Multimorbidity among the Indigenous population: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Multimorbidity, the concurrent presence of multiple chronic health conditions in an individual, represents a mounting public health challenge. Chronic illnesses are prevalent in the Indigenous populations, which contributes to multimorbidity. However, the epidemiology of multimorbidity in this population is not well studied. This review aimed to elucidate the extent, determinants, consequences, and prevention of multimorbidity within Indigenous populations globally, contrasting findings with non-Indigenous populations. METHODS: Adhering to the PRISMA guidelines, this systematic review assimilated peer-reviewed articles and grey literature, focusing on the prevalence, determinants, implications, and preventive strategies of multimorbidity in global Indigenous populations. Emphasis was given to original, English-language, full-text articles, excluding editorials, and conference abstracts. FINDINGS: Of the 444 articles identified, 13 met the inclusion criteria. Five studies are from Australia, and the rest are from the USA, Canada, New Zealand, and India. The study indicated a higher multimorbidity prevalence among Indigenous populations, with consistent disparities observed across various age groups. Particularly, Indigenous individuals exhibited a 2-times higher likelihood of multimorbidity compared to non-Indigenous populations. Noteworthy findings underscored the elevated severity of certain comorbid conditions, especially strokes, within Indigenous groups, with further revelations highlighting their significant pairing with conditions such as heart diseases and diabetes. INTERPRETATION: The findings affirm the elevated burden of multimorbidity among Indigenous populations. Prevalence and risk of developing multimorbidity are significantly higher in this population compared to their non-Indigenous counterparts. Future research should prioritize harmonized research methodologies, fostering insights into the multimorbidity landscape, and promoting strategies to address health disparities in Indigenous populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".