Beyond Black vs White: racial/ethnic disparities in chronic pain including Hispanic, Asian, Native American, and multiracial US adults
Bibliographic record
Abstract
ABSTRACT: Previous literature on race/ethnicity and pain has rarely included all major US racial groups or examined the sensitivity of findings to different pain operationalizations. Using data from the 2010 to 2018 National Health Interview Surveys on adults 18 years or older (N = 273,972), we calculated the weighted prevalence of 6 definitions of pain to provide a detailed description of chronic pain in White, Black, Hispanic, Asian, Native American, and multiracial groups. We also estimated modified Poisson models to obtain relative disparities, net of demographic and socioeconomic (SES) factors including educational attainment, family income, and home ownership; finally, we calculated average predicted probabilities to show prevalence disparities in absolute terms. We found that Asian Americans showed the lowest pain prevalence across all pain definitions and model specifications. By contrast, Native American and multiracial adults had the highest pain prevalence. This excess pain was due to the lower SES among Native Americans but remained significant and unexplained among multiracial adults. The pain prevalence in White, Black, and Hispanic adults fell in between the 2 extremes. In this trio, Hispanics showed the lowest prevalence, an advantage not attributable to immigrant status or SES. Although most previous research focuses on Black-White comparisons, these 2 groups differ relatively little. Blacks report lower prevalence of less severe pain definitions than Whites but slightly higher prevalence of severe pain. Net of SES, however, Blacks experienced significantly lower pain across all definitions. Overall, racial disparities are larger than previously recognized once all major racial groups are included, and these disparities are largely consistent across different operationalizations of pain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".