2026 Poster Presentations
P301: SOCIOECONOMIC DISPARITIES IN TREATMENT ACCESS AND SURVIVAL IN SINONASAL ADENOID CYSTIC CARCINOMA: A POPULATION-BASED ANALYSIS
Emma J Anisman, BA; Emma Tam, BA; Abdulghafoor Alani, BS; Benjamin F Bitner, MD; Marc Rosen, MD; Mindy Rabinowitz, MD; Elina Toskala, MD, PhD, MBA; Gurston Nyquist, MD; Thomas Jefferson University
Background: Sociodemographic factors including socioeconomic status (SES) and residence type may influence access to care and survival in patients with sinonasal adenoid cystic carcinoma (ACC), but data are limited. This retrospective study examines how SES and rural or urban zip code impact treatment and survival in ACC patients in the United States using the Surveillance, Epidemiology, and End Results (SEER) program database.
Methods: A total of 872 patients with adenoid cystic carcinoma of the sinonasal cavity between 2000 and 2022 were retrospectively evaluated using the SEER program database. International Classification of Diseases for Oncology (ICD-O) codes for subsite and histology were used to identify patients which included C30.0 (Nasal cavity) C31.0 (Maxillary Sinus), C31.1 (Ethmoid Sinus) C31.2 (Frontal sinus) C31.3 (Sphenoid sinus) C31.8 (Overlapping lesion of accessory sinuses), C31.9 (Accessory sinus, NOS), C11 (Nasopharynx) and ICD-O histology code 8200/3 (Adenoid Cystic Carcinoma). County-level median household income was grouped into quartiles (Q1–Q4: Q1 <$40,000–54,999; Q2 $55,000–74,999; Q3 $75,000–94,999; Q4 ≥$95,000, respectively). Rurality was dichotomized from Rural-Urban Continuum codes to urban (metro) vs rural (non-metro). In a secondary analysis, income was dichotomized as <$50,000 (lower income) vs ≥$50,000 (higher income). Records with unknown values were excluded from each analysis. Multivariable logistic regression and Cox proportional hazards were used for treatment receipt and survival respectively. All models adjusted for age, sex, race, tumor subsite, and stage.
Results: Higher income was associated with lower odds of delayed treatment initiation (>30 days) compared with the lowest quartile (Q2 OR 0.315, 95% CI 0.139–0.712; Q3 OR 0.291, 95% CI 0.129–0.659; Q4 OR 0.436, 95% CI 0.187–1.020; overall p=0.009), while rural or urban residence was not significantly associated with delay (OR 0.519, 95% CI 0.235–1.150; p=0.106). Income was also associated with receiving surgery compared with the lowest quartile (Q2 OR 2.691, 95% CI 1.218–5.944; Q3 OR 2.599, 95% CI 1.187–5.691; Q4 OR 3.395, 95% CI 1.469–7.846; overall p=0.039). Rural or urban residence was not significantly associated with receiving surgery (OR 1.379, 95% CI 0.601–3.165; p=0.448). Survival was not associated with rural or urban residence (p=0.494) or income quartile (p=0.254). Income <$50,000, however, was associated with decreased survival time and 2.53-fold higher hazard of death (95% CI 1.38–4.63; p=0.003).
Conclusion: After adjustment for tumor stage, site, age, and other demographics, lower income individuals had significantly delayed time to treatment initiation and were less likely to receive surgery than higher income patients. While income quartile was not significantly associated with survival, the lowest income patients had significantly decreased survival compared to all others. These results suggest that low SES negatively impacts timely treatment and treatment access, with the greatest impact on survival affecting the lowest income patients. These findings highlight the need for further investigation into defining and mitigating structural barriers for the most economically disadvantaged patients.