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Cancer Epidemiol ; 92: 102645, 2024 Aug 14.
Article in English | MEDLINE | ID: mdl-39146873

ABSTRACT

OBJECTIVE: Rural-urban healthcare disparities have been demonstrated throughout the United States, particularly in acquiring oncologic care. In this study, we aim to discern the role of rural-urban health disparities in thymic cancer incidence and uncover potential survival disparities. METHODS: The Surveillance, Epidemiology, and End Results (SEER) 17-State database was queried for all cases of thymoma (ICD-O-3/3 codes: 8580-8585) and thymic carcinoma (8586) located in the thymus (primary site code C37.9) diagnosed between 2000 and 2020. Residence was established using SEER Rural-Urban Continuum Codes. Incidence trend modeling for rural versus urban patients was completed using Joinpoint Regression Software. Chi-square, Kaplan-Meier with log-rank testing, and Cox proportional hazards was completed using SPSS, with significance set to p <0.05. RESULTS: Joinpoint analysis revealed a significant growth in incidence in the urban population compared to a stagnant incidence among the rural population. Disease specific survival was higher among urban patients on univariate modeling (p = 0.010), and confirmed on multivariate analysis, whereby rural living conferred an adjusted hazard ratio of 1.263 (95 % CI 1.045-1.527; p = 0.016) in comparison to urban patients. CONCLUSIONS: These findings demonstrate differences between thymic cancer incidence and outcomes in patients living in urban versus rural environments and demonstrate an important disparity.

7.
Dermatol Surg ; 2024 Jul 05.
Article in English | MEDLINE | ID: mdl-38968088

ABSTRACT

BACKGROUND: Field cancerization is poorly defined in dermatology. The author group previously proposed and applied a classification system in an original cohort to risk-stratify patients with field cancerization. OBJECTIVE: Apply the authors' classification system within a validation cohort. METHODS: Patients with keratinocyte carcinoma history completed a survey regarding demographic information, medical history, and chemoprevention use. Patients were assigned a field cancerization class, and differences between validation and original cohorts were assessed. RESULTS: A total of 363 patients were enrolled (mean age 67.4; 61.7% male). After comparing validation and original cohorts, there were differences in age between class II (p = .02) and class IVb (p = .047), and differences in chemoprevention use in class III (p = .04). Similar to the original cohort, the validation cohort was associated with increases in total number of skin cancers in the last year (p < .001), 5 years (p < .001), lifetime (p < .001), years since first skin cancer (p < .001), and chemoprevention use (p < .001). In the validation cohort, there were increases in age (p = .03) and immunocompromised status (p = .04) with increasing class, which were not observed in the original cohort. CONCLUSION: Differences among field cancerization classes were similar in a validation cohort, further highlighting the importance of class-specific treatment and management.

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