Educational disparities in joint pain within and across US states: do macro sociopolitical contexts matter?
Bibliographic record
Abstract
ABSTRACT: Despite growing recognition of the importance of social, economic, and political contexts for population health and health inequalities, research on pain disparities relies heavily on individual-level data, while neglecting overarching macrolevel factors such as state-level policies and characteristics. Focusing on moderate or severe arthritis-attributable joint pain-a common form of pain that considerably harms individuals' quality of life-we (1) compared joint pain prevalence across US states; (2) estimated educational disparities in joint pain across states; and (3) assessed whether state sociopolitical contexts help explain these 2 forms of cross-state variation. We linked individual-level data on 407,938 adults (ages 25-80 years) from the 2017 Behavioral Risk Factor Surveillance System with state-level data on 6 measures (eg, the Supplemental Nutrition Assistance Program [SNAP], Earned Income Tax Credit, Gini index, and social cohesion index). We conducted multilevel logistic regressions to identify predictors of joint pain and inequalities therein. Prevalence of joint pain varies strikingly across US states: the age-adjusted prevalence ranges from 6.9% in Minnesota to 23.1% in West Virginia. Educational gradients in joint pain exist in all states but vary substantially in magnitude, primarily due to variation in pain prevalence among the least educated. At all education levels, residents of states with greater educational disparities in pain are at a substantially higher risk of pain than peers in states with lower educational disparities. More generous SNAP programs (odds ratio [OR] = 0.925; 95% confidence interval [CI]: 0.963-0.957) and higher social cohesion (OR = 0.819; 95% CI: 0.748-0.896) predict lower overall pain prevalence, and state-level Gini predicts higher pain disparities by education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".