Hello all! A few weeks ago at a Talking Data Equity presentation on Measuring Gaps, Heather, you brilliantly presented a powerful slide on “How much are each of our structural determinants contributing to this gap?” And the visual explained that the structural determinants like economic, housing, health care, social, and physical environments contributed to gaps in the likelihood of childhood mortality much more so for Black people compared to White people. I would love to create a similar visual for our STD/HIV diagnosis data (and also include additional racial identities.) In the literature, I’ve seen structural barriers to prevention, treatment, and care described, but not quantified in this way. Thank you!
Thank you so much @amyzlot! If you do try this out, let me know how it goes. I’d love to see it.
@Heather Can you share your source for the analysis that was used to produce slide #31? I’d love to look at the methodology, so we can reproduce something similar with our data. Thank you!
Hey @amyzlot . I’d be happy to - I need a bit more help - can you tell me specifically what slide deck the slide you’re interest in is from? I lost the thread a bit
Oops! Apologies my question was taken out of context, @Heather . I was referring to your great talk on Measuring Gaps and Data Equity slide #31. Thank you!!
Got it! Thanks @amyzlot! Currently, that project is still in process with the Foundation that is conducting it, so I can’t share it yet.
However, I can tell you that what they’re doing is to build a predictive/causal model that uses each of those structural determinants of health as predictors (or covariates) and the maternal mortality as the outcome (or dependent variable). They’re doing this at both the individual level and the county level, depending on what data they have.
Does this work? Feel free to share a few more details about your project and I’m happy to help brainstorm methods and models.
Thanks, @Heather. Yes, that works! I look forward to learning more about the study upon its completion. I’m in the beginning stages of exploring county and indiv level predictors of congenital syphilis. Maybe we can compare notes!