Maria Glymour

Maria Glymour

Adjunct Assistant Professor of Social and Behavioral Sciences

Department of Social and Behavioral Sciences

Research Interests

  • Social determinants of health in aging
  • Cognitive change in the elderly
  • Socioeconomic and geographic determinants of stroke incidence and outcomes
  • Causal inference in social epidemiology

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 cognitively engaging exposures.  Current cohorts of elderly in the US were exposed to profound social changes during the early 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 early 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, and cognitive outcomes in particular.  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!  For this reason, 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. 

Students interested in research collaborations related to the social epidemiology of stroke, dementia, and aging are welcome to send me an email. 


S.D., 2004, Harvard School of Public Health
S.M., 2004, Harvard School of Public Health
A.B., 1996, University of Chicago, The College

Selected Publications

Kaufman J, Glymour MM. Splitting the Differences: Problems in Using Twin Controls to Study the Effects of BMI on Mortality. Epidemiology (Commentary) (Forthcoming, 2011). 

Glymour MM, Maselko J, Gilman SE, Patton KK, Avendano MP. Depressive Symptoms Predict Incident Stroke Independently of Memory Impairments. Neurology, 2010, 75: 2063-70.

Glymour MM, Mujahid M, Wu Q, White K, Tchetgen Tchetgen EJ. Neighborhood disadvantage and self-assessed health, disability, and depressive symptoms: longitudinal results from the Health and Retirement Study. Annals of Epidemiology, 2010, 20: 856-61.

Rehkopf DH, Jencks CS, and Glymour MM. The association of earnings with health in middle age: do self-reported earnings for the previous year tell the whole story? Social Science and Medicine, 2010; 71: 431-439.

Glymour MM, Kosheleva A, and Boden-Albala B. Birth and adult residence in the Stroke Belt independently predict stroke mortality. Neurology 2009; 73: 1858-1865.

Maselko, J, Bates, L, Avendano M, and Glymour MM. The intersection of sex, marital status, and cardiovascular risk factors in shaping stroke incidence. Journal of the American Geriatric Society (Brief Report) 2009.

DeFries TB, Avendano MP, and Glymour MM. Level and change in cognitive test scores predict risk of first stroke. Journal of the American Geriatric Society 2009; 57: 499-505.

Glymour, MM and Avendano MP. Can self-reported strokes be used to study stroke incidence and risk factors? Evidence from the Health and Retirement Study. Stroke 2009; 40: 873-879. Available at:

Helzner EP, Luchsinger J, Scarmeas N, Cosentino S, Brickman A, Glymour MM, Stern Y. Contribution of vascular risk factors to disease progression in Alzheimer’s Disease. Archives of Neurology 2009; 66: 343-348. Available at:

Avendano M, Glymour MM, Banks J, and Mackenbach J.  The health disadvantage in the United States: Are poor Europeans healthier than wealthy Americans? American Journal of Public Health 2009; 99: 540-548.

Glymour MM, Ertel KA, and Berkman LF. What can life-course epidemiology tell us about health inequalities in old age? In Annual Review of Gerontology and Geriatrics, Volume 29, 2009: Life Course Perspectives on Late Life Health Inequalities, Antonucci TC and Jackson JS, eds. 29:27-56.

Ertel KA, Glymour MM, and Berkman LF.  Social networks and health: a life course perspective integrating observational and experimental evidence. Journal of Social and Personal Relationships 2009; 26: 73-92.

Glymour MM, and Manly JJ. Lifecourse social conditions and racial and ethnic patterns of cognitive aging. Neuropsychology Reviews 2008; 3: 223-254.

Glymour MM, Weuve J, and Chen J. Methodological challenges in causal research on racial and ethnic patterns of cognitive trajectories: measurement, selection, and bias. Neuropsychology Reviews 2008; 3: 194-213.

Glymour MM, Avendaño MP, Haas S, and Berkman LF. Lifecourse social conditions and racial disparities in incidence of first stroke. Annals of Epidemiology 2008; 18: 904-912.

Glymour MM, Defries TB, Kawachi I, and Avendano MP. Spousal smoking and incidence of first stroke in the Health and Retirement Study.  American Journal of Preventive Medicine (Brief Report) 2008; 35: 245-248.

Ertel K, Glymour MM, and Berkman LF. Effects of Social Integration on Preserving Memory Function in a Nationally Representative US Elderly Population. American Journal of Public Health 2008: 98; 1215-1220. Available at:

Glymour MM, Weuve J, Fay M, Glass T, and Berkman L. Social ties and cognitive recovery after stroke: does social integration promote cognitive resilience?  Neuroepidemiology 2008; 31: 10-20.

Glymour, MM, Kawachi, I, Jencks, C, and Berkman L. Does childhood schooling affect old age memory and cognitive function? Using state schooling laws as natural experiments.  Journal of Epidemiology and Community Health 2008; 62: 532-537. 

Glymour MM. and Greenland S. Causal diagrams.  In Modern Epidemiology, 3rd edition, Rothman KJ, Greenland S, and Lash T, eds. Lippincott-Raven (Book chapter, March, 2008).

Glymour MM. Sensitive period and first difference models: integrating etiologic thinking into econometric techniques. Commentary on “Neomaterialist theory and the temporal relationship between income inequality and longevity change.”  Social Science and Medicine (Commentary) 2008; 66: 1895-1902. 

Avendaño M, and Glymour MM. Socioeconomic disparities in stroke incidence: the effects of wealth, income, and education in the HRS. Stroke 2008; 39: 1533-1540.

Subramanian SV, Glymour MM. and Kawachi I. Identifying causal ecologic effects on health: a methodological assessment.  In Macrosocial Determinants of Health, Galea S, ed., Springer Media (Book chapter, 2007).

Glymour MM. Selected samples and nebulous measures: some methodological difficulties in lifecourse epidemiology.  International Journal of Epidemiology (Invited commentary) 2007; 36: 566-568.  Text available at:   
Glymour MM, Avendaño M, and Berkman LF.  Is the Stroke Belt worn from childhood?  Risk of first stroke and place of residence in childhood and adulthood. Stroke 2007; 38: 2415-2421.  Text available at:

Ertel K, Glymour MM, Fay ME, Glass TA, and Berkman LF. Frailty modifies effects of psychosocial intervention in recovery from stroke.  Clinical Rehabilitation 2007; 21: 511-522.  

Glymour MM. When bad genes look good: APOE-e4, cognitive decline, and diagnostic thresholds.  American Journal of Epidemiology 2007; 165: 1239-1246. Text available at:

Glymour MM. Using causal diagrams to understand common problems in social epidemiology.  In Methods in Social Epidemiology, Oakes M, and Kaufman J, eds. Jossey-Bass. 2006    

Glymour MM. Natural experiments and instrumental variables analyses in social epidemiology.  In Methods in Social Epidemiology, Oakes M, and Kaufman J, eds. Jossey-Bass. 2006.    

Berkman L, and Glymour MM. How society shapes aging: the centrality of variability.  Daedalus: Journal of the American Academy of Arts and Sciences 2006; 135: 105-114. 

Glymour MM, and Kawachi I. Here’s a proposal for editors that may help reduce publication bias.  British Medical Journal, (Letter) 2005; 331: 638.  Available at:
Glymour MM, Weuve J, Kawachi I, Berkman L, Robins J. Baseline adjustment in models of change: an example with education and cognitive change.  American Journal of Epidemiology 2005; 162: 267-78.    Available at: