Editorial: Equity in health: placing human diversity at the heart of neuroscientific research
Notice bibliographique
Résumé
Medical research that meaningfully serves global populations must reflect the complexity of human diversity. Yet, historically, much of biomedical and public health research has failed to incorporate key dimensions such as sex/gender, ethnicity, culture, and socioeconomic status (Braveman & Gottlieb, 2014;George et al., 2014;Oh et al., 2015;Subramaniapillai et al., 2024). Health outcomesparticularly those related to brain and neurological function-are shaped not only by biological factors but also by environmental factors and structural inequities that influence access to care, exposure to risk, and inclusion in scientific research (National Academies of Sciences, Engineering, and Medicine, 2023; Legaz et al., 2024;Williams & Collins, 2013). This Research Topic brings together five timely contributions that illuminate how social, economic, and demographic disparities intersect to shape major neurological and public health challenges-from stroke and concussion to gender-based violence and premature mortality. Together, these contributions underscore the urgent need for equity-driven approaches in both research and policy, offering insight into the systemic reforms needed to close persistent gaps in health outcomes.One major theme emerging from these studies is the influence of social and geographic disparities on neurological outcomes. Using global data spanning three decades, Liu et al (2025) examine the burden of ischaemic stroke among adults aged 20 years and older from 1990 to 2021. By incorporating subnational geographic divisions in addition to national stratification, they reveal how changes in agestandardized mortality and disability-adjusted life years (DALYs) are linked to the sociodemographic index, a composite marker of income, education, and fertility rate. The findings underscore the need for locally tailored health policy interventions that reflect both regional and population-specific needs.Such disparities are not limited to the global scale. In the United States, stark health gaps exist between population subgroups even within the same geographic regions. Nadeau et al (2025) investigate this issue by analyzing premature mortality among American Indians in North Dakota, who experience some of the highest age-adjusted mortality rates in the country, revealing deep-rooted health inequities (Carron, 2020). By stratifying data by age group and sex, they highlight the social determinants underlying these elevated mortality rates and propose community-informed, culturally sensitive solutions, from prevention and outreach to institutional collaborations designed to close persistent care gaps.The consequences of overlooking diversity are also evident in how research itself is conducted.MacEachern et al (2024) examined whether concussion literature accurately represents Black/African American individuals, who are overrepresented in high-risk sports but often underdiagnosed. Their metaanalysis reveals striking inconsistencies across data sources-hospital records, national surveys, and sports clinics-with concussion rates varying depending on the setting. These inconsistencies raise concerns about how structural bias, whether through study design, access to care, or diagnostic criteria, can distort our understanding of injury risk and outcomes, ultimately limiting the relevance of care delivery.In parallel, a gendered lens on brain health reveals how sex and socioeconomic context interact to influence neurological risk and vulnerability. Ronne-Engström and Friberg (2025) examined the socioeconomic profiles of 890 Swedish patients with spontaneous subarachnoid hemorrhage, compared to matched controls. Living in sparsely populated areas emerged as a key risk factor, but striking sex differences were also evident. Women with subarachnoid hemorrhage were more economically vulnerable-often unemployed, single, and with lower income-while men showed a different vulnerability related to changes in civil status. These findings suggest the need for targeted, preventative healthcare strategies that consider socioeconomic context, particularly in rural and marginalized populations.This need for intersectional, context-sensitive healthcare is brought into sharper focus by Taiebine's (2025) opinion article, which draws attention to the neurological and psychological consequences of gender-based violence on brain and mental health among refugee and migrant women.Despite global action plans that recognize gender-based violence as a public health issue (e.g., World Health Organization, 2016), the neurological consequences of trauma, especially in the context of forced displacement and limited access to care, remain underexamined. Taiebine argues for the development of integrated, trauma-informed, and culturally sensitive interventions that address the full scope of survivors' needs across health, legal, and social systems. This Research Topic contributes to an evolving body of evidence calling for intersectional and community-engaged approaches in neuroscience research and public health policy. These articles show that structural determinants are integral to understanding brain and health outcomes. More importantly, they demonstrate that addressing health inequities requires not just more diverse data collection, but a transformation in how public health systems identify risk, allocate resources, and design inclusive interventions. Yet if research is to serve all people, equity must also extend to who gets to participate in shaping the science itself. Open-access fees, publishing barriers, and structural biases in academia disproportionately affect researchers from low-resource settings and historically marginalized communities (Byrne 2024;Frank et al., 2023;Kwon, 2022). Without addressing these limitations, we risk perpetuating the very exclusions we seek to correct. As editors and contributors, we believe that equity in research must also include equity in access and authorship.Looking forward, we encourage continued work that integrates intersectional frameworks, culturally grounded definitions of health, and inclusive research methods spanning genomics, epidemiology, and implementation science. In doing so, we can build a research ecosystem where diversity is not an afterthought but a guiding principle, advancing both the science and the equity it seeks to serve.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,010 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,011 | 0,008 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,020 | 0,024 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,018 |
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 ».