The impact of regulatory restrictions on pregabalin use in Saudi Arabia: An interrupted time series analysis
Bibliographic record
Abstract
PURPOSE: The Saudi Food and Drug Authority (SFDA) added pregabalin to the list of controlled substances in December 2017 to minimize the risk of its possible abuse and misuse. This study was aimed at assessing the impact of this decision on the overall use of pregabalin in Saudi Arabia and in comparison with drugs prescribed to treat neuropathic pain (i.e., vs. gabapentin, tramadol, duloxetine, and amitriptyline). METHODS: This was an interrupted time-series analysis of the Saudi quarterly sale data of the study drugs from October/2015 to September/2020. These data were obtained from IQVIA and were converted into use estimates (defined daily dose per 1000 inhabitant-days [DDD/TID]). Segmented regression models were conducted to assess the direct (level) and prolonged (trend) changes in use data after the decision. All analyses were completed using RStudio Version 1.4.1103. RESULTS: Before the SFDA's decision, there was an increased quarter-to-quarter use of pregabalin (DDD/TID: 0.16; 95% confidence interval [CI] 0.04 to 0.28). Pregabalin overall use dropped sharply by -1.85 DDD/TID (95% CI -2.71 to -0.99) directly after the decision with a prolonged quarter-to-quarter declining effect (DDD/TID: -0.22, CI to -0.37 to -0.05). The decision was associated with a direct increase in the use of gabapentin by 0.62 DDD/TID (95% CI 0.52-0.72) without any impact on the use of other drugs. CONCLUSIONS: The results of our study showed that the SFDA decision was associated with a decrease in the overall use of pregabalin, which may help minimize the risk of its abuse and misuse.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".