Prevalence of youth type 2 diabetes in global Indigenous populations: a systematic review
Bibliographic record
Abstract
AIMS/HYPOTHESIS: We aimed to synthesise global prevalence estimates of type 2 diabetes among Indigenous youth aged under 25 years, and examine age- and gender-specific differences and secular trends. METHODS: We searched MEDLINE, Embase, CINAHL and Cochrane, and bibliographies of included studies, from 1 January 1980 to 14 September 2024. We included cross-sectional observational studies that reported diabetes point prevalence estimates (per 1000) and prevalence trends in Indigenous youth aged under 25 years from all regions. Age- and gender-specific analysis and secular trends were reported. Study quality was assessed using a modified Newcastle-Ottawa Scale adapted for Indigenous health research. RESULTS: From 2342 records and 27 additional references, 49 studies were retained for data extraction. Total type 2 diabetes prevalence, reported in 33 of 49 studies from 36 distinct populations across six countries and two self-governing states, varied widely (0-44 per 1000), with 75% (27/36) of the populations reporting a prevalence of over 1 per 1000. Age-specific data, available in 44 studies, showed increased prevalence with age: 0-4 per 1000 at age <10 years; 0-44 per 1000 at age 10-19 years; and 0-64 per 1000 at age 15-25 years. Of 22 studies with gender-specific data, 77% showed a female predominance. Secular trends, examined in 12 studies since 1981, showed a rising prevalence in young adults (aged 15-25 years) in eight of ten studies, and in youth aged under 15 years in six of nine studies. Heterogeneity in study design, diagnostic criteria, and incomplete age- and gender disaggregation precluded meta-analysis. CONCLUSIONS/INTERPRETATION: Youth type 2 diabetes prevalence in Indigenous populations is very high, particularly in young adulthood, and among the female sex. Prevalence has increased over time. Future research should stratify data by age and pubertal status, and identify both protective and risk factors to inform targeted prevention strategies. Indigenous-led, community-specific approaches that actively engage youth are critical in the development and implementation of diabetes surveillance, prevention and management programmes. TRIAL REGISTRATION: PROSPERO registration no. CRD42021278418.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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".