM medication was assessed. This category quantifies the financial burden associated with these drugs and offers insights into the healthcare system’s expenditure for MM treatment. Reimbursement was calculated for each drug quarterly and then yearly. The currency used for the study was US dollars.
- Price: The price of each MM medication was calculated by dividing the total reimbursement by the number of prescriptions, yielding the reimbursement amount per prescription. This metric serves as a proxy for the medication’s price and contributes to understanding the pricing and affordability of the studied drugs.
2.2. Inclusion and Exclusion Criteria
3. Results
3.1. MM Drug Utilization
3.1.1. Ixazomib
3.1.2. Daratumumab
3.1.3. Elotuzumab
3.2. MM Drug Reimbursement
3.2.1. Ixazomib
3.2.2. Daratumumab
3.2.3. Elotuzumab
3.3. MM Drug Prices
3.3.1. Ixazomib
3.3.2. Daratumumab
3.3.3. Elotuzumab
3.4. MM Drug Utilization Market Share
3.5. MM Drug Reimbursement Market Shares
3.6. Joinpoint Regression
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Cowan, A.J.; Green, D.J.; Kwok, M.; Lee, S.; Coffey, D.G.; Holmberg, L.A.; Tazon, S.; Gopal, A.K.; Libby, E.N. Diagnosis and Management of Multiple Myeloma: A Review. JAMA 2022, 327, e0003. [Google Scholar] [CrossRef]
- Gadó, K.; Domján, G.; Gadó, K.; Domján, G. Quality of Life Issues of Patients with Multiple Myeloma. In Multiple Myeloma—A Quick Reflection on the Fast Progress; IntechOpen: London, UK, 2013; ISBN 978-953-51-1083-5. [Google Scholar]
- MacEwan, J.P.; Batt, K.; Yin, W.; Peneva, D.; Sison, S.; Vine, S.; Chen, C. Economic Burden of Multiple Myeloma among Patients in Successive Lines of Therapy in the United States. Leuk. Lymphoma 2018, 59, 941–949. [Google Scholar] [CrossRef]
- Kanas, G.; Clark, O.; Keeven, K.; Nersesyan, K.; Sansbury, L.; Hogea, C. Estimate of Multiple Myeloma Patients by Line of Therapy in the USA: Population-Level Projections 2020–2025. Future Oncol. 2021, 17, 921–930. [Google Scholar] [CrossRef] [PubMed]
- Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 2021, 71, 7–33. [Google Scholar] [CrossRef] [PubMed]
- Girnius, S.; Munshi, N.C. Challenges in Multiple Myeloma Diagnosis and Treatment. Leuk. Suppl. 2013, 2, S3–S9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Das, S.; Juliana, N.; Yazit, N.A.A.; Azmani, S.; Abu, I.F. Multiple Myeloma: Challenges Encountered and Future Options for Better Treatment. Int. J. Mol. Sci. 2022, 23, 1649. [Google Scholar] [CrossRef] [PubMed]
- Bazarbachi, A.H.; Al Hamed, R.; Malard, F.; Harousseau, J.-L.; Mohty, M. Relapsed Refractory Multiple Myeloma: A Comprehensive Overview. Leukemia 2019, 33, 2343–2357. [Google Scholar] [CrossRef] [PubMed]
- Cook, R. Economic and Clinical Impact of Multiple Myeloma to Managed Care. J. Manag. Care Pharm. 2008, 14, 19–25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Richter, J.; Anupindi, V.R.; Yeaw, J.; Kudaravalli, S.; Zavisic, S.; Shah, D. Real-World Treatment Patterns in Relapsed/Refractory Multiple Myeloma: Clinical and Economic Outcomes in Patients Treated with Pomalidomide or Daratumumab. J. Oncol. Pharm. Pract. 2022, 28, 395–409. [Google Scholar] [CrossRef] [PubMed]
