The Impact of Enrolment in Methadone Maintenance Therapy on Initiation of Heavy Drinking among People Who Use Heroin
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Bibliographic record
Abstract
BACKGROUND: There is equivocal evidence regarding whether people who use heroin substitute heroin for alcohol upon entry to methadone maintenance therapy (MMT). We aimed to examine the impact of MMT enrolment on the onset of heavy drinking among people who use heroin. METHODS: We derived data from prospective, community-based cohorts of people who inject drugs in Vancouver, Canada, between December 1, 2005, and May 31, 2014. Multivariable extended Cox regression analysis examined the effect of MMT enrolment on the onset of heavy drinking among people who used heroin at baseline. RESULTS: In total, 357 people who use heroin were included in this study. Of these, 208 (58%) enrolled in MMT at some point during follow-up, and 115 (32%) reported initiating heavy drinking during follow-up for an incidence density of 7.8 events (95% CI 6.4-9.5) per 100 person-years. The incidence density of heavy drinking was significantly lower among those enrolled in MMT at some point during follow-up compared to those who did not (4.6 vs. 16.2; p < 0.001). MMT enrolment was not significantly associated with time to initiate heavy drinking (adjusted relative hazard (ARH) 1.27; 95% CI 0.78-2.07) after adjustment for relevant demographic and substance-use characteristics. Age and cannabis use were the only variables that were independently associated with the time to onset of heavy drinking (ARH 0.74; 95% CI 0.58-0.94 and ARH 2.06; 95% CI 1.32-3.19, respectively). CONCLUSION: In this study, MMT enrolment did not predict heavy drinking and may even appear to decrease the initiation of heavy drinking. Our findings suggest younger age and cannabis use may predict heavy drinking. These findings could help inform on-going discussions about the effects of opioid agonist therapy on alcohol consumption among people who use heroin.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it