Rates of new HIV diagnoses among Indigenous peoples in Canada, Australia, New Zealand, and the United States: 2009–2017
Bibliographic record
Abstract
OBJECTIVE: To compare rates and trends of HIV diagnoses among Indigenous peoples in Canada (First Nations, Métis, Inuit, and other non-specified), Australia (Aboriginal and Torres Strait Islanders), the USA (American Indian, Alaska Native, Native Hawaiian, and Other Pacific Islanders), and New Zealand (Māori). DESIGN: We employed publicly available surveillance data from 2009 to 2017 to estimate the rate per 100 000 of HIV diagnoses. Estimated annual percentage change (EAPC) in diagnosis rates was calculated using Poisson regression. SETTING: The four countries have passive population-based HIV surveillance programs. PARTICIPANTS: Population estimates from respective census programs were used as rate denominators. MAIN OUTCOME MEASURES: Estimated annual HIV diagnosis rate per 100 000 and EAPC were calculated for total Indigenous peoples, women, and men. RESULTS: As of 2017, rates of HIV were highest in Canada (16.22, 95% confidence interval (CI): 14.30--18.33) and lowest in New Zealand (1.36, 95% CI: 0.65--2.50). Australia had a rate of 3.81 (95% CI: 2.59--5.40) and the USA 3.22 (95% CI: 2.85--3.63). HIV diagnosis rates among the total Indigenous population decreased in Canada (-7.92 EAPC, 95% CI: -9.34 to -6.49) and in the USA (-4.25 EAPC, 95% CI: -5.75 to -2.73) but increased in Australia (5.10 EAPC, 95% CI: 0.39--10.08). No significant trends over time were observed in New Zealand (2.23 EAPC, 95% CI: -4.48 to 9.47). CONCLUSION: Despite limitations to conducting cross-national comparisons, there are substantial differences in HIV diagnosis rates in these four countries that may be reflective of divergent national policies and systems that affect the health status of Indigenous peoples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".