Correlation Between Opioid Drug Prescription and Opioid-Related Mortality in Spain as a Surveillance Tool: Ecological Study
Bibliographic record
Abstract
BACKGROUND: Opioid drug prescription (ODP) and opioid-related mortality (ORM) have increased in Spain. However, their relationship is complex, as ORM is registered without considering the type of opioid (legal or illegal). OBJECTIVE: This ecological study aimed to examine the correlation between ODP and ORM in Spain and discuss their usefulness as a surveillance tool. METHODS: This was an ecological descriptive study using retrospective annual data (2000-2019) from the Spanish general population. Data were collected from people of all ages. Information on ODP was obtained from the Spanish Medicines Agency in daily doses per 1000 inhabitants per day (DHD) for total ODP, total ODP excluding those with better safety protocols (codeine and tramadol), and each opioid drug separately. Rates of ORM (per 1,000,000 inhabitants) were calculated based on deaths registered (International Classification of Diseases, 10th Revision codes) as opioid poisoning by the National Statistics Institute, derived from the drug data recorded by medical examiners in death certificates. Opioid-related deaths were considered to be those that indicated opioid consumption (accidental, infringed, or self-inflicted) as the main cause of death: death due to accidental poisoning (X40-X44), intentional self-inflicted poisoning (X60-X64), drug-induced aggression (X85), and poisoning of undetermined intention (Y10-Y14). A descriptive analysis was carried out, and correlations between the annual rates of ORM and DHD of the prescribed opioid drugs globally, excluding medications of the least potential risk of overdose and lowest treatment tier, were analyzed using Pearson linear correlation coefficient. Their temporal evolution was analyzed using cross-correlations with 24 lags and the cross-correlation function. The analyses were carried out using Stata and StatGraphics Centurion 19. RESULTS: The rate of ORM (2000-2019) ranged between 14 and 23 deaths per 1,000,000 inhabitants, with a minimum in 2006 and an increasing trend starting in 2010. The ODP ranged between 1.51 to 19.94 DHD. The rates of ORM were directly correlated with the DHD of total ODP (r=0.597; P=.006), total ODP without codeine and tramadol (r=0.934; P<.001), and every prescribed opioid except buprenorphine (P=.47). In the time analysis, correlations between DHD and ORM were observed in the same year, although not statistically significant (all P≥.05). CONCLUSIONS: There is a correlation between greater availability of prescribed opioid drugs and an increase in opioid-related deaths. The correlation between ODP and ORM may be a useful tool in monitoring legal opiates and possible disturbances in the illegal market. The role of tramadol (an easily prescribed opioid) is important in this correlation, as is that of fentanyl (the strongest opioid). Measures stronger than recommendations need to be taken to reduce off-label prescribing. This study shows that not only is opioid use directly related to the prescribing of opioid drugs above what is desirable but also an increase in deaths.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".