1. Give an example of a research question for investigating racial/ethnic health disparities where: [1] SES is a confounder; [2] SES is an effect modifier; [3] SES is a mediator. Briefly discuss the interpretations/implications of each approach as it relates to understanding health disparities by race/ethnicity.
Note: There are many markers of SES, including but not limited to: education level, occupation, household income, insurance status, etc. For simplicity, I will not refer to these markers separately in the example research questions below, and will instead refer to SES generally. In actual practice, the various SES factors should be addressed separately during data analysis.
[1] SES is a confounder
Research question: What is the effect of race/ethnicity on the incidence of coronary artery disease (CAD)?
Implications: If SES is a confounder, meaning it influences both race/ethnicity and CAD, then SES would need to be controlled for during data analysis. We would interpret our findings as follows: After adjustment for the potential confounder of SES, we found an association (or no association) between race/ethnicity and CAD.
[2] SES is an effect modifier
Research question: What is the effect of race/ethnicity on the occurrence of pediatric asthma exacerbations?
Implications: If SES is an effect modifier, that means it modifies the effect of race/ethnicity on the occurrence of pediatric asthma exacerbations. It can also be said that there is an interaction between SES and race/ethnicity in the occurrence of pediatric asthma exacerbations. During data analysis, a statistical test of interaction between SES and race/ethnicity would need to be performed.
[3] SES is a mediator
Research question: What is the effect of race/ethnicity on the incidence of depression?
Implications: If SES is a mediator, then that means that in this scenario, race/ethnicity influences SES, which then influences depression. Once we adjust for SES, whatever association is leftover can be attributed to the direct effect of race/ethnicity on depression (assuming that no other mediators exist).
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 would be interested in investigating the association between race/ethnicity and the receiving of surveillance mammography among breast cancer survivors. In this case, SES could be a potential effect modifier in that it modifies the effect of race/ethnicity on the rate of receiving surveillance mammography among breast cancer survivors. To study whether this relationship exists, I would need to perform a statistical test of interaction during data analysis. If an interaction is found, then SES would need to be adjusted for (e.g. stratification or mathematical regression).