Initial cancer treatment and survival in children, adolescents, and young adults with Hodgkin lymphoma: A population-based study
Menée à partir des données 2007-2017 du registre californien des cancers portant sur 4 807 enfants, adolescents et jeunes adultes atteints d'un lymphome hodgkinien (âge : inférieur ou égal à 40 ans), cette étude identifie les facteurs sociodémographiques et les facteurs liés aux traitements associés à la survie globale et à la survie spécifique (durée médiane de suivi : 4,4 ans)
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.