"Toxic Liberality” in Health Care Research
It Has no Place!
from: https://www.macdc.org/news/defining-health-equity-%E2%80%9Ci-know-it-when-i-see-it%E2%80%9D
The simple truth is that if you really are interested in “health equity” you must throw away Critical Race Theory.
I want to be clear: I am no proponent of “Diversity”, “Equity” and “Inclusion” as they are currently practiced. These terms are euphemistic descriptions of what are really “Orthodoxy”, “Inequality” and “Discrimination”. However, I, as I think all healthcare professionals are, or should be, in favor of maximizing the health of all. I would like to see everyone’s health increased to an equal optimum. The problem is that Critical Race Theory will not do that. And I base this conclusion on actual evidence, not political wishful thinking. I see things as they are, not as how I think they should be.
Those who have been reading my substack know that I view health and healthcare as a Complex Adaptive System. This means that an accurate understanding of “starting point” is necessary to make sense out of change. Complex Adaptive Systems move according to “emergent” order. Attempting to impose order only results in disastrous unintended consequences. Movement is done through the use of “attractors” which may not be known ahead of time as cause and effect are seen after the fact: “retrospective coherence”. We must augment the positive attractors (what works) and dampen the negative attractors (what doesn’t work). Since the time horizon in Complex Adaptive Systems is so short, we must frequently evaluate our efforts and make adjustments.
This runs counter to the classic Scientific Method in which we formulate a hypothesis ahead of time and then test that hypothesis. It works well in things that are merely complicated, but not those that are truly complex. It can certainly improve performance of coronary artery surgery, but it is not so successful in ameliorating the cause of that coronary artery disease.
A few years ago, I was invited to join the visiting adjunct faculty at Arizona State University to help bring an understanding of Complexity Science to what was then the School for the Science of Healthcare Delivery. I was particularly interested to see if Complexity Science could be used to understand the relationships between “Health Factors” and “Health Outcomes”.
The assumption is that healthy factors will lead to healthy outcomes. In the merely complicated world, they are seen as cause and effect. If you want to improve the health outcomes of people, you must improve their health factors: get them to eat healthy food, exercise, stop smoking, have regular preventive care, avoid environmental health risks, etc... Fortunately, we have a tool to study this, “The County Health Rankings and Roadmaps.”
This is a research data repository supported by the Robert Wood Johnson Foundation and has taken objective data from the University of Wisconsin Population Health Institute:
https://uwphi.pophealth.wisc.edu
collected across a vast swath of human experience and correlating the health of virtually all the counties in the United States. The purpose of the rankings and roadmaps is articulated on the website:
The CHR&R program provides data, evidence, guidance, and examples to build awareness of the multiple factors that influence health and support leaders in growing community power to improve health equity. The Rankings are unique in their ability to measure the health of nearly every county in all 50 states, and are complemented by guidance, tools, and resources designed to accelerate community learning and action. CHR&R is known for effectively translating and communicating complex (my emphasis added) data and evidence-informed policy (my emphasis added) into accessible models, reports, and products that deepen the understanding of what makes communities healthy and inspires and supports improvement efforts. County Health Rankings & Roadmaps’ work is rooted in a sincere belief in health equity, the idea that everyone deserves a fair and just opportunity to be as healthy as possible (my emphasis added).
It sounded great, so I decided to investigate this further. I looked at the data for Health Outcomes and Health Factors in 71 counties in Wisconsin and found this:
Yes, Health Outcomes seemed to be related to Health Factors, but in a very loose fashion. At least 40% of the Health Outcome Rank is explained by something other than the Health Factor rank.
Then I looked at the same data from the 15 counties in Arizona:
Here 60% of the Health Outcome Rank is explained by something other than Health Factor rank. What is going on? And why are there several counties (red box) that do quite a bit better in Outcome than would be expected by Factors? Santa Cruz county ranked 9th in Factors, but 1st in Outcomes. Yuma County ranked 10th in Factors but 3rd in Outcomes.
That question sparked our major investigation into what is known as “The Hispanic Paradox” in healthcare. Recent immigrants, particularly those who do not speak English at home, have health outcomes much better than would be expected. The finding does not exist to the same degree in neighboring New Mexico, which has a higher percentage of Hispanic population:
From our study:
The basis for the Hispanic Paradox is the fact that a population has better health results than would be expected. In order to investigate the potential disconnection between Health Factors and Health Outcomes, the Z-Score for the Health Outcomes were subtracted from the Z-Score for Health Factors. A positive result would indicate that county “overperfomed” and had better Health Outcomes than would ordinarily be expected if there was a linear relationship between Health Factors and Health Outcomes. The results are given in Figure 2.
Figure 2. Percentage of Hispanic population graphed against “Expected vs Actual Performance”. Positive numbers indicate performance better than expected. In Arizona, all but one of the counties with a Hispanic population above 45% had Health Outcomes better than their Health Factors would indicate.
