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

• Social determinants of health in aging
• Cognitive change in the elderly
• Causal inference in social epidemiology

I am interested in 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 a profound social change during the early 20th century when we revolutionized access to high school.  One thread of my research focuses on how changes in schooling laws and quality in the early 20th century might have influenced the health and cognitive functioning of current cohorts of elderly.  My results suggested 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 thread of my research focuses on overcoming methodological problems encountered in analyses of cognitive outcomes. For many reasons, research on lifecourse epidemiology and on 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 of induced experiments are one promising example.       

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 (Forthcoming).    

 Ertel K, Glymour MM, and Berkman LF. Social integration and memory loss over six years of follow-up in the Health and Retirement Study. American Journal of Public Health (Forthcoming, 2008).    

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

 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, Forthcoming, 2007).    

 
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: http://stroke.ahajournals.org/cgi/reprint/STROKEAHA.107.482059?ijkey=GSPFGzV8uEPQfKc&keytype=ref

 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.  Selected samples and nebulous measures: some methodological difficulties in lifecourse epidemiology.  International Journal of Epidemiology 2007; 36: 566-568. 

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

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:
http://bmj.bmjjournals.com/cgi/content/full/331/7517/638-a?ijkey=dz5cJdfEo952xWp&keytype=ref
   

 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:
http://aje.oxfordjournals.org/cgi/reprint/kwi187?ijkey=BiK3zWG64lzVYqA&keytyp e=ref

 Glymour MM, Saha S, Schwartz M and Bigby J.  Physician ethnicity, professional satisfaction, and work-related stress: results from the physician worklife study.  Journal of the National Medical Association 2004; 96: 1283-9.    
 
Glass T, Berkman L, Hiltunen E, Furie K, Glymour MM, Fay M, and Ware J.  The Families In Recovery From Stroke Trial (F.I.R.S.T.): primary study results.  Psychosomatic Medicine 2004; 66: 889-97.