The opioid epidemic: A worldwide exploratory study using the WHO pharmacovigilance database
Bibliographic record
Abstract
Abstract Background and Aims The current opioid epidemic in the United States began 20 years ago and has become the leading cause of accidental deaths in the country. This crisis prompted us to explore trends in opioid abuse and dependence worldwide. We sought to identify other countries at high‐risk of opioid use disorders, using the World Health Organization's (WHO) pharmacovigilance database. Methods We performed a disproportionality analysis using VigiBase, the WHO Global Individual Case Safety Report (ICSR) database. Five opioids used worldwide were included: oxycodone, fentanyl, morphine, tramadol, and codeine. We extracted all ICSRs associated with the drugs of interest, considered as suspect medication and recorded up until 5 June 2021, using the narrow Standardised MedDRA Query (SMQ) for drug abuse and dependence. Countries with at least one ICSR for each of the five opioids were retained. The relationship between the use of a drug (i.e. an opioid) and the occurrence of an adverse drug reaction (i.e. drug abuse and dependence) for each country was assessed by calculating the information component (IC) and its 99.9% CI [IC 0005 ; IC 9995 ], using a quasi‐Bayesian confidence propagation neural network (BCPNN). A hierarchical cluster analysis (Ward's method) of the IC 0005 value for each of the five opioids was performed to identify subgroups of countries with similar reported risks of opioid abuse and dependence. Results Among 21 countries, the optimal number of clusters was calculated to be four, each with a Jaccard index >0.5 (0.95, 0.78, 0.65 and 0.75, respectively). Six countries with the highest signals of drug abuse and dependence were identified in cluster 1, with significant CIs for the five opioids of interest (IC 0005 > 0), ranging from 0.9 to 5.8 for the lower endpoint. Conclusions There appear to be four distinct clusters of countries with similar opioid abuse and dependence profiles. The group with the highest reported risk for the opioids oxycodone, fentanyl, morphine, tramadol and codeine includes Australia, Canada, France, Germans, the United Kingdom and the United States.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".