Medicare Part D low-income subsidies alone are insufficient to improve the uptake and equitable use of high-cost, orally administered antimyeloma therapy.
Objectives: The Medicare Part D low-income subsidy program drastically reduces patient cost sharing and may improve access to and equitable use of high-cost antimyeloma therapy. We compared initiation of and adherence to orally administered antimyeloma therapy between full-subsidy and nonsubsidy enrollees and assessed the association between full subsidies and racial/ethnic inequities in orally administered antimyeloma treatment use.
Study Design: Retrospective cohort study.
Methods: We used Surveillance, Epidemiology, and End Results–Medicare data to identify beneficiaries diagnosed with multiple myeloma between 2007 and 2015. Separate Cox proportional hazards models assessed time from diagnosis to treatment initiation and time from therapy initiation to discontinuation. Modified Poisson regression examined therapy initiation in the 30, 60, and 90 days following diagnosis and adherence to and discontinuation of treatment in the 180 days following initiation.
Results: Receipt of full subsidies was not associated with earlier initiation of or improved adherence to orally administered antimyeloma therapy. Full-subsidy enrollees were 22% (adjusted HR [aHR], 1.22; 95% CI, 1.08-1.38) more likely to experience earlier treatment discontinuation than nonsubsidy enrollees. Receipt of full subsidies did not appear to reduce racial/ethnic inequities in orally administered antimyeloma therapy use. Black full-subsidy and nonsubsidy enrollees were 14% less likely than their White counterparts to ever initiate treatment (full subsidy: aHR, 0.86; 95% CI, 0.73-1.02; nonsubsidy: aHR, 0.86; 95% CI, 0.74-0.99).
Conclusions: Full subsidies alone are insufficient to increase uptake or equitable use of orally administered antimyeloma therapy. Addressing known barriers to care (eg, social determinants of health, implicit bias) could improve access to and use of high-cost antimyeloma therapy.
Am J Manag Care. 2023;29(5):246-254. https://doi.org/10.37765/ajmc.2023.89357
Increasing pharmaceutical prices1 and variations in cost sharing across the phases of Medicare Part D2 have raised concerns about patients’ financial burden and the suboptimal use of orally administered antimyeloma therapy.1,3,4 Specifically, 29% of patients with high out-of-pocket costs have delayed antimyeloma treatment, and 43% of patients have either partially filled a prescription for or discontinued antimyeloma therapy due to financial burden.3 Cost-related treatment delays and nonadherence are more pronounced among persons of color (POC).3,5-7 Compared with White patients, POC are less likely to receive any systemic antimyeloma therapy6 and, among those who are treated, experience shorter periods of using novel, orally administered medications.5,7 Given that timely initiation and continued use of therapy are necessary for optimal clinical outcomes,8 identifying and evaluating policy solutions that address cost-related barriers to care are imperative.
The Medicare Part D low-income subsidy (LIS) program substantially reduces out-of-pocket costs for beneficiaries with limited incomes (eg, < 135% of the federal poverty level) and assets (eg, ≤ $9470 for individuals).2,4,9 For example, in 2021 a 28-day supply of lenalidomide—one of the most expensive first-line, orally administered antimyeloma medications—cost only $9.20 for a full-subsidy enrollee9 compared with an average of $3000 for a nonsubsidized enrollee.10 Prior research has suggested that LIS program participation improves the uptake of and short-term adherence to anticancer therapies11-13; however, the role of reduced out-of-pocket costs in narrowing racial/ethnic inequities and the continuous use of therapy in older adults with multiple myeloma is limited. Our objectives were to compare the initiation of and adherence to orally administered antimyeloma therapy between full-subsidy and nonsubsidy enrollees and to assess the association between the receipt of full subsidies and racial/ethnic inequities in antimyeloma treatment use.
We used the Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database to identify beneficiaries who were 66 years or older with a primary diagnosis of multiple myeloma (SEER site recode 34000) between January 2007 and December 2015. Eligible patients were (1) continuously enrolled in fee-for-service Medicare Parts A and B in the 12 months prior to diagnosis; (2) covered by fee-for-service Medicare Parts A, B, and D at diagnosis; (3) not diagnosed at autopsy or death; (4) not eligible for Medicare benefits due to end-stage renal disease; and (5) not receiving Medicare Part D partial subsidies at diagnosis. We further restricted the cohort to patients who were non-Hispanic/Latinx White (herein referred to as White), non-Hispanic/Latinx Black or African American (herein referred to as Black), Hispanic/Latinx, or Asian or Pacific Islander (herein referred to as Asian)7,14 and who had no prior evidence of antimyeloma treatment use (Figure 1).
