Global incidence of suicide among Indigenous peoples: a systematic review
Bibliographic record
Abstract
BACKGROUND: Suicide is the second leading cause of death among adolescents worldwide, and is a major driver of health inequity among Indigenous people in high-income countries. However, little is known about the burden of suicide among Indigenous populations in low- and middle-income nations, and no synthesis of the global data is currently available. Our objective was to examine the global incidence of suicide among Indigenous peoples and assess disparities through comparisons with non-Indigenous populations. METHODS: We conducted a systematic review of suicide rates among Indigenous peoples worldwide and assessed disparities between Indigenous and non-Indigenous populations. We performed text word and Medical Subject Headings searches in PubMed, MEDLINE, Embase, Cumulative Index of Nursing and Allied Health (CINAHL), PsycINFO, Latin American and Caribbean Health Sciences Literature (LILACS), and Scientific Electronic Library Online (SciELO) for observational studies in any language, indexed from database inception until June 1, 2017. Eligible studies examined crude or standardized suicide rates in Indigenous populations at national, regional, or local levels, and examined rate ratios for comparisons to non-Indigenous populations. RESULTS: The search identified 13,736 papers and we included 99. Eligible studies examined suicide rates among Indigenous peoples in 30 countries and territories, though the majority focused on populations in high-income nations. Results showed that suicide rates are elevated in many Indigenous populations worldwide, though rate variation is common, and suicide incidence ranges from 0 to 187.5 suicide deaths per 100,000 population. We found evidence of suicide rate parity between Indigenous and non-Indigenous populations in some contexts, while elsewhere rates were more than 20 times higher among Indigenous peoples. CONCLUSIONS: This review showed that suicide rates in Indigenous populations vary globally, and that suicide rate disparities between Indigenous and non-Indigenous populations are substantial in some settings but not universal. Including Indigenous identifiers and disaggregating national suicide mortality data by geography and ethnicity will improve the quality and relevance of evidence that informs community, clinical, and public health practice in Indigenous suicide prevention.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".