School of Public Health, The University of Queensland, Herston, QLD, Australia.
Department of Medical Laboratory Science, Bahir Dar University, Bahir Dar, Ethiopia.
BMC Infect Dis. 2024 May 17;24(1):498. doi: 10.1186/s12879-024-09393-y.
Antimicrobial resistance (AMR) represents a significant threat to global health with Neisseria gonorrhoea emerging as a key pathogen of concern. In Australia, the Australian Gonococcal Surveillance Program (AGSP) plays a critical role in monitoring resistance patterns. However, antibiotic susceptibility test (AST) uptake - a crucial component for effective resistance surveillance - remains to be a limiting factor. The study aims to model the processes involved in generating AST tests for N. gonorrhoea isolates within the Australian healthcare system and assess the potential impact of systematic and policy-level changes.
Two models were developed. The first model was a mathematical stochastic health systems model (SHSM) and a Bayesian Belief Network (BBN) to simulate the clinician-patient dynamics influencing AST initiation. Key variables were identified through systematic literature review to inform the construction of both models. Scenario analyses were conducted with the modification of model parameters.
The SHSM and BBN highlighted clinician education and the use of clinical support tools as effective strategies to improve AST. Scenario analysis further identified adherence to guidelines and changes in patient-level factors, such as persistence of symptoms and high-risk behaviours, as significant determinants. Both models supported the notion of mandated testing to achieve higher AST initiation rates but with considerations necessary regarding practicality, laboratory constraints, and culture failure rate.
The study fundamentally demonstrates a novel approach to conceptualising the patient-clinician dynamic within AMR testing utilising a model-based approach. It suggests targeted interventions to educational, support tools, and legislative framework as feasible strategies to improve AST initiation rates. However, the research fundamentally highlights substantial research gaps in the underlying understanding of AMR.
抗菌药物耐药性（AMR）对全球健康构成重大威胁，淋球菌已成为一个关键的关注病原体。在澳大利亚，澳大利亚淋球菌监测计划（AGSP）在监测耐药模式方面发挥着关键作用。然而，抗生素药敏试验（AST）的采用——这是有效耐药监测的关键组成部分——仍然是一个限制因素。本研究旨在对澳大利亚医疗保健系统中产生淋球菌分离物 AST 试验的过程进行建模，并评估系统和政策层面变化的潜在影响。
开发了两种模型。第一种模型是一个数学随机健康系统模型（SHSM）和一个贝叶斯信念网络（BBN），用于模拟影响 AST 启动的临床医生-患者动态。通过系统文献回顾确定了关键变量，为两个模型的构建提供信息。通过修改模型参数进行了情景分析。
SHSM 和 BBN 突出了临床医生教育和使用临床支持工具作为提高 AST 的有效策略。情景分析进一步确定了遵循指南和改变患者层面的因素，如症状持续存在和高风险行为，是重要的决定因素。这两种模型都支持采用强制性检测来实现更高的 AST 启动率，但需要考虑到实用性、实验室限制和培养失败率等问题。
本研究从根本上展示了一种新颖的方法，利用基于模型的方法来概念化 AMR 检测中的患者-临床医生动态。它提出了针对教育、支持工具和立法框架的有针对性的干预措施，作为提高 AST 启动率的可行策略。然而，该研究从根本上强调了对抗菌药物耐药性的基本理解存在重大研究空白。
