Racial and Ethnic Disparities in the Prescribing of Pain Medication in US Primary Care Settings, 1999–2019: Where Are We Now?
Bibliographic record
Abstract
BACKGROUND: Policy initiatives have attempted to reduce healthcare inequalities in the USA, but evidence on whether these initiatives have reduced racial and ethnic disparities in pain treatment in primary care is lacking. OBJECTIVE: To determine whether racial and ethnic disparities in medication prescribed for pain in primary care settings have diminished over a 21-year period from 1999 to 2019. DESIGN: An annual, representative cross-sectional probability sample of visits to US primary care physicians, taken from the National Ambulatory Medical Care Survey. PATIENTS: Pain-related visits to primary care physicians. MAIN MEASURES: Prescriptions for opioid and non-opioid analgesics. KEY RESULTS: Of 599,293 (16%) sampled visits, 94,422 were pain-related, representing a population-weighted estimate of 143 million visits made annually to primary care physicians for pain. Relative risk analysis controlling for insurance, pain type, and other potential confounds showed no difference in pain medication prescribed between Black and White patients (p = .121). However, White patients were 1.61 (95% CI 1.32-1.97) and Black patients 1.57 (95% CI 1.26-1.95) times more likely to be prescribed opioids than a more underrepresented group consisting of Asian, Native-Hawaiian/Pacific-Islander, and American-Indian/Alaska-Natives (ps < .001). Non-Hispanic/Latino patients were 1.32 (95% CI 1.18-1.45) times more likely to receive opioids for pain than Hispanic/Latino patients (p < .001). Penalized cubic spline regression found no substantive narrowing of disparities over time. CONCLUSIONS: These findings suggest that additional intervention strategies, or better implementation of existing strategies, are needed to eliminate ethnic and racial disparities in pain treatment towards the goal of equitable healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".