Sex-Specific Risk Factors and Health Disparity Among Hepatitis C Positive Patients Receiving Pharmacotherapy for Opioid Use Disorder: Findings From a Propensity Matched Analysis
Bibliographic record
Abstract
BACKGROUND: The incidence of opioid-related fatality has reached unparalleled levels across North America. Patients with comorbid hepatitis C virus (HCV) remain the most vulnerable and difficult to treat. Considering the unique challenges associated with this population, we aimed to re-examine the impact of HCV on response to medication assistant treatment for opioid use disorder and establish sex-specific risk factors affecting care. METHODS: This study employs a multi-center prospective cohort design, with 1-year follow-up. Patients aged ≥18, receiving methadone for opioid use disorder were recruited from a network of outpatient opioid addiction treatment centers across Southern Ontario, Canada. Patients with ≥50% positive opioid urine screens over 1 year of follow-up were classified as poor responders. The prognostic impact of HCV on response was established using a propensity score matched analysis. Sex-specific regression models were constructed to evaluate risk factors for treatment response. RESULTS: Among participants eligible for inclusion (n = 1234), HCV was prevalent in 25% (n = 307). HCV patients exhibited significantly higher rates of high-risk opioid consumption patterns 35.29% (standard deviation 0.478). Sex-specific examination revealed females with HCV incur a 2 times increased risk for high-risk opioid consumption behaviors (female odds ratio: 1.95, 95% confidence interval 1.23, 3.10; P = 0.01). CONCLUSIONS: Findings from this study establish the link between HCV and poor treatment response, with differentially higher risk among female patients. In light of the high potential for overdose among this population, concerted efforts are required for distinguishing the source for sex-based disparities, in addition to establishing trauma and gender informed treatment protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".