The burden of stomach cancer in indigenous populations: a systematic review and global assessment
Bibliographic record
Abstract
OBJECTIVE: Stomach cancer is a leading cause of cancer death, especially in developing countries. Incidence has been associated with poverty and is also reported to disproportionately affect indigenous peoples, many of whom live in poor socioeconomic circumstances and experience lower standards of health. In this comprehensive assessment, we explore the burden of stomach cancer among indigenous peoples globally. DESIGN: The literature was searched systematically for studies on stomach cancer incidence, mortality and survival in indigenous populations, including Indigenous Australians, Maori in New Zealand, indigenous peoples from the circumpolar region, native Americans and Alaska natives in the USA, and the Mapuche peoples in Chile. Data from the New Zealand Health Information Service and the Surveillance Epidemiology and End Results (SEER) Program were used to estimate trends in incidence. RESULTS: Elevated rates of stomach cancer incidence and mortality were found in almost all indigenous peoples relative to corresponding non-indigenous populations in the same regions or countries. This was particularly evident among Inuit residing in the circumpolar region (standardised incidence ratios (SIR) males: 3.9, females: 3.6) and in Maori (SIR males: 2.2, females: 3.2). Increasing trends in incidence were found for some groups. CONCLUSIONS: We found a higher burden of stomach cancer in indigenous populations globally, and rising incidence in some indigenous groups, in stark contrast to the decreasing global trends. This is of major public health concern requiring close surveillance and further research of potential risk factors. Given evidence that improving nutrition and housing sanitation, and Helicobacter pylori eradication programmes could reduce stomach cancer rates, policies which address these initiatives could reduce inequalities in stomach cancer burden for indigenous peoples.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".