Data-driven stewardship intelligence for antimicrobial resistance in India

Abstract

Antimicrobial resistance (AMR) is one of the most urgent public health threats of the present century. Antimicrobial stewardship and surveillance are integral pillars of the response to AMR. However, connecting these pillars requires interpretable evidence on resistance levels, temporal change and local context. This thesis develops analytical and translational approaches to turn routine surveillance data into evidence for stewardship prioritisation.The first objective is to characterise longitudinal and geographical patterns of AMR in India using multicentric IAMRSN data from bloodstream and urinary tract infections. Resistance trends varied across organisms, antibiotics and regions. Imipenem resistance increased by an estimated 0.22 percentage points per month in Escherichia coli bloodstream isolates and 0.37 percentage points per month in Pseudomonas aeruginosa urinary isolates. Bayesian network modelling identified conditional associations with state-level Sustainable Development Goal indicators, without establishing causality.Building on these patterns, the second objective moved beyond retrospective description towards examining temporal lead-lag relationships using cross-correlation. Cefotaxime resistance in E. coli bloodstream isolates showed a leading temporal association with imipenem and meropenem resistance at approximately 9–10 months. These findings identify candidate surveillance indicators whose robustness and prospective predictive performance require further evaluation. The third objective is to develop an interpretable trajectory-based scorecard AMROrbit for AMR monitoring. AMROrbit jointly captures resistance burden and temporal momentum by estimating both baseline resistance and directional change, enabling countries, hospitals, or pathogen–drug combinations to be classified into interpretable stewardship trajectories. Applied to 5,920 country– organism–antibiotic–specimen trajectories, we observed 17.7% were spiralling-out in comparison to only 13.4% as spiralling-in. AMROrbit has been implemented as a web-based tool to analyse 10 susceptibility testing data. It is currently being evaluated with clinical partners at AIIMS New Delhi and Max Hospital, with planned scale-up across multiple AIIMS in collaboration with CDAC. This objective extends AMR surveillance from trend estimation to prioritisation, allowing decision-makers to identify where resistance is not only high, but also worsening. The fourth objective defined a translational pathway through AMRSense for generating and triangulating contextual baseline evidence from community records, awareness assessments, prescriptions, antibiotic-use data and laboratory surveillance. Completed contributions included the conceptual architecture, application prototypes, awareness materials, a Delphi-validated KAP instrument, and development and pilot testing of the community data-collection workflow. KAP data collection is ongoing, while wider deployment and evaluation of effects on antibiotic-use practices and resistance remain future work. AMRaura is a proposed multidimensional integration framework whose development and prospective validation fall outside the completed work reported in this thