Multilevel Social Determinant Assessments of Skull-Base Squamous Cell Carcinoma Treatment Disparities in the United States

Abstract Utilizing a national patient population of skull-base squamous cell carcinoma patients (SBSC) and multilevel social determinants of health (SDoH) models comprising individual- and community-level factors, including the Yost-SES-Index, to observe how multilevel social determinant factors associate with SBSC treatment disparities across the United States. Retrospective cohort study assessed SBSC patients between 2010 and 2018 from SEER, and these patients were analyzed by age-adjusted multivariable, multilevel logistic regression models utilizing covariates of individual-level sex and race/ethnicity and census-level rurality–urbanicity and Yost-socioeconomic status (SES)-Index (composite measure of poverty, education, income, housing) were logistically regressed, stratified across non-/nasopharyngeal-SBSC types. Surgical treatment, radiation therapy, chemotherapy, and delay-in-treatment-initiation (3-or-more months after diagnosis) outcomes Across 8,852 patients, lower Yost-SES-Index (odds ratio: 0.73, 95% confidence interval: 0.63–0.85) was a negative predictor of non-nasopharyngeal-SBSC-surgery. For radiation therapy receipt, female sex (1.33, 1.16–1.54) was a non-nasopharyngeal SBSC-positive predictor, whereas lower Yost-SES-Index (0.75, 0.64–0.87) was a nasopharyngeal-SBSC negative predictor. For chemotherapy, minority race/ethnicity was a non-nasopharyngeal-SBSC positive predictor (1.25, 1.07–1.46), whereas lower Yost-SES-Index (0.85, 0.73–0.98) was a nasopharyngeal-SBSC-negative predictor. Finally, female sex (1.40, 1.17–1.69), minority race/ethnicity (1.30, 1.08–1.57), and lower Yost-SES-Index (1.49, 1.24–1.79) were markedly positive predictors for delays-in-treatment. For SBSC, minority race/ethnicity, female sex, and community-level SES were the greatest independent factor associated with treatment disparities. Rurality–urbanicity was not significantly associated with treatment disparities. Multilevel modeling objectively assessed the associative effects of each factor for conferring treatment disparities.