Hepatitis C among vulnerable populations: A seroprevalence study of homeless, people who inject drugs and prisoners in London
Bibliographic record
Abstract
Injecting drugs substantially increases the risk of hepatitis C virus (HCV) infection and is common in the homeless and prisoners. Capturing accurate data on disease prevalence within these groups is challenging but is essential to inform strategies to reduce HCV transmission. The aim of this study was to estimate the prevalence of HCV in these populations. We conducted a cross-sectional study between May 2011 and June 2013 in London and, using convenience sampling, recruited participants from hostels for the homeless, drug treatment services and a prison. A questionnaire was administered and blood samples were tested for hepatitis C. We recruited 491 individuals who were homeless (40.7%), 205 drug users (17%) and 511 prisoners (42.3%). Eight per cent of patients (98/1207, 95% CI: 6.7%-9.8%) had active HCV infection and 3% (38/1207, 95% CI: 2.3%-4.3%) past HCV infection. Overall, one quarter (51/205) of people recruited in drug treatment services, 13% (65/491) of people from homeless residential sites and 4% (20/511) prisoners in this study were anti-HCV positive. Seventy-seven of the 136 (56.6%, 95% CI: 47.9%-65%) of HCV infected participants identified had a history of all three risk factors (homelessness, imprisonment and drug use), 27.3% (95% CI: 20.1%-35.6%) had 2 overlapping risk factors, and 15.4% (95% CI: 10.6%-23.7%) one risk factor. Drug treatment services, prisons and homelessness services provide good opportunities for identifying hepatitis C-infected individuals. Effective models need to be developed to ensure case identification in these settings that can lead to an effective treatment and an efficient HCV prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".