2914

Lung cancer is the leading cause of cancer death worldwide and approximately 55% of people die within one year of being diagnosed. Small advances in lung cancer survival to date are primarily attributable to individual-level interventions in tobacco use, early diagnosis and improved treatment. Persistent poor survival suggests that new approaches are needed to identify modifiable risk factors to improve lung cancer survival. While there is strong evidence for the relationship between long term exposure to ambient fine particulate matter (PM_2.5) and lung cancer risk, relatively little is known about how air pollution affects survival after a lung cancer diagnosis. Recent studies have reported associations between exposure to ambient air pollution after diagnosis and poor cancer survival. However, the number of studies is limited, and the specific etiologic mechanism remains unknown. Distributed lag models (DLM) can be particularly robust for considering exposures with both cumulative and delayed exposure effects. These models allow for the identification of specific exposure windows of increased sensitivity that may be informative for identifying etiologic mechanisms and creating targeted interventions. In the context of lung cancer survival, DLMs can be used to estimate the time-varying association between continuous PM_2.5 exposure following diagnosis and lung cancer survival, while allowing for the identification of exposure periods that contribute differentially to survival. PM_2.5 exposure can be modeled as a smooth function of the exposure period using natural cubic splines, which allows for the estimation of the impact of specific exposure periods as well as the cumulative exposure effect. To the best of our knowledge, no studies have utilized DLMs to examine the association between ambient air pollution and lung cancer survival. The objective of this study is to assess the association between PM_2.5 and lung cancer survival in the California Cancer Registry from 2000-2013 (n=164,346).