Tariffs, the War on Drugs, and Predominantly Biomedical Approaches to Addiction
Bibliographic record
Abstract
The threat of tariffs from the current US administration is top of mind for most Canadians of late. Tariffs have the potential for damaging the Canadian economy, putting people out of work, and sowing the seeds for discontent, substance use, and addiction. One of the reasons cited for the imposition of tariffs was that Canada lacks border security, supposedly allowing vast quantities of fentanyl to enter the United States, despite a lack of evidence to support this claim.1 It also runs contrary to most effective approaches to deal with illicit drugs. In an underacknowledged landmark report,2 the RAND Corporation found that addressing the demand for cocaine by treating heavy users of cocaine was vastly more cost-effective than measures to curtail supply. In comparing the relative cost benefits of source-country control (coca leaf eradication and seizures in source countries), interdiction (cocaine and asset seizures by US Customs, US Coast Guard, US Army, and Immigration and Naturalization Services), domestic enforcement (cocaine seizures, asset seizures, and arrests of dealers and their agents by federal, state, and local law enforcement, as well as imprisonment of convicted drug dealers and their agents), and treatment of heavy users (outpatient and bed-based treatment programs), for every 1 dollar spent, source-country control returned 15 cents on the dollar, interdiction returned 32 cents on the dollar, and domestic enforcement returned 52 cents on the dollar. Comparatively, the treatment of heavy users returned $7.46 on every dollar spent. The report recommended diverting spending away from attempting to curtail supply or enforcement, as has been the predominant focus of the War on Drugs, to the treatment of heavy cocaine users. Previously, up to 70% of funding in Canada for addressing illicit substance use was allocated to enforcement strategies, resulting in increased incarceration rates,3 but over the years Canada has developed a more balanced approach where of the $513.4 million spent annually on the anti-drug strategy, 40% goes to enforcement, 37% to treatment, and 23% to prevention.4 The prior focus on enforcement also had the unintended consequence of shifting the drug supply from naturally sourced substances, like cocaine and heroin, to chemically created substances that are highly concentrated as they are less likely to be stopped by source-country control and interdiction as they can be mailed or domestically made from chemical precursors.5,6 As Fischer et al6 note in their important commentary in this issue of the Canadian Journal of Addiction, fentanyl and the other high potency synthetics are here to stay and require a more coherent and comprehensive treatment approach rather than the current piecemeal one. What is also increasingly apparent is that a predominantly biomedical approach alone will also not be effective. The interesting paper by Conway et al,7 also in this issue, identifies that the provision of safer supply prescription opioids did not appear to change the use of illicit drugs and that about 50% of the prescription opioids were taken irregularly or not at all. However, they may have been associated with slightly lower opioid overdose events and mortality rates. Buprenorphine and methadone are WHO essential medicines with demonstrated efficacy, reducing all-cause mortality and overdose in persons with opioid use disorder (OUD) while retained on Opioid Agonist Therapy (OAT).8,9 However, barriers to accessing OAT remain and up to half of persons started on OAT are not retained in treatment at 6 months.10 Addressing the often-complex psychological and social needs of our patients with frequent in-person contact to help them in the recovery process and avert potential relapse needs to become more part of daily addiction medicine practice again to enhance retention and long-term outcomes. Potentially, it was the ongoing provision of care that led to the trend in reduced overdose and mortality found by Conway et al,7 rather than the safer supply. Taken together, it appears clear that although evidence-based prescribing does play a major role in addiction treatment, especially with indicated pharmacotherapies, a predominantly biomedical approach will not work, just as policies that perpetuate the War on Drugs by predominantly focusing on enforcement also have not. The core work of addiction medicine remains direct and active, talking with the people we see to engage them where they are at, trying to address their often-complex biopsychosocial needs supportively, working with them to change their behaviors over time, and keeping them retained in follow-up care, knowing that addiction is a chronic disorder prone to relapse.
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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) | 0.000 | 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 | 0.000 |
| 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".