3760 - Assessing Barriers to Care and Resource Utilization in a Rural Radiation Oncology Setting
Presenter(s)
C. Y. Zhao1, M. N. Kitonyi2, K. T. Dentley3, M. Hall2, A. L. Stockham2, and A. E. Garda2; 1Mayo Clinic, Alix School of Medicine, Rochester, MN, 2Mayo Clinic, Department of Radiation Oncology, Rochester, MN, 3Bethune-Cookman University, Daytona Beach, FL
Purpose/Objective(s): Cancer care in rural settings poses unique logistical and psychosocial challenges, especially during prolonged treatment. In this study, we examine self-reported barriers to care and factors associated with care team resource utilization during radiotherapy (RT).
Materials/Methods: We analyzed radiation oncology consultations at a community satellite clinic in La Crosse, WI from 2018-2024. Potential barriers to care were identified through social determinants of health questionnaires and chart review. Rurality was determined using 2022 Rural-Urban Continuum Codes. Care team support was measured by contacts and minutes spent with nursing care coordinators, advanced practice providers, and social workers from 30 days pre- to 90 days post-RT, normalized as contacts per fraction (CPF) and minutes per fraction (MPF). Associations were evaluated using t-tests unless otherwise stated.
Results: Among 1254 patients (1526 RT courses), 50.2% were male, 65.3% retired, and 66.2% had a life partner. Substance use was common (tobacco 62.7%, alcohol 52.1%, cannabis 5.8%). The most commonly treated sites were thoracic (24.8%), breast (23.5 %), and genitourinary (22.9%) cancers; 28% were treated for metastatic disease. Median number of fractions was 15 (range: 1-45). Most patients (51.6%) lived in small metropolitan areas (<250,000), while 46.6% were from rural or non-metro areas. Pre-treatment survey showed 14.4% of patients anticipated barriers, most commonly distance (3.7%), transportation (3.1%), and finances (2.7%). Care team interactions varied by diagnosis and social factors. CPF and MPF were highest in esophageal, hematologic, and thoracic cancers (ANOVA p < 0.001), and lowest in breast, gynecologic, and soft tissue cancers. Metastatic disease increased resource utilization (CPF 0.43 vs. 0.14, p<0.001; MPF 13.3 vs. 3.4, p<0.001). Tobacco (CPF: 7.30 vs 4.48, p < 0.001; MPF: 0.35 vs. 0.23, p < 0.001) and cannabis use (CPF: 0.47 vs 0.29; p = 0.001; MPF: 11.5 vs. 5.9; p = 0.008) were associated with greater care team engagement, while alcohol use was not (CPF: p = 0.052; MPF: p = 0.138). Neither distance (CPF: p = 0.896; MPF: p = 0.996) nor rurality (CPF: p=0.399; MPF: p=0.086) impacted resource use.
Conclusion: Cancer type, substance use, and treatment for metastatic disease significantly influenced resource use. Geographic factors such as distance and rurality were not associated with increased care team engagement, suggesting that clinical and social determinants may be stronger drivers of support needs than logistical barriers alone. These findings underscore the importance of incorporating patient-specific factors into predictive staffing models and resource allocation strategies to optimize support for patients facing greater barriers to care.