A novel multisite model to facilitate hepatitis C virus elimination in people experiencing homelessness
Bibliographic record
Abstract
Background /aims: Only a handful of countries are on target to achieve hepatitis C virus (HCV) elimination by 2030. People experiencing homelessness (PEH) remain an important HCV reservoir. The END C study evaluated clinical, patient reported and health economic outcomes of a decentralised integrated model. Methods This prospective study assessed a decentralised regional service based at multiple homeless sites in southeast England. Novel linkage-care strategies were utilised. We assessed generic and liver specific health-related quality of life (HRQoL) (SF-12v2; EQ-5D-5L and SFLDQol) pre/post HCV treatment and cost per HCV case detected and cured. Primary outcome was intention-to-treat (ITT) sustained virological response (SVR12). Results We recruited 418 individuals , mean age 44.45 ± 10.6, 78% were male, 74% being currently homeless, current injecting drug use/alcohol use being 25% and 65% respectively. Prevalence of cirrhosis (liver stiffness measurement >12kPa) was 12%. Twenty-eight percent (n=116) were HCV PCR positive, of whom n=105 received direct acting antiviral treatment. ITT SVR12 rates were 81% (95% CI 72%-88%), the only predictor of SVR12 being >80% treatment adherence (OR 20.69, 95% CI 6.227-68.772, p<0.001). HRQoL improved significantly after SVR12: SF-12v2 (General Health, Mental Health, Social Functioning, Mental Health Composite Score p<0.049); SFLDQoL (Symptoms/Effects of Liver Disease, Distress, Loneliness p<0.004) and EQ-5D-5L (Index Score, Visual Analog Scale p<0.001). Costs (British pound 2022) per HCV case detected and per case cured were £359 and £257 respectively. Reinfection rates were 6.82/100 person years. Conclusion The END C study endorses a multisite decentralised service for PEH enabling excellent linkage to care, high SVR12 rates and significant improvements in generic and liver specific HRQoL, all being achieved at modest costs. Such services are paramount to help achieve HCV elimination. Impact and implications In people experiencing homeless, we found a high prevalence of HCV, alcohol and substance misuse including overdoses and mental health issues. Despite this, an integrated and decentralised service resulted in excellent linkage to care with high SVR12 rates. Even in this complex cohort with multiple comorbidities, SVR12 was associated with significant improvements in both generic and liver specific health-related quality of life. This was all achieved at modest costs in a community setting. Such models of care are feasible, easy to replicate and essential if we are to achieve HCV elimination.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".