Treatment initiation. We identified Medicare Part D–covered medications indicated for or commonly used to treat multiple myeloma: thalidomide, lenalidomide, pomalidomide, ixazomib, panobinostat, melphalan, cyclophosphamide, and etoposide (eAppendix Table 1 [eAppendix available at ajmc.com]). We evaluated initiation in 2 ways: (1) time from diagnosis to orally administered therapy initiation in the full cohort (n = 6972) (Figure 1; cohort 1); and (2) the probability of starting antimyeloma treatment within 30, 60, and 90 days4 of diagnosis among patients continuously enrolled in a Medicare Part D stand-alone plan for at least 3 months after diagnosis (n = 6268) (Figure 1; cohort 2).
Treatment adherence and discontinuation. Adherence to and discontinuation of orally administered antimyeloma therapy were assessed among beneficiaries who were continuously enrolled in a Medicare Part D stand-alone plan in the 6 months following treatment initiation (n = 3091) (Figure 1; cohort 3). We defined adherence using the proportion of days covered (PDC)15 and allowed for switching between orally administered therapies (eg, due to treatment intolerance, therapy nonresponse, or disease progression).16 Consistent with prior research and Pharmacy Quality Alliance thresholds,15,16 patients were categorized as adherent to antimyeloma therapy if their PDC was 80% or greater. Discontinuation was defined as a gap of at least 60 consecutive days following exhaustion of available drug supply.16 We separately evaluated time from orally administered therapy initiation to discontinuation among patients enrolled in a Medicare Part D stand-alone plan at time of initiation (n = 3563) (Figure 1; cohort 4).
LIS program participation. LIS program participation in the month of diagnosis was identified in the Medicare Part D enrollment file and beneficiaries were categorized as either full-subsidy (dual-eligible or deemed eligible for reduced co-payments) or nonsubsidy (not eligible for cost-sharing subsidy) enrollees.4,11
Covariates. Covariates included age at diagnosis, sex, marital status, urbanicity, quarter and year of diagnosis, comorbidity score (measured in the 12 months prior to diagnosis with the Klabunde modification of the Charlson Comorbidity Index score),11,13 clinical markers of symptomatic multiple myeloma (diagnoses of hypercalcemia, renal impairment, anemia, and bone loss or lesions),17 prior health service use (hospitalizations and emergency department visits in the 12 months prior to diagnosis), and socioeconomic status (SES) and SES-related factors (Census tract–level median household income, poverty level, high school education, and English proficiency).
We used separate Cox proportional hazards models to evaluate time to orally administered antimyeloma therapy initiation and discontinuation. In the initiation model, we accounted for death as a competing risk13,18 and censored patients at Medicare Part D stand-alone plan disenrollment. In the discontinuation model, beneficiaries were censored at hospice enrollment, Medicare Part D stand-alone plan disenrollment, or death (whichever came first). We tested the proportionality assumption using martingale and Schoenfeld residuals19 and by adding time-dependent variables (interaction between covariate and log-time) to each model.20 We also used modified Poisson regression21 with robust error variance to estimate the likelihood of starting therapy within 30, 60, and 90 days of diagnosis and adherence to and discontinuation of treatment in the 180 days following initiation.
Propensity score weighting. To assess the independent association between subsidies and antimyeloma treatment use, we used stabilized inverse probability of treatment weights to balance observed patient- and Census tract–level factors between full-subsidy and nonsubsidy enrollees. Characteristics were considered balanced if absolute standardized differences were less than 10% (eAppendix Tables 2 and 3).22 Factors that were imbalanced following propensity score weighting were added as covariates to the regression models (ie, doubly robust).23
Measuring racial/ethnic inequities. Consistent with the Institute of Medicine’s definition of disparities, we used a multivariable (nonweighted) model to first assess the independent effect of race/ethnicity by controlling only for health status (age at diagnosis, sex, marital status, aforementioned clinical characteristics, and prior health service utilization).24,25 We then adjusted for urbanicity and Census tract–level SES and SES-related factors to determine if racial/ethnic differences in antimyeloma therapy use were attenuated.24,25 Finally, we stratified the models by subsidy status to understand whether full subsidies modify inequities in antimyeloma treatment use.
