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.
[1] SES is a confounder in the association between CKD awareness and incident end-stage kidney disease. SES causes low CKD awareness and also causes health behaviors which contribute to a higher rate of incident kidney failure.
[2] SES is an effect modifier in the effect of patient education on CKD awareness. Educating patients about CKD may be effective in high socioeconomic groups, but not in low socioeconomic groups that lack health literacy.
[3] SES is a mediator in the association between race and home dialysis uptake. Black and Hispanic patients are less likely to use home dialysis (peritoneal dialysis or home hemodialysis). Black and Hispanic patients have lower socioeconomic status due to historical and interpersonal discrimination, which causes more housing insecurity and homelessness, which may result in lower home dialysis usage. Controlling for SES would allow you to evaluate for healthcare bias in counseling on home dialysis options, independent of low home dialysis uptake due to SES factors.
Black race --> Low SES --> Worse housing --> Lower home dialysis
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 am studying the role of social risk factor adjustment, beyond traditional adjustment for comorbidities, for performance measures in kidney disease. An example is the standardized readmission rate in the ESRD Quality Incentive Program. Those with low SES may be less likely to access their medications from the pharmacy, travel to follow-up appointments, and afford ancillary services which may contribute to a higher readmission rate. To study this, I would first develop a risk prediction model for readmissions that includes patient demographics and comorbidities. I would then add SES variables at either the individual-level or neighborhood-level, such as Dual-eligible status or zip code median income, to the risk prediction model and assess the change in C-statistics.