Objective. To examine the impact of patient demographics on mortality in breast cancer patients receiving care at a safety net academic medical center. Patients and Methods. 1128 patients were diagnosed with breast cancer at our institution between August 2004 and October 2011. Patient demographics were determined as follows: race/ethnicity, primary language, insurance type, age at diagnosis, marital status, income (determined by zip code), and AJCC tumor stage. Multivariate logistic regression analysis was performed to identify factors related to mortality at the end of follow-up in March 2012. Results. There was no significant difference in mortality by race/ethnicity, primary language, insurance type, or income in the multivariate adjusted model. An increased mortality was observed in patients who were single (OR = 2.36, CI = 1.28–4.37, p = 0.006), age > 70 years (OR = 3.88, CI = 1.13–11.48, p = 0.014), and AJCC stage IV (OR = 171.81, CI = 59.99–492.06, p < 0.0001). Conclusions. In this retrospective study, breast cancer patients who were single, presented at a later stage, or were older had increased incidence of mortality. Unlike other large-scale studies, non-White race, non-English primary language, low income, or Medicaid insurance did not result in worse outcomes.

No abstract

PURPOSE: The financial toxicity of cancer care is a source of significant distress for patients with cancer. The purpose of this study is to understand factors associated with financial toxicity in three distinct care systems. METHODS: We conducted a cross-sectional survey of patients in three care systems, Stanford Cancer Institute (SCI), VA Palo Alto Health Care System (VAPAHCS), and Santa Clara Valley Medical Center (SCVMC), from October 2017 to May 2019. We assessed demographic factors, employment status, and out-of-pocket costs (OOPCs) and administered the validated COmprehensive Score for financial Toxicity tool. We calculated descriptive statistics and conducted linear regression models to analyze factors associated with financial toxicity. RESULTS: Four hundred forty-four of 578 patients (77%) completed the entire COmprehensive Score for financial Toxicity tool and were included in the analysis. Most respondents at SCI were White, with annual household income (AHI) > $50,000 USD and Medicare insurance. At the VAPAHCS, most were White, with AHI ≤ $50,000 USD and insured by the Veterans Administration. At SCVMC, most were Asian and/or Pacific Islander, with AHI ≤ $25,000 USD and Medicaid insurance. Low AHI ( P < .0001), high OOPCs ( P = .003), and employment changes as a result of cancer diagnosis ( P < .0001) were associated with financial toxicity in the pooled analysis. There was variation in factors associated with financial toxicity by site, with employment changes significant at SCI, OOPCs at SCVMC, and no significant factors at the VAPAHCS. CONCLUSION: Low AHI, high OOPCs, and employment changes contribute to financial toxicity; however, there are variations based on site of care. Future studies should tailor financial toxicity interventions within care delivery systems.

Globally, cancer care delivery is marked by inequalities, where some economic, demographic , and sociocultural groups have worse outcomes than others. In this review, we sought to identify patient-facing interventions designed to reduce disparities in cancer care in both high- and low-income countries. We found two broad categories of interventions that have been studied in the current literature: Patient navigation and telehealth. Navigation has the strongest evidence base for reducing disparities, primarily in cancer screening. Improved outcomes with navigation interventions have been seen in both high- and low-income countries. Telehealth interventions remain an active area of exploration, primarily in high income countries, with the best evidence being for the remote delivery of palliative care. Ongoing research is needed to identify the most efficacious, cost-effective, and scalable interventions to reduce barriers to the receipt of cancer care globally.

Purpose and objectiveThe aim of this study was to examine the impact of patient demographics, tumor characteristics, and treatment type on time to treatment (TTT) in patients with breast cancer treated at a safety net medical center with a diverse patient population.Patients and methodsA total of 1,130 patients were diagnosed and treated for breast cancer between 2004 and 2014 at our institution. We retrospectively collected data on patient age at diagnosis, race/ethnicity, primary language spoken, marital status, insurance coverage, American Joint Committee on Cancer (AJCC) stage, hormone receptor status, and treatment dates. TTT was determined from the date of breast cancer biopsy to treatment start date. Nonparametric Mann–Whitney U-test (or Kruskal–Wallis test when appropriate) and multivariable quantile regression models were employed to assess for significant differences in TTT associated with each factor.ResultsLonger median TTT was noted for Black (P=0.002) and single (P=0.002) patients. AJCC stage IV patients had shorter TTT (27.5 days) compared to earlier AJCC patients (36, 35, 37, 37 days for stage 0, I, II, III, respectively), P=0.028. Age, primary language spoken, insurance coverage, and hormone receptor status had no significant impact on TTT. On multivariate analysis, race/ethnicity remained the only significant factor with Black reporting longer TTT, P=0.025. However, race was not a significant factor for time from first to second treatment. More Black patients were noted to be single (P<0.0001) and received chemotherapy as first treatment (P=0.008) compared to White, Hispanic, or other race/ethnicity patients.ConclusionIn this retrospective analysis, Black patients had longer TTT, were more likely to receive chemotherapy as first treatment, and have a single marital status. These patient factors will help identify vulnerable patients and guide further research to understand the barriers to care and the impact of treatment delays on outcomes.