Real-world evidence on harm reduction strategies: A multidisciplinary evaluation
Bibliographic record
Abstract
People who inject drugs (PWID) are at elevated risks of adverse health outcomes, including overdose and HIV. To reduce these drug-related harms, several strategies have been employed, including needle and syringe programs (NSP), opioid agonist treatment (OAT), and supervised consumption sites (SCS).Skin, soft tissue, and vascular infections (SSTVI) are the leading cause of morbidity in PWID globally. However, earlier studies examining the effectiveness of NSP and SCS have overlooked these endpoints. For studies comparing OAT medications, treatment retention is the most common measure of effectiveness. Despite available evidence, no studies have unified findings from different healthcare practices to strengthen clinical treatment protocols. To address these limitations, I sought to examine the effectiveness of these harm reduction strategies from the clinical, economic, and health service utilization angles. The overall goal of my thesis was to investigate how these strategies have mitigated drug-related adverse health outcomes in PWID.First, I developed a microsimulation model to assess clinical and cost-effectiveness of NSP with respect to SSTVI compared to a counterfactual scenario without NSP. I assessed the cost-effectiveness of NSP, estimated the hazard of SSTVI mortality, and examined health service utilization patterns under the two NSP scenarios. The incremental cost-effectiveness ratio was $70,278 per quality-adjusted life years (QALY), which was due to low incremental QALY as well as high incremental costs from continued health service use and NSP costs among those who were alive. My study establishes the effectiveness of NSP while capturing the real-world complexities surrounding individuals’ unique clinical pathways and interactions with the healthcare system to treat SSTVI.Second, I conducted a systematic review of randomized controlled trials (RCT) comparing treatment retention percentage between four medications prescribed as OAT: buprenorphine, methadone, naltrexone, and slow-release oral morphine (SROM). I ran a Bayesian network meta-analysis to enable direct and indirect comparison of the medications and rank them based on the likelihood of treatment retention. Methadone was ranked the highest, while the non-pharmacotherapeutic control group was ranked the lowest. Due to a small number of high-quality trials, confidence in the network estimates of treatment pairs involving naltrexone and SROM remains low. Additional high-quality RCT are needed to estimate more accurately the extent of efficacy of naltrexone and SROM relative to other medications.Third, I conducted a quasi-experimental study to assess the effect of Montreal’s four SCS on the incidence of SSTVI among PWID in the city. I ran an interrupted time series analysis that accounted for autocorrelation and seasonality. After the opening of the four SCS in 2017, there was a level increase with positive time trend in outpatient visits. During the same post-intervention period, there was a moderate decline in time trend in emergency department visits and hospitalizations. The findings from my study demonstrate that SCS resulted in increased healthcare service use while mitigating more serious cases of SSTVI.Findings from my thesis shed light on important additional benefits of harm reduction interventions beyond cost savings and number of infections averted. The use of state-of-the-art methods drawn from decision science, biostatistics, and econometrics enabled rigorous examination of NSP, OAT, and SCS. The results of my thesis have broad applications not only to opioid prescribing physicians who seek to establish clinical best practice but also to public health stakeholders who wish to expand existing services
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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 | 0.093 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.025 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".