Risk of overdose and death following codeine prescription among immigrants
Bibliographic record
Abstract
BACKGROUND: Immigrants may be at a higher risk of adverse drug reactions, in that poor language proficiency reduces individuals understanding of drug label instructions. Additionally, there are reports of severe or fatal toxicity due to CYP2D6 ultrarapid hepatic metabolism of codeine to morphine among some ethnic groups, especially those from Eastern Africa. METHODS: Between 2002 and 2012 we conducted a population-based cohort study among residents of Ontario, Canada. We used administrative health databases that linked immigrants and Canadian-born individuals to both prescription medication use and emergency department visits and hospital admissions. The primary composite outcome was the risk of drug overdose or all-cause mortality within 30 days of codeine prescription, comparing patients from various world regions to Canadian-born individuals. A secondary analysis stratified by codeine dose and ability to speak English and/or French. RESULTS: There were 553 504 individuals exclusively prescribed codeine. Relative to an incidence rate of 57.1/100 000 person-days among Canadian-born codeine recipients, those who migrated from various world regions were at a lower risk of drug overdose or death. For example, Eastern Africans had an adjusted HR of 0.60 (95% CI 0.31 to 1.17) on controlling for potential confounders such as age, sex, income and physician visits. Patients unable to speak English or French who were prescribed codeine were at a lower risk of the composite outcome relative to those proficient in either language (adjusted HR 0.63, 95% CI 0.54 to 0.74). INTERPRETATION: Overdose and death following the institution of codeine therapy are not more commonly observed among immigrants from world regions with a high prevalence of ultrarapid CYP2D6 status relative to those born in Canada. Lower proficiency in English or French also did not appear to heighten the risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".