HW SES

HW SES

by Jorge -
  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 understanding health disparities by race/ethnicity.

SES may act as confounder or mediator, It could also consider the main outcome of measure. For example in the Headen et al paper, SES acts as a confounder, because correlates directly with both the dependent variable (pregnancy weight gain) and an independent variable (racial/ethnic disparities) in the study. The mediator effect is seen in Lorch et al paper, because the influence of racial and ethnic disparities in fetal death is mediated by SES meaning that the  observed relationship between the dependent variable (pregnancy weight gain) and an independent variable (racial/ethnic disparities) is via the inclusion of SES, so it is not a direct causal relationship between the independent variable and the dependent variable. MLRA, as seen in Merlo et al, could be helpful to understand the influence of SES on the outcome variable.

  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.

 

In relation to my research area, I will be interested in the differences in long-term survival in patients with dementia according to SES. I think that patients with short-term survival after diagnosis will have low SES, anyway this will be a complex analysis so a MLRA analysis will be necessary to control for access to health care, community, and social support, educational differences, progression rate etc.

 

 

Re: HW SES

by Samuel Washington -

I think looking at dementia using an MLRA would be an interesting topic. Looking at differences in diagnosis by various healthcare providers would also be interesting, to see how stringent criteria versus say a resident adding the ICD-10 code for a patient who is forgetful would affect the rates observed within the population. Clustering based upon common socioeconomic environments would provide an interesting lens through which to investigate this issue.

I think access to care is something that is quite impactful on outcomes and can be difficult to measure. In terms of social support other than how many family members live in the same household, maybe it would be interesting to find ways to objectively measure that social network/support system by looking at factors such as financial support (in cases where children/relatives provide funds for bills/living) may be another thing to factor into the analysis.

Re: HW SES

by Christine Dehlendorf -

Thanks Jorge. In terms of SES, I think you mean that SES could in fact be one of the predictors of interest? If so, you are absolutely right, and often times both race and SES are of interest. Other approaches to consider is SES as an effect modifier and as a contextual factor. I know you couldn't be at the lecture today, but I really encourage you to watch it and see how the DAGs map on to these different modeling approaches. One other thing to keep in mind is that mediation can be partial - so the direct pathway doesn't have to be completely eliminated by the inclusion of the mediator.

For dementia, I think policy context with respect to access to in home services and other social services, as well as attitudes toward the elderly, would be important contextual factors. Also, age distribution within the community would have an effect. The importance thing to remember about multi level models is that you are including something that can't be just measured at the individual level, but is really about the context that the individual is in.