Ethnic differences in injury mortality rates among adult emergency healthcare service users in high-income countries: a scoping review
Bibliographic record
Abstract
Background: Ethnic disparities in healthcare access and outcomes have been widely reported across different settings. In this scoping review, we aimed to explore whether adults from minority racial and ethnic backgrounds face higher risks of death after presenting with injuries to emergency healthcare services in high-income countries. Methods: (American Psychological Association, Washington, DC, USA)] for peer-reviewed studies published between January 2010 and March 2024. We included studies that compared mortality outcomes by race or ethnicity in emergency healthcare settings such as ambulance services, trauma centres and hospital emergency departments in high-income countries. Results: Out of the 1873 articles identified, 32 met the inclusion criteria. Of these, 20 reported higher risk of mortality for ethnic minority patients compared to White patients following injury. Most studies were conducted in the USA with limited representation from other high-income countries such as Canada and Israel. This strong emphasis on USA-based research limits how well the findings apply to other countries with different healthcare systems. A major issue identified across the studies was the inconsistency in how race and ethnicity were recorded and reported. This lack of standardisation makes it difficult to compare results across studies and may hide the true extent of disparities. Future work: To better understand and address ethnic disparities in trauma care, future research should adopt consistent and inclusive ethnicity coding to improve data quality and comparability across studies. Studies should be conducted in a wider range of high-income countries and include pre-hospital settings, where disparities may first appear. This will help build a more globally relevant evidence base. Researchers should also take an intersectional approach, examining how ethnicity combines with other social determinants to influence outcomes. In addition to mortality, future studies using longitudinal and mixed-methods designs should explore long-term recovery and access to rehabilitation to gauge the full impact of these health disparities. Limitations: The review focused solely on mortality outcomes, limiting insight into broader health outcomes such as long-term recovery, quality of life or patient experiences. It also did not explore how ethnicity interacts with other social factors such as gender, income, disability or immigration status. These gaps obscure the full extent of inequalities in emergency care. Conclusion: This review adds to the growing evidence that ethnic minority patients in high-income countries could be at a higher risk of injury-related deaths. However, inconsistent ethnicity coding and a USA-centric evidence base limit the generalisability of findings. To create fairer and more effective emergency care systems, future research must improve data quality, broaden its geographic scope and consider the complex social factors that shape health outcomes. Funding: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR132744.
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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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".