MétaCan
Menu
Back to cohort
Record W2965968591 · doi:10.1016/j.eclinm.2019.08.001

Joint Trajectories of Heroin Use and Treatment Utilisation: Who Will Benefit in the Long Term?

2019· article· en· W2965968591 on OpenAlexaff
Huiru Dong, Thomas Kerr

Bibliographic record

VenueEClinicalMedicine · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of British Columbia
Fundersnot available
KeywordsMedicineHeroinHarm reductionBuprenorphinePublic healthMethadone(+)-NaloxonePsychological interventionPsychiatryOpioidDrugInternal medicineNursing

Abstract

fetched live from OpenAlex

Heroin use continues to result in significant harm to the health of individuals, including dependence, blood borne viral infection transmission [[1]Lawrinson P. Ali R. Buavirat A. et al.Key findings from the WHO collaborative study on substitution therapy for opioid dependence and HIV/AIDS.Addiction. 2008; 103: 1484-1492Crossref PubMed Scopus (180) Google Scholar], and fatal and non-fatal overdose [[2]Martins S.S. Sampson L. Cerdá M. Galea S. Worldwide prevalence and trends in unintentional drug overdose: a systematic review of the literature.Am J Public Health. 2015; 105: e29-e49Crossref PubMed Scopus (206) Google Scholar]. The steep rise in opioid use and related overdose deaths has rendered the “opioid epidemic” a major public health challenge in various settings. In 2017, the annual prevalence of opioid (mainly heroin) use in North America is estimated to be 0.7% [[3]United Nations Office on Drugs and Crime World Drug Report 2019. United Nations publication.https://wdr.unodc.org/wdr2019/Date: 2019Google Scholar]. In Europe, with nearly 3.8 million opioid users, heroin remains the main drug type for which people receive treatment [[3]United Nations Office on Drugs and Crime World Drug Report 2019. United Nations publication.https://wdr.unodc.org/wdr2019/Date: 2019Google Scholar]. In response to this ongoing crisis, a range of various pharmacological treatments (e.g., methadone, buprenorphine/naloxone) and other psychosocial interventions are being implemented with the aim of decreasing opioid use and thereby limiting overdose risk, reducing criminal activity, and improving quality of life [[4]Veilleux J.C. Colvin P.J. Anderson J. York C. Heinz A.J. A review of opioid dependence treatment: pharmacological and psychosocial interventions to treat opioid addiction.Clin Psychol Rev. 2010; 30: 155-166Crossref PubMed Scopus (227) Google Scholar]. It has been well-established that heroin dependence is a chronic relapsing condition [[5]Darke S. The life of the heroin user: typical beginnings, trajectories and outcomes. Cambridge University Press, 2011Crossref Scopus (57) Google Scholar], and there is considerable variation in treatment seeking behaviour and related outcomes at the individual level. While there is value in assessing the effectiveness of single-treatment episode, studies that evaluate patterns and outcomes of multiple, sequential interventions can provide better understanding of the natural history of heroin dependence and the impacts of treatment in the long term. Marel and colleagues [[6]Marel C. Mills K.L. Slade T. Darke S. Ross J. Teesson M. Modelling long-term joint trajectories of heroin use and treatment utilisation: findings from the Australian Treatment Outcome Study.EClinicalMedicine. 2019; 14: 71-79Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar] are among the first to provide meaningful insight into the relationship between trajectories of heroin use and treatment utilisation over a period of 10–11 years among a cohort of Australians with heroin dependence. Adding to the growing evidence of the effectiveness of treatment for opioid use disorder, the authors revealed considerable heterogeneity regarding the long-term joint trajectories. It is particularly interesting to learn that approximately 13% of study participants achieved and maintained abstinence without ongoing treatment; however, roughly an equal number of people continued using heroin even after engaging in long-term treatment. Discovering these distinct trajectories can improve our understanding of heroin use progression and treatment responses, and also point to future areas for research. For example, increasing attention has been given to “natural recovery” processes and more work is now needed to better understand how some people achieve sustained abstinence without the aid of treatment, and what other facilitating factors may be involved in such processes [[7]Sobell L.C. Ellingstad T.P. Sobell M.B. Natural recovery from alcohol and drug problems: methodological review of the research with suggestions for future directions.Addiction. 