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综合基因组监测对澳大利亚非伤寒感染的影响:一项生态学研究。

The impact of integrated genomic surveillance on non-typhoidal infection in Australia: an ecological study.

作者信息

Nghiem Son, Mai Nhung, Tran My, Cribb Danielle M, Bulfone Liliana, Andersson Patiyan, Zahedi Alireza, Hoang Tuyet, Zulfiqar Tehzeeb, Ferdinand Angeline, Glass Katie, Kirk Martyn D, Sintchenko Vitali, Jennison Amy V, Howden Benjamin P, Lancsar Emily

机构信息

National Centre for Epidemiology and Population Health, The Australian National University, Canberra, ACT, Australia.

Centre for Health Services Research, The University of Queensland, Brisbane, Australia.

出版信息

Lancet Reg Health West Pac. 2025 Jun 17;59:101592. doi: 10.1016/j.lanwpc.2025.101592. eCollection 2025 Jun.

Abstract

BACKGROUND

Whole Genome Sequencing (WGS) is a powerful technology for monitoring and detecting outbreaks of infectious pathogens, including non-typhoidal (NTS). Despite its higher cost than traditional typing methods, WGS offers numerous advantages, including higher resolution and potentially quicker turnaround time. However, evidence regarding its effectiveness in NTS surveillance has predominantly stemmed from micro-simulations or small-scale data. Notably, a recent systematic review identified a lack of real-world, large-scale evidence on the impact of WGS application in NTS surveillance. Our study fills this gap by estimating the effects of WGS on NTS surveillance in Australia using national notifiable disease datasets.

METHODS

The main dataset was the National Notifiable Diseases Surveillance System (NNDSS) for NTS from 2009 to 2024. The treatment variable was defined as a binary variable representing the period when WGS was implemented in each jurisdiction of Australia. To minimise the effects of unobserved confounders, we employed a two-stage difference-in-difference (2sDiD) approach. This method estimated the parameters of state and period fixed-effects and then adjusted the observed outcomes from those fixed-effects in the first stage. The average treatment effect was obtained in the second stage by regressing the adjusted outcome against the treatment in the second stage. We also conducted a sensitivity analysis using a multi-period DiD model with a double machine-learning estimator.

FINDINGS

Compared to the pre-WGS periods, the introduction of WGS was associated with an average of 11.6% reduction in NTS cases when a static specification was applied. Results of a dynamic specification were slightly higher, with a 12.7% reduction in NTS cases after WGS. The estimated effects increased to 17.5% when a multi-period DiD model with a double machine learning estimator was applied.

INTERPRETATION

Our study shows that WGS was associated with a significant reduction (11.6%-17.5%) of NTS cases in Australia. Using the cost and break-even point of NTS from previous Australian studies, our findings suggest that WGS is associated with 7200-10,900 cases of NTS averted, saving US$11.3 m-US$17.0 m per year.

FUNDING

Australian National Health and Medical Research Council, Medical Research Futures Fund (FSPGN00049), and Investigator Grant (GNT1196103) to BPH.

摘要

背景

全基因组测序(WGS)是一种用于监测和检测包括非伤寒沙门氏菌(NTS)在内的传染性病原体爆发的强大技术。尽管其成本高于传统分型方法,但WGS具有许多优势,包括更高的分辨率和可能更快的周转时间。然而,关于其在NTS监测中的有效性的证据主要来自微观模拟或小规模数据。值得注意的是,最近的一项系统评价发现缺乏关于WGS应用于NTS监测影响的真实世界、大规模证据。我们的研究通过使用国家法定传染病数据集估计WGS对澳大利亚NTS监测的影响来填补这一空白。

方法

主要数据集是2009年至2024年NTS的国家法定传染病监测系统(NNDSS)。治疗变量被定义为一个二元变量,代表澳大利亚每个司法管辖区实施WGS的时期。为了尽量减少未观察到的混杂因素的影响,我们采用了两阶段差分法(2sDiD)。该方法估计了州和时期固定效应的参数,然后在第一阶段从这些固定效应中调整观察到的结果。在第二阶段,通过将调整后的结果与治疗进行回归来获得平均治疗效果。我们还使用具有双机器学习估计器的多时期DiD模型进行了敏感性分析。

结果

与WGS实施前的时期相比,在应用静态规范时,WGS的引入与NTS病例平均减少11.6%相关。动态规范的结果略高,WGS实施后NTS病例减少12.7%。当应用具有双机器学习估计器的多时期DiD模型时,估计效果增加到17.5%。

解读

我们的研究表明,WGS与澳大利亚NTS病例的显著减少(11.6%-17.5%)相关。根据澳大利亚先前研究中NTS的成本和盈亏平衡点,我们的研究结果表明,WGS与避免7200-10900例NTS病例相关,每年节省1130万美元至1700万美元。

资金来源

澳大利亚国家卫生与医学研究委员会、医学研究未来基金(FSPGN00049)以及授予BPH的研究员资助(GNT1196103)。

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