Rural-Urban Disparities of Alzheimer's Disease and Related Dementias: A Scoping Review
Bibliographic record
Abstract
Background & Aim: 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. Methods: 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 utilized to identify relevant articles. The search was performed on August 16, 2024, and included articles published from 2000 onward. Results: 62 articles met the eligibility criteria for data extraction and synthesis. Most articles were published after 2010 (90.3%) and were concentrated in the US, China, and Canada (64.5%). A majority had cross-sectional (59.7%) or cohort study designs (24.2%), primarily examining prevalence (40.3%) or incidence (11.3%). Findings often indicated a higher prevalence and incidence in rural areas, although inconsistent rural-urban classification systems were noted. Common risk factors included female gender, lower education level, lower income, and comorbidities such as diabetes and cerebrovascular diseases. Environmental (12.9%) and lifestyle (14.5%) 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. Conclusion: 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, such as environmental, lifestyle, and genetic influences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".