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Vol. 16 No. 1 (2025)
Vol. 16 Núm. 1 (2025): Vol. 16 Núm. 1 (2025): Vol 16, Núm.1 (2025): Enero - Junio

Artículos de investigación / Research papers

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

https://doi.org/10.19053/uptc.01217488.v16.n1.2025.17528

Published 2025-04-20

  • Jhoana Romero
    • ,
  • Kernel Prieto
    • ,
  • Felipe Benavides

Jhoana Romero
University of Manitoba

Kernel Prieto


Felipe Benavides


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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