Overdose deaths involving non-BZD hypnotic/sedatives in the USA: Trends analyses
Bibliographic record
Abstract
Background: There is sparse knowledge on overdose deaths resulting from non-benzodiazepines and gabapentinoids usage. We examined overdose death rate across demographics categories and the overdose death trends over time. Methods: Using data from the National Center for Health Statistics (USA), we identified 21,167 persons that died with an overdose ICD code as the underlying cause of death and had a T42.6/T42.7 ICD code, which include gabapentinoids and z-drugs, among their multiple causes of death. The overdose death rate was calculated per 100,000 persons for every year between 2000 and 2018. We used joinpoint regression analyses to assess trends over time. Results: We identified a rise in the proportion of deaths with a T42.6/T42.7 ICD code between 2000 and 2006 (yearly change: +0.06) and between 2006 and 2015 (yearly change: +0.32). From 2000 to 2008, the proportion of deaths with any other T code rose significantly (yearly change: +3.56). Between 2008 and 2018, there was also a significant rise (yearly change: +1.31). From 2000 to 2015, the proportion of deaths with a T42.6/T42.7 ICD code with any other T code rose (yearly change: +2.58). From 2000 to 2015, the proportion of deaths with a T42.6/T42.7 ICD code with a concurrent benzodiazepine T code rose (yearly change: +1.98). From 2000 to 2005, the proportion of alcohol T codes rose non-significantly (yearly change: +0.35). Finally, the proportion of alcohol T codes fell significantly between 2008 and 2018 (yearly change: - 0.74). Interpretation: Deaths due to non-benzodiazepine hypnotics and gabapentinoids increased significantly over the last two decades. Clinicians should not assume that replacing benzodiazepines and opioids with these medications necessarily lowers risk to the patient. Funding: This study was funded by an internal grant from the Columbia University President's Global Innovation Fund (PI: Martins).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".