Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, EPH, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
PLoS Med. 2024 Mar 14;21(3):e1004301. doi: 10.1371/journal.pmed.1004301. eCollection 2024 Mar.
Antibiotic usage, contact with high transmission healthcare settings as well as changes in immune system function all vary by a patient's age and sex. Yet, most analyses of antimicrobial resistance (AMR) ignore demographic indicators and provide only country-level resistance prevalence values. This study aimed to address this knowledge gap by quantifying how resistance prevalence and incidence of bloodstream infection (BSI) varied by age and sex across bacteria and antibiotics in Europe.
We used patient-level data collected as part of routine surveillance between 2015 and 2019 on BSIs in 29 European countries from the European Antimicrobial Resistance Surveillance Network (EARS-Net). A total of 6,862,577 susceptibility results from isolates with age, sex, and spatial information from 944,520 individuals were used to characterise resistance prevalence patterns for 38 different bacterial species and antibiotic combinations, and 47% of these susceptibility results were from females, with a similar age distribution in both sexes (mean of 66 years old). A total of 349,448 isolates from 2019 with age and sex metadata were used to calculate incidence. We fit Bayesian multilevel regression models by country, laboratory code, sex, age, and year of sample to quantify resistant prevalence and provide estimates of country-, bacteria-, and drug-family effect variation. We explore our results in greater depths for 2 of the most clinically important bacteria-antibiotic combinations (aminopenicillin resistance in Escherichia coli and methicillin resistance in Staphylococcus aureus) and present a simplifying indicative index of the difference in predicted resistance between old (aged 100) and young (aged 1). At the European level, we find distinct patterns in resistance prevalence by age. Trends often vary more within an antibiotic family, such as fluroquinolones, than within a bacterial species, such as Pseudomonas aeruginosa. Clear resistance increases by age for methicillin-resistant Staphylococcus aureus (MRSA) contrast with a peak in resistance to several antibiotics at approximately 30 years of age for P. aeruginosa. For most bacterial species, there was a u-shaped pattern of infection incidence with age, which was higher in males. An important exception was E. coli, for which there was an elevated incidence in females between the ages of 15 and 40. At the country-level, subnational differences account for a large amount of resistance variation (approximately 38%), and there are a range of functional forms for the associations between age and resistance prevalence. For MRSA, age trends were mostly positive, with 72% (n = 21) of countries seeing an increased resistance between males aged 1 and 100 years and a greater change in resistance in males. This compares to age trends for aminopenicillin resistance in E. coli which were mostly negative (males: 93% (n = 27) of countries see decreased resistance between those aged 1 and 100 years) with a smaller change in resistance in females. A change in resistance prevalence between those aged 1 and 100 years ranged up to 0.51 (median, 95% quantile of model simulated prevalence using posterior parameter ranges 0.48, 0.55 in males) for MRSA in one country but varied between 0.16 (95% quantile 0.12, 0.21 in females) to -0.27 (95% quantile -0.4, -0.15 in males) across individual countries for aminopenicillin resistance in E. coli. Limitations include potential bias due to the nature of routine surveillance and dependency of results on model structure.
In this study, we found that the prevalence of resistance in BSIs in Europe varies substantially by bacteria and antibiotic over the age and sex of the patient shedding new light on gaps in our understanding of AMR epidemiology. Future work is needed to determine the drivers of these associations in order to more effectively target transmission and antibiotic stewardship interventions.
抗生素的使用、与高传播性医疗保健环境的接触以及免疫系统功能的变化因患者的年龄和性别而异。然而,大多数对抗菌药物耐药性(AMR)的分析都忽略了人口统计学指标,只提供了国家层面的耐药流行率值。本研究旨在通过量化欧洲血流感染(BSI)中细菌和抗生素的耐药流行率和发生率如何随年龄和性别而变化来填补这一知识空白。
我们使用了 2015 年至 2019 年期间在欧洲抗菌药物耐药性监测网络(EARS-Net)中从 29 个欧洲国家收集的关于 BSI 的患者水平数据。使用了来自 944,520 个人的年龄、性别和空间信息的 38 种不同细菌物种和抗生素组合的 6,862,577 个分离物的药敏结果来描述耐药流行率模式,其中 47%的药敏结果来自女性,男女的年龄分布相似(平均年龄为 66 岁)。使用了 2019 年的 349,448 个带有年龄和性别元数据的分离物来计算发病率。我们通过国家、实验室代码、性别、年龄和样本年份拟合贝叶斯多水平回归模型,以量化耐药流行率并提供国家、细菌和药物家族效应变化的估计。我们为两种最具临床重要性的细菌-抗生素组合(大肠埃希菌的氨基青霉素耐药性和金黄色葡萄球菌的甲氧西林耐药性)更深入地探索了我们的结果,并提出了一个简化的指示性指数,用于预测老年人(100 岁)和年轻人(1 岁)之间的耐药差异。在欧洲层面,我们发现耐药流行率按年龄有明显的差异。在抗生素家族内,如氟喹诺酮类药物,趋势的变化通常比在细菌种类内,如铜绿假单胞菌,更为明显。甲氧西林耐药金黄色葡萄球菌(MRSA)的耐药性明显增加,而铜绿假单胞菌对几种抗生素的耐药性在大约 30 岁时达到峰值。对于大多数细菌物种,感染发病率随年龄呈 U 形模式,男性发病率较高。一个重要的例外是大肠埃希菌,女性在 15 至 40 岁之间的发病率较高。在国家层面,国家以下层面的差异导致了大量的耐药性变异(约占 38%),年龄和耐药流行率之间的关系有多种功能形式。对于 MRSA,年龄趋势大多为正,在 1 至 100 岁的男性中,72%(n=21)的国家看到耐药性增加,男性的耐药性变化更大。相比之下,大肠埃希菌中氨基青霉素耐药性的年龄趋势大多为负(男性:93%(n=27)的国家在 1 至 100 岁的人群中看到耐药性降低),女性的耐药性变化较小。一个国家内 1 至 100 岁人群的耐药性流行率变化幅度高达 0.51(中位数,使用后验参数范围模拟的流行率的 95%分位数为 0.48,0.55 在男性)对于一个国家的 MRSA,但在个别国家,从 0.16(95%分位数 0.12,0.21 在女性)到-0.27(95%分位数-0.4,-0.15 在男性)之间变化。
在这项研究中,我们发现欧洲血流感染中细菌和抗生素的耐药流行率在患者的年龄和性别方面存在显著差异,这为我们对抗菌药物耐药性流行病学的理解提供了新的视角。未来需要进一步研究这些关联的驱动因素,以便更有效地针对传播和抗生素管理干预措施。