Using oral and parenteral formulation of AWaRe antibiotics as a proxy estimate of primary healthcare and inpatient hospital sector use
Bibliographic record
Abstract
Synopsis Background Benchmarking antibiotic use across different healthcare sectors is crucial to improve use and implement the UNGA 70% Access target. Many countries only have available aggregate sales data, which do not have sector-specific usage information. The objective of this study is to estimate the proportion of oral antibiotic use across different healthcare sectors. Materials/methods We used IQVIA MIDAS ® Quarterly Sales data and Global Point Prevalence Survey (Global-PPS) inpatient data from eight countries, including Belgium, Canada, China, Netherlands, Philippines, Saudi Arabia, Singapore, and United Kingdom, in 2019. Our analysis focused on Access and Watch antibiotics. In the main analysis, we assumed that all parenteral antibiotics were used exclusively in inpatient settings, an assumption we then relaxed through sensitivity analyses. The observed ratios of oral-to-parenteral antibiotics in the patient-level Global-PPS data were calculated, by dividing the volume of oral antibiotic use by that of parenteral antibiotic use, and then this calculated ratio was multiplied to the IQVIA MIDAS sales data to estimate oral antibiotic use outside of the inpatient sector. Results The ratios of oral-to-parenteral use among inpatients in the Global-PPS data ranged between 0.05 (95%Credible Interval [CrI]: 0.03-0.09) and 1.01 (95%CrI: 0.56-1.80) in the main analysis. We estimated that overall, less than 7% of national oral antibiotics were used by inpatients, assuming exclusive parenteral use in inpatient settings in the main analysis, and less than 9% when assuming 90% of parenteral use was non-inpatient in the sensitivity analyses. Conclusion Our results suggest that where patient-level data are unavailable, alternative sources, such as antibiotic import data including routes of administration, can reasonably estimate sector-specific national use.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".