Background: Preventing hospital-acquired infections (HAIs) to curb the rise of AMR (antimicrobial resistance) is an increasingly urgent global health challenge. Infection prevention and control (IPC) measures and antimicrobial stewardship (AMS) in healthcare facilities in low- and middle-income countries (LMICs), however, remain inadequate. Current research on IPC and AMS largely focuses on individual behaviour, neglecting underlying systems issues. This PhD develops a complex systems science approach called System Dynamics (SD) to investigate systems factors influencing IPC for HAI and AMR prevention in hospitalised neonates in a large public referral hospital in Botswana as an exemplar case study.

Methods: A systematic review was conducted to evaluate how SD incorporates economic efficiency analyses for decision-making. 67 semi-structured interviews were conducted with key stakeholders and a participatory Group Model Building workshop was held. The analysis informed the development of causal loop diagrams (CLDs) to represent the interactions and causal relationships between systems factors influencing IPC and AMR in the neonatal unit.

Results: The systematic review revealed that SD modelling is underutilized in assessing complex public sector challenges, particularly in health. Engaging a diverse group of stakeholders revealed the impact of systems constraints on IPC implementation by healthcare workers, the mismatch between IPC best practices and LMIC realities, and the experiences of mothers with IPC, crucial in settings where they provide significant care for hospitalised neonates. 62 systems variables and 92 variable relationships were identified and represented in CLDs.

Conclusion: This research advances a qualitative SD approach for addressing IPC and AMR challenges, revealing its utility and usability for IPC and AMR research which have been dominated by linear reductionist approaches. The CLD serves as a generalisable representation in similar settings, capturing variables shaping HAIs and AMR in neonatal care. Finally, it provides a foundation for future quantitative modelling and intervention testing.