Tracking global trends in the effectiveness of antibiotic therapy using the Drug Resistance Index
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background Evaluating trends in antibiotic resistance and communicating the results to a broad audience are important for dealing with this global threat. The Drug Resistance Index (DRI), which combines use and resistance into a single measure, was developed as an easy-to-understand measure of the effectiveness of antibiotic therapy. We demonstrate its utility in communicating differences in the effectiveness of antibiotic therapy across countries. Methods We calculated the DRI for countries with data on antibiotic use and resistance for the disease-causing organisms considered by the WHO as priority pathogens: Acinetobacter baumannii , Escherichia coli , Klebsiella pneumoniae , Pseudomonas aeruginosa , Staphylococcus aureus , Enterococcus faecium and Enterococcus faecalis . Additionally, we estimated pooled worldwide resistance rates for these pathogens. Results 41 countries had the requisite data and were included in the study. Resistance and use rates were highly variable across countries, but A. baumannii resistance rates were uniformly higher, on average, than other organisms. High-income countries, particularly Sweden, Canada, Norway, Finland and Denmark, had the lowest DRIs; the countries with the highest DRIs, and therefore the lowest effectiveness of antibiotic therapy, were all low-income and middle-income countries. Conclusions The DRI is a useful indicator of the problem of resistance. By combining data on antibiotic use with resistance, it captures a snapshot of how the antibiotics a country typically uses match their resistance profiles. This single measure of the effectiveness of antibiotic therapy provides a means of benchmarking against other countries and can, over time, indicate changes in drug effectiveness that can be easily communicated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it