Policy, practice, and prediction: model-based approaches to evaluating N. gonorrhoeae antibiotic susceptibility test uptake in Australia.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Strengthening antimicrobial resistance surveillance systems: a scoping review.

BACKGROUND: Antimicrobial resistance (AMR) is an emerging global public health crisis. Surveillance is a fundamental component in the monitoring and evaluation of AMR mitigation endeavours. The primary aim of the scoping review is to identify successes, barriers, and gaps in implementing AMR surveillance systems and utilising data from them. METHODS: PubMed, Web of Science, SCOPUS, and EMBASE databases were searched systematically to identify literature pertaining to implementation, monitoring, and evaluation of AMR surveillance systems. A thematic analysis was conducted where themes within the literature were inductively grouped based on the described content. RESULTS: The systematic search yielded 639 journal articles for screening. Following deduplication and screening, 46 articles were determined to be appropriate for inclusion. Generally, most studies focused on human AMR surveillance (n = 38, 82.6%). Regionally, there was equal focus on low- and middle-income countries (n = 7, 15.2%) and trans-national contexts (n = 7, 14.5%). All included articles (n = 46, 100.0%) discussed barriers to either implementing or utilising AMR surveillance systems. From the scoping review, 6 themes emerged: capacity for surveillance, data infrastructure, policy, representativeness, stakeholder engagement, and sustainability. Data infrastructure was most frequently discussed as problematic in evaluation of surveillance systems (n = 36, 75.0%). The most frequent success to surveillance system implementation was stakeholder engagement (n = 30, 65.2%). CONCLUSIONS: Experiences of AMR surveillance systems are diverse across contexts. There is a distinct separation of experiences between systems with emerging surveillance systems and those with established systems. Surveillance systems require extensive refinement to become representative and meet surveillance objectives.

Enhancing Insights into Australia's Gonococcal Surveillance Programme through Stochastic Modelling.

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.