1. What are 3 different ways to account for SES in an analytic model when investigating racial/ethnic health disparities? (describe a fourth for extra bonus points!). Briefly discuss the interpretations/implications of each approach as it relates to the interest in understand health disparities by race/ethnicity.
Generalized estimating equations: especially useful for health disparities research because they can be used to model data from clustered or multi-level studies. GEEs estimate a generalized linear model when there is a chance of unknown correlations between outcomes. Headen et al. used GEEs with log link functions to estimate risk ratios for race and gestational weight gain (GWG) that took account of pregnancy clustering within women.
Mediation analysis: can be carried out using logistic regression models. The mediation model is one in which there is a pathway between the predictor and outcome that travels through the mediator. Baron and Kenny outlined a framework for mediation that involved 4 key criteria: (1) racial/ethnic group is associated with the outcome, (2) racial/ethnic group is associated with a set of potential mediating factors, (3) a set of potential mediating factors are associated with the risk of the outcome, and (4) including both racial/ethnic group and the set of mediating factors in a model changes the association between outcome and r/e group seen in criteria 1. Lorch et al. applied this framework to their study of the factors that mediate racial/ethnic disparities in US fetal death rates.
Multilevel modeling: the approach used by Coley et al. when they wanted to look at associations between neighborhood factors and individual outcomes, with regard to racial disparities in low birth weight among infants born to teen mothers. This approach is useful in health disparities research because oftentimes disparities are the result of multiple proximate and distal factors that occur in different settings, for different lengths of time, and to affect individuals to a varying degree. Multilevel modeling allows researchers to examine the pathway between predictor and outcome in the context of all this variability.
2. Describe a potential effect modifier, mediator, or contextual variable (for definition of contextual variable, see first page of option Merlo reading) for an association of interest to you and relevant to health disparities. For example, for investigating the association between SES and maternal mortality, I might be interested in the contextual variable of exposure to violence in the neighborhood. Describe how you would study whether this relationship exists.
Contextual variable: my current work is examining the relationship between race/ethnicity (predictor) and time to blood pressure control (outcome). One example of a contextual variable in this context is neighborhood walkability. I would hypothesize that environmental characteristics like neighborhood walkability could account for some of the relationship between race/ethnicity and disparities in time to blood pressure control.