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PURPOSE: Recognizing that receiving healthcare can be time intensive and burdensome, time toxicity has been conceptualized as the time spent by patients seeking healthcare. This study investigates the association between age at diagnosis and time toxicity for patients with Metastatic Breast Cancer (MBC) and identifies major components of care that confer the greatest time toxicity. METHODS: We conducted a retrospective cohort study among patients with MBC aged 67 or older using the SEER-Medicare database. We assessed time toxicity using the number of encounter days patients interacted with the healthcare system per 100 days, within the first year of starting cancer treatment. We used a Poisson model to analyze the association between age and encounter days, adjusting for clinical and sociodemographic factors. We stratified the mean encounter days for each age cohort by treatment types. FINDINGS: The final sample included 2949 patients; 51.4% were between 70 and 79 years old, and 81.3% were white. Although unadjusted analysis showed an association between older age and more encounter days (Rate Ratio (RR) 1.12; 95% CI 1.02, 1.22), there was no significant association after adjusting for comorbidities and treatment type. Patients with more than three comorbidities had significantly higher encounter days compared to those without comorbidities [RR 1.36 (95% CI 1.26, 1.46)]. Receipt of radiotherapy [RR: 1.45 95% CI (1.37, 1.54)] was associated with more encounter days compared to not receiving radiotherapy, while receipt of bone-modifying agents was associated with fewer encounter days compared to not using Bone modifying agents [RR 0.75 (95% CI 0.70, 0.79)]. CONCLUSION: Our study identified comorbidities and cancer treatment modality, including radiotherapy, as the factors affecting time toxicity in older patients with MBC. Assessment of an individual's comorbid medical conditions and types of treatment planned are crucial to understanding age-related impacts on encounter days and to support shared decision making in older patients.
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OBJECTIVE: To examine Medicaid-insurance acceptance at facilities treating urologic cancers following implementation of the Affordable Care Act (ACA). METHODS: We conducted a retrospective, longitudinal study with a pre-post design. We accessed 2010-2017 data from the National Cancer Database, calculating the facility-level change in proportion of urologic cancer patients with Medicaid following implementation of the ACA. We used multivariable logistic regression to assess baseline clinical and demographic factors associated with changes in the proportion of patients at a facility insured through Medicaid. RESULTS: We identified 630 facilities, including 287 in Medicaid expansion states and 343 in non-expansion states associated with 436,082 urologic cancer patients. The mean facility-level change in proportion of patients with Medicaid was + 5.8% (95% CI 5.0%-6.5%) in expansion states versus + 0.6% (95% CI 0.2%-0.9%) in non-expansion states. There were 179 facilities that experienced a decrease in the post-ACA period, representing 13.6% of facilities in expansion states and 40.8% in non-expansion states (P <.001). Factors associated with a decrease in proportion of urologic cancer patients insured by Medicaid included non-expansion state status (OR 8.9, 95% CI 5.3-15.6, P <.001), higher baseline proportion of patients with Medicaid (highest quartile vs lowest: OR 4.6, 95% CI 2.3-9.4, P <.001) and high-income zip code (highest vs lowest quartile: OR 3.1, 95% CI 1.5-6.6, P <.001). CONCLUSION: Urologic cancer care for Medicaid-insured Americans remains unevenly distributed across cancer care centers, even in states that expanded coverage. Our findings suggest that this variation may reflect the effort of some facilities to reduce their financial exposure to increased numbers of Medicaid patients in the wake of ACA-supported state expansions.
