Approximately one-half of individuals with cancer face personal economic burdens associated with the disease and its treatment, a problem known as financial toxicity (FT). FT more frequently affects socioeconomically vulnerable individuals and leads to subsequent adverse economic and health outcomes. Whereas multilevel systemic factors at the policy, payer, and provider levels drive FT, there are also accompanying intervenable patient-level factors that exacerbate FT in the setting of clinical care delivery. The primary strategy to intervene on FT at the patient level is financial navigation. Financial navigation uses comprehensive assessment of patients' risk factors for FT, guidance toward support resources, and referrals to assist patient financial needs during cancer care. Social workers or nurse navigators most frequently lead financial navigation. Oncologists and clinical provider teams are multidisciplinary partners who can support optimal FT management in the context of their clinical roles. Oncologists and clinical provider teams can proactively assess patient concerns about the financial hardship and employment effects of disease and treatment. They can respond by streamlining clinical treatment and care delivery planning and incorporating FT concerns into comprehensive goals of care discussions and coordinated symptom and psychosocial care. By understanding how age and life stage, socioeconomic, and cultural factors modify FT trajectory, oncologists and multidisciplinary health care teams can be engaged and informative in patient-centered, tailored FT management. The case presentations in this report provide a practical context to summarize authors' recommendations for patient-level FT management, supported by a review of key supporting evidence and a discussion of challenges to mitigating FT in oncology care.

To examine the racial/ethnic, rural-urban, and regional variations in the trends of diabetes-related lower-extremity amputations (LEAs) among hospitalized U.S. adults from 2009 to 2017. RESEARCH DESIGN AND METHODSWe used the National Inpatient Sample (NIS) (2009-2017) to identify trends in LEA rates among those primarily hospitalized for diabetes in the U.S. We conducted multivariable logistic regressions to identify individuals at risk for LEA based on race/ethnicity, census region location (North, Midwest, South, and West), and rurality of residence. RESULTSFrom 2009 to 2017, the rates of minor LEAs increased across all racial/ethnic, rural/urban, and census region categories. The increase in minor LEAs was driven by Native Americans (annual percent change [APC] 7.1%, P < 0.001) and Asians/ Pacific Islanders (APC 7.8%, P < 0.001). Residents of non-core (APC 5.4%, P < 0.001) and large central metropolitan areas (APC 5.5%, P < 0.001) experienced the highest increases over time in minor LEA rates. Among Whites and residents of the Midwest and non-core and small metropolitan areas there was a significant increase in major LEAs. Regression findings showed that Native Americans and Hispanics were more likely to have a minor or major LEA compared with Whites. The odds of a major LEA increased with rurality and was also higher among residents of the South than among those of the Northeast. A steep decline in major-to-minor amputation ratios was observed, especially among Native Americans. CONCLUSIONSDespite increased risk of diabetes-related lower-limb amputations in underserved groups, our findings are promising when the major-to-minor amputation ratio is considered.

BACKGROUND:Patients with ovarian cancer often present with late-stage disease and nonspecific symptoms, but little is known about factors affecting the time to diagnosis (TTD) in the United States. METHODS: A retrospective, population-based study of the Surveillance, Epidemiology, and End Results-Medicare database was conducted. It included women 66 years old or older with stage II to IV epithelial ovarian cancer with at least 1 code for abdominal/pelvic pain, bloating, difficulty eating, or urinary symptoms within 1 year of the cancer diagnosis. TTD was defined from the first claim with a prespecified symptom to the ovarian cancer diagnosis. Kruskal-Wallis tests were used to assess for differences in TTD by group medians. Univariate and generalized linear models with a log-link function evaluated TTD by covariables. RESULTS: For the 13,872 women analyzed, the mean and median times to diagnosis were 2.9 and 1.1 months, respectively. The median TTD differed significantly by first symptom (P < .001), number of symptoms (P < .001), and first physician specialty seen (P < .001). In a multivariable analysis, TTD differed significantly according to race/ethnicity (P < .001), geographic region (P = .001), urban-rural location (P = .031), emergency room presentation (P < .001), and number of specialties seen (P < .001). A shorter TTD was associated with a diagnosis in 2006-2010 (relative risk [RR], 0.92; 95% confidence interval [CI], 0.87-0.98) or 2011-2015 (RR, 0.87; 95% CI, 0.81-0.93) in comparison with 1992-1999. CONCLUSIONS: The time from a symptomatic presentation to care to a diagnosis of ovarian cancer is influenced by clinical and demographic variables. This study's findings reinforce the importance of educating all physicians on ovarian cancer symptoms to aid in diagnosis. Cancer 2021;127:4151-4160.