We conducted several sensitivity analyses to check the robustness of our findings. First, individuals who qualified for Medicare due to a disability may have health needs that influence antimyeloma therapy uptake and utilization; therefore, we restricted analyses to patients whose reason for entitlement was age. Second, to further account for cases of symptomatic multiple myeloma, we restricted time-to-initiation analyses to patients treated within 12 months of diagnosis.7 Third, we examined initiation of infused antimyeloma treatment (eAppendix Table 1) as a negative control because out-of-pocket costs are expected to be low for both full-subsidy and nonsubsidy enrollees.11 Fourth, because adherence and discontinuation are related measures, we evaluated orally administered therapy adherence among patients who did not discontinue treatment.16 Fifth, to account for expected clinical use of antimyeloma therapy among beneficiaries receiving an autologous stem cell transplant, we measured adherence in the first 120 days following treatment initiation.16 Lastly, orally administered antimyeloma medications have varied toxicity profiles that could influence continued use; therefore, we included initial therapy and days’ supply in the propensity score models used to assess adherence and discontinuation.
Study Population Characteristics
Among the 6972 beneficiaries with multiple myeloma, approximately 30% were receiving full subsidies and 29% were POC (Table 1 [part A and part B]17). Compared with nonsubsidy enrollees, full-subsidy enrollees were less likely to be White (38% vs 85%) and married (31% vs 57%) but more likely to have multiple comorbidities (49% vs 32%) and live in areas with high rates of poverty (44% vs 17%). Following propensity score weighting, baseline characteristics were well balanced between full-subsidy and nonsubsidy enrollees (eAppendix Table 2).
A smaller proportion of full-subsidy enrollees (43%) started orally administered antimyeloma therapy any time after diagnosis compared with nonsubsidy enrollees (54%) (Figure 2, panel A). However, treatment uptake within 30, 60, and 90 days of diagnosis was similar between full-subsidy and nonsubsidy enrollees (eAppendix Figure, panels A-C). In propensity score–weighted models, receipt of full subsidies was not associated with earlier orally administered treatment initiation or starting antimyeloma therapy in the 30, 60, and 90 days following diagnosis (eAppendix Table 4).
When assessing uptake by race/ethnicity, a lower percentage of Black full-subsidy and nonsubsidy enrollees initiated orally administered treatment any time and in the 90 days after diagnosis compared with their White, Hispanic/Latinx, and Asian counterparts (Figure 2, panel A and eAppendix Figure, panel C). In the health status–adjusted models, receipt of full subsidies did not appear to narrow inequities in therapy initiation. Among both full-subsidy and nonsubsidy enrollees, Black patients were less likely than White patients to experience earlier orally administered treatment initiation (full subsidy: adjusted HR [aHR], 0.86; 95% CI, 0.73-1.02; nonsubsidy: aHR, 0.86; 95% CI, 0.74-0.99) (Table 2).
Treatment Adherence and Discontinuation
Less than half of full-subsidy (35%) and nonsubsidy (41%) enrollees were adherent to orally administered therapy (Figure 2, panel B), but a larger proportion of full-subsidy enrollees discontinued treatment compared with nonsubsidy enrollees (40% vs 33%) (Figure 2, panel C). In weighted models, receipt of full subsidies was not associated with adherence to or discontinuation of antimyeloma treatment in the 180 days following initiation (eAppendix Table 5). However, full-subsidy enrollees had a 22% (aHR, 1.22; 95% CI, 1.08-1.38) increased likelihood of earlier therapy discontinuation relative to nonsubsidy enrollees.
We observed low rates of adherence across all races/ethnicities (Figure 2, panel B). For example, only 30% of Black full-subsidy enrollees and 31% of Asian nonsubsidy enrollees adhered to antimyeloma therapy. A larger percentage of White, Black, Hispanic/Latinx, and Asian full-subsidy enrollees discontinued treatment relative to their nonsubsidized counterparts, with the largest difference observed between Black full-subsidy and nonsubsidy enrollees (43% vs 25%) (Figure 2, panel C). In the health status–adjusted models, we did not observe statistically significant differences in orally administered therapy adherence and discontinuation between White patients and Black, Hispanic/Latinx, and Asian patients who received full subsidies (Table 3). Compared with White nonsubsidy enrollees, Black nonsubsidy enrollees were 27% (adjusted risk ratio [aRR], 0.73; 95% CI, 0.57-0.95) and 26% (aHR, 0.74; 95% CI, 0.56-0.98) less likely to discontinue antimyeloma therapy or experience earlier treatment discontinuation, respectively. Findings were similar when controlling for SES and SES-related factors.
Full-subsidy enrollees were less likely than nonsubsidy enrollees to experience earlier infused therapy initiation (aHR, 0.81; 95% CI, 0.75-0.88) or to start infused treatment within 60 (aRR, 0.84; 95% CI, 0.73-0.98) or 90 (aRR, 0.88; 95% CI, 0.77-1.00) days of diagnosis (eAppendix Table 6). Results of remaining sensitivity analyses were consistent with our primary findings and are not shown.
