Exploring the Role of Macro-Level Factors and Antibiotic Consumption in MDR of E. coli and K. pneumoniae: A Multi-Method Study in European Countries
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) is a significant global public health concern, with rising multidrug-resistant (MDR) infections. The emergence and spread of MDR bacteria result from a complex interaction of factors across individual, community, and macro levels. While considerable research has explored individual and community factors, the impact of macro-level factors, such as healthcare systems and policies, on MDR bacteria development and spread remains relatively unexplored.Objective: To investigate the impact of community-based antimicrobial consumption as a private-factor, and broader macro-level factors such as socioeconomic and governance aspects, on the development of MDR in two commonly encountered community-acquired bacteria: E. coli and K. pneumoniae, over time and across European countries.Methods: The authors analyzed data from sources such as the European Antimicrobial Resistance Surveillance System, World Health Organization, and World Bank. Descriptive analyses were performed on the datasets to identify their key characteristics. Two methods, Extra Tree Regressor (ETR) and Pooled Ordinary Least Squares Regression on Data-Panel (POLS), were compared to evaluate the impact of predictor variables on MDR behavior in E. coli and K. pneumoniae.Results: Notable differences between the two approaches in determining factors influencing E. coli and K. pneumoniae. In the case of E. coli, the data-panel approach recognized the human development index (HDI) and out-of-pocket health expenses as significant factors. In contrast, the machine learning approach deemed out-of-pocket expenses the most crucial variable. For K. pneumoniae, the data-panel approach emphasized antibiotic community-level consumption as the most critical factor. In contrast, the machine learning approach highlighted the governance index as the most crucial variable.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".