Do Low-Income Subsidy Participants Get Optimal Cancer Care?

TOPLINE:
Patients with cancer who participated in the Medicare Low-Income Subsidy program were less likely to receive any systemic cancer therapy compared with patients not participating in the program. Among patients who did receive systemic cancer therapy, Low-Income Subsidy participants were more likely to get inferior or nonrecommended treatments, a recent analysis found.
METHODOLOGY:
Medicare’s Low-Income Subsidy program helps low-income Medicare beneficiaries afford oral prescription drugs, covered by Medicare Part D. The program, however, does not include clinician-administered drugs, covered under Medicare Part B.
To determine whether patients eligible for the Low-Income Subsidy are as likely to receive optimal cancer care as those without the subsidy, researchers analyzed linked SEER-Medicare data from 2015 to 2017 and Medicare claims through 2018.
The researchers divided systemic therapy options into several categories using treatment recommendations from the National Comprehensive Cancer Network Evidence Blocks: Optimal treatment (highest scores for a cancer type), lower but noninferior regimen (lower than the optimal options), inferior treatment (lower on all measures), nonrecommended regimen, and unclassifiable regimen.
The primary outcome was receipt of any systemic therapy. Among those who received systemic therapy, researchers also assessed factors associated with a patient’s likelihood of getting optimal vs less optimal to nonrecommended care.
The analysis included 9290 patients with cancer, aged 66 years or more, who had continuous Medicare Part A, B, and D coverage. Among all patients, 73.1% received no Low-Income Subsidy, 24.6% received full subsidy, and 2.3% received partial subsidy.
TAKEAWAY:
Overall, 57.4% of patients received systemic therapy and 42.6% received no systemic therapy. Among those who received systemic therapy, 31.9% received optimal treatment, 38.2% received lower but noninferior treatment, 16.9% received inferior or nonrecommended treatment, and 12.9% received an unclassifiable treatment.
Compared with patients not receiving the subsidy, those with the full Low-Income Subsidy were less likely to receive any systemic therapy (odds ratio [OR], 0.64). Among those who received systemic therapy, patients with full Low-Income Subsidy participation were more likely to receive inferior or nonrecommended treatments (OR, 1.31) compared with nonparticipants.
Other factors associated with lower odds of receiving any systemic therapy include older age, having three or more comorbidities (OR, 0.84), and not being treated at a National Cancer Institute (NCI)-designated hospital (OR, 0.85).
Among patients who received systemic treatment, older patients (those aged 75 years and above) were more likely to receive inferior or nonrecommended treatments than younger patients (aged 66-70 years), and patients with more comorbidities were more likely to receive lower but noninferior treatments (OR, 1.26 for two comorbidities; OR, 1.30 for three or more comorbidities vs no comorbidities) or an unclassifiable regimen (OR, 1.57 and 1.95, respectively).
IN PRACTICE:
This study “observed less frequent use of the highest-recommended cancer treatments for older and more comorbid patients, which may reflect appropriate patient selection. However,
less frequent use of these treatments for [Low-Income Subsidy] recipients suggests persistent financial barriers among low-income individuals,” the authors concluded.
SOURCE:
The study, led by Aaron P. Mitchell, MD, MPH, Memorial Sloan Kettering Cancer Center in New York City, was published online in Journal of Clinical Oncology.
LIMITATIONS:
The study’s observational design limits causal inference. The findings are applicable only to Medicare beneficiaries with part D coverage and the study period of 2015-2018.
DISCLOSURES:
This study was supported by the National Institutes of Health and NCI. Several authors declared ties with various sources. Additional disclosures are noted in the original article.
This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.
 
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