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Association of a Positive Drug Screening for Cannabis With Mortality and Hospital Visits Among Veterans Affairs Enrollees Prescribed Opioids

2022· article· en· W4311692428 on OpenAlexaff
Salomeh Keyhani, Samuel Leonard, Amy L. Byers, Tauheed Zaman, Erin E. Krebs, Peter C. Austin, Tristan Moss-Vazquez, Charles Austin, Friedhelm Sandbrink, Dawn M. Bravata

Bibliographic record

VenueJAMA Network Open · 2022
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Toronto
FundersNational Institutes of HealthAustralian Pain SocietyHealth Services Research and DevelopmentPatient-Centered Outcomes Research InstituteU.S. Department of Veterans Affairs
KeywordsMedicineHazard ratioProportional hazards modelVeterans AffairsConfoundingOpioidCannabisMedical prescriptionPropensity score matchingCohort studyInternal medicineEmergency departmentCohortEmergency medicinePsychiatryConfidence intervalPharmacology

Abstract

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Importance: Cannabis has been proposed as a therapeutic with potential opioid-sparing properties in chronic pain, and its use could theoretically be associated with decreased amounts of opioids used and decreased risk of mortality among individuals prescribed opioids. Objective: To examine the risks associated with cannabis use among adults prescribed opioid analgesic medications. Design, Setting, and Participants: This cohort study was conducted among individuals aged 18 years and older who had urine drug screening in 2014 to 2019 and received any prescription opioid in the prior 90 days or long-term opioid therapy (LTOT), defined as more than 84 days of the prior 90 days, through the Veterans Affairs health system. Data were analyzed from November 2020 through March 2022. Exposures: Biologically verified cannabis use from a urine drug screen. Main Outcomes and Measures: The main outcomes were 90-day and 180-day all-cause mortality. A composite outcome of all-cause emergency department (ED) visits, all-cause hospitalization, or all-cause mortality was a secondary outcome. Weights based on the propensity score were used to reduce confounding, and hazard ratios [HRs] were estimated using Cox proportional hazards regression models. Analyses were conducted among the overall sample of patients who received any prescription opioid in the prior 90 days and were repeated among those who received LTOT. Analyses were repeated among adults aged 65 years and older. Results: Among 297 620 adults treated with opioids, 30 514 individuals used cannabis (mean [SE] age, 57.8 [10.5] years; 28 784 [94.3%] men) and 267 106 adults did not (mean [SE] age, 62.3 [12.3] years; P < .001; 247 684 [92.7%] men; P < .001). Among all patients, cannabis use was not associated with increased all-cause mortality at 90 days (HR, 1.07; 95% CI, 0.92-1.22) or 180 days (HR, 1.00; 95% CI, 0.90-1.10) but was associated with an increased hazard of the composite outcome at 90 days (HR, 1.05; 95% CI, 1.01-1.07) and 180 days (HR, 1.04; 95% CI, 1.01-1.06). Among 181 096 adults receiving LTOT, cannabis use was not associated with increased risk of all-cause mortality at 90 or 180 days but was associated with an increased hazard of the composite outcome at 90 days (HR, 1.05; 95% CI, 1.02-1.09) and 180 days (HR, 1.05; 95% CI, 1.02-1.09). Among 77 791 adults aged 65 years and older receiving LTOT, cannabis use was associated with increased 90-day mortality (HR, 1.55; 95% CI, 1.17-2.04). Conclusions and Relevance: This study found that cannabis use among adults receiving opioid analgesic medications was not associated with any change in mortality risk but was associated with a small increased risk of adverse outcomes and that short-term risks were higher among older adults receiving LTOT.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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".

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Citations4
Published2022
Admission routes1
Has abstractyes

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