Evaluating the effectiveness of the Ministry of Health restriction policy on seasonal antibiotic consumption trends in Saudi Arabia, 2016–2020
Bibliographic record
Abstract
Background: Understanding antibiotic consumption patterns over time is essential to optimize prescribing practices and minimizing antimicrobial resistance. This study aimed to determine whether the antibiotics restriction policy launched by the Saudi Ministry of Health in April 2018 has impacted antibiotic use by assessing changes and seasonal variations following policy enforcement. Methods: Quarterly sales data of J01 antibacterial for systemic use in standard units were obtained from the IQVIA-MIDAS database, spanning from the first quarter of 2016 to the last quarter of 2020. Antibiotics consumption was measured in defined daily doses per 1,000 inhabitant per day- in a quarter (DDDdq). A comparative analysis of antibiotic consumption pre- and post-policy periods introduction was conducted by computing the average consumption values for each period. Statistical comparison of the mean differences between the two periods were then made using independent samples t-test, Mann-Whitney U Test where needed. Time series analysis was employed to estimate the projected antibiotic consumption in the post-policy period if the restriction policy had not been implemented, which was then compared to actual consumption values to evaluate the effectiveness of the restriction policy. Results: During the pre-policy, there were seasonal trends of the total and oral antibiotic consumption through quarters, with higher consumption observed in the first and fourth quarters. In contrast, parenteral antibiotic consumption did not appear to follow a clear seasonal pattern. Following the restriction policy, there was a significant reduction in total and oral antibiotic use, with mean reductions of −96.9 DDDdq ( p -value = 0.002) and −98 DDDdq ( p -value = 0.002), respectively. Conversely, a significant increase in parenteral antibiotic consumption was observed with a mean increase of +1.4 DDDdq ( p -value < 0.0001). The comparison between the forecasted and actual models showed that the actual antibiotics consumption for total, oral, and parenteral were lower than the corresponding forecasted values by 30%, 31%, and 34%, respectively. Conclusion: Overall, our analysis of antibiotics consumption from 2016 to 2020 displays great success for the policy implemented by the Saudi Ministry of Health in significantly reducing the total and oral use of antibiotics. However, future studies are needed to explore the increased consumption of the parenteral antibiotics as well as the persistent high consumption patterns during the fall and winter months even after the implementation of the restriction policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".