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
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: 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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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