There appears to be a phase shift at a Hispanic population of @ 45% in Arizona. Below that, there is a suggestion that a low percent of Hispanic population is initially associated with a worse than expected performance. This evens out from 20-40%. In New Mexico, no clear association can be seen….
The analysis shows that an “Hispanic Paradox” seems to be clearly present in Yuma and Santa Cruz counties in Arizona for all the years studied. In these counties, Health Outcomes were always better than average, and always better than expected. Both counties are located on the United States/Mexico Border. Both are majority Hispanic with that percentage increasing 2011-2016 (Yuma @ 57-62%, Santa Cruz @80-83%). Both have high Non-English Speakers, with that value decreasing 2011-2016 (Yuma @23-14%; Santa Cruz @39-21%).
The situation in New Mexico is less clear. Although the percentage of Hispanic population is higher, it is more dispersed with the highest percentage of those of Hispanic heritage located in the north central portion of the state.
The underlying reason for this “Hispanic Paradox” appears to be rooted in a concept termed “Curanderismo”. This this is a folk world-view common to Latino culture in the Western Hemisphere that produces a syncretic, eclectic and holistic social support system that is not captured by our view of “Health Factors” but is extremely powerful:
One of the negative consequences of acculturation is seen in the disappearance of this positive element in the Hispanic communities of New Mexico. In this regard, the experience is very much like the “Roseto Effect” describe over a half century ago:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1695733/
Italian immigrants living in Roseto, Pennsylvania were found to have markedly less incidence of heart disease. Investigation showed it was not due to genetics, diet or other postulated causes such as smoking or other lifestyle choices. It was due to a similar robust social support network that is operative in the “Hispanic Paradox”. Unfortunately, like the effect of acculturation on Hispanics in New Mexico, the protective effect was lost after modern life diminished the need and desire for such a support system.
We realized that there was indeed a “truly complex” and not “merely complicated” relationship between the Health Factors identified in the County Rankings and Roadmaps. When we looked at this relationship of a function modifying Health Factors to produce Health Outcomes, although we could not identify the additional elements producing this modification, we could measure it!
We used the allometric scaling co-efficient found to be operative in both biologic processes as well as the equation used in the transformation function found in biologic processes as well as aspects of cities and corporations to find a modifying constant, k, for each county in Arizona:
Health Outcomes = k (Health Factors)0.25
This constant was remarkably similar for each county across the years 2011-2016:
In 2021 as COVID was winding down, we were curious if the “Hispanic Paradox” was still operative, or if the lockdowns associated with our response to the emergency would have curtailed this complex social support system. We investigated funding opportunities as we thought it would be a great fit for studies of “health disparities”. Unfortunately, as we proceeded with the grant application it was clear that the only “health disparity” in which the organization had an interest were in exposing negative health disparities due to “systemic racism”. As our evidence pointed to a situation where the health disparity was positive for the non-white population, it was clear it never would even be considered, and the project was dropped.
Recently I did a cursory investigation of the 2023 data for Arizona in the County Health Rankings and Roadmaps website. I wanted to just answer the question if Yuma and Santa Cruz counties continued to have better than expected Health Outcomes, based on their Health Factors. They do. This is a simple graph of Health Factors Rank minus Health Outcomes Ranks. If a county has a positive number (Yuma and Santa Cruz) the Outcomes are better than expected. If it has a negative number (Yavapai and Coconino) the Outcomes are worse than expected.
The County Constants have the same general relationships with each other, but have different values:
It is exceedingly frustrating that there appears to be a tremendous amount of useful information that could be gleaned from a further investigation into this data.
What was the impact of COVID and especially our response on the social support network in Yuma and Santa Cruz Counties? Positive attractors are at work, but what are they and how can we augment them? How can the lessons of the Hispanic Paradox be exported to the population as a whole? What are the unknown elements that modify the impact of Health Factors on Health Outcomes? They seem to behave like “Dark Matter:
http://rinergroup.com/healthcare-future-known-unknown/
in so far as we can measure them, but we can’t identify them. Would it be a better use of resources to address this modifier of Health Factors rather than the Health Factors themselves? The modifier MULTIPLIES the total of the Health Factors instead of just arithmetically adding to them.
These are all questions crying out for investigation. Unfortunately, since the answer to these questions does not fit the political agenda of Toxic Liberality, they will not be given any attention. Ironically, the elites controlling medical science are more interested in pointing their fingers at “systemic racism” and supporting their concept of Critical Race Theory than understanding how some non-white populations have learned something that could materially help the rest of the world. It’s almost like they wish to keep them victims rather than acknowledge their true accomplishment. What a waste…. I look forward to the day when science, especially medical science, waits to articulate the conclusion until after the research is performed.
While most of this info is far above my "Educational Pay Grade" I can see the intrinsic benefits of a social togetherness in "Mental and Emotional" health that might be better for the aftermath of everyday health problems. My own observations of DEI are mainly political and I really never thought about the application of them in a medical sense. Doug