Racial Disparities in Opioid Prescribing in the United States from 2011 to 2021: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: This meta-analysis is an update to a seminal meta-analysis on racial/ethnic disparities in pain treatment in the United States (US) published in 2012. Since then, literature has accumulated on the topic and important policy changes were made. Objective: Examining racial/ethnic disparities in pain management and investigating key moderators of the association between race/ethnicity and pain outcomes in the US. Methods: We performed a systematic search of publications (between January 2011 and February 2021) from the Scopus database. Search terms included: race, racial, racialized, ethnic, ethnicity, minority, minorities, minoritized, pain treatment, pain management, and analgesia. All studies were observational, examining differences in receipt of pain prescription medication in various settings, across racial or ethnic categories in US adult patient populations. Two binary analgesic outcomes were extracted: 1) prescription of "any" analgesia, and 2) prescription of "opioid" analgesia. We analyzed these outcomes in two populations: 1) Black patients, with White patients as a reference; and 2) Hispanic patients, with non-Hispanic White patients as a reference. Results: The meta-analysis included twelve studies, and the systematic review included forty-three studies. Meta-analysis showed that, compared to White patients, Black patients were less likely to receive opioid analgesia (OR 0.83, 95% CI [0.73-0.94]). Compared to non-Hispanic White patients, Hispanic patients were less likely to receive opioid analgesia (OR 0.80, 95% CI [0.72-0.88]). Conclusion: Despite a decade's gap, the findings indicate persistent disparities in prescription of, and access to opioid analgesics for pain among Black and Hispanic populations in the US.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".