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Record W2800841433 · doi:10.1016/s2468-2667(18)30044-6

Distribution of take-home opioid antagonist kits during a synthetic opioid epidemic in British Columbia, Canada: a modelling study

2018· article· en· W2800841433 on OpenAlexafffundabout
Michael A. Irvine, Jane A. Buxton, Michael Otterstatter, Robert Balshaw, Réka Gustafson, Mark Tyndall, Perry Kendall, Thomas Kerr, Mark Gilbert, Daniel Coombs

Bibliographic record

VenueThe Lancet Public Health · 2018
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBritish Columbia Centre on Substance UseVancouver Coastal HealthMinistry of HealthUniversity of ManitobaGeorge & Fay Yee Centre for Healthcare InnovationUniversity of British ColumbiaBC Centre for Disease Control
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMedicineFentanylOpioid overdose(+)-NaloxonePopulationHeroinDrug overdoseEmergency medicineOpioidPoison controlEnvironmental healthPharmacologyDrugInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Illicit use of high-potency synthetic opioids has become a global issue over the past decade. This misuse is particularly pronounced in British Columbia, Canada, where a rapid increase in availability of fentanyl and other synthetic opioids in the local illicit drug supply during 2016 led to a substantial increase in overdoses and deaths. In response, distribution of take-home naloxone (THN) overdose prevention kits was scaled up (6·4-fold increase) throughout the province. The aim of this study was to estimate the impact of the THN programme in terms of the number of deaths averted over the study period. METHODS: We estimated the impact of THN kits on the ongoing epidemic among people who use illicit opioids in British Columbia and explored counterfactual scenarios for the provincial response. A Markov chain model was constructed explicitly including opioid-related deaths, fentanyl-related deaths, ambulance-attended overdoses, and uses of THN kits. The model was calibrated in a Bayesian framework incorporating population data between Jan 1, 2012, and Oct 31, 2016. FINDINGS: 22 499 ambulance-attended overdoses and 2121 illicit drug-related deaths (677 [32%] deaths related to fentanyl) were recorded in the study period, mostly since January, 2016. In the same period, 19 074 THN kits were distributed. We estimate that 298 deaths (95% credible interval [CrI] 91-474) were averted by the THN programme. Of these deaths, 226 (95% CrI 125-340) were averted in 2016, following a rapid scale-up in distribution of kits. We infer a rapid increase in fentanyl adulterant at the beginning of 2016, with an estimated 2·3 times (95% CrI 2·0-2·9) increase from 2015 to 2016. Counterfactual modelling indicated that an earlier scale-up of the programme would have averted an additional 118 deaths (95% CrI 64-207). Our model also indicated that the increase in deaths could parsimoniously be explained through a change in the fentanyl-related overdose rate alone. INTERPRETATION: The THN programme substantially reduced the number of overdose deaths during a period of rapid increase in the number of illicit drug overdoses due to fentanyl in British Columbia. However, earlier adoption and distribution of the THN intervention might have had an even greater impact on overdose deaths. Our findings show the value of a fast and effective response at the start of a synthetic opioid epidemic. We also believe that multiple interventions are needed to achieve an optimal impact. FUNDING: Canadian Institutes of Health Research Partnerships for Health Systems Improvement programme (grant 318068) and Natural Science and Engineering Research Council of Canada (grant 04611).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.041
GPT teacher head0.291
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations111
Published2018
Admission routes3
Has abstractyes

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