Surge in Adverse Events for Prescription Opioids and Opioid Overdose Treatments during the COVID-19 Pandemic
Bibliographic record
Abstract
The opioid epidemic is one of the most pressing public health issues of our time, with hundreds of deaths daily due to opioid overdose.This research investigates the number of reported adverse events related to the use of prescription opioids and opioid overdose treatments during the COVID-19 pandemic, lending further insight into the impact the COVID-19 pandemic has had on the opioid epidemic.We hypothesized that adverse events for both prescription opioids and opioid overdose treatments rose during the COVID-19 pandemic, due to isolation and lack of access to healthcare services.Using data from the Food and Drug Administration Adverse Event Reporting System (FAERS), we analyzed the number of adverse drug events (ADE) in the years 2020 and 2021 compared to 2019, specifically for the medications Naloxone(G), Naloxone Hydrochloride(G), Oxycodone(G), Oxycodone Hydrochloride(G), and Oxycontin(P).We also analyzed the most commonly reported types of adverse reactions and the age of the reporters.The data reveals an alarming spike in the number of ADEs attributed to Naloxone(G) from 2019 to 2020, increasing by 148% and then another 29% in 2021.Similarly, the number of ADEs reported for Naloxone Hydrochloride(G) nearly rose four-fold from 66 to 246.For the prescription opioid Oxycodone(G), there was a 78% increase in ADEs from 2019 to 2020.More concerningly, there was a 434% spike in the number of ADEs for Oxycodone Hydrochloride(G) and more than thirteen-fold the number of cases in 2020 than 2019 for Oxycontin(P).Finally, we found the most commonly reported reactions were "overdose," "drug dependence," "drug withdrawal syndrome," and "drug abuse"; the 18-64-year-old age group reported the majority of the cases.These results highlight the need to increase focus on the opioid epidemic, specifically monitoring the use of prescription opioids.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".