Spatial-, neighborhood- and healthcare system-related drivers of lung cancer treatment disparities
Abstracts
Initial Award Abstract |
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Lung cancer is the leading cause of cancer-related death for men and women in the United States. In 2019, it is projected that 228,150 Americans will be diagnosed and 142,670 will die from lung cancer. Smoking is the most common risk factor for lung cancer, contributing to 80%-90% of lung cancer deaths. In 2013, the U.S. Preventative Task Force began recommending lung cancer screening for people at high risk, which has resulted in an earlier stage diagnosis for many tobacco users. However, disparities in who receives screening and appropriate treatment for lung cancer have been observed across different racial/ethnic groups, among socioeconomically disadvantaged populations, and underserved rural populations. Favorable prognosis is highly dependent on a patient's stage of diagnosis and receipt of proper treatment in a timely manner. California has a highly diverse population with more Hispanics, Asians, and American Indians than any other state, a combined nonwhite population that is the second highest of any state, and one of the highest poverty rates nationwide. The objective of this dissertation is to better understand the mechanisms of disparities in lung cancer patient's receipt of proper treatment in California. Specifically, to identify factors that explain racial/ethnic disparities in receipt of proper care from three distinct domains: spatial proximity and accessibility of treatment facilities, racial composition and segregation of a patient's neighborhood, and aspects of the patient-provider relationship within the healthcare system. Several population-based data sources will be used, including geocoded statewide cancer registry data linked with electronic health records from a large healthcare system in Northern California. The fellow will earn a Doctor of Philosophy in Epidemiology, with mentorship and planned training in geospatial analysis, minority health disparities, healthcare delivery, methods for explanation in causal inference, and use of electronic health records for observational research. |
