RESUMEN
Importance: The longitudinal experience of patients is critical to the development of interventions to identify and reduce financial hardship. Objective: To evaluate financial hardship over 12 months in patients with newly diagnosed colorectal cancer (CRC) undergoing curative-intent therapy. Design, Setting, and Participants: This prospective, longitudinal cohort study was conducted between May 2018 and July 2020, with time points over 12 months. Participants included patients at National Cance Institute Community Oncology Research Program sites. Eligibility criteria included age at least 18 years, newly diagnosed stage I to III CRC, not started chemotherapy and/or radiation, treated with curative intent, and able to speak English. Data were analyzed from December 2022 through April 2023. Main Outcomes and Measures: The primary end point was financial hardship, measured using the Comprehensive Score for Financial Toxicity (COST), which assesses the psychological domain of financial hardship (range, 0-44; higher score indicates better financial well-being). Participants completed 30-minute surveys (online or paper) at baseline and 3, 6, and 12 months. Results: A total of 450 participants (mean [SD] age, 61.0 [12.0] years; 240 [53.3%] male) completed the baseline survey; 33 participants (7.3%) were Black and 379 participants (84.2%) were White, and 14 participants (3.1%) identified as Hispanic or Latino and 424 participants (94.2%) identified as neither Hispanic nor Latino. There were 192 participants (42.7%) with an annual household income of $60â¯000 or greater. There was an improvement in financial hardship from diagnosis to 12 months of 0.3 (95% CI, 0.2 to 0.3) points per month (P < .001). Patients with better quality of life and greater self-efficacy had less financial toxicity. Each 1-unit increase in Functional Assessment of Cancer Therapy-General (rapid version) score was associated with an increase of 0.7 (95% CI, 0.5 to 0.9) points in COST score (P < .001); each 1-unit increase in self-efficacy associated with an increase of 0.6 (95% CI, 0.2 to 1.0) points in COST score (P = .006). Patients who lived in areas with lower neighborhood socioeconomic status had greater financial toxicity. Neighborhood deprivation index was associated with a decrease of 0.3 (95% CI, -0.5 to -0.1) points in COST score (P = .009). Conclusions and Relevance: These findings suggest that interventions for financial toxicity in cancer care should focus on counseling to improve self-efficacy and mitigate financial worry and screening for these interventions should include patients at higher risk of financial burden.
Asunto(s)
Neoplasias Colorrectales , Neoplasias del Recto , Humanos , Masculino , Persona de Mediana Edad , Femenino , Estrés Financiero , Estudios Longitudinales , Estudios Prospectivos , Calidad de Vida , Neoplasias del Recto/terapia , Neoplasias Colorrectales/terapia , Medición de Resultados Informados por el PacienteRESUMEN
Dynamic failure events have occurred in the underground coal mining industry since its inception. Recent NIOSH research has identified geochemical markers that correlate with in situ reportable dynamic event occurrence, although the causes behind this correlative relationship remain unclear. In this study, NIOSH researchers conducted machine learning analysis to examine whether a model could be constructed to assess the probability of dynamic failure occurrence based on geochemical and petrographic data. Linear regression, random forest, dimensionality reduction, and cluster analyses were applied to a catalog of dynamic failure and control data from the Pennsylvania Coal Sample Databank, cross-referenced with accident data from the Mine Safety and Health Administration (MSHA). Analyses determined that 7 of the 18 geochemical parameters that were examined had the biggest impact on model performance. Classifications based on logistic regression and random forest models attained precision values of 85.7% and 96.7%, respectively. Dimensionality reduction was used to explore patterns and groupings in the data and to search for relationships between compositional parameters. Cluster analyses were performed to determine if an algorithm could find clusters with given class memberships and to what extent misclassifications of dynamic failure status occurred. Cluster analysis using a hierarchal clustering algorithm after dimensionality reduction resulted in four clusters, with one relatively distinct dynamic failure cluster, and three clusters mostly consisting of control group members but with a small number of dynamic failure members.