Rural–urban disparities of Alzheimer's disease and related dementias: A scoping review
Notice bibliographique
Résumé
The rising age of the global population has made Alzheimer's disease and related dementias (ADRD) a critical public health problem, with significant health-related disparities observed between rural and urban areas. However, no previous reviews have examined the scope and determinant factors contributing to rural-urban disparities of ADRD-related health outcomes. This study aims to systematically collate and synthesize peer-reviewed articles on rural-urban disparities in ADRD, identifying key determinants and research gaps to guide future research. We conducted a systematic search using key terms related to rural-urban disparities and ADRD without restrictions on geography or study design. Five search engines-MEDLINE, CINAHL, Web of Science, PubMed, and Scopus-were used to identify relevant articles. The search was performed on August 16, 2024, and included English-language articles published from 2000 onward. Sixty-three articles met the eligibility criteria for data extraction and synthesis. Most articles were published after 2010 (85.7%) and were concentrated in the United States, China, and Canada (66.7%). A majority had cross-sectional (58.7%) or cohort study designs (23.8%), primarily examining prevalence (41.3%) or incidence (11.1%). Findings often indicated a higher prevalence and incidence in rural areas, although inconsistent rural-urban classification systems were noted. Common risk factors included female sex, lower education level, lower income, and comorbidities such as diabetes and cerebrovascular diseases. Environmental (12.7%) and lifestyle (14.3%) factors for ADRD have been less explored. The statistical methods used were mainly traditional analyses (e.g., logistic regression) and lacked advanced techniques such as machine learning or causal inference methods. The gaps identified in this review emphasize the need for future research in underexplored geographic regions and encourage the use of advanced methods to investigate understudied factors contributing to ADRD disparities, such as environmental, lifestyle, and genetic influences. Highlights: Few studies on rural-urban ADRD disparities focus on low- and middle-income countries.Common risk factors include female sex, low education attainment, low income, and comorbidities.Inconsistent definitions of "rural" complicate cross-country comparisons.Environmental and lifestyle factors affecting ADRD are underexplored.Advanced statistical methods, such as machine learning and causal inference, are recommended.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 tête enseignante, 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 ».