1. What are 3 different ways to account for SES in a analytic models when investigating racial/ethnic health disparities? Briefly discuss the interpretations/implications of each approach as it relates to understanding health disparities by race/ethnicity.
Effect modifier: as SES changes, this changes the magnitude of association of the outcome. Helpful to determine how differing SES may impact outcomes among different race/ethnicity groups.
Effect mediator: if we consider SES an effect mediator we can include it in a model to attempt to understand the disparity attributable directly to race/ethnicity (or other uncontrolled for mediators) when known common causes are controlled for. Helpful to attempt to describe disparity directly associated with race/ethnicity
Contextual effect: group (eg neighborhood) SES may impact individual association between race/ethnicity and health outcome/disparity. May be helpful to understand how community or surrounding environs impact health outcomes.
2. Describe a potential effect modifier, mediator, or contextual variable (for definition of contextual variable, see Diez-Roux reading) for an association of interest to you and relevant to health disparities. For example, for investigating the association between education and hypertension, I might be interested in evaluating whether the association between years of education and hypertension is different for Black men than for White men. Describe how you would study whether this relationship exists.
I'm interested in the effect of testosterone on the development of endometrial neoplasia (cancer and precursor lesions). There is a known health disparity among Black women and white women in endometrial cancer diagnosis and survival. For me, it will be important to include race/ethnicity in my analysis to see if this disparity is replicated among trans people using testosterone therapy. I would include race/ethnicity as an effect modifier.