Depression and Outcomes of Methadone and Buprenorphine Treatment Among People with Opioid Use Disorders: A Literature Review
Bibliographic record
Abstract
Objective: Depression is the most common psychiatric comorbidity among people with opioid use disorders (OUDs). However, whether and how comorbid depression is associated with the outcomes of opioid agonist therapy (OAT) remains poorly understood. The objective of this review was to identify and describe the association between depression and main outcomes (opioid use and treatment retention) of methadone and buprenorphine treatment among people with OUDs. Methods: A literature review was conducted by searching five electronic databases (MEDLINE, PubMed, Embase, Evidence-Based Medicine Reviews [EBMR], and Cumulative Index of Nursing and Allied Health Literature [CINAHL] Complete) from January 1970 to April 2019. Two independent reviewers screened titles and abstracts of the identified records by using pre-established eligibility criteria. Next, full texts were reviewed and studies that met inclusion criteria were selected. Finally, a descriptive synthesis of extracted data was performed. Results: In total, 12,296 records were identified and 18 studies that met inclusion criteria were retained. Of these, six studies reported reduced opioid use and seven reported increased opioid use during methadone or buprenorphine treatment. In addition, three studies reported an increased retention rate and four documented a decreased retention rate during methadone or buprenorphine treatment. The remaining studies did not find any significant association between depression and opioid use or treatment retention. Overall, the evidence did not demonstrate a consistent association between depression and outcomes of methadone or buprenorphine treatment. Conclusions: Although the inconsistent nature of the current evidence prohibited us from drawing definitive conclusions, we posit that the presence of depression among OUDs patients may not always predict negative outcomes related to retention and drug use during the course of OAT. Particularly, the hypothesis that adequate treatment of depression can improve treatment retention is promising and is in line with the call for increased efforts to provide integrated care for comorbid mental health disorders and addiction. Future studies with rigorous methodology are essential to better characterize the complex interplay between depression, OAT, and OUDs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".