Hepatitis C treatment outcomes for Australian First Nations Peoples: equivalent SVR rate but higher rates of loss to follow-up
Bibliographic record
Abstract
Abstract Background First Nations Peoples of Australia are disproportionally affected by hepatitis C (HCV) infection. Through a prospective study we evaluated the outcome of direct-acting antiviral (DAA) therapy among First Nations Peoples with HCV infection. Methods Adults who initiated DAA therapy at one of 26 hospitals across Australia, 2016–2019 were included in the study. Clinical data were obtained from medical records and the Pharmaceutical and Medicare Benefits Schemes. Outcomes included sustained virologic response (SVR) and loss to follow-up (LTFU). A multivariable analysis assessed factors associated with LTFU. Results Compared to non-Indigenous Australians (n = 3206), First Nations Peoples (n = 89) were younger (p < 0.001), morel likely to reside in most disadvantaged (p = 0.002) and in regional/remote areas (p < 0.001), and had similar liver disease severity. Medicines for mental health conditions were most commonly dispensed among First Nations Peoples (55.2% vs. 42.8%; p = 0.022). Of 2910 patients with follow-up data, both groups had high SVR rates (95.3% of First Nations Peoples vs. 93.2% of non-Indigenous patients; p = 0.51) and ‘good’ adherence (90.0% vs. 86.9%, respectively; p = 0.43). However, 28.1% of First Nations Peoples were LTFU vs. 11.2% of non-Indigenous patients (p < 0.001). Among First Nations Peoples, younger age (adj-OR = 0.93, 95% CI 0.87–0.99) and treatment initiation in 2018–2019 vs. 2016 (adj-OR = 5.14, 95% CI 1.23–21.36) predicted LTFU, while higher fibrosis score was associated with better engagement in HCV care (adj-OR = 0.71, 95% CI 0.50–0.99). Conclusions Our data showed that First Nations Peoples have an equivalent HCV cure rate, but higher rates of LTFU. Better strategies to increase engagement of First Nations Peoples with HCV care are needed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".