What Drives the Contemporary Black–White Racial Disparities in Gout in the US? Impact of Social Determinants of Health
Bibliographic record
Abstract
Together with the substantial role of genetic factors, serum urate levels and the occurrence of gout are also heavily driven by environmental and clinical factors, including adiposity, dietary patterns, alcohol, kidney function, and diuretic medication use. These are, in turn, greatly influenced by the social determinants of health, which encompass access to health care, availability of healthy foods, and opportunities for physical activity but also education, income, social norms, and racism, among other forces. Gout-related health disparities have been described for Māori and Pacific people in New Zealand, but racial disparities in gout prevalence and outcomes between Black and White Americans have been under-recognised, and particularly, sex-specific data are scarce. In this article we review evidence from prior cohort studies and contemporary national-level data which show the incidence and prevalence of gout and hyperuricemia in Black adults in the US have come to exceed that in White adults and are disproportionately greater in Black women. Importantly, this emerging disparity can be attributed entirely to social determinants of health, including higher levels of adiposity and poverty in Black women compared to White women and lower kidney function and poorer quality diet among Black men compared to White men. Furthermore, Black patients with gout have received poorer quality gout-related care and experienced higher levels of healthcare use, especially Black women. While identifying targets for culturally safe interventions for addressing risk factor disparities is essential, evidence gaps remain about potential disparities in longer-term outcomes of gout, including cardio-metabolic-kidney endpoints and premature mortality. Sociodemographically diverse, population-based longitudinal cohort studies, research on implementation strategies for improved gout care delivery models for underserved groups, and efforts to minimise structural racism and its effects are key to achieving health equity in gout.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".