Indigenous disparities in disease-specific mortality, a cross-country comparison: New Zealand, Australia, Canada, and the United States.
Bibliographic record
Abstract
AIMS: To compare the disease-specific mortality rates of the indigenous populations of New Zealand, Australia, Canada, and the United States with the non-indigenous populations in each country. METHODS: For New Zealand, Australia, Canada, and the United States, we compiled and calculated (from crude data) ethnic-specific mortality rates by primary cause of death in 1999 for the indigenous and non-indigenous populations in each country. We calculated age-adjusted mortality rates, using direct standardisation and weights based on the World Health Organization world population. RESULTS: Australia experienced the largest relative and absolute disparities in life expectancy between indigenous and non-indigenous populations. For specific causes of death, New Zealand Maori, and Australian Aboriginals and Torres Strait Islanders experienced the highest levels of disparities when compared to their respective non-indigenous population group. Large disparities exist for indigenous peoples in all four countries for diabetes mortality. CONCLUSION The indigenous peoples of New Zealand and Australia suffer from high disease-specific mortality rates. The relative size of indigenous/non-indigenous mortality disparities are highest in New Zealand and Australia. There appears to be a number of common issues that adversely affect the quality of the mortality data that is available in the four countries. Action is required to address indigenous health disparities and to improve the quality of indigenous mortality data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".