Ambient air pollution and survival among Black women with epithelial ovarian cancer across diverse geographical regions of the United States
作者
Ekaterina Chirikova,Courtney E. Johnson,Anke Huels,Pushkar P. Inamdar,Elisa V. Bandera,Lawrence H. Kushi,J.A. Doherty,Joellen M. Schildkraut,Hari S. Iyer,Melissa L. Bondy,Edward Peters,Kendra L. Ratnapradipa,Jeffrey S. Marks,Christopher R. Pierson,Theresa A. Hastert,Kristian Hallermalm,Grace M. Christensen,Salma Shariff‐Marco,Scarlett Lin Gomez,Andrew Lawson
Background: Ovarian cancer is a leading cause of gynecologic cancer mortality, with Black women experiencing 5-year survival rates of only 41%. Disproportionate air pollution exposure may impact survival. We evaluated associations of fine particulate matter (PM 2.5 ) and nitrogen dioxide (NO 2 ) exposure with survival among Black women with epithelial ovarian cancer using data from the California Cancer Registry (CCR, n = 540) and the multi-state African American Cancer Epidemiology Study (AACES, n = 766). Methods: Annual PM 2.5 and NO 2 levels were estimated at a 1 km resolution using well-validated ensemble-based prediction models derived from the Socioeconomic Data and Application Center and assigned to the participants’ residential addresses per their year of diagnosis (2004−2016). Weibull accelerated failure time models with participant-level frailty were used to assess air pollutant exposure associations with overall survival. Results: Average PM 2.5 and NO 2 exposures were 11.3 μg/m³ and 25.8 ppb in CCR and 9.7 μg/m³ and 17.5 ppb in AACES. There was little evidence of an association between air pollution exposures and survival, with event time ratios (> 1 indicate longer survival) in CCR of 1.08 (95% CI = 0.97, 1.20) per 1 μg/m³ PM 2.5 and 1.07 (95% CI = 0.99, 1.15) per 10 ppb NO 2 , and in AACES of 1.00 (95% CI = 0.93, 1.07) per 1 μg/m³ PM 2.5 and 1.04 (95% CI = 0.91, 1.19) per 10 ppb NO 2 . Conclusions: Findings were modest and consistent across both cohorts and sensitivity analyses, supported by the use of advanced exposure modeling. Future research should use time-varying, long-term exposure data and examine interactions with occupation, physical activity, and neighborhood stressors.