501. Impact of Implementation of World Health Organization (WHO) National Action Plans (NAP) on Antibiotic Rates: A Time Series Analysis of 38 Countries
Bibliographic record
Abstract
Abstract Background Antimicrobial Resistance (AMR) poses a major threat to global health. In 2015, the World Health Organization (WHO) advised that member nations should develop National Action Plans (NAPs) to improve antibiotic stewardship and decrease AMR. However, the association between NAP implementation and antibiotic use is unknown. The objective of this study, therefore, was to examine post NAP-implementation changes in antibiotic utilization.Figure 1:Relative change in the population-based antibiotic rate after 2 years of implementation of National Action Plans (NAP) Methods We conducted a longitudinal repeated cross-sectional study of antibiotic purchases leveraging IQVIA’s MIDAS database. Our sample included 38 countries which implemented NAPs from June 2013 to January 2018. Quarterly purchases were reported in standardized units (1 pill/capsule/vial/5mL oral liquid) per 1 million population. We conducted an interrupted time-series analysis using mixed effects negative binomial models with a log link to assess level and trend changes in the antibiotic purchasing rate in the 8 quarters after each country’s NAP implementation, relative to 8 quarters pre-implementation. We included sinusoidal terms to account for seasonality, as well as random effects to estimate country-specific changes in rates after 8 quarters. Results Across all countries, the average level change post-NAP implementation was .03 log units (p=.44), and the average trend decreased by -.0014 log units per quarter (p=.72). After two years, the overall relative change in the purchasing rate was 1.9% [-7.8, 12.5], with country-specific effects ranging from -38.6% to 216% (See Figure 1). Only 4 countries (Norway, Jordan, South Africa, and Indonesia) experienced significant decreases. Conclusion Implementation of the NAP was not associated with overall changes in antibiotic purchasing two years later. The findings of this study may be useful for evaluating the effectiveness of these policies in terms of their stated objectives as well as identifying factors that led to the success of those countries that did observe decreases in antibiotic utilization. Disclosures All Authors: No reported disclosures
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".