Alzheimer’s Disease and Related Dementia in Indigenous Populations: A Systematic Review of Risk Factors
Bibliographic record
Abstract
BACKGROUND: There remains a lack of information and understanding of the prevalence and incidence of Alzheimer's disease and related dementia in Indigenous populations. Little evidence available suggests that Indigenous peoples may have disproportionately high rates of Alzheimer's disease and related dementia (ADRD). OBJECTIVE: Given this information, this study systematically explores what risk factors may be associated with ADRD in Indigenous populations. METHODS: A search of all published literature was conducted in October 2016, March 2018, and July 2019 using Medline, Embase, and PsychINFO. Subject headings explored were inclusive of all terms related to Indigenous persons, dementia, and risk. All relevant words, phrases, and combinations were used. To be included in this systematic review, articles had to display an association of a risk factor and ADRD. Only studies that reported a quantifiable measure of risk, involved human subjects, and were published in English were included. RESULTS: Of 237 articles originally identified through database searches, 45 were duplicates and 179 did not meet a priori inclusion criteria, resulting in 13 studies eligible for inclusion in this systematic review. CONCLUSION: The large number of potentially modifiable risk factors reported relative to non-modifiable risk factors illustrates the importance of socioeconomic context in the pathogenesis of ADRD in Indigenous populations. The tendency to prioritize genetic over social explanations when encountering disproportionately high disease rates in Indigenous populations can distract from modifiable proximal, intermediate, and distal determinants of health.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".