Prevalence of dementia among Indigenous populations of countries with a very high Human Development Index: a systematic review
Bibliographic record
Abstract
Dementia is a health priority for Indigenous peoples. Here, we reviewed studies on the prevalence of dementia or cognitive impairment among Indigenous populations from countries with a very high Human Development Index (≥0·8). Quality was assessed using the Joanna Briggs Institute risk-of-bias tool and CONSolIDated critERia for strengthening the reporting of health research involving Indigenous peoples (CONSIDER), with oversight provided by an Indigenous Advisory Board. After screening, 23 studies were included in the Review. Relative to the respective non-Indigenous populations, greater age-standardised prevalence ratios were observed in the Australian Aboriginal and Torres Strait Islander (2·5-5·2), Aotearoa-New Zealand Māori (1·2-2·0), and Singaporean Malay (1·3-1·7) populations, and greater crude prevalence ratios were observed in the Canadian First Nation (1·3), Singaporean Malay (2·3), Malaysian Melanau (1·7-4·0), American Indian and Alaska Native (1·0-3·2), and Chamorro of Guam (1·2-2·0) populations. The prevalence ratios were greater across younger age groups, predominantly comprising those younger than 70 years. 14 studies presented a moderate risk of bias and few studies reported Indigenous involvement. Despite improved management of risk factors, a greater prevalence of dementia persists in Indigenous populations, overall and at younger ages than in non-Indigenous populations. Future epidemiological work involving Indigenous populations should uphold and prioritise Indigenous perspectives.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".