Impact of Race, Ethnicity, and Socioeconomic Status on Nasopharyngeal Carcinoma Disease-Specific and Conditional Survival

Abstract

Introduction Race, ethnicity, and socioeconomic status (SES) are complex, interconnected social determinants of health outcomes. This study uses multivariable analysis on a combination of large national datasets to examine the effects of these factors on 5-year disease-specific survival (DSS) and conditional DSS (CDSS) for nasopharyngeal carcinoma (NPC). Methods A retrospective study of adults with NPC between 2000 and 2017 from the Surveillance, Epidemiology, End Results (SEER) registry was performed, using the National Cancer Institute Yost Index, a census tract–level composite score of SES to categorize patients. Kaplan–Meier analysis and Cox's regression for DSS and CDSS were stratified by SES. Logistic regression was conducted to identify risk factors for advanced cancer stage at time of diagnosis and receiving multimodal therapy. Results Our analysis included 5,632 patients. DSS was significantly associated with race and SES (p < 0.01). Asian/Pacific Islander patients exhibited increased survival when controlling for other variables (hazard ratio [HR] = 0.73, p < 0.01). Although Black patients were more likely to be diagnosed with advanced disease (Black odds ratio [OR] = 1.47, p < 0.01), Black patients were also less likely to receive multimodal therapy; however, this relationship lost statistical significance once SES was incorporated into the multivariable analysis. DSS was decreased among the lowest (first) and middle (second) tertiles of SES (first HR = 1.34, p < 0.01; second HR = 1.20, p < 0.01) compared with the highest (third). Conclusion Our results indicate that race, ethnicity, and SES significantly affect survival, stage at diagnosis, and treatment of NPC. An interplay of tumor biology and inequalities in access to care likely drives these disparities.

Association of social vulnerability on survival, treatment, and presentation in oral cavity cancer

“…Receiving adjuvant therapy requires a number of regimented postoperative visits that can be difficult to adhere to, and disparities in utilization of such therapy have been documented for a number of different tumors. [35][36][37][38][39] Mechanisms of this disparity include travel difficulties as well as other barriers including factors as simple as parking costs. 40,41 Finally, another study by Yu et al found that oral cancer patients of certain racial/ethnic/ socioeconomic backgrounds were less likely to be treated in high-volume hospitals than their more advantaged counterparts which can result in poor utilization of standard of care or more extensive treatments.…”

“…Despite these recommendations, our study identified variations in utilization of multimodal therapy for patients with certain racial and socioeconomic backgrounds even after controlling for stage of disease in multivariable models. Receiving adjuvant therapy requires a number of regimented postoperative visits that can be difficult to adhere to, and disparities in utilization of such therapy have been documented for a number of different tumors 35–39 . Mechanisms of this disparity include travel difficulties as well as other barriers including factors as simple as parking costs 40,41 .…”

Trends in nasopharyngeal cancer mortality in the United States, 1999–2020

“…Slight elevations in NPC mortality have been noted in regions along the Gulf and Atlantic Coasts; however, the higher mortality rates in coastal regions could not be explained by significant geographic variations in mortality 43 . It is possible that slight differences in economic sub‐regions might explain slight elevations and stationary trends in NPC, since the disease is associated with lower socioeconomic status 3,44 …”

Avoiding bias in artificial intelligence

“…Furthermore, for rhinologic malignancies, patients of minority demographic groups and lower socioeconomic status appear more likely to present with advanced stage disease 9–12 . Considering that such disorders are quite rare at baseline, this disparity suggests that as a specialty, our collective datasets lack proportionally representative demographic minority patients with early‐stage cancer.…”

“…Furthermore, for rhinologic malignancies, patients of minority demographic groups and lower socioeconomic status appear more likely to present with advanced stage disease. [9][10][11][12] Considering that such disorders are quite rare at baseline, this disparity suggests that as a specialty, our collective datasets lack proportionally representative demographic minority patients with early-stage cancer. What would these data yield when used as input for a machine-learning algorithm programmed to screen radiology studies or histopathology of biopsies for, say, malignant conversion of inverted papilloma?…”