Hepatitis C Virus Infection in Indigenous Populations in the United States and Canada
Bibliographic record
Abstract
American Indian/Alaska Native (AI/AN) and Canadian Indigenous people are disproportionally affected by hepatitis C virus (HCV) infection yet are frequently underrepresented in epidemiologic studies and surveys often used to inform public health efforts. We performed a systematic review of published and unpublished literature and summarized our findings on HCV prevalence in these Indigenous populations. We found a disparity of epidemiologic literature of HCV prevalence among AI/AN in the United States and Indigenous people in Canada. The limited data available, which date from 1995, demonstrate a wide range of HCV prevalence in AI/AN (1.49%-67.60%) and Indigenous populations (2.28%-90.24%). The highest HCV prevalence in both countries was reported in studies that either included or specifically targeted people who inject drugs. Lower prevalence was reported in studies of general Indigenous populations, although in Canada, the lowest prevalence was up to 3-fold higher in Aboriginal people compared with general population estimates. The disparity of available data on HCV prevalence and need for consistent and enhanced HCV surveillance and reporting among Indigenous people are highlighted. HCV affects Indigenous peoples to a greater degree than the general population; thus we recommend tribal and community leaders be engaged in enhanced surveillance efforts and that funds benefitting all Indigenous persons be expanded to help prevent and cover health care expenses to help stop this epidemic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".