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Rapid Access Addiction Medicine Clinics for People With Problematic Opioid Use

2023· article· en· W4388907469 on OpenAlexafffundabout
Kim Corace, Kednapa Thavorn, Kelly D. Suschinsky, Melanie Willows, Pamela Leece, Meldon Kahan, Larry Nijmeh, Natalie Aubin, Michael Roach, Gord Garner, Refik Saskin, Eliane Kim, Danielle B. Rice, Sheena Taha, Gary Garber, Brian Hutton

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsCanadian Centre on Substance Use and AddictionMcMaster UniversityLaurentian UniversityPublic Health OntarioQueen's UniversityLakeridge HealthWomen's College HospitalHealth Sciences NorthUniversity of TorontoUniversity of OttawaInstitute for Clinical Evaluative SciencesSt. Joseph’s Healthcare HamiltonRoyal Ottawa Mental Health CentreOttawa Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineEmergency departmentTriageFamily medicinePropensity score matchingEmergency medicinePopulationCohortHealth careRetrospective cohort studyAddiction medicineAddictionPsychiatryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Importance: New approaches are needed to provide care for individuals with problematic opioid use (POU). Rapid access addiction medicine (RAAM) clinics offer a flexible, low-barrier, rapid access care model for this population. Objective: To assess the associations of RAAM clinics with emergency department (ED) visits, hospitalizations, and mortality for people with POU. Design, Setting, and Participants: A retrospective cohort study involving a matched control group was performed using health administrative data from Ontario, Canada. Anonymized data from 4 Ontario RAAM clinics (cities of Ottawa, Toronto, Oshawa, and Sudbury) were linked with health administrative data. Analyses were performed on a cohort of individuals who received care at participating RAAM clinics and geographically matched controls who did not receive care at a RAAM clinic. All visits occurred between October 2, 2017, and October 30, 2019, and data analyses were completed in spring 2023. A propensity score-matching approach was used to balance confounding factors between groups, with adjustment for covariates that remained imbalanced after matching. Exposures: Individuals who initiated care through the RAAM model (including assessment, pharmacotherapy, brief counseling, harm reduction, triage to appropriate level of care, navigation to community services and primary care, and related care) were compared with individuals who did not receive care through the RAAM model. Main Outcomes and Measures: The primary outcome was a composite measure of ED visits for any reason, hospitalization for any reason, and all-cause mortality (all measured up to 30 days after index date). Outcomes up to 90 days after index date, as well as outcomes looking at opioid-related ED visits and hospitalizations, were also assessed. Results: In analyses of the sample of 876 patients formed using propensity score matching, 440 in the RAAM group (mean [SD] age, 36.5 [12.6] years; 276 [62.7%] male) and 436 in the control group (mean [SD] age, 36.8 [13.8] years; 258 [59.2%] male), the pooled odds ratio (OR) for the primary, 30-day composite outcome of all-cause ED visit, hospitalization, or mortality favored the RAAM model (OR, 0.68; 95% CI, 0.50-0.92). Analysis of the same outcome for opioid-related reasons only also favored the RAAM intervention (OR, 0.47; 95% CI, 0.29-0.76). Findings for the individual events of hospitalization, ED visit, and mortality at both 30-day and 90-day follow-up also favored the RAAM model, with comparisons reaching statistical significance in most cases. Conclusions and Relevance: In this cohort study of individuals with POU, RAAM clinics were associated with reductions in ED visits, hospitalizations, and mortality. These findings provide valuable evidence toward a broadened adoption of the RAAM model in other regions of North America and beyond.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.357
Teacher spread0.294 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2023
Admission routes3
Has abstractyes

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