Ethnic differences in injury mortality rates among adult emergency healthcare service users in high-income countries: a scoping review
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,008 |
| Bibliométrie | 0,021 | 0,019 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».