The effect of spatial variation on linkage to care and treatment rates among patients with hepatitis C: A Canadian population-based study
Bibliographic record
Abstract
Introduction: Despite significant global efforts towards eliminating hepatitis C virus (HCV) infection, multiple challenges remain in achieving this goal. In this study, we assessed the impact of geographical variation on HCV diagnosis and treatment rates in Alberta, Canada. Methods: Adults aged ≥20 years with a positive HCV antibody were identified from the provincial administrative sources from the fiscal years 2012 through 2017. To assess the differences across Alberta's rural-urban continuum, high-resolution spatial analyses using global and local spatial autocorrelation were applied to the HCV age- and sex-standardized prevalence rate at the sub-local geographic area level. We compared and tested differences in HCV RNA tests, HCV RNA positivity rates, and HCV treatment status across the different areas. Results: Between 2012 and 2017, we identified 18,768 patients who had tested positive for HCV Ab. Within this cohort, only 63.1% had HCV RNA repetitive. The HCV RNA positivity rate was 42.1%, and 65.3% had received HCV treatment after testing as HCV RNA positive. HCV Ab positive case counts exhibited a spatial distribution consistent with that of the population at risk: 67.5% in metro, 11.1% in urban, 19.7% in rural, and 1.8% in remote areas. The metropolitan area of Edmonton's age-and sex-standardized prevalence of 8.2 (95% CI 8.0–8.4) per 1,000 persons was higher than Calgary's standardized prevalence of 5.0 (95% CI 5.1–5.4) per 1,000 persons ( p < 0.001). HCV RNA and HCV treatment rates demonstrated significant spatial variation. Conclusions: HCV prevalence, diagnosis, and treatment exhibit significant spatial variation across rural-urban Alberta. Implementation of geographically oriented screening and treatment interventions would result in a time- and cost-efficient HCV elimination strategy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".