Justine M Kahn1, Frances B Maguire2, Qian Li3, Renata Abrahão3,4, Jamie E Flerlage4,5, Elysia Alvarez3,6, Theresa H M Keegan3. 1. Department of Pediatrics, Columbia University Irving Medical Center, New York, New York. 2. California Cancer Reporting and Epidemiologic Surveillance Program, University of California Davis Comprehensive Cancer Center, Sacramento, California. 3. Center for Oncology Hematology Outcomes Research and Training and Division of Hematology and Oncology, University of California Davis School of Medicine, Sacramento, California. 4. Center for Healthcare Policy and Research, University of California Davis School of Medicine, Sacramento, California. 5. Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee. 6. Department of Pediatrics, University of California Davis School of Medicine, Sacramento, California.
Abstract
BACKGROUND: Hodgkin lymphoma (HL) is a treatable tumor affecting children, adolescents and young adults (AYAs; 15-39 years old). Population-based studies report worse survival for non-White children and AYAs but have limited data on individual therapeutic exposures. This study examined overall and HL-specific survival in a population-based cohort of patients while adjusting for sociodemographic factors and treatment. METHODS: Data for 4807 patients younger than 40 years with HL (2007-2017) were obtained from the California Cancer Registry. Individual treatment information was extracted from text fields; chemotherapy regimens were defined by standard approaches for pediatric and adult HL. Multivariable Cox models examined the influence of patient and treatment factors on survival. RESULTS: At a median follow-up of 4.4 years, 95% of the patients were alive. Chemotherapy differed by age, with 70% of 22- to 39-year-olds and 41% of <22-year-olds receiving doxorubicin, bleomycin, vinblastine, and dacarbazine (P < .001). In multivariable models, older patients (22-39 vs < 21 y; hazard ratio [HR], 1.53; 95% confidence interval [CI], 1.11-2.10), Black (vs White patients); HR, 1.90; 95% CI, 1.25-2.88), and Hispanic patients (HR, 1.45; 95% CI, 1.06-1.99) experienced worse survival; among those < 21 y, Black race was associated with a 3.3-fold increased risk of death (HR, 3.26; 95% CI, 1.43-7.42). CONCLUSIONS: In children and AYAs with HL, older age and non-White race/ethnicity predicted worse survival after adjustments for treatment data. Further work is needed to identify the biological and nonbiological factors driving disparities in these at-risk populations.
BACKGROUND: Hodgkin lymphoma (HL) is a treatable tumor affecting children, adolescents and young adults (AYAs; 15-39 years old). Population-based studies report worse survival for non-White children and AYAs but have limited data on individual therapeutic exposures. This study examined overall and HL-specific survival in a population-based cohort of patients while adjusting for sociodemographic factors and treatment. METHODS: Data for 4807 patients younger than 40 years with HL (2007-2017) were obtained from the California Cancer Registry. Individual treatment information was extracted from text fields; chemotherapy regimens were defined by standard approaches for pediatric and adult HL. Multivariable Cox models examined the influence of patient and treatment factors on survival. RESULTS: At a median follow-up of 4.4 years, 95% of the patients were alive. Chemotherapy differed by age, with 70% of 22- to 39-year-olds and 41% of <22-year-olds receiving doxorubicin, bleomycin, vinblastine, and dacarbazine (P < .001). In multivariable models, older patients (22-39 vs < 21 y; hazard ratio [HR], 1.53; 95% confidence interval [CI], 1.11-2.10), Black (vs White patients); HR, 1.90; 95% CI, 1.25-2.88), and Hispanic patients (HR, 1.45; 95% CI, 1.06-1.99) experienced worse survival; among those < 21 y, Black race was associated with a 3.3-fold increased risk of death (HR, 3.26; 95% CI, 1.43-7.42). CONCLUSIONS: In children and AYAs with HL, older age and non-White race/ethnicity predicted worse survival after adjustments for treatment data. Further work is needed to identify the biological and nonbiological factors driving disparities in these at-risk populations.
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