- Tran, D.; Kamalakar, R.; Manthena, S.; Karve, S. Economic Burden of Multiple Myeloma: Results from a Large Employer-Sponsored Real-World Administrative Claims Database, 2012 to 2018. Blood 2019, 134, 3414. [Google Scholar] [CrossRef]
- Kumar, S.K.; Dispenzieri, A.; Lacy, M.Q.; Gertz, M.A.; Buadi, F.K.; Pandey, S.; Kapoor, P.; Dingli, D.; Hayman, S.R.; Leung, N.; et al. Continued Improvement in Survival in Multiple Myeloma: Changes in Early Mortality and Outcomes in Older Patients. Leukemia 2014, 28, 1122–1128. [Google Scholar] [CrossRef] [Green Version]
- Facon, T.; Kumar, S.K.; Plesner, T.; Orlowski, R.Z.; Moreau, P.; Bahlis, N.; Basu, S.; Nahi, H.; Hulin, C.; Quach, H.; et al. Daratumumab, Lenalidomide, and Dexamethasone versus Lenalidomide and Dexamethasone Alone in Newly Diagnosed Multiple Myeloma (MAIA): Overall Survival Results from a Randomised, Open-Label, Phase 3 Trial. Lancet Oncol. 2021, 22, 1582–1596. [Google Scholar] [CrossRef] [PubMed]
- Myeloma—Cancer Stat Facts. Available online: https://seer.cancer.gov/statfacts/html/mulmy.html (accessed on 16 May 2023).
- Rajkumar, S.V. Multiple Myeloma: 2022 Update on Diagnosis, Risk Stratification, and Management. Am. J. Hematol. 2022, 97, 1086–1107. [Google Scholar] [CrossRef] [PubMed]
- Kazandjian, D. Multiple Myeloma Epidemiology and Survival: A Unique Malignancy. Semin. Oncol. 2016, 43, 676–681. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kiss, S.; Gede, N.; Hegyi, P.; Nagy, B.; Deák, R.; Dembrovszky, F.; Bunduc, S.; Erőss, B.; Leiner, T.; Szakács, Z.; et al. Addition of Daratumumab to Multiple Myeloma Backbone Regimens Significantly Improves Clinical Outcomes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials. Sci. Rep. 2021, 11, 21916. [Google Scholar] [CrossRef]
- Durie, B.G.M.; Hoering, A.; Abidi, M.H.; Rajkumar, S.V.; Epstein, J.; Kahanic, S.P.; Thakuri, M.; Reu, F.; Reynolds, C.M.; Sexton, R.; et al. Bortezomib with Lenalidomide and Dexamethasone versus Lenalidomide and Dexamethasone Alone in Patients with Newly Diagnosed Myeloma without Intent for Immediate Autologous Stem-Cell Transplant (SWOG S0777): A Randomised, Open-Label, Phase 3 Trial. Lancet 2017, 389, 519–527. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moreau, P.; Attal, M.; Hulin, C.; Arnulf, B.; Belhadj, K.; Benboubker, L.; Béné, M.C.; Broijl, A.; Caillon, H.; Caillot, D.; et al. Bortezomib, Thalidomide, and Dexamethasone with or without Daratumumab before and after Autologous Stem-Cell Transplantation for Newly Diagnosed Multiple Myeloma (CASSIOPEIA): A Randomised, Open-Label, Phase 3 Study. Lancet 2019, 394, 29–38. [Google Scholar] [CrossRef] [PubMed]
- Kabat, G.C.; Matthews, C.E.; Kamensky, V.; Hollenbeck, A.R.; Rohan, T.E. Adherence to Cancer Prevention Guidelines and Cancer Incidence, Cancer Mortality, and Total Mortality: A Prospective Cohort Study. Am. J. Clin. Nutr. 2015, 101, 558–569. [Google Scholar] [CrossRef] [Green Version]
- Wöckel, A.; Kurzeder, C.; Geyer, V.; Novasphenny, I.; Wolters, R.; Wischnewsky, M.; Kreienberg, R.; Varga, D. Effects of Guideline Adherence in Primary Breast Cancer—A 5-Year Multi-Center Cohort Study of 3976 Patients. Breast 2010, 19, 120–127. [Google Scholar] [CrossRef] [PubMed]
- Research Center for Dietary Evaluation. FDA Approves Daratumumab and Hyaluronidase-Fihj for Multiple Myeloma; FDA: Silver Spring, MD, USA, 2021. [Google Scholar]
