Multilevel Diabetes Prevention Interventions to Address Population Inequities in Diabetes Risk: Scoping Review
Notice bibliographique
Résumé
Background: Type 2 diabetes risk is disproportionately higher among structurally marginalized communities, partly due to influences from social, economic, and environmental determinants of health. Individual-level diabetes prevention strategies address proximal factors, such as modifiable behaviors, often overlooking the role of multilevel socioecological factors that contribute to diabetes risk and inequities. Multilevel diabetes prevention interventions involve actions that address multiple health determinants across the individual, community, and systemic levels of influence, offering a promising approach to reducing inequities in diabetes risk. Objective: This scoping review aimed to systematically map the types of health determinants addressed in multilevel diabetes prevention interventions that have been implemented for addressing population inequities in diabetes risk and to describe what evidence exists regarding their effectiveness. Methods: A comprehensive literature search was conducted in PubMed, CINAHL, MEDLINE, Embase, Web of Science, and gray literature sources (websites of government agencies and local/international nongovernmental health organizations) for studies published from the year 2000 to 2024. The research team developed a conceptual framework to guide the scoping review and define multilevel interventions for eligibility. Eligibility criteria included studies focusing on multilevel diabetes prevention interventions targeting diabetes relevant risk factors at more than one level of influence (micro, meso, and macro) and where intervention outcomes were reported. Data extraction included study characteristics, intervention target populations and coverage, targeted health determinants, and intervention outcomes and was completed by 2 independent reviewers. Data synthesis involved mapping health determinants addressed by each multilevel intervention according to our conceptual framework and a narrative synthesis of findings on themes corresponding to intervention types and reported outcomes. Results: Of 7813 articles retrieved, a total of 25 studies met the inclusion criteria. Interventions consisted of targeted interventions for high-risk populations (n=7), environmental-based interventions (n=7), and community-based interventions (n=11). Most interventions addressed health determinants at 2 levels (micro and macro) (14/25, 56%) or 3 levels (micro, meso, and macro) (11/25, 44%). All studies reported on proximal outcomes, most frequently on weight, physical activity, and dietary behaviors. One-third (8/25, 32%) of studies reported outcomes on changes in metabolic risk. None of the studies reported on equity outcomes related to changes in population inequities in diabetes incidence. Only 8% (n=2) of studies reported an equity outcome that captures disparities in a diabetes risk factor level between disadvantaged and advantaged population groups. Conclusions: Our review identified a research gap in that outcomes on population inequities in diabetes risk have not been consistently measured in multilevel diabetes prevention interventions, and the impact of these interventions on reducing population inequities in diabetes incidence is not consistently examined or reported. Future research should prioritize equity outcomes in evaluations of multilevel diabetes prevention interventions and emphasize impacts on disadvantaged populations and population inequities.
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 | 0,026 | 0,107 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,008 |
| Bibliométrie | 0,020 | 0,017 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».