Evidence map›Paper›PMID 33405314›Full record

ArticleAddiction (Abingdon, England)2021

Sex differences in factors predicting post-treatment opioid use.

Jordan P Davis, David Eddie, John Prindle, Emily R Dworkin, Nina C Christie, Shaddy Saba, Graham T DiGuiseppi, John D Clapp, John F Kelly

Open access · greenAbstract read
In one paragraph

Article in Addiction (Abingdon, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 46 citations in OpenAlex.

  1. Trial
  2. Article
  3. Central amygdalar PKCδ neurons mediate fentanyl withdrawal.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Opioids and restless legs syndrome: a double-edged sword.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Jordan P DavisSuzanne Dworak-Peck School of Social Work, USC Center for Artificial Intelligence in Society, USC Center for Mindfulness Science, USC Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0002-6108-4936
David EddieRecovery Research Institute, Center for Addiction Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-0619-7209
John PrindleUniversity of Southern California, Los Angeles, CA, USA.
Emily R DworkinUniversity of Washington School of Medicine, Seattle, WA, USA.
Nina C ChristieDepartment of Psychology and the USC Brain and Creativity Institute, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0003-0976-6195
Shaddy SabaSuzanne Dworak-Peck School of Social Work, USC Center for Artificial Intelligence in Society, USC Center for Mindfulness Science, USC Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA.
Graham T DiGuiseppiSuzanne Dworak-Peck School of Social Work, USC Center for Artificial Intelligence in Society, USC Center for Mindfulness Science, USC Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA.
John D ClappSuzanne Dworak-Peck School of Social Work, USC Keck School of Medicine, USC Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA.
John F KellyRecovery Research Institute, Center for Addiction Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
University of Southern California · USHarvard University · USUniversity of Washington · US

Funding

Bringing real-time stress detection to scale: Development of a biosensor driven, stress detection classifier for smartwatchesK23AA027577 · NIAAA · MASSACHUSETTS GENERAL HOSPITAL · PI EDDIE, DAVID · 2020 to 2024
$919k
Preventing Risky Drinking and PTSD After Sexual Assault: A Web-Based InterventionR00AA026317 · NIAAA · UNIVERSITY OF WASHINGTON · PI DWORKIN, EMILY RAPHAEL · 2019 to 2021
$730k
NIAAA NIH HHS K23 AA027577NIAAA NIH HHS R00 AA026317
6 · The paper itself

Abstract

BACKGROUND AND

aimsSeveral reports have documented risk factors for opioid use following treatment discharge, yet few have assessed sex differences, and no study has assessed risk using contemporary machine learning approaches. The goal of the present paper was to inform treatments for opioid use disorder (OUD) by exploring individual factors for each sex that are most strongly associated with opioid use following treatment.

designSecondary analysis of Global Appraisal of Individual Needs (GAIN) database with follow-ups at 3, 6 and 12 months post-OUD treatment discharge, exploring demographic, psychological and behavioral variables that predict post-treatment opioid use.

settingOne hundred and thity-seven treatment sites across the United States.

participantsAdolescents (26.9%), young adults (40.8%) and adults (32.3%) in treatment for OUD. The sample (n = 1,126) was 54.9% male, 66.1% white, 20% Hispanic, 9.8% multi-race/ethnicity, 2.8% African American and 1.3% other. MEASUREMENT: Primary outcome was latency to opioid use over 1 year following treatment admission.

resultsFor women, regularized Cox regression indicated that greater withdrawal symptoms [hazard ratio (HR) = 1.31], younger age (HR = 0.88), prior substance use disorder (SUD) treatment (HR = 1.11) and treatment resistance (HR = 1.11) presented the largest hazard for post-treatment opioid use, while a random survival forest identified and ranked substance use problems [variable importance (VI) = 0.007], criminal justice involvement (VI = 0.006), younger age (VI = 0.005) and greater withdrawal symptoms (VI = 0.004) as the greatest risk factors. For men, Cox regression indicated greater conduct disorder symptoms (HR = 1.34), younger age (HR = 0.76) and multiple SUDs (HR = 1.27) were most strongly associated with post-treatment opioid use, while a random survival forests ranked younger age (VI = 0.023), greater conduct disorder symptoms (VI = 0.010), having multiple substance use disorders (VI = 0.010) and criminal justice involvement (VI = 0.006) as the greatest risk factors.

conclusionRisk factors for relapse to opioid use following opioid use disorder treatment appear to be, for women, greater substance use problems and withdrawal symptoms and, for men, younger age and histories of conduct disorder and multiple substance use disorder.

Indexed as

Opioid-Related DisordersSubstance Withdrawal SyndromeAdolescentAnalgesics, OpioidCriminal LawFemaleHumansMaleSex CharacteristicsUnited StatesYoung AdultAnalgesics, OpioidAdolescentsmachine learningopioidsopioid use disorderrisk factorstraumatreatment

Identifiers

PMID33405314
PMCPMC8254742
OpenAlexW3120734665

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.