- Research Center for Dietary Evaluation; FDA Grants. Accelerated Approval to Darzalex Faspro for Newly Diagnosed Light Chain Amyloidosis; FDA: Silver Spring, MD, USA, 2021. [Google Scholar]
- Al Hamed, R.; Bazarbachi, A.H.; Bazarbachi, A.; Malard, F.; Harousseau, J.-L.; Mohty, M. Comprehensive Review of AL Amyloidosis: Some Practical Recommendations. Blood Cancer J. 2021, 11, 97. [Google Scholar] [CrossRef]
- Heald, A.; Bramham-Jones, S.; Davies, M. Comparing Cost of Intravenous Infusion and Subcutaneous Biologics in COVID-19 Pandemic Care Pathways for Rheumatoid Arthritis and Inflammatory Bowel Disease: A Brief UK Stakeholder Survey. Int. J. Clin. Pract. 2021, 75, e14341. [Google Scholar] [CrossRef]
- Alonso Torres, A.M.; Arévalo Bernabé, A.G.; Becerril Ríos, N.; Hellín Gil, M.F.; Martínez Sesmero, J.M.; Meca Lallana, V.; Ramió-Torrentà, L.; Rodríguez-Antigüedad, A.; Gómez Maldonado, L.; Triana Junco, I.; et al. Cost-Analysis of Subcutaneous vs Intravenous Administration of Natalizumab Based on Patient Care Pathway in Multiple Sclerosis in Spain. PharmacoEconomics—Open 2023, 7, 431–441. [Google Scholar] [CrossRef] [PubMed]
- McCloskey, C.; Ortega, M.T.; Nair, S.; Garcia, M.J.; Manevy, F. A Systematic Review of Time and Resource Use Costs of Subcutaneous Versus Intravenous Administration of Oncology Biologics in a Hospital Setting. PharmacoEconomics—Open 2023, 7, 3–36. [Google Scholar] [CrossRef] [PubMed]
- Aremu, T.O.; Oluwole, O.E.; Adeyinka, K.O.; Schommer, J.C. Medication Adherence and Compliance: Recipe for Improving Patient Outcomes. Pharm. J. Pharm. Educ. Pract. 2022, 10, 106. [Google Scholar] [CrossRef]
- U.S. Bureau of Labor. Statistics Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing. Available online: https://fred.stlouisfed.org/series/PCU32543254 (accessed on 5 June 2023).
- Alrasheed, M.; Hincapie, A.L.; Guo, J.J. Drug Expenditure, Price, and Utilization in the U.S. Medicaid: A Trend Analysis for SSRI and SNRI Antidepressants from 1991 to 2018. J. Ment. Health Policy Econ. 2021, 24, 3–11. [Google Scholar]
- Almalki, Z.S.; Yue, X.; Xia, Y.; Wigle, P.R.; Guo, J.J. Utilization, Spending, and Price Trends for Quinolones in the US Medicaid Programs: 25 Years’ Experience 1991–2015. PharmacoEconomics—Open 2017, 1, 123–131. [Google Scholar] [CrossRef] [Green Version]
- Chiu, S.-F.; Kelton, C.M.L.; Guo, J.J.; Wigle, P.R.; Lin, A.C.; Szeinbach, S.L. Utilization, Spending, and Price Trends for Short- and Long-Acting Beta-Agonists and Inhaled Corticosteroids in the Medicaid Program, 1991–2010. Am. Health Drug Benefits 2011, 4, 140–149. [Google Scholar] [PubMed]
- Atzinger, C.B.; Guo, J.J. Biologic Disease-Modifying Antirheumatic Drugs in a National, Privately Insured Population: Utilization, Expenditures, and Price Trends. Am. Health Drug Benefits 2017, 10, 27–36. [Google Scholar] [PubMed]
- Bian, B.; Kelton, C.M.L.; Guo, J.J.; Wigle, P.R. ACE Inhibitor and ARB Utilization and Expenditures in the Medicaid Fee-for-Service Program from 1991 to 2008. J. Manag. Care Pharm. JMCP 2010, 16, 671–679. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- NHE Fact Sheet. CMS. Available online: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet (accessed on 31 May 2023).
