Persistence of resistance: a panel data analysis of the effect of antibiotic usage on the prevalence of resistance
Bibliographic record
Abstract
The use of antibiotics promotes the emergence of resistant bacteria in the patient and the environment. The extent of this well-documented biological relationship is, however, not well characterized at an ecological level. To make good policy around antibiotic use, it is important to understand the empirical connection between usage and resistance. We provide a consistent approach to estimate this relationship using national-level surveillance data. This paper estimates the effect of antibiotic usage on antibiotic resistance using an 11-year panel of data on both usage and resistance for 26 antibiotic-bacteria combinations in 26 European countries. Using distributed-lag models and event-study specifications, we provide estimates of the rate at which increases in antibiotic usage at the national level affect antibiotic resistance nationally and internationally. We also calculate the persistence of resistance and analyze how resistance behaves asymmetrically with respect to increases and decreases in usage. Our analysis finds the prevalence of resistant bacteria increases immediately after usage and continues to increase for at least 4 years after usage. We show that a decrease in usage has little identifiable impact on resistance over the same period. Usage in neighboring countries increases resistance in a country, independent of usage in that country. Trends in usage-related resistance vary across European regions and across bacterial classifications.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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".