2000; 95: 749-764Crossref PubMed Scopus (285) Google Scholar]. These findings should also be interpreted in the context of specific treatment settings and other social-structural conditions (e.g., availability of harm reduction programs and other social and health supports) operating in the study setting. Future studies in other settings can thus hopefully improve our understanding of the long-term treatment effect on heroin use patterns. Early detection of individuals who are not responsive to available treatments or who are at high risk of relapse is crucial. Such information can provide valuable information for clinical practice in targeting those with the greatest need for treatment. However, as demonstrated in Marel et al. [[6]Marel C. Mills K.L. Slade T. Darke S. Ross J. Teesson M. Modelling long-term joint trajectories of heroin use and treatment utilisation: findings from the Australian Treatment Outcome Study.EClinicalMedicine. 2019; 14: 71-79Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar], while entering methadone/buprenorphine therapy and residential rehabilitation at baseline could potentially help predict the joint trajectories, no baseline demographic, drug use history, physical or mental health factors were predictive of the patterns. Therefore, in light of dynamic drug-use behaviours and treatment process, recognizing the early signs of such trajectories is challenging. However, given a recent review conducted by Hser and colleagues [[8]Hser Y. Evans E. Grella C. Ling W. Anglin D. Long-term course of opioid addiction.Harv Rev Psychiatry. 2015; 23: 76-89Crossref PubMed Scopus (165) Google Scholar] indicating that longer treatment retention and multiple treatment episodes are associated with a greater likelihood of abstinence and eventual cessation, treatment engagement is clearly something that should continue to be encouraged and facilitated. These findings from Marel et al. [[6]Marel C. Mills K.L. Slade T. Darke S. Ross J. Teesson M. Modelling long-term joint trajectories of heroin use and treatment utilisation: findings from the Australian Treatment Outcome Study.EClinicalMedicine. 2019; 14: 71-79Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar] raise many interesting questions for future research. For example, it is critical to investigate the reasons for poor treatment outcomes. Research has indicated that inadequate dosing in the context of opioid agonist therapy may result in the use of substances during treatment [[9]Heikman P.K. Muhonen L.H. Ojanperä I.A. Polydrug abuse among opioid maintenance treatment patients is related to inadequate dose of maintenance treatment medicine.BMC Psychiatry. 2017; 17: 245Crossref PubMed Scopus (39) Google Scholar]. Additionally, can intensity of heroin use, routes of administration, past treatment experience, treatment timing be predictive of distinct trajectories? How do life-changing events, such as loss of key relationships, overdose, and incarceration, influence trajectory patterns? In the context of the continuing opioid overdose crisis, how are the trajectories different for people who misuse opioids other than heroin? In summary, the study by Marel and colleagues [[6]Marel C. Mills K.L. Slade T. Darke S. Ross J. Teesson M. Modelling long-term joint trajectories of heroin use and treatment utilisation: findings from the Australian Treatment Outcome Study.EClinicalMedicine. 2019; 14: 71-79Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar] in this issue of EClinicalMedicine takes an important first step in examining the relationship between patterns of heroin use and treatment utilisation, predictors of the joint trajectories, and their results highlight the benefits of treatment engagement. At the same time, their findings underscore the importance of identifying and attending to diversity during the treatment process, and highlight the need for adopting patient-centered strategies for treating heroin dependence. Modelling Long-Term Joint Trajectories of Heroin Use and Treatment Utilisation: Findings from the Australian Treatment Outcome StudyThe role of treatment in recovery from heroin dependence is undeniable; however, a considerable proportion of people are able achieve and maintain abstinence without the need for ongoing treatment. An equally significant proportion will continue to use heroin despite being in long-term treatment. Full-Text PDF Open Access

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)0.000
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 score0.007
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.055
GPT teacher head0.323
Teacher spread0.268 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEClinicalMedicineSame topicOpioid Use Disorder TreatmentFrench-language works237,207