Current State and Demographic Trends of Medically Underserved Populations in Rare Disease Research in the United States
Notice bibliographique
Résumé
Abstract Background Addressing inequities and health disparities for medically underserved populations (MUPs) is critical, as they already face systemic bias and barriers, such as historical mistrust of healthcare systems. To achieve health equity, we need systematic approaches to measuring, quantifying, and reporting diversity, equity, inclusion, and accessibility (DEIA) metrics. Methods The objective of the study was to analyze literature and clinical trials to summarize the current state of demographics and socioeconomic factors (SF) reporting for MUPs in US-based RD clinical research. PubMed, Cochrane Library, and ClinicalTrials.gov were searched (1983– 2023). A universal set of 30,303 unique RD terms from the Genetic and Rare Diseases Information Center, Orphanet, Rare-X, and ClinicalTrials.gov was used to filter publications and clinical trials. Publications that reported demographics or SFs, were US-based, and involved one or more RDs were included for analysis. Clinical trials that were US-based, involved an RD, and had study results posted were also included. Age, sex or gender, race, ethnicity, and SF data were extracted and analyzed using descriptive statistics. Race and ethnicity data were compared with the US census. The representation of MUPs in RD clinical research was assessed based on the frequency of publications and clinical trials reporting 13 variables. Results We reviewed 234 publications and 8475 RD clinical trials. Age was the most reported demographic variable (publications: 94%; clinical trials: 100%), followed by sex or gender (86.3%; 100%). Race (50%; 45.7%) and ethnicity (29.9%; 38.5%) were less frequently reported and often in a variable format in publications compared with the ClinicalTrials.gov database. At least one SF was reported in 15.8% of the publications and 0.2% of the trials. American Indian or Native Alaskan, Asian, Hispanic, and Latino participants were significantly underrepresented compared with the US census averages. Data were largely absent for other MUPs: lesbian, gay, bisexual, transgender, and queer or questioning individuals, rural residents, veterans, immigrants, and those affected by disability and poverty. Conclusions Significant gaps exist in demographics and SF reporting in RD clinical research, and several MUPs are underrepresented. Therefore, a framework to enhance DEIA in RD research is urgently needed.
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 | 0,072 | 0,207 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,037 | 0,042 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».