Impact of pregabalin reclassification as a controlled substance in Egypt on gabapentinoid and opioid utilization: A repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Pregabalin is commonly used for treating pain but is also recognized for its misuse potential. In response to rising abuse, Egyptian health authorities reclassified pregabalin as a controlled substance in August 2019, aiming to curb misuse and regulate its distribution. This study evaluated the impact of the 2019 policy on gabapentinoid (pregabalin and gabapentin) and opioid sales in Egypt. METHODS: An interrupted time-series analysis using Autoregressive Integrated Moving Average (ARIMA) models was conducted on IQVIA MIDAS® quarterly volume sales data obtained under license from IQVIA for the period 2012-2023. Copyright IQVIA. All rights reserved. Drug volume sales were standardized per 1,000 individuals based on population estimates. ARIMA modelling was used to capture immediate and delayed effects of the August 2019 policy change. Percent changes for 3-, 6- and 12-months were also calculated. RESULTS: Overall gabapentinoid sales increased steadily until the second quarter of 2019. Following reclassification, a significant decline in total gabapentinoid sales (-67%) was observed, driven by a 99% drop in pregabalin sales, while gabapentin sales surged by 198%. ARIMA analyses of gabapentinoid sales showed a significant short-term effect (pulse: p < 0.001) and a notable gradual long-term change (ramp: p = 0.008). In contrast, opioids exhibited a significant short-term sustained increase (step: p = 0.010) but a non-significant gradual long-term change (ramp: p = 0.256), with sales rising by up to 49.5% at one year post-policy. CONCLUSIONS: Reclassifying pregabalin effectively reduced its utilization but prompted a shift to gabapentin use. Our findings highlight the complexity of drug policy interventions, underscoring the need for continuous monitoring to mitigate unintended substitution effects and better understand policy impacts of the treatment of pain.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".