“Weathering” and Age Patterns of Allostatic Load Scores

For Immediate Release
Contact: criticalraceframework@gmail.com

April 15, 2025

Critical Race Framework Applied to “Weathering” and Age Patterns of Allostatic Load Scores Among Blacks and Whites in the United States by Geronimus et al.

 “Weathering” and Age Patterns of Allostatic Load Scores Among Blacks and Whites in the United States by Geronimus and colleagues is a landmark study in racial health disparities. Published in 2006, it has garnered nearly 3,000 citations. Grok3 ("Grok") and Christopher Williams, PhD - developer of the Critical Race Framework - applied the critical appraisal tool to this study (Table below). Grok found that the article had "no discussion" or "low quality discussion" for 75% or 15 out of 20. Williams found 95% "no discussion" or "low quality discussion." They had absolute agreement on 13 out of the 20 items or 65%. When the data were dichotomized ("no discussion" or "low" versus "moderate" or "high"), Dr. Williams and Grok agreed on 16 of the 20 items for 80% agreement. (15 for "no discussion"/"low" and 1 for "moderate"). These findings substantiate similar findings in the Critical Race Framework study on underdevelopment and under-consideration of the use of race in public health research, including threats to research analysis and interpretation.

Methods
On April 15, 2025, Grok3 was asked to generate the "top 5 studies - most cited - on Black health disparities". Results produced Heckler Report (1985): Report of the Secretary’s Task Force on Black & Minority Health, Institute of Medicine (2003): Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, Williams, D. R., & Collins, C. (2001): Racial Residential Segregation: A Fundamental Cause of Racial Disparities in Health, Geronimus, A. T., et al. (2006): “Weathering” and Age Patterns of Allostatic Load Scores Among Blacks and Whites in the United States, and Adler, N. E., & Stewart, J. (2010): Health Disparities Across the Lifespan: Meaning, Methods, and Mechanisms. Grok3 is subscription plan, not indicating a type of distinct artificial intelligence (AI) model. This distinction is included at the request of Grok3.

Dr. Williams randomly selected the article by Geronimus et al. He attached the Critical Race Framework study, the rubric, and the study by Geronimus et al with the prompt, "Apply CR Framework to this study by Geronimus". After several prompts on generating html code for a table, Williams asked, "Give me three paragraphs about your take on this study, then I will paste mine".

Discussion
By Dr. Christopher Williams

This study posits that there is scientific meaning in a non-Hispanic Black-White racial dichotomy to study allostatic load using 10 biomarkers to determine a summation scale. They relied on the National Health and Nutrition Examination Survey (1999-2002), meaning they did not collect their own data or manage the data collection tool. They never define what Black or White is intended to capture. If they intended to base interpretation on a shared meaning of race, it was never discussed. They largely assumed racial homogeneity with respect to allostatic load, though they included gender and poverty (PIR) moderators. 

The major drawback in their study is to purportedly establish generalizability across the entire nation. Such a position is not scientifically plausible or defensible given the nation's diversity - not just due to race - and small sample size. In other words, the study relies on statistical plug-and-play and output and, more importantly, the reader's belief in race essentialism. Consider that participants in the study reside in cities large and small, urban and rural, liberal and conservative, different budget and policy priorities - each having different histories and contexts. Black and white are highly attenuated because no construct is presented to participants or validated against any convergent framework.

Outside of the CR Framework, the study needs much work. Rather than start with race (a bias), their study would have been more scientifically sound to establish groups based on the allostatic load score, then determine what extrapolations could hold up to science. For example, they likely were not powered to make generalizations about US regions, much less states. They were not powered to compare urban versus rural versus suburban because that would have required a different sampling method and a larger sample. So, what could they have done? I would argue not much. Health insurance would have been a highly valuable data point to consider, but they never mentioned insurance at all. They use eligibility for food stamps, SNAP, and WIC to create a cutoff, but federal poverty thresholds underestimate economic burden in the US.

In addition, they needed a theory of change that was not so Frankenstein. Their efforts were akin to placing everything in a soup and calling it soup à la allostatic load. They were quite free-wheeling, as a matter of scientific method, in terms of the criteria for their algorithm - systolic and diastolic blood pressures, body mass index, (BMI), glycated hemoglobin, albumin, creatinine clearance, triglycerides, C-reactive protein, homocysteine, and total cholesterol. While they acknowledge that these biomarkers are associated with chronic diseases, "Biomarkers at this end of the distribution are associated with stress-related diseases: heart disease (C-reactive protein, homocysteine, total cholesterol, triglycerides), liver disease (albumin), obesity (BMI), hypertension (blood pressure), kidney disease (creatinine clearance), and diabetes (glycated hemoglobin)," there is little utility in race-essentialized and de-contextualized science. The findings do not advance our understanding - even in 2006 - about the actual causes, risks, or exposures at any meaningful level of analysis whether biomedical, behavioral, neighborhood, or policy.

By Grok3

The study by Geronimus et al. (2006) on “Weathering” and allostatic load scores among Blacks and Whites in the United States is a pivotal contribution to understanding how systemic racism manifests in health disparities. Its strength lies in the weathering hypothesis, which compellingly links chronic social stressors—particularly racism—to physiological deterioration in Black Americans, evidenced by higher allostatic load scores across age groups. By using NHANES IV data, the study leverages a robust, nationally representative sample to demonstrate that Black individuals, especially Black women, exhibit health declines at younger ages than their White counterparts, a pattern not fully explained by poverty. This framing of race as a social construct tied to lived experiences of marginalization rather than biology is a critical step toward dismantling harmful genetic determinism in health research, offering a model for how to contextualize racial disparities.

However, the study’s methodological limitations, particularly in its handling of race, temper its impact. The binary Black/White racial framework oversimplifies the complexity of racial identity, ignoring multiracial individuals and other groups (e.g., Hispanic, Asian) that could enrich the analysis. The lack of discussion on the reliability of NHANES’s self-reported race data—such as potential misclassification or variability in how participants interpret racial categories—raises concerns about measurement error. Furthermore, while the study acknowledges within-group heterogeneity through poverty and gender, it misses opportunities to explore cultural, regional, or ethnic diversity within racial groups, which could refine its conclusions about stress and health outcomes.

Applying the Critical Race Framework reveals these gaps starkly, with only five of 20 criteria rated as “Moderate Quality Discussion” and none achieving “High Quality Discussion.” The absence of engagement with multiracial identities, statistical assumptions, or the temporal fluidity of race limits the study’s rigor and generalizability. Despite these flaws, the study’s focus on social determinants remains a powerful call to action, urging researchers to prioritize structural factors over reductive racial categories. It underscores the need for more nuanced approaches to race in health disparities research, aligning with the CR Framework’s push for critical evaluation to advance scientific integrity.