Perhaps you can consider structural equation modeling. SEM allows for inclusion of both latent and observed variables in your parameterization model.
To evaluate causal mediation (with consideration for temporality) you may consider longitudinal SEM, and it sounds like you are also interested in multi-level factors, so ML SEM for longitudinal data.
Statistical Horizons in Philadelphia offers several short seminar courses in SEM. I really enjoyed their ML SEM course.
Best of luck,
Ramona Kyabaggu, PhD(c), CHIM
Assistant Professor
Johnson-Shoyama Graduate School of Public Policy, CB 334.7
University of Regina
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306.585.4548e:
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-------- Original message --------
Date: 2022-12-03 7:18 p.m. (GMT-06:00)
Subject: Re: [SDOH] Data analysis methods
Good question,
The informatics group at Johns Hopkins is doing interesting work in your area.
Another difficult problem, in my opinion, is how to measure health outcomes. Diagnostic labels do not tell you how
sick someone is. Consider diabetes or pneumonia. The label indicates why someone might be sick, but not how sick they are. Other proxies also present problems of interpretation.
Most health record systems don't systematically capture health status and changes in health associated with care. Consider
that in Canada after years of seasonal flu vaccine no one can answer people who ask "Do people who are flu shot acceptors live, overall, longer and healthier lives compared with flu shot rejectors (adjusting for other variables that influence health status
and whether or not you routinely accept seasonal flu vaccine)
P.S. I would be delighted if someone could point me to a study that looks at all cause mortality and morbidity in seasonal flu shot acceptors or flu shot rejectors.
DZ
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CAUTION: The Sender of this email is not from within Dalhousie.
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Hi,
Does anyone know what statistical methods would be appropriate for examining the effects of structural determinants, through a mediator, on health outcomes? All of these would be different units of analysis. For example, a political/structural variable at
the state level --> state budget --> health outcomes in actual people in that state. And how do researchers deal with the time lag issue, that is, how to decide when to measure health outcomes in relation to the upstream structural variables, and the issue
of people moving in and out of states or other geographical units? We just finished a study using state-and year fixed-effects models, and I want to follow it up with a mediational model.
I feel like this is a crucial question for examining social/political determinants of health & health inequities, but I have no idea what kind of analysis would be appropriate. A lot of the studies I have looked at so far are more in the form of predictor
--> outcome & not in the form of predictor --> mediator --> outcome. Or they follow individual people in longitudinal studies, but then the focus is on the people as they move through time and space, not as much on the political/structural determinants in
a specific jurisdiction.
I'm wading into areas that are far, far away from what I was trained in decades ago, so I'd appreciate any help or any examples of studies that have tried to tackle this question.
Thanks,
Canan
--
Canan Karatekin, Ph.D.
Associate Professor | Institute of Child Development, 206C
| icd.umn.edu
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