You have learned that random error and bias must be considered as possible explanations for an observed association between an exposure and disease. This week we will examine the role of confounding. Unlike random error or bias, confounding is a property of the study population, and occurs when the effect of an exposure on an outcome is mixed together with the effect of a third variable. The following exercise will examine the properties of confounders and describe methods to adjust for confounding through both study design and analysis. (Please see Aschengrau & Seage Chapter 11 for more information).

Faculty Highlight: Dr. Sandro Galea

Dr. Galea is the Gelman Professor and Chair of the Department of Epidemiology at Columbia University's Mailman School of Public Health. He is a physician and an epidemiologist. His primary research has been on the causes of mental disorders, particularly common mood-anxiety disorders and substance abuse, and on the role of traumatic events in shaping population health. In particular, his work seeks to uncover how determinants at multiple levels of influence--including policies, features of the social environment, molecular, and genetic factors--jointly produce the health of urban populations.

Read more about Dr. Galea work

Galea S, Riddle M, Kaplan GA. Causal thinking and complex system approaches in epidemiology. Int J Epidemiol. 2010 Feb;39(1):97-106. Epub 2009 Oct 9.

Uddin M, Aiello AE, Wildman DE, Koenen KC, Pawelec G, de Los Santos R, Goldmann E, Galea S. Epigenetic and immune function profiles associated with posttraumatic stress disorder. Proc Natl Acad Sci U S A. 2010 May 18;107(20):9470-5. Epub 2010 May 3.

Good luck and have fun!