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.
1. Effect Modifier: Differing levels of SES, such as income, wealth and education, can change the magnitude or strength of the association of the exposure on the outcome. If we stratified SES categories and evaluated how race/ethnicity association varied with health outcomes depending on the SES category we could account for effect modification. This is helpful in trying to understand how race/ethnicity interventions can impact different levels of SES category and where policy could invest more programming/funding.
2.Effect Mediator: SES as an effect mediator could explain the relationship between race/ethnicity and health a health disparity. Adding SES to the model as a mediator may explain the relationship between race/ethnicity and a health disparity so much that the relationship may become insignificant.
3. Contextual Effect: Collective or group SES may have an effect on individual-level association between race/ethnicity and health disparities. Contexts like neighborhood and community behavior may impact health behaviors and 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 in between years of education and hypertension is different for Black men than for White men. Describe how you would study whether this relationship exists.
Is the association between income and low birth weight different for indigenous Guatemalan women vs non indigenous Guatemalan women. A potential mediator could be prenatal health behaviors. Prenatal health status may be related to ethnicity in terms of empowerment, nutrition availability and maternal age at pregnancy. Perhaps using multilevel modeling using mediation testing to understand the mechanism of how income affects low birth weight through prenatal health status.
3. Respond to one other person's post on the forum with a comment or suggestion.
In response to the model of discrimination as a mediator for the association between race/ethnicity to depression-would you enter the mediator in the first or second model to compare total effect vs the direct effect? I’m curious to know if order of entry in modeling matters or it only depends on how the researcher reports the findings.