The impact of individual-level income predicted from the BRFSS on the association between insurance status and overall survival among adults with cancer from the SEER program.

INTRODUCTION: Among patients with cancer in the United States, Medicaid insurance is associated with worse outcomes than private insurance and with similar outcomes as being uninsured. However, prior studies have not addressed the impact of individual-level socioeconomic status, which determines Medicaid eligibility, on the associations of Medicaid status and cancer outcomes. Our objective was to determine whether differences in cancer outcomes by insurance status persist after accounting for individual-level income. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 18-64 year-old individuals with cancer from 2014-2016. Individual-level income was imputed using a model trained on Behavioral Risk Factors Surveillance Survey participants including covariates also present in SEER. The association of 1-year overall survival and insurance status was estimated with and without adjustment for estimated individual-level income and other covariates. RESULTS: A total of 416,784 cases in SEER were analyzed. The 1-yr OS for patients with private insurance, Medicaid insurance, and no insurance was 88.7%, 76.1%, and 73.7%, respectively. After adjusting for all covariates except individual-level income, 1-year OS differences were worse with Medicaid (-6.0%, 95% CI = -6.3 to -5.6) and no insurance (-6.7%, 95% CI = -7.3 to -6.0) versus private insurance. After also adjusting for estimated individual-level income, the survival difference for Medicaid patients was similar to privately insured (-0.4%, 95% CI = -1.9 to 1.1) and better than uninsured individuals (2.1%, 95% CI = 0.7 to 3.4). CONCLUSIONS: Income, rather than Medicaid status, may drive poor cancer outcomes in the low-income and Medicaid-insured population. Medicaid insurance coverage may improve cancer outcomes for low-income individuals.

Socio-economic factors do not affect overall survival in soft tissue sarcoma when patients treated at a single high-volume center.

BACKGROUND: Treatments for soft tissue sarcoma (STS) include extensive surgical resection, radiation and chemotherapy, and can necessitate specialized care and excellent social support. Studies have demonstrated that socioeconomic factors, such as income, marital status, urban/rural residence, and educational attainment as well as treatment at high-volume institution may be associated with overall survival (OS) in STS. METHODS: In order to explore the effect of socio-economic factors on OS in patients treated at a high-volume center, we performed a retrospective analysis of STS patients treated at a single institution. RESULTS: Overall, 435 patients were included. Thirty-seven percent had grade 3 tumors and 44% had disease larger than 5 cm. Patients were most commonly privately insured (38%), married (67%) and retired or unemployed (43%). Median distance from the treatment center was 42 miles and median area deprivation index (ADI) was 5 (10 representing most deprived communities). The majority of patients (52%) were treated with neoadjuvant therapy followed by resection. As expected, higher tumor grade (HR 3.1), tumor size > 5 cm (HR 1.3), and involved lymph nodes (HR 3.2) were significantly associated with OS on multivariate analysis. Demographic and socioeconomic factors, including sex, age at diagnosis, marital status, employment status, urban vs. rural location, income, education, distance to the treatment center, and ADI were not associated with OS. CONCLUSIONS: In contrast to prior studies, we did not identify a significant association between socioeconomic factors and OS of patients with STS when patients were treated at a single high-volume center. Treatment at a high volume institution may mitigate the importance of socio-economic factors in the OS of STS.

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.

BACKGROUND: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). METHODS: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. RESULTS: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. CONCLUSIONS: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Evaluation of near-miss and adverse events in radiation oncology using a comprehensive causal factor taxonomy.

PURPOSE: Incident learning systems (ILSs) are a popular strategy for improving safety in radiation oncology (RO) clinics, but few reports focus on the causes of errors in RO. The goal of this study was to test a causal factor taxonomy developed in 2012 by the American Association of Physicists in Medicine and adopted for use in the RO: Incident Learning System (RO-ILS). METHODS AND MATERIALS: Three hundred event reports were randomly selected from an institutional ILS database and Safety in Radiation Oncology (SAFRON), an international ILS. The reports were split into 3 groups of 100 events each: low-risk institutional, high-risk institutional, and SAFRON. Three raters retrospectively analyzed each event for contributing factors using the American Association of Physicists in Medicine taxonomy. RESULTS: No events were described by a single causal factor (median, 7). The causal factor taxonomy was found to be applicable for all events, but 4 causal factors were not described in the taxonomy: linear accelerator failure (n = 3), hardware/equipment failure (n = 2), failure to follow through with a quality improvement intervention (n = 1), and workflow documentation was misleading (n = 1). The most common causal factor categories contributing to events were similar in all event types. The most common specific causal factor to contribute to events was a "slip causing physical error." Poor human factors engineering was the only causal factor found to contribute more frequently to high-risk institutional versus low-risk institutional events. CONCLUSIONS: The taxonomy in the study was found to be applicable for all events and may be useful in root cause analyses and future studies. Communication and human behaviors were the most common errors affecting all types of events. Poor human factors engineering was found to specifically contribute to high-risk more than low-risk institutional events, and may represent a strategy for reducing errors in all types of events.