- Kansteiner, F. J&J’s Next-Gen Darzalex Sparks Drug Delivery Royalties Battle. 24 September 2020. Available online: https://www.fiercepharma.com/pharma/genmab-takes-j-j-to-arbitration-court-over-subq-darzalex-royalties (accessed on 13 June 2023).
- Janssen, J. Victorious in Arbitration Battle Vs. Genmab over Darzalex Royalties. Available online: https://www.biospace.com/article/a-win-for-j-and-j-janssen-beats-genmab-in-arbitration-over-royalty-paymentsrbitr/ (accessed on 13 June 2023).
- Mullins, C.D. Applying Oncology Formulary and Benefit Design Innovations to the Management of Multiple Myeloma in the Managed Care Setting. J. Manag. Care Pharm. 2012, 18, S13–S19. [Google Scholar] [CrossRef] [Green Version]
- Bennink, C.; de Mul, M.; van der Klift, M.; Broijl, A.; Tick, L.; de Jongh, E.; Garvelink, M.; Lobbezoo, D.; Sonneveld, P.; Hazelzet, J. Improving Outcome-Driven Care in Multiple Myeloma Using Patient-Reported Outcomes: A Qualitative Evaluation Study. Patient 2023, 16, 255–264. [Google Scholar] [CrossRef] [PubMed]
- Health Insurance Status. U.S. Population. 2021. Available online: https://www.statista.com/statistics/238866/health-insurance-status-of-the-total-us-population/ (accessed on 31 May 2023).
Year | Quarter | Number of Prescriptions (Utilization) | Total Spending (Reimbursement) ($) | Price (Reimbursement Per Prescription) ($) | Market Share (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ixazomib | Daratumumab | Elotuzumab | Ixazomib | Daratumumab | Elotuzumab | Ixazomib | Daratumumab | Elotuzumab | Number of Prescriptions (Utilization) | Total Spending (Reimbursement) | ||||||
Ixazomib | Daratumumab | Elotuzumab | Ixazomib | Daratumumab | Elotuzumab | |||||||||||
2016 | Q1 | 49 | 59 | 26 | 418,974 | 184,150 | 85,397 | 8550 | 3121 | 3285 | 37 | 44 | 19 | 61 | 27 | 12 |
Q2 | 124 | 103 | 56 | 1,075,014 | 548,096 | 249,870 | 8669 | 5321 | 4462 | 44 | 36 | 20 | 57 | 29 | 13 | |
Q3 | 188 | 248 | 73 | 1,640,289 | 966,903 | 400,191 | 8725 | 3899 | 5482 | 37 | 49 | 14 | 55 | 32 | 13 | |
Q4 | 221 | 385 | 139 | 1,962,145 | 1,238,592 | 480,770 | 8878 | 3217 | 3459 | 30 | 52 | 19 | 53 | 34 | 13 | |
Total/Average | 582 | 795 | 294 | 5,096,423 | 2,937,741 | 1,216,228 | 8706 | 3890 | 4172 | 37 | 45 | 18 | 57 | 30 | 13 | |
2017 | Q1 | 213 | 588 | 172 | 1,921,693 | 2,124,497 | 711,852 | 9022 | 3613 | 4139 | 22 | 60 | 18 | 40 | 45 | 15 |