In this study comparing the use of orally administered antimyeloma therapy between Medicare Part D full-subsidy and nonsubsidy enrollees, we found no statistically significant differences in earlier therapy initiation or rates of treatment adherence. In addition, reduced cost sharing did not appear to minimize racial/ethnic inequities in multiple myeloma care. We observed inequities in the uptake of orally administered therapy among both full-subsidy and nonsubsidy enrollees; however, no statistically significant differences in therapy adherence were observed by race/ethnicity and subsidy status.
One possible explanation for our findings and inconsistencies with previous studies11,13,26 is that utilization management processes27 and restricted distribution networks27,28 likely contributed to therapy delays for both full-subsidy and nonsubsidy enrollees. In regard to the former, research has demonstrated median treatment delays of 22 days for initial prior authorization approval and an additional 30 days for an appeal to be filed and approved.29,30 In terms of the latter, risk evaluation and mitigation strategy programs limit the number of specialty pharmacies that are allowed to dispense immunomodulatory agents (lenalidomide, thalidomide, pomalidomide),28 which has resulted in therapy delays for approximately one-fourth of patients.28 Although efforts to streamline processes and evaluate programs for barriers to care have been proposed,28,31 payers and policy makers should consider permanent reforms to ensure timely access to life-extending antimyeloma therapies.
High out-of-pocket costs may have also limited access to orally administered therapy for some nonsubsidy enrollees. Evidence suggests that out-of-pocket costs greater than $2000 are associated with abandonment of prescriptions for immunomodulatory agents.32 Although some patients may eventually fill a prescription (eg, after obtaining financial support), others may choose to initiate infused therapy due to the generosity of coverage for outpatient medical services. Because most fee-for-service Medicare beneficiaries have supplemental insurance to aid with out-of-pocket expenses,33 reduced cost sharing may only partially explain why nonsubsidy enrollees were more likely than full-subsidy enrollees to initiate infused antimyeloma therapy. Potential reasons for the undertreatment of full-subsidy enrollees include poor patient-provider communication (eg, limited discussions regarding treatment options),34 inadequate social support,35 structural barriers (eg, lack of reliable transportation, dearth of health care providers),35,36 and patients’ beliefs and preferences (eg, understanding of disease severity and treatment benefits or adverse effects).27,35,37
Common barriers to accessing antimyeloma therapy could also explain why full-subsidy enrollees had a 22% higher probability of discontinuing orally administered treatment; however, our findings indicate that reduced cost sharing may not improve the continuous use of high-cost therapies. Studies have shown that out-of-pocket costs—even those less than $3—can adversely affect the prolonged use of medications among low-income patients.12 Although full-subsidy enrollees often pay $1 for a monthly supply of anticancer therapy,26,38 approximately 1.2 million are exposed to greater cost sharing because they are enrolled in Medicare Part D plans for which they pay a monthly premium.39 Full-subsidy enrollees could switch to plans that minimize cost sharing,39 yet many have a poor understanding of the Medicare Part D program40 and thus do not enroll in a plan that aligns with their health and financial needs. As the number of no-premium plans is expected to decrease by 24% in 2022,41 federal agencies should consider additional resources to aid full-subsidy enrollees with plan selection to ensure that the LIS program is not inadvertently acting as a financial barrier to anticancer therapy use.
We also observed that Black full-subsidy and nonsubsidy enrollees had a lower likelihood of ever initiating orally administered therapy. This, to our knowledge, is not only a novel finding, but it also suggests that subsidies alone are insufficient to narrow racial/ethnic inequities in antimyeloma care. Research indicates that health care provider characteristics (eg, demographics, implicit bias, training) independently influence quality of care, including the underprescribing of medication and timely receipt of aggressive therapies.42,43 For example, implicit bias during racially discordant patient-provider interactions has been associated with limited patient-centered communication43,44 and shared decision-making,44,45 patients’ lack of confidence in and nonadherence with recommended treatment plans,43 and patients’ overall perceptions of mistrust of and dissatisfaction with the health care system.44,45 Given the low rates of treatment initiation3,5 and high risk of excess mortality5 among Black patients with multiple myeloma, future studies should focus on the development of interventions that improve patient-provider communication and address health care providers’ biases.
