P137 Understanding barriers and enablers to hepatitis C treatment initiation: a systematic review of qualitative studies
Bibliographic record
Abstract
Many factors associated with disengagement from care between hepatitis C diagnosis and treatment initiation have been identified in the direct-acting antiviral (DAA) era. We aimed to better understand and contextualise these factors by undertaking a systematic review of qualitative studies on DAA treatment initiation. We performed a systematic review of the barriers and enablers to hepatitis C DAA treatment initiation, in high income settings with free publicly-funded universal healthcare provision. We searched eight bibliographic databases for studies published between 2014 and May 2022 using pre-defined keywords. We included provider and patient interview-based qualitative studies investigating positive (enablers) or negative (barriers) associations with DAA treatment initiation. The Capability, Opportunity, Motivation, Behaviour (COM-B) framework was used to synthesise our findings. Of 5198 articles identified, 21 studies conducted in Australia, Canada and Europe were included. Overall quality was good. Barrier themes included: lack of knowledge of DAA availability and effectiveness, poor mental and physical health and deferral to stabilise drug use first (Capability); previous negative healthcare experiences including stigma, competing priorities and resource limitations of the patient (such as housing) as well as of the provider and healthcare context (Opportunity); and beliefs about consequences of hepatitis C and of DAA treatment (Motivation). Enabler themes included: peer advocate-led awareness-raising and education (Capability); building trusting patient-provider relationships, simplified integrated/co-located multidisciplinary care services to address multiple needs (Opportunity); and changing beliefs about consequences and individual capabilities (Motivation). Interview-based studies of barriers and enablers to DAA treatment explain multiple factors associated with lower treatment uptake borne out in quantitative analyses, such as low socioeconomic status, insecure housing, active drug use, disease stage, age and treatment experience. By identifying an individual’s Capability, Opportunity and Motivation barriers, pre-emptive supportive interventions can be tailored to efficiently enable engagement with DAA treatment. References O’Sullivan M, Jones AM, Gage H, et al. ITTREAT (Integrated Community Test - Stage - TREAT) Hepatitis C service for people who use drugs: real-world outcomes. Liver International. 2020;40:1021–31. Valerio H, Alavi M, Silk D, et al. Progress towards elimination of hepatitis c infection among people who inject drugs in australia: The ETHOS engage study. Clin Infect Dis. 2021;73(1):e69-e78. Michie S, Van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation science 2011;6:42. Cane J, O’Connor D, Michie S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implementation Science. 2012;7:37.
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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 | 0.069 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".