Elizabeth Rose Mayeda, PhD
|School||UCSF School of Medicine|
|Department||Epidemiology & Biostatistics|
|Address||600 16th Street|
San Francisco CA 94158
|University of California, San Francisco||PhD, Epidemiology and Translational Science||2013|
|Columbia University Mailman School of Public Health||MPH, Epidemiology||2009|
|University of California, Berkeley||BA, Integrative Biology and Public Health||2007|
||2017||American Heart Association Postdoctoral Fellowship|
||2015||UCSF Center for Aging in Diverse Communities Pilot Grant|
I am a postdoctoral fellow working with Professor Maria Glymour in the UCSF Department of Epidemiology and Biostatistics. I am also affiliated with the UCSF Center for Vulnerable Populations and the UCSF Center for Aging in Diverse Communities.
The substantive focus of my research is on identifying the origins and lifecourse mechanisms of racial and ethnic health disparities, especially in Alzheimer’s disease and related disorders. To address these research questions, my work emphasizes methods to strengthen causal inference, especially methods to quantify and address selection bias. Drawing on prominent frameworks for social inequalities, there are many theoretical reasons we would expect racial/ethnic disparities in dementia. Prior research indicates that older African Americans have 40-100% higher risk of dementia than older non-Latino Whites, but less is known about dementia risk in other racial/ethnic groups. I am currently evaluating inequalities in dementia across multiple racial/ethnic groups, which lays the foundation for my long-term agenda on identifying how upstream social processes and cardiovascular risk factors across the lifecourse contribute to disparities in dementia. I completed my dissertation research on the effects of diabetes on dementia and methodological challenges introduced by selective survival. Selective survival poses a threat to causal inference in aging research because of differential survival to old age. I have developed a flexible simulation platform to quantify potential biases in studies of determinants of cognitive decline. The simulation incorporates features of cognitive aging data, including auto-correlation and measurement error. Although I originally focused on methods to evaluate selective survival bias in the context of dementia, the concepts are widely applicable to a range of problems in lifecourse epidemiology, including trends of deteriorating life expectancy for low education women in the United States and the age attenuation of black-white disparities in stroke incidence.
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