Commentary on Krawczyk <i>et al</i>. (2020): Reinforcing the case for evidence‐based treatment of opioid use disorder
Bibliographic record
Abstract
Opioid agonist treatment (OAT) for opioid use disorder (OUD) is evidence-based and has several advantages over non-medication treatment approaches, but OAT coverage in the United States remains low. OAT's benefits hold up regardless of the illicit drug supply context, underscoring the urgent need for improved access to this evidence-based intervention in the United States. Although the availability of OAT increased in U.S. treatment facilities from 2007 (20%) to 2016 (36%), levels remain low [4]. The increase was largely attributable to the expansion of buprenorphine/naloxone and extended-release naltrexone in office-based settings [4], whereas methadone has remained strictly regulated. The number of methadone-prescribing facilities in the United States has been relatively constant since 2002, comprising 8% of all substance abuse treatment facilities in 2002 and 10% in 2016 [4, 5]. Other barriers to access persist, including low insurance coverage [6, 7], variable geographic coverage [8], regulatory restrictions [8], and stigma [9], along with political/ideological barriers related to the criminalization of drug use [10]. Limited OAT coverage in the United States indicates a major treatment gap, which has been at least partially filled by non-medication treatments (e.g. detoxification, psychotherapy, education). While some of these approaches may be complementary to OAT (e.g. psychological interventions), there is little evidence suggesting they are effective on their own. [1, 11, 12] There is a need for improved evidence on when, how, and for whom non-medication treatments can benefit people with OUD. More importantly, however, there is a need to rapidly scale up the evidence-based approaches that we already know work [1, 12, 13]. Treatment is about more than reducing drug use; it creates connections to a healthcare system. As Krawczyk and colleagues highlight [1], there is a need to couple treatment with education and harm reduction. From this perspective, OAT confers additional advantages. Compared with non-medication treatments, OAT, being naturally embedded in healthcare and yielding longer retention outcomes, is much better suited as a point of connection. For instance, longer retention means more opportunities to provide naloxone and education on safer injecting; providing treatment in a healthcare setting facilitates connections with primary care and treatment of comorbid medical conditions [14], which are highly prevalent among people with OUD [15]. Given the chronic, recurrent nature of OUD, it is essential to increase opportunities for connection to harm reduction to prevent overdoses and mortality during inevitable periods off treatment. Access to OAT, healthcare, and harm reduction have become even more important in the United States in recent years. Indeed, Krawczyk and colleagues [1] observed an increased risk of overdose death in the periods directly after discontinuing treatment (medication or non-medication). The overall trend is consistent with other literature, [3, 16] however, they observed a larger effect size than that reported internationally, which the authors attributed to the increased potency of the drug supply in Baltimore over the study period. [1] In settings hard hit by fentanyl, access to and retention in OAT has become even more protective than before. In British Columbia, Canada, we recently reported a >2-fold increase in the relative risk of mortality following the introduction of fentanyl to the illicit drug supply; however, this increase was only recorded for individuals off of OAT. We found no change in the risk of mortality for individuals continuously engaged in OAT [13]. Our findings and the findings of Krawczyk et al. highlight that even with changes to the drug supply, OAT remains an essential medication [1, 17]. OAT's effectiveness in promoting long-term retention in care, preventing mortality, and connecting clients to other health services is well-established; the study by Krawczyk et al. [1] adds to growing literature suggesting these benefits persist even in contexts with a highly potent illicit drug supply. Nonetheless, logistical, ideological, and regulatory barriers continue to impede access to this treatment and other essential harm reduction services in the United States. Findings from Krawczyk et al. [1] therefore highlight a treatment gap in the United States and serve as a loud and urgent call for improved access to evidence-based approaches for OUD. No financial or other relevant links to companies with an interest in the topic of this article.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".