Q2 | 258 | 1105 | 282 | 2,459,439 | 4,685,435 | 993,303 | 9533 | 4240 | 3522 | 16 | 67 | 17 | 30 | 58 | 12 | |
Q3 | 272 | 1091 | 325 | 2,609,437 | 4,800,696 | 950,493 | 9594 | 4400 | 2925 | 16 | 65 | 19 | 31 | 57 | 11 | |
Q4 | 316 | 1495 | 268 | 3,018,335 | 6,537,390 | 757,884 | 9552 | 4373 | 2828 | 15 | 72 | 13 | 29 | 63 | 7 | |
Total/Average | 1059 | 4279 | 1047 | 10,008,905 | 18,148,017 | 3,413,532 | 9425 | 4157 | 3353 | 17 | 66 | 17 | 33 | 56 | 11 | |
2018 | Q1 | 313 | 1514 | 236 | 2,845,100 | 8,413,147 | 576,247 | 9090 | 5557 | 2442 | 15 | 73 | 11 | 24 | 71 | 5 |
Q2 | 334 | 1618 | 332 | 2,710,233 | 7,070,271 | 1,050,124 | 8114 | 4370 | 3163 | 15 | 71 | 15 | 25 | 65 | 10 | |
Q3 | 331 | 1665 | 298 | 2,707,603 | 7,519,141 | 922,973 | 8180 | 4516 | 3097 | 14 | 73 | 13 | 24 | 67 | 8 | |
Q4 | 340 | 1942 | 301 | 3,401,326 | 7,906,849 | 982,065 | 10,004 | 4071 | 3263 | 13 | 75 | 12 | 28 | 64 | 8 | |
Total/Average | 1318 | 6739 | 1167 | 11,664,261 | 30,909,408 | 3,531,409 | 8847 | 4629 | 2991 | 14 | 73 | 13 | 25 | 67 | 8 | |
2019 | Q1 | 343 | 2287 | 362 | 3,316,103 | 10,286,895 | 1,241,526 | 9668 | 4498 | 3430 | 11 | 76 | 12 | 22 | 69 | 8 |
Q2 | 406 | 2526 | 418 | 4,034,498 | 9,921,096 | 1,465,808 | 9937 | 3928 | 3507 | 12 | 75 | 12 | 26 | 64 | 10 | |
Q3 | 415 | 2617 | 475 | 4,183,645 | 10,570,097 | 1,598,221 | 10,081 | 4039 | 3365 | 12 | 75 | 14 | 26 | 65 | 10 | |
Q4 | 366 | 2228 | 408 | 3,677,176 | 8,737,377 | 1,168,238 | 10,047 | 3922 | 2863 | 12 | 74 | 14 | 27 | 64 | 9 | |
Total/Average | 1530 | 9658 | 1663 | 15,211,421 | 39,515,466 | 5,473,792 | 9933 | 4097 | 3291 | 12 | 75 | 13 | 25 | 66 | 9 | |
2020 | Q1 | 400 | 4048 | 551 | 4,022,046 | 16,046,282 | 1,669,349 | 10,055 | 3964 | 3030 | 8 | 81 | 11 | 19 | 74 | 8 |
Q2 | 499 | 3839 | 449 | 5,030,400 | 15,736,047 | 1,943,357 | 10,081 | 4099 | 4328 | 10 | 80 | 9 | 22 | 69 | 9 | |
Q3 | 476 | 3639 | 371 | 4,876,649 | 15,865,742 | 1,613,262 | 10,245 | 4360 | 4348 | 11 | 81 | 8 | 22 | 71 | 7 | |
Q4 | 479 | 3676 | 367 | 4,934,713 | 16,638,493 | 1,715,746 | 10,302 | 4526 | 4675 | 11 | 81 | 8 | 21 | 71 | 7 | |
Total/Average | 1854 | 15,202 | 1738 | 18,863,808 | 64,286,564 | 6,941,713 | 10,171 | 4237 | 4095 | 10 | 81 | 9 | 21 | 71 | 8 | |
2021 | Q1 | 485 | 4341 | 328 | 5,139,634 | 21,125,781 | 1,724,861 | 10,597 | 4867 | 5259 | 9 | 84 | 6 | 18 | 75 | 6 |