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Importance: Area-level measures of sociodemographic disadvantage may be associated with racial and ethnic disparities with respect to receipt of treatment for metastatic renal cell carcinoma (mRCC) but have not been investigated previously, to our knowledge. Objective: To assess the association between area-level measures of social vulnerability and racial and ethnic disparities in the treatment of US Medicare beneficiaries with mRCC from 2015 through 2019. Design, Setting, and Participants: This retrospective cohort study included Medicare beneficiaries older than 65 years who were diagnosed with mRCC from January 2015 through December 2019 and were enrolled in fee-for-service Medicare Parts A, B, and D from 1 year before through 1 year after presumed diagnosis or until death. Data were analyzed from November 22, 2022, through January 26, 2024. Exposures: Five different county-level measures of disadvantage and 4 zip code-level measures of vulnerability or deprivation and segregation were used to dichotomize whether an individual resided in the most vulnerable quartile according to each metric. Patient-level factors included age, race and ethnicity, sex, diagnosis year, comorbidities, frailty, Medicare and Medicaid dual enrollment eligibility, and Medicare Part D low-income subsidy (LIS). Main Outcomes and Measures: The main outcomes were receipt and type of systemic therapy (oral anticancer agent or immunotherapy from 2 months before to 1 year after diagnosis of mRCC) as a function of patient and area-level characteristics. Multivariable regression analyses were used to adjust for patient factors, and odds ratios (ORs) from logistic regression and relative risk ratios (RRRs) from multinomial logistic regression are reported. Results: The sample included 15â¯407 patients (mean [SD] age, 75.6 [6.8] years), of whom 9360 (60.8%) were men; 6931 (45.0%), older than 75 years; 93 (0.6%), American Indian or Alaska Native; 257 (1.7%), Asian or Pacific Islander; 757 (4.9%), Hispanic; 1017 (6.6%), non-Hispanic Black; 12â¯966 (84.2%), non-Hispanic White; 121 (0.8%), other; and 196 (1.3%), unknown. Overall, 8317 patients (54.0%) received some type of systemic therapy. After adjusting for individual factors, no county or zip code-level measures of social vulnerability, deprivation, or segregation were associated with disparities in treatment. In contrast, patient-level factors, including female sex (OR, 0.78; 95% CI, 0.73-0.84) and LIS (OR, 0.48; 95% CI, 0.36-0.65), were associated with lack of treatment, with particularly limited access to immunotherapy for patients with LIS (RRR, 0.25; 95% CI, 0.14-0.43). Associations between individual-level factors and treatment in multivariable analysis were not mediated by the addition of area-level metrics. Disparities by race and ethnicity were consistently and only observed within the most vulnerable areas, as indicated by the top quartile of each vulnerability deprivation index. Conclusions and Relevance: In this cohort study of older Medicare patients diagnosed with mRCC, individual-level demographics, including race and ethnicity, sex, and income, were associated with receipt of systemic therapy, whereas area-level measures were not. However, individual-level racial and ethnic disparities were largely limited to socially vulnerable areas, suggesting that efforts to improve racial and ethnic disparities may be most effective when targeted to socially vulnerable areas.
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Carcinoma de Células Renais , Disparidades em Assistência à Saúde , Neoplasias Renais , Medicare , Humanos , Masculino , Feminino , Idoso , Carcinoma de Células Renais/terapia , Carcinoma de Células Renais/etnologia , Estados Unidos , Estudos Retrospectivos , Medicare/estatística & dados numéricos , Neoplasias Renais/terapia , Neoplasias Renais/etnologia , Disparidades em Assistência à Saúde/estatística & dados numéricos , Disparidades em Assistência à Saúde/etnologia , Idoso de 80 Anos ou mais , Vulnerabilidade Social , Populações Vulneráveis/estatística & dados numéricos , Fatores SocioeconômicosRESUMO
BACKGROUND: Immunotherapy (IO) and oral anticancer agents (OAA) have improved outcomes for metastatic renal cell carcinoma (mRCC) but there is a need to understand real-world costs from the perspective of payers and patients. METHODS: We used retrospective fee-for-service Medicare 100% claims data to study patients diagnosed with mRCC in 2015-2019. We identified initial treatment type and costs (the year after diagnosis) and analyzed differences in monthly and 12-month costs over time and between OAA, IO, and combination groups and the association between OOP costs and adherence. RESULTS: We identified 15,407 patients with mRCC (61% male; 85% non-Hispanic white). 6,196 received OAA, IO or combination OAA/IO as initial treatment. OAA use decreased (from 31% to 11%) with a simultaneous rise in patients receiving IO (3% to 26%) or combination IO/OAA therapy (1% to 11%). Medicare payments for all patients with mRCC increased by 41%, from $60,320 (95% CI: 58,260-62,380) in 2015 to $85,130 (95% CI: 82,630-87,630) in 2019. Payments increased in patients who received OAA, IO or combination OAA/IO but were stable in those with other/no treatment. Initial higher OOP responsibility ($200-$1000) was associated with 13% decrease in percent days covered in patients receiving OAA in the first 90 days of treatment, compared to those whose OOP responsibility was <$200. CONCLUSION: From 2015-2019, costs for Medicare patients with mRCC rose substantially due to more patients receiving IO or IO/OAA combined therapy and increases in costs among those receiving those therapies. Increased OOP costs was associated with decreased adherence.