Contrary to prior research,12 we found that Black nonsubsidy enrollees were less likely than their White counterparts to discontinue orally administered treatment. White nonsubsidy enrollees may have been prescribed a shorter duration of antimyeloma therapy (eg, 3 to 4 cycles of induction therapy with lenalidomide) in preparation for an autologous stem cell transplant. Approximately 28% of older adults (≥ 70 years) receive an autologous stem cell transplant,46 yet White patients are undergoing transplantation at a greater frequency than Black patients.5,6 Although it may appear that racial inequities in autologous stem cell transplantation are contributing to the continuous use of orally administered therapy among Black nonsubsidy enrollees, future research is needed to identify the underlying factors and the short- and long-term clinical outcomes associated with these inequities.
This study had several limitations. First, causality could not be assessed in our observational study and residual confounding resulting from unobserved characteristics may not have been addressed by our statistical models. Second, we were not able to discern reasons for not starting, delaying, or stopping orally administered antimyeloma therapy. Future research should assess health care providers’ and patients’ treatment preferences to identify barriers to and the root causes of inequities in therapy uptake and continued use. Third, we lacked data on the receipt of prescription assistance outside of the Medicare Part D program. Cost-sharing support could have provided enrollees with the financial resources to start and/or adhere to orally administered therapy. Fourth, our analysis did not account for phases of the Medicare Part D benefit. Nonsubsidy enrollees who entered the catastrophic coverage phase likely had relatively low out-of-pocket costs that contributed to continued treatment use. Fifth, we were limited to Census tract–level SES and SES-related factors and thus may have misclassified individuals’ SES. Future studies should examine both patient- and area-level SES to better understand the associations among out-of-pocket costs, race/ethnicity, and orally administered anticancer medication use.12 Sixth, our analysis did not account for known barriers to health equity.47,48 Future research should examine the role of structural racism (eg, residential segregation, scientific racism) in access to and use of anticancer therapy.47,48 Seventh, our analysis focused on fee-for-service beneficiaries, which may limit the generalizability of our findings to patients covered by Medicare Advantage plans. Eighth, some beneficiaries may have remained untreated due to asymptomatic multiple myeloma49,50; however, a growing body of literature suggests that early intervention with orally administered therapy may prevent progression to symptomatic disease and improve overall survival.49,50 Ninth, PDC measures only prescription refills and we were not able to determine if patients actually ingested medication as prescribed. Last, although we did not account for hospitalizations, both adherent and nonadherent hospitalized patients experienced short durations of stay (median, ≤ 3 days) that would not have meaningfully changed our measures of orally administered treatment use.
Receipt of Medicare Part D full subsidies does not appear to improve the use of or reduce racial/ethnic inequities in the initiation of orally administered antimyeloma therapy. Policy makers, health care providers, and researchers should focus on identifying the underlying causes of suboptimal treatment use and developing interventions to address known barriers to access. Together, these actions could improve the uptake and equitable use of high-cost antimyeloma therapy.
This study used the linked Surveillance, Epidemiology, and End Results (SEER)–Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the National Cancer Institute; the CMS Office of Research, Development and Information; Information Management Services (IMS), Inc; and the SEER Program tumor registries in the creation of the SEER-Medicare database.
Author Affiliations: Department of Health Policy, Vanderbilt University School of Medicine (SAJ, SBD), Nashville, TN; Department of Health Policy and Management, University of North Carolina at Chapel Hill Gillings School of Global Public Health (SAJ, CAS-R, JGT), Chapel Hill, NC; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill (CAS-R, WAW, JGT), Chapel Hill, NC; Flatiron Health, Inc (CAS-R), New York, NY; Department of Medicine, University of North Carolina at Chapel Hill School of Medicine (WAW), Chapel Hill, NC; Department of Population Health Sciences, Duke University School of Medicine (LLZ), Durham, NC; Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System (LLZ), Durham, NC; Vanderbilt-Ingram Cancer Center (SBD), Nashville, TN.
Source of Funding: The database infrastructure used for this project was funded by the CER Strategic Initiative of University of North Carolina (UNC)’s Clinical & Translational Science Award (UL1TR002489), the UNC School of Medicine, and the UNC Lineberger Comprehensive Cancer Center’s University Cancer Research Fund via the State of North Carolina. Dr Jazowski’s time drafting and revising the manuscript was supported by grant number T32 HS026122 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.
