Maria Glymour, ScD, MS

Maria Glymour, ScD, MS

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Professor, Epidemiology & Biostatistics, School of Medicine, UCSF
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My research focuses on how social factors experienced across the lifecourse, from infancy to adulthood, influence cognitive function, dementia, stroke, and other health outcomes in old age. I am especially interested in education and other exposures amenable to policy interventions. Current cohorts of elderly in the US were exposed to profound social changes during the 20th century when we revolutionized access to high school. One thread of my research examines how changes in schooling laws and school quality in the 20th century might have influenced the health and cognitive functioning of current cohorts of elderly. My results suggest that extra schooling has substantial benefits for memory function in the elderly independent of any “innate” characteristics.

My recent work has also focused on understanding the social and geographic patterning of stroke and stroke recovery. In the United States, there is a longstanding pattern of excess stroke incidence and mortality suffered by residents of southern states. We are examining whether this might be attributable to early life exposure, rather than adult place of residence. By studying stroke, I hope to improve understanding of factors that influence neurologic risk and resilience and how these conditions are shaped by social inequalities from childhood through adulthood. In particular, the intersection of stroke and dementia is an inadequately understood area.

A separate theme of my research focuses on overcoming methodological problems encountered in analyses of social determinants of health, Alzheimer's disease, and dementia. For many reasons, research focusing on lifecourse epidemiology as well as cognitive aging introduces substantial methodological challenges. Sometimes, these are conceptual challenges, and clear causal thinking can help! Many of these challenges are being addressed in the MELODEM (MEthods in LOngitudinal research on DEMentia) initiative, an international group of researchers focusing on analytic challenges in research on dementia and cognitive aging. MELODEM is organized into working groups on measurement, selection/survival, time-scale definitions, complex confounding, high-dimensional data.

I have advocated the use of causal directed acyclic graphs (DAGs) as a standard research tool to represent our causal hypotheses and help elucidate potential biases in proposed analyses. In other cases, the methodological problems require more analytical solutions that have been developed elsewhere in epidemiology or in other disciplines, but are rarely applied to these research questions. Instrumental variables analyses of natural or induced experiments are one promising example. Genetic variations have recently been advanced as possible instrumental variables to estimate the health effects of a wide range of phenotypes, an approach sometimes called “Mendelian Randomization.” Using genetic polymorphisms as instrumental variables could provide a very powerful tool for social epidemiology, but the inferences from such analyses rest on strong assumptions. Thus I am currently working with a team to explore approaches to evaluating the plausibility of those assumptions in applications for social epidemiology.

I currently serve as the Director for the UCSF PhD program in Epidemiology and Translational Science (…). With Drs. Mary Haan and Bob Hiatt, I co-lead the UCSF T32 training grant on Aging and Chronic Disease, which offers financial support for pre- and post-doctoral researchers.

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