A cost benefit analysis of a virtual overdose monitoring service/mobile overdose response service: the national overdose response service
Bibliographic record
Abstract
BACKGROUND: The overdose crisis continues across Canada which calls for novel harm reduction strategies. Previous research indicates that a majority of eHealth solutions are cost-effective however current literature on the cost-benefit of eHealth for harm reduction is sparse. The National Overdose Response Service (NORS) is a Canada-wide telephone-based harm reduction service. Service users can call the phone number and connect to a peer who can virtually monitor the substance use session and dispatch appropriate interventions in the case of overdose. OBJECTIVES OF THE RESEARCH/PROJECT: We aim to assess the cost-benefit of NORS by comparing the estimated cost-savings from prevented overdose mortality to the operating costs of the program, alongside healthcare costs associated with its operation. METHODS: Data around systems costs and operational costs were gathered for our calculations. Our primary outcome was cost-benefit ratios, derived from estimates and models of mortality rates in current literature and value of life lost. We presented our main results across a range of values for costs and the probability of death following an unwitnessed overdose. These values were utilized to calculate cost-benefit ratios and value per dollar spent on service provision by NORS over the length of the program's operation (December 2020-2022). RESULTS: Over the total funded lifespan of the program, and using a Monte Carlo estimate, the benefit-to-cost ratio of the NORS program was 8.59 (1.53-15.28) per dollar spent, depending on estimated mortality rates following unwitnessed overdose and program operation costs. Further, we conservatively estimate that early community-based naloxone intervention results in healthcare system savings of $4470.82 per overdose response. CONCLUSIONS: We found the NORS program to have a positive benefit-to-cost ratio when the probability of death following an unwitnessed overdose was greater than 5%. NORS and potentially other virtual overdose monitoring services have the potential to be cost-effective solutions for managing the drug poisoning crisis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| 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".