Author Disclosures: Dr Samuel-Ryals was an associate professor at the University of North Carolina at Chapel Hill at the time of this research; she is currently an employee at Flatiron Health, Inc, which is an independent subsidiary of the Roche Group; she reports owning Roche stock. Dr Wood reports research funding to his institution from Genentech and Pfizer. Dr Zullig reports consulting fees from Novartis and honoraria from Pfizer. Dr Dusetzina reports research funding from Arnold Ventures, Leukemia & Lymphoma Society, the Commonwealth Fund, and the Robert Wood Johnson Foundation, as well as consulting fees from the National Academy for State Health Policy and honoraria from West Health and the Institute for Clinical and Economic Review, and she is a member of the Medicare Payment Advisory Commission. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (SAJ, CAS-R, WAW, JGT, SBD); acquisition of data (SAJ, SBD); analysis and interpretation of data (SAJ, CAS-R, WAW, LLZ, SBD); drafting of the manuscript (SAJ); critical revision of the manuscript for important intellectual content (CAS-R, WAW, LLZ, JGT, SBD); statistical analysis (SAJ); and supervision (CAS-R, LLZ, JGT, SBD).
Address Correspondence to: Shelley A. Jazowski, PhD, MPH, Department of Health Policy, Vanderbilt University School of Medicine, 2525 West End Ave, Ste 1200, Nashville, TN 37203. Email: email@example.com.
1. Dusetzina SB, Huskamp HA, Keating NL. Specialty drug pricing and out-of-pocket spending on orally administered anticancer drugs in Medicare Part D, 2010 to 2019. JAMA. 2019;321(20):2025-2027. doi:10.1001/jama.2019.4492
2. Davidoff AJ, Hendrick FB, Zeidan AM, et al. Patient cost sharing and receipt of erythropoiesis-stimulating agents through Medicare Part D. J Oncol Pract. 2015;11(2):e190-e198. doi:10.1200/JOP.2014.001527
3. Huntington SF, Weiss BM, Vogl DT, et al. Financial toxicity in insured patients with multiple myeloma: a cross-sectional pilot study. Lancet Haematol. 2015;2(10):e408-e416. doi:10.1016/S2352-3026(15)00151-9
4. Olszewski AJ, Dusetzina SB, Eaton CB, Davidoff AJ, Trivedi AN. Subsidies for oral chemotherapy and use of immunomodulatory drugs among Medicare beneficiaries with myeloma. J Clin Oncol. 2017;35(29):3306-3314. doi:10.1200/JCO.2017.72.2447
5. Ailawadhi S, Parikh K, Abouzaid S, et al. Racial disparities in treatment patterns and outcomes among patients with multiple myeloma: a SEER-Medicare analysis. Blood Adv. 2019;3(20):2986-2994. doi:10.1182/bloodadvances.2019000308
6. Fakhri B, Fiala MA, Tuchman SA, Wildes TM. Undertreatment of older patients with newly diagnosed multiple myeloma in the era of novel therapies. Clin Lymphoma Myeloma Leuk. 2018;18(3):219-224. doi:10.1016/j.clml.2018.01.005
7. Ailawadhi S, Frank RD, Advani P, et al. Racial disparity in utilization of therapeutic modalities among multiple myeloma patients: a SEER-Medicare analysis. Cancer Med. 2017;6(12):2876-2885. doi:10.1002/cam4.1246
8. Mikhael J, Ismaila N, Cheung MC, et al. Treatment of multiple myeloma: ASCO and CCO joint clinical practice guideline. J Clin Oncol. 2019;37(14):1228-1263. doi:10.1200/JCO.18.02096
9. 2021 resource and cost-sharing limits for low-income subsidy (LIS). CMS. October 30, 2020. Accessed August 5, 2022. https://www.cms.gov/files/document/2021-lis-resource-limits-memo.pdf
10. Brooks A. Medicare Part D patients may pay more than $15K for their specialty medications. GoodRx Health. June 2, 2021. Accessed August 5, 2022. https://www.goodrx.com/insurance/medicare/medicare-part-d-patients-out-of-pocket-costs-for-specialty-medications
11. Chou YT, Farley JF, Stinchcombe TE, Proctor AE, Lafata JE, Dusetzina SB. The association between Medicare low-income subsidy and anticancer treatment uptake in advanced lung cancer. J Natl Cancer Inst. 2020;112(6):637-646. doi:10.1093/jnci/djz183
12. Biggers A, Shi Y, Charlson J, et al. Medicare D subsidies and racial disparities in persistence and adherence with hormonal therapy. J Clin Oncol. 2016;34(36):4398-4404. doi:10.1200/JCO.2016.67.3350
13. Winn AN, Keating NL, Dusetzina SB. Factors associated with tyrosine kinase inhibitor initiation and adherence among Medicare beneficiaries with chronic myeloid leukemia. J Clin Oncol. 2016;34(36):4323-4328. doi:10.1200/JCO.2016.67.4184
14. Jarrín OF, Nyandege AN, Grafova IB, Dong X, Lin H. Validity of race and ethnicity codes in Medicare administrative data compared to gold-standard self-reported race collected during routine home health care visits. Med Care. 2020;58(1):e1-e8. doi:10.1097/MLR.0000000000001216
15. Hess LM, Raebel MA, Conner DA, Malone DC. Measurement of adherence in pharmacy administrative databases: a proposal for standard definitions and preferred measures. Ann Pharmacother. 2006;40(7-8):1280-1288. doi:10.1345/aph.1H018
16. Dusetzina SB, Huskamp HA, Jazowski SA, et al. Oral oncology parity laws, medication use, and out-of-pocket spending for patients with blood cancers. J Natl Cancer Inst. 2020;112(10):1055-1062. doi:10.1093/jnci/djz243.
