Commentary on Nolan<i>et al</i>. (2014): Opiate substitution treatment and hepatitis C virus prevention: building an evidence base?
Bibliographic record
Abstract
The beneficial effects of opiate substitution treatment (OST) for people who inject drugs (PWID) encompass multiple domains and outcomes. This includes decreasing HIV acquisition risk by half 1, reducing drug-related mortality 2, 3, possibly enhancing adherence to HIV anti-retroviral treatment 4, diminishing crime 5 and the societal costs associated with drug use 6, increasing quality of life 5 and sometimes employment status 7, 8. However, until recently, the evidence for OST or any harm reduction intervention reducing the risk of hepatitis C virus (HCV) acquisition was classified as insufficient 9, 10. This situation began to change 3 years ago, when a pooled UK analysis of selected observational studies suggested for the first time that OST could reduce HCV acquisition risk among PWID by more than 50% and that the combination of OST and high-coverage needle and syringe distribution could reduce HCV acquisition risk by up to 80% 11. In recent months there has been a further strengthening of the evidence base, with results from the Vancouver Injecting Drug Use Study (VIDUS) published in this issue of Addiction 12 and two other prospective studies of PWID from Australia 13 and San Francisco, USA 14, each reporting that OST can reduce the risk of HCV acquisition by 50–80% (Table 1). Despite a similar effect size across all four studies, an important difference between the Australian paper 13 and the analyses from Vancouver and San Francisco is that White et al. included PWID only for whom OST was potentially indicated—i.e. those who reported primarily injecting heroin or other opioids 13. In contrast, both the Vancouver and San Francisco papers were inclusive of all cohort participants, including those for whom OST may not be indicated (such as methamphetamine and cocaine injectors), so the protective effects may be underestimated. While it is encouraging that the size of the protective effect is consistent across the studies in multiple sites, we recognize that these studies are all observational and at greater risk of selection bias and confounding than randomized controlled trials. For instance, in the Nolan study 12 there was a considerable difference in the HCV prevalence among people receiving and not receiving OST at baseline (24 versus 76%), as well as differences in drug-using patterns, which may suggest that the difference in risk may not entirely be due to the direct effects of OST on injecting behaviours. Importantly, methadone and buprenorphine are essential medicines that cannot be randomized in future studies and so the evidence base will have to be built from non-randomized observational studies such as these. What are the implications of these results for designing HCV prevention strategies? First, as highlighted by a recent modelling analysis 15, OST averts infections, with projections from the United Kingdom suggesting that current high coverage levels of OST (50% of PWID are currently on OST in the United Kingdom) may have contributed to reducing the chronic HCV prevalence from 57 to the 40% chronic prevalence it is now. OST may also have an accumulating effect—the longer the average duration on OST the greater the impact on reducing HCV risk 12 and drug-related mortality 2. Indeed, because economic analyses suggest that OST could be cost-saving when societal benefits are accounted for 6, or at least highly cost-effective if only health benefits are considered 6, then it seems that there should be no argument against scaling-up OST in all settings. There is a long way to go until we achieve the high levels of OST coverage that currently exist in some settings, such as the United Kingdom and Australia. Data from the last systematic review of intervention coverage among PWID suggested that the world-wide coverage of OST was, at best, 8% 16, and although many countries have since initiated OST programmes, in most settings recent data continue to show inadequate coverage of OST 17. This raises the spectre of the potentially enormous global prevention gap. For example, adapted results from our previous modelling analysis 15 suggest that scaling-up OST world-wide could avert between 1 and 2 million HCV infections during the next 10 years if it was scaled-up from less than 10 to 50% coverage (8 million) of all PWID. Although these calculations warrant more detailed modelling to capture the heterogeneities in different epidemics, they none the less highlight the potential substantial prevention benefit of scaling up OST. It is important to note, however, that although recent results (Table 1) suggest that OST is an essential component of any future HCV prevention strategy, it is not the only answer to HCV prevention. HCV prevalence remains persistently high in many countries despite high coverage of OST and needle and syringe distribution. It is likely that only by also scaling-up antiviral treatment and prophylactic vaccine development for HCV that prevalence can be significantly reduced 18, 19. None.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".