Contextualizing Mental Health Comorbidities in SUD Patients Leading to Increased Risk of Overdose: A Systematic Literature Review
Bibliographic record
Abstract
Introduction: Despite increased awareness of the risk associated with drug overdose, it remains a significant public health concern in North America. In the case of Substance Use Disorder (SUD), comorbid mental illness has been found to increase risk of overdose. This systematic review aims to identify, assess and contextualize common comorbidities associated with SUD that have contributed to overdose occurrences. Methods: A comprehensive literature search was conducted to identify major comorbidities associated with SUD. PubMed, Embase, MEDLINE, and Web of Science were utilized. Peer-reviewed primary studies were included if they examined the prevalence of SUD in conjunction with a comorbidity involving mental illness. To establish a correlation with real-world overdose cases, the collected data was compared with coroner data to determine if current drug and comorbidity research in literature was reflective of the most prevalent forms of overdose in the general population. Results were summarized following the PRISMA guidelines. Results: A total of 60 papers investigating SUD with a comorbidity involving mental illness comorbidity were identified. Alcohol and cannabis were the most frequently studied substances, while Depressive and Anxiety disorders were the most common mental illness comorbidities examined. Geographically, these findings were consistent with studies from the US. However, in Canada, opioids were the most extensively studied substances, with Depressive and Neurodevelopmental disorders being the most commonly investigated mental illness comorbidities. Discussion: Comparison with coroner data suggests that greater research focus should be directed towards substances with greater potential for harm and fatal overdose. This emphasis on specific drugs can help improve overall mortality rates among SUD patients with comorbid mental illnesses. In Canada, this could involve conducting further research on stimulants such as cocaine and methamphetamine, and in the US with fentanyl. Conclusion: A disconnect between the substances studied in the literature and their real-world impact was found. Bridging this gap is essential to develop evidence-based interventions for comorbid SUD. More research on SUD, mental health comorbidity and overdose trends are needed to improve relevance to real-world scenarios.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".