Decreasing Trends in Antibiotic Consumption in Public Hospitals from 2014 to 2017 Following the Decentralization of Drug Procurement in Myanmar
Bibliographic record
Abstract
(1) Background: In 2014, drug procurement for public hospitals in Myanmar was decentralized to a pull system. This might lead to increasing trends in the consumption of broad-spectrum and last-resort antibiotics. For fiscal years 2014-2017, we assessed annual antibiotic consumption trends and patterns in total defined daily doses (DDDs). (2) Methods: We followed World Health Organization (WHO) methodology for surveillance of antimicrobial consumption based on hospital antibiotic procurement records (as a proxy). (3) Results: In 32% of all public hospitals where data were retrieved, total antibiotic consumption reduced by 19% between 2014 (7,122,852 DDD) and 2017 (5,794,904 DDD). Consumption per 1000 inhabitants per day (<200 bed hospitals) also reduced from 0.6 to 0.3. Over 60% of procurement was for beta-lactam antibiotics and quinolones; quinolones decreased over time. Consumption of first-line antibiotics increased (42% in 2014 to 54% in 2017), whereas broad-spectrum antibiotics decreased (46% in 2014 to 38% in 2017). Linezolid was the only last-resort antibiotic procured. There was a progressive reduction in per capita government current health expenditure from approximately 9.2 US$ in 2014 to 8.3 US$ in 2017. (4) Conclusions: Antibiotic consumption decreased over time in public hospitals. This first study provides a baseline for developing an antibiotic consumption surveillance system in Myanmar.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".