The association between age of onset of opioid use and comorbidity among opioid dependent patients receiving methadone maintenance therapy
Bibliographic record
Abstract
BACKGROUND: Opioid use disorder (OUD) affects approximately 21.9 million people worldwide. This study aims to determine the association between age of onset of opioid use and comorbid disorders, both physical and psychiatric, in patients receiving methadone maintenance treatment (MMT) for OUD. Understanding this association may inform clinical practice about important prognostic factors of patients on MMT, enabling clinicians to identify high-risk patients. METHODS: This study includes data collected between June 2011 and August 2016 for the Genetics of Opioid Addiction research collaborative between McMaster University and the Canadian Addiction Treatment Centers. All patients were interviewed by trained health professionals using the Mini-International Neuropsychiatric Interview and case report forms. Physical comorbidities were verified using patients' electronic medical records. A multi-variable logistic regression model was constructed to determine the strength of the association between age of onset of opioid use and the presence of physical or psychiatric comorbidity while adjusting for current age, sex, body mass index, methadone dose and smoking status. RESULTS: Data from 627 MMT patients with a mean age of 38.8 years (SD = 11.07) were analyzed. Individuals with an age of onset of opioid use younger than 18 years were found to be at higher odds for having a physical or psychiatric comorbid disorder compared to individuals with an age of onset of opioid use of 31 years or older (odds ratio 2.94, 95% confidence interval 1.20, 7.19, p = 0.02). A significant association was not found between the risk of having a comorbidity and an age of onset of opioid use between 18 and 25 years or 26 and 30 years, compared to an age of onset of opioid use of 31 years or older. CONCLUSION: Our study demonstrates that the younger one begins to use opioids, the greater their chance of having a physical or psychiatric co-morbidity. Understanding the risk posed by an earlier onset of opioid use for the later development of comorbid disorders informs clinical practice about important prognostic predictors and aids in the identification of high-risk patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".