17. Fiala MA, Dukeman J, Tuchman SA, Keller M, Vij R, Wildes TM. Development of an algorithm to distinguish smoldering versus symptomatic multiple myeloma in claims-based data sets. JCO Clin Cancer Inform. 2017;1:CCI.17.0089. doi:10.1200/CCI.17.00089
18. Dignam JJ, Zhang Q, Kocherginsky MN. The use and interpretation of competing risks regression models. Clin Cancer Res. 2012;18(8):2301-2308. doi:10.1158/1078-0432.CCR-11-2097
19. Allison PD. Survival Analysis Using SAS: A Practical Guide. 2nd ed. SAS Institute Inc; 2010.
20. Testing the proportional hazard assumption in Cox models. UCLA Institute for Digital Research & Education. Accessed August 5, 2022. https://stats.idre.ucla.edu/other/examples/asa2/testing-the-proportional-hazard-assumption-in-cox-models/
21. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702-706. doi:10.1093/aje/kwh090
22. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083-3107. doi:10.1002/sim.3697
23. Nguyen TL, Collins GS, Spence J, et al. Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance. BMC Med Res Methodol. 2017;17(1):78. doi:10.1186/s12874-017-0338-0
24. Samuel CA, Landrum MB, McNeil BJ, Bozeman SR, Williams CD, Keating NL. Racial disparities in cancer care in Veterans Affairs health care system and the role of site of care. Am J Public Health. 2014;104(suppl 4):S562-S571. doi:10.2105/AJPH.2014.302079
25. Le Cook B, McGuire TG, Lock K, Zaslavsky AM. Comparing methods of racial and ethnic disparities measurement across different settings of mental health care. Health Serv Res. 2010;45(3):825-847. doi:10.1111/j.1475-6773.2010.01100.x
26. Rivera DR, Enewold L, Barrett MJ, et al. Population-based utilization and costs associated with tyrosine kinase inhibitors for first-line treatment of chronic myelogenous leukemia among elderly patients. J Manag Care Spec Pharm. 2020;26(12):1494-1504. doi:10.18553/jmcp.2020.26.12.1494
27. Sweiss K. Oral antimyeloma therapy: barriers to patient adherence and tips for improvement. HOPA News. 2018;15(3):3-6.
28. Schepers AJ, Jones AR, Reeves BN, Tuchman SA, Bates JS. A comparison of response in the presence or absence of a delay in induction therapy with bortezomib, lenalidomide, and dexamethasone. J Oncol Pharm Pract. 2019;25(7):1692-1698. doi:10.1177/1078155218815283
29. Cavallo J. Study finds prior authorization may be associated with delayed receipt of oral anticancer drugs. The ASCO Post. September 20, 2021. Accessed August 5, 2022. https://ascopost.com/news/september-2021/study-finds-prior-authorization-may-be-associated-with-delayed-receipt-of-oral-anticancer-drugs/
30. Wallace ZS, Harkness T, Fu X, Stone JH, Choi HK, Walensky RP. Treatment delays associated with prior authorization for infusible medications: a cohort study. Arthritis Care Res (Hoboken). 2020;72(11):1543-1549. doi:10.1002/acr.24062
31. Newcomer LN, Weininger R, Carlson RW. Transforming prior authorization to decision support. J Oncol Pract. 2017;13(1):e57-e61. doi:10.1200/JOP.2016.015198
32. Doshi JA, Li P, Huo H, Pettit AR, Armstrong KA. Association of patient out-of-pocket costs with prescription abandonment and delay in fills of novel oral anticancer agents. J Clin Oncol. 2018;36(5):476-482. doi:10.1200/JCO.2017.74.5091
33. Koma W, Cubanski J, Neuman T. A snapshot of sources of coverage among Medicare beneficiaries in 2018. Kaiser Family Foundation. March 23, 2021. Accessed August 5, 2022. https://www.kff.org/medicare/issue-brief/a-snapshot-of-sources-of-coverage-among-medicare-beneficiaries-in-2018/
34. Institute of Medicine. Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis. The National Academies Press; 2013.
35. Seal BS, Anderson S, Shermock KM. Factors associated with adherence rates for oral and intravenous anticancer therapy in commercially insured patients with metastatic colon cancer. J Manag Care Spec Pharm. 2016;22(3):227-235. doi:10.18553/jmcp.2016.22.3.227
36. Yang W, William JH, Hogan PF, et al. Projected supply of and demand for oncologists and radiation oncologists through 2025: an aging, better-insured population will result in a shortage. J Oncol Pract. 2014;10(1):39-45. doi:10.1200/JOP.2013.001319
37. Eek D, Krohe M, Mazar I, et al. Patient-reported preferences for oral versus intravenous administration for the treatment of cancer: a review of the literature. Patient Prefer Adherence. 2016;10:1609-1621. doi:10.2147/PPA.S106629
38. Caram MEV, Oerlin MK, Dusetzina S, et al. Adherence and out-of-pocket costs among Medicare beneficiaries who are prescribed oral targeted therapies for advanced prostate cancer. Cancer. 2020;126(23):5050-5059. doi:10.1002/cncr.33176
39. Cubanski J, Damico A, Neuman T. Medicare Part D in 2018: the latest on enrollment, premiums, and cost sharing. Kaiser Family Foundation. May 17, 2018. Accessed August 5, 2022. https://www.kff.org/medicare/issue-brief/medicare-part-d-in-2018-the-latest-on-enrollment-premiums-and-cost-sharing/
40. Riley GF, Warren JL, Harlan LC, Blackwell SA. Endocrine therapy use among elderly hormone receptor–positive breast cancer patients enrolled in Medicare Part D. Medicare Medicaid Res Rev. 2011;1(4):0.001.04.a04. doi:10.5600/mmrr.001.04.a04
41. An overview of the Medicare Part D prescription drug benefit. Kaiser Family Foundation. October 19, 2022. Accessed August 5, 2022. https://www.kff.org/medicare/fact-sheet/an-overview-of-the-medicare-part-d-prescription-drug-benefit/
42. Ost DE, Niu J, Elting LS, Buchholz TA, Giordano SH. Determinants of practice patterns and quality gaps in lung cancer staging and diagnosis. Chest. 2014;145(5):1097-1113. doi:10.1378/chest.13-1628
43. Penner LA, Dovidio JF, Gonzalez R, et al. The effects of oncologist implicit racial bias in racially discordant oncology interactions. J Clin Oncol. 2016;34(24):2874-2880. doi:10.1200/JCO.2015.66.3658
44. Thornton RL, Powe NR, Roter D, Cooper L. Patient-physician social concordance, medical visit communication and patients’ perceptions of health care quality. Patient Educ Couns. 2011;85(3):e201-e208. doi:10.1016/j.pec.2011.07.015
45. Penner LA, Dovidio JF, West TV, et al. Aversive racism and medical interactions with black patients: a field study. J Exp Soc Psychol. 2010;46(2):436-440. doi:10.1016/j.jesp.2009.11.004
46. Munshi PN, Vesole D, Jurczyszyn A, et al. Age no bar: a CIBMTR analysis of elderly patients undergoing autologous hematopoietic cell transplantation for multiple myeloma. Cancer. 2020;126(23):5077-5087. doi:10.1002/cncr.33171
47. Bailey ZD, Feldman JM, Bassett MT. How structural racism works – racist policies as a root cause of U.S. racial health inequities. N Engl J Med. 2021;384(8):768-773. doi:10.1056/NEJMms2025396
48. Essien UR, Dusetzina SB, Gellad WF. A policy prescription for reducing health disparities—achieving pharmacoequity. JAMA. 2021;326(18):1793-1794. doi:10.1001/jama.2021.17764
49. Bolli N, Sgherza N, Curci P, et al. What is new in the treatment of smoldering multiple myeloma? J Clin Med. 2021;10(3):421. doi:10.3390/jcm10030421
50. Zhao AL, Shen KN, Wang JN, Huo LQ, Li J, Cao XX. Early or deferred treatment of smoldering myeloma: a meta-analysis on randomized controlled trials. Cancer Manag Res. 2019;11:5599-5611. doi:10.2147/CMAR.S205623