Do Phu Cong, Alemu Yibeltal Assefa, Reid Simon Andrew
School of Public Health, Faculty of Medicine, University of Queensland, Herston, QLD 4006, Australia.
Pathogens. 2023 Jul 4;12(7):907. doi: 10.3390/pathogens12070907.
Continued surveillance of antimicrobial resistance is critical as a feedback mechanism for the generation of concerted public health action. A characteristic of importance in evaluating disease surveillance systems is representativeness. Scenario tree modelling offers an approach to quantify system representativeness. This paper utilises the modelling approach to assess the Australian Gonococcal Surveillance Programme's representativeness as a case study. The model was built by identifying the sequence of events necessary for surveillance output generation through expert consultation and literature review. A scenario tree model was developed encompassing 16 dichotomous branches representing individual system sub-components. Key classifications included biological sex, clinical symptom status, and location of healthcare service access. The expected sensitivities for gonococcal detection and antibiotic status ascertainment were 0.624 (95% CI; 0.524, 0.736) and 0.144 (95% CI; 0.106, 0.189), respectively. Detection capacity of the system was observed to be high overall. The stochastic modelling approach has highlighted the need to consider differential risk factors such as sex, health-seeking behaviours, and clinical behaviour in sample generation. Actionable points generated by this study include modification of clinician behaviour and supplementary systems to achieve a greater contextual understanding of the surveillance data generation process.
持续监测抗菌药物耐药性作为协调公共卫生行动的反馈机制至关重要。评估疾病监测系统时一个重要的特征是代表性。情景树建模提供了一种量化系统代表性的方法。本文以澳大利亚淋球菌监测项目为例，利用该建模方法评估其代表性。该模型通过专家咨询和文献回顾确定产生监测输出所需的事件序列来构建。开发了一个情景树模型，包含16个二分法分支，代表各个系统子组件。关键分类包括生物性别、临床症状状态和获得医疗服务的地点。淋球菌检测和抗生素状态确定的预期敏感度分别为0.624（95%置信区间；0.524，0.736）和0.144（95%置信区间；0.106，0.189）。总体观察到该系统的检测能力较高。随机建模方法突出了在样本生成中考虑性别、求医行为和临床行为等不同风险因素的必要性。本研究产生的可操作要点包括改变临床医生行为和补充系统，以更好地从背景角度理解监测数据生成过程。