Q2 | 464 | 4631 | 364 | 5,014,912 | 24,167,953 | 1,451,278 | 10,808 | 5219 | 3987 | 8 | 85 | 7 | 16 | 79 | 5 | |
Q3 | 456 | 5366 | 506 | 4,828,613 | 27,089,448 | 1,658,160 | 10,589 | 5048 | 3277 | 7 | 85 | 8 | 14 | 81 | 5 | |
Q4 | 401 | 4949 | 366 | 4,225,339 | 26,601,581 | 1,419,905 | 10,537 | 5375 | 3880 | 7 | 87 | 6 | 13 | 82 | 4 | |
Total/Average | 1806 | 19,287 | 1564 | 19,208,499 | 98,984,763 | 6,254,204 | 10,633 | 5127 | 4101 | 8 | 85 | 7 | 16 | 79 | 5 | |
2022 | Q1 | 640 | 10,259 | 701 | 7,236,890 | 60,599,589 | 2,754,584 | 11,308 | 5907 | 3930 | 6 | 88 | 6 | 10 | 86 | 4 |
Q2 | 644 | 9989 | 558 | 7,296,106 | 61,131,335 | 2,278,834 | 11,329 | 6120 | 4084 | 6 | 89 | 5 | 10 | 86 | 3 | |
Q3 | 333 | 6152 | 329 | 3,814,904 | 37,095,479 | 1,355,151 | 11,456 | 6030 | 4119 | 5 | 90 | 5 | 9 | 88 | 3 | |
Q4 | 273 | 4495 | 210 | 3,119,600 | 26,961,227 | 805,931 | 11,427 | 5998 | 3838 | 5 | 90 | 4 | 10 | 87 | 3 | |
Total/Average | 1890 | 30,895 | 1798 | 21,467,500 | 185,787,630 | 7,194,501 | 11,380 | 6014 | 3993 | 5 | 90 | 5 | 10 | 87 | 3 | |
Total/Average | 10,039 | 86,855 | 9271 | 101,520,817 | 440,569,590 | 34,025,380 | 9871 | 4593 | 3714 | 15 | 74 | 12 | 27 | 65 | 8 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Alrasheed, M.; Alsuhibani, A.; Balkhi, B.; Guo, J.J. Drug Expenditure, Price, and Utilization in US Medicaid: A Trend Analysis for New Multiple Myeloma Medications from 2016 to 2022. Healthcare 2023, 11, 2265. https://doi.org/10.3390/healthcare11162265
Alrasheed M, Alsuhibani A, Balkhi B, Guo JJ. Drug Expenditure, Price, and Utilization in US Medicaid: A Trend Analysis for New Multiple Myeloma Medications from 2016 to 2022. Healthcare. 2023; 11(16):2265. https://doi.org/10.3390/healthcare11162265
Chicago/Turabian StyleAlrasheed, Marwan, Abdulrahman Alsuhibani, Bander Balkhi, and Jeff Jianfei Guo. 2023. "Drug Expenditure, Price, and Utilization in US Medicaid: A Trend Analysis for New Multiple Myeloma Medications from 2016 to 2022" Healthcare 11, no. 16: 2265. https://doi.org/10.3390/healthcare11162265
APA StyleAlrasheed, M., Alsuhibani, A., Balkhi, B., & Guo, J. J. (2023). Drug Expenditure, Price, and Utilization in US Medicaid: A Trend Analysis for New Multiple Myeloma Medications from 2016 to 2022. Healthcare, 11(16), 2265. https://doi.org/10.3390/healthcare11162265