HIV Serostatus and Having Access to a Physician for Regular Hepatitis C Virus Care Among People Who Inject Drugs
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWIDs) and who are living with HIV and hepatitis C virus (HCV) infection are vulnerable to a range of health-related harms, including liver cirrhosis, hepatocellular carcinoma, and death. There is limited evidence describing how HIV serostatus shapes access to a physician for regular HCV care among PWID. SETTING: Data were collected through the Vancouver Injection Drug Users Study (VIDUS), the AIDS Care Cohort to evaluate Exposure to Survival Services (ACCESS), and the At-Risk Youth Study (ARYS), 3 prospective cohorts involving people who use illicit drugs in Vancouver, Canada, between 2005 and 2015. METHODS: Using generalized estimating equations, we examined the relationship between HIV-seropositivity and having access to a physician for regular HCV care. We conducted a mediation analysis to examine whether this association was mediated by increased frequency of engagement in health care. RESULTS: In total, 1627 HCV-positive PWID were eligible for analysis; 582 (35.8%) were HIV-positive at baseline; and 31 (1.9%) became HIV-positive during follow-up. In multivariable analyses, after adjusting for a range of confounders, HIV serostatus [adjusted odds ratio = 1.99; 95% confidence interval: 1.77 to 2.24] was significantly associated with having access to HCV care. Approximately 26% of the effect was due to mediation. CONCLUSION: Our results demonstrate a positive relationship between HIV-seropositivity and having access to a physician for regular HCV care, which is partially explained through increased frequency of engagement in health care. These findings highlight the need to address patterns of inequality in access to HCV care among PWID.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".