Disaggregation of Asian American and Pacific Islander Women With Stage 0-II Breast Cancer Unmasks Disparities in Survival and Surgery-to-Radiation Intervals: A National Cancer Database Analysis From 2004 to 2017

Abstract

PURPOSE: Aggregation of Asian Americans (AAs) with Native Hawaiians and Other Pacific Islanders (NHPIs) masks significant health disparities. We evaluated overall survival (OS) and surgery-to-radiation intervals (STRIs) among AA and NHPI women with early-stage breast cancer. METHODS: This National Cancer Database study included women with stage 0-II breast cancer diagnosed between 2004 and 2017. STRI was defined as days from surgery to radiation. Patients were stratified by adjuvant treatment. AAs were disaggregated into geographically relevant subpopulations: East, South, and Southeast Asians. Kaplan-Meier estimates and log-rank tests assessed survival. Cox proportional hazard and linear regression were adjusted for clinical and sociodemographic factors. RESULTS: In total, 578,927 women were included (median age 61 years, median follow-up 65 months, and 10-year OS 83%). AA and NHPI 10-year OS was 91% overall; subpopulation 10-year OS was 92% for East Asian, 90% for South Asian, 90% for Southeast Asian, and 83% for NHPI. On multivariable analysis, compared with non-Hispanic White, NHPI women had worse survival (adjusted hazard ratio [aHR] = 1.38; 95% CI, 1.09 to 1.77); all AA subpopulations had improved survival: East Asian (aHR = 0.57; 95% CI, 0.48 to 0.69), South Asian (aHR = 0.66; 95% CI, 0.51 to 0.84), and Southeast Asian (aHR = 0.78; 95% CI, 0.65 to 0.94). The AA and NHPI median STRI for was 73 days overall; the disaggregated median STRI was 68 days for East Asian, 80 days for South Asian, 77 days for Southeast Asians, and 81 days for NHPI. On adjusted analysis, compared with non-Hispanic White, Southeast Asians and NHPI had longer STRI by 6.6 (95% CI, 4.3 to 8.9) and 10.0 (95% CI, 5.8 to 14) days, respectively. CONCLUSION: Breast cancer disparities exist among disaggregated AA and NHPI subpopulations. Data disaggregation insights may lead to interventions to overcome these disparities, such as optimizing time-to-treatment for select populations.