探索抗生素消费与耐药性之间的关联:使用值回归方法的加泰罗尼亚 (西班牙) 的证据
Toni Mora1,2, David Roche1,2, Carmen Cabezas Peña3
1Universitat Internacional de Catalunya, Barcelona, Spain.
BMJ public health
|March 16, 2026
概括
消费抗生素强烈预测了耐药性. 针对过度使用的干预措施,特别是在确定门以下,可以通过考虑非线性动态来减轻抗生素耐药性.
科学领域:
- 流行病学 流行病学
- 药理学 药理学是指药理学的学科.
- 公共卫生 公共卫生
背景情况:
- 抗生素耐药性是一个日益增长的全球健康威胁.
- 了解抗生素耐药性的个体水平预测因素对于有效的干预至关重要.
- 抗生素使用和耐药性之间的关系可能是非线性的,具有潜在的值.
研究的目的:
- 在个人层面上确定抗生素耐药性的关键预测因素.
- 评估抗生素使用和耐药性之间的关系在不同累积抗生素消费水平下如何变化.
- 使用值回归来捕捉非线性动态.
主要方法:
- 在加泰罗尼亚 (2014-2021) 进行了一项基于人口的纵向观测研究,涉及近300万个人.
- 机器学习技术 (堆叠组合) 用于识别与抗生素耐药性相关的因素.
- 基因算法优化的值回归模型分析了抗生素消耗和耐药性的非线性.
主要成果:
- 机器学习模型显示出良好的预测性能 (AUC 0.90).
- 抗生素消费是抗生素耐药性的重要预测因素.
- 值回归表明,抗生素使用对超出特定值的耐药性的边际影响正在减少.
结论:
- 针对过度使用抗生素的干预措施,特别是低于确定值的干预措施,可以帮助缓解耐药性.
- 调整干预措施以适应社会人口和区域背景是很重要的.
- 考虑非线性消费-耐药性动态对于抗生素管理政策至关重要.
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