Project Summary/Abstract Despite steep declines in asbestos usage since the 1970s, the burden of mesothelioma within the United States has not decreased proportionately. The lack of decline is particularly pronounced among women, in whom mesothelioma rates have remained stable for more than three decades. Although the relationship between occupational asbestos exposure and mesothelioma is well-described, our understanding of non- occupational risk factors is limited. Current lags in the cancer registry network enrollment contribute to delayed patient identification, preventing early intervention. Due to delayed patient identification, previous studies primarily relied on unvalidated proxy interviews for risk exposure assessment. In this project, I will collaborate with the California Department of Public Health's Occupational Health Branch (OHB) and the California Cancer Registry (CCR) to create a novel case ascertainment system in order to mitigate these issues. This study's central objective is to use rapid case ascertainment and exposure assessment to elucidate non-occupational asbestos exposures that are associated with the development of mesothelioma. To assess this, I will conduct a case-control study to determine the non-occupational exposures to asbestos utilizing a novel case ascertainment strategy (Aim 1). The secondary objective of this study is to assess the feasibility of creating a rapid mesothelioma case ascertainment program that could facilitate future research and treatment (Aim 2). A rapid cancer-reporting system will create a mechanism for rapid exposure assessments within the public health surveillance system, allowing us to assess cancer risk factors accurately. The proposal is directly responsive to NHLBI's Strategic Vision to "develop and optimize clinical and implementation research to improve health and reduce disease" (Objective 6). Additionally, by developing innovative approaches to integrate multiple forms of data such as natural language processing, exposure history questionnaires, registry metadata, geo-mapping, exposure assessments, and outcome data, our project also fulfills the goal of "leverag[ing] emerging opportunities in data science to open new frontiers in HLBS research" (Objective 7). Through improved public health surveillance and risk assessment, exposures can be mitigated, bolstering cancer prevention efforts. This is particularly important to elucidating the preventable, non-occupational asbestos exposures related to mesothelioma. Furthermore, this award will serve as a foundation for training Dr. Sheiphali Gandhi, a fellow at the University of California San Francisco, to prepare her for her academic research career. This fellowship will provide her with the necessary support to hone her epidemiology and biostatistical skills in order to become an independent physician-scientist studying the epidemiology of occupational and environmental lung diseases.