Prevalence and incidence of dementia among indigenous populations: a systematic review
Bibliographic record
Abstract
BACKGROUND: Indigenous populations may be at increased risk, compared with majority populations, for the development of dementia due to lower education levels and socio-economic status, higher rates of diabetes, hypertension, cardiovascular disease and alcohol abuse, an aging population structure, and poorer overall health. This is the first systematic review investigating the prevalence and incidence of dementia in indigenous populations worldwide. METHODS: This systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE, Embase, and PsycInfo for relevant papers published up to April 2015. Studies were included if they reported prevalence or incidence, the disease typically occurred after the age of 45, the study population included indigenous people, and the study was conducted in the general population. RESULTS: Fifteen studies representing five countries (Canada, Australia, the USA, Guam, Brazil) met the inclusion criteria. Dementia prevalence ranged from 0.5% to 20%. Retrospective studies relying on medical records for diagnoses had much lower prevalence rates and a higher risk of bias than population-based prospective studies performing their own diagnoses with culturally appropriate cognitive assessment methods. CONCLUSIONS: The prevalence of dementia among indigenous populations appears to be higher than it is for non-indigenous populations. Despite a building body of evidence supporting the need for dementia research among indigenous populations, there is a paucity of epidemiological research, none of which is of high quality.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".