Incidence of Stroke in Indigenous Populations of Countries With a Very High Human Development Index
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cardiovascular disease contributes significantly to disease burden among many Indigenous populations. However, data on stroke incidence in Indigenous populations are sparse. We aimed to investigate what is known of stroke incidence in Indigenous populations of countries with a very high Human Development Index (HDI), locating the research in the broader context of Indigenous health. METHODS: We identified population-based stroke incidence studies published between 1990 and 2022 among Indigenous adult populations of developed countries using PubMed, Embase, and Global Health databases, without language restriction. We excluded non-peer-reviewed sources, studies with fewer than 10 Indigenous people, or not covering a 35- to 64-year minimum age range. Two reviewers independently screened titles, abstracts, and full-text articles and extracted data. We assessed quality using "gold standard" criteria for population-based stroke incidence studies, the Newcastle-Ottawa Scale for risk of bias, and CONSIDER criteria for reporting of Indigenous health research. An Indigenous Advisory Board provided oversight for the study. RESULTS: From 13,041 publications screened, 24 studies (19 full-text articles, 5 abstracts) from 7 countries met the inclusion criteria. Age-standardized stroke incidence rate ratios were greater in Aboriginal and Torres Strait Islander Australians (1.7-3.2), American Indians (1.2), Sámi of Sweden/Norway (1.08-2.14), and Singaporean Malay (1.7-1.9), compared with respective non-Indigenous populations. Studies had substantial heterogeneity in design and risk of bias. Attack rates, male-female rate ratios, and time trends are reported where available. Few investigators reported Indigenous stakeholder involvement, with few studies meeting any of the CONSIDER criteria for research among Indigenous populations. DISCUSSION: In countries with a very high HDI, there are notable, albeit varying, disparities in stroke incidence between Indigenous and non-Indigenous populations, although there are gaps in data availability and quality. A greater understanding of stroke incidence is imperative for informing effective societal responses to socioeconomic and health disparities in these populations. Future studies into stroke incidence in Indigenous populations should be designed and conducted with Indigenous oversight and governance to facilitate improved outcomes and capacity building. REGISTRATION INFORMATION: PROSPERO registration: CRD42021242367.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".