HMWK Week 8

HMWK Week 8

by Brianna Michelle Singleton -

  1. What are the different ways to account for SES in an analytic model when investigating racial/ethnic health disparities? (Hint: you should have three options). Discuss the interpretations/implications of each approach as it relates to the interest in understand health disparities by race/ethnicity. Draw a DAG for each option and reference it in your response (you do not have to post this!).

    1. Mediation— SES has an impact on the outcome in addition to the independent variable impacting the dependent variables. For example, if medication use was the study's dependent variable and insurance status and age were the independent variables were, SES could be a mediating variable. The impact of SES could be due to the person’s ability to cover the difference of the cost of medication (SES mediating insurance status) or a person being older and having multiple co-morbidities (SES mediating age).   

    2. Interaction and effect modification- An interaction magnifies the impact of a variable. An example is obesity can be magnified by SES because cheaper foods are comparatively lower nutritional density and have higher caloric

    3. Clustering- Is looking at multiple factors that are highly correlated to one another that are not easily teased apart.



  1. Think about multilevel influences on a health outcome of interest to you. Discuss how you would study this, including measurement and analytic approaches you would use to account for exposures across multiple levels.

    1. I really appreciated the readings on MLRA, and what caught my eye was the sentence, “MLRA is a suitable statistical technique that can be used to operationalise conceptual schemas in multilevel epidemiology.” When approaching sleep health, I could look at it from an individual’s perspective. I could measure how much caffeine, alcohol, and nicotine use. I could measure screen time or other poor sleep hygiene behaviors. From a community level, I could explore job demand, pressure and stress. I could also look at how much control a person has and how that impacts a person’s well being. Analytically I could cluster workers by what type of work they do, where they work, level of experience or other factors that could make their job less stressful.

Re: HMWK Week 8

by Christine Dehlendorf -

I love your multilevel thinking! Very interesting. Indeed, things like decision latitude and other markers of empowerment/control could be context dependent and would be interesting to study. 

Another option for modeling of SES is confounding, although, as we talked about in class, this is complicated because the direction of the causal arrow from SES to race/ethnicity does not make sense - so it is really more of a "third variable" whose treatment depends on what your research question is (e.g. do you want to know what the race/ethnicity association is independent of certain SES variables, acknowledging they are usually imperfect measures, or do you want to know the association of an outcome with race/ethnicity without controlling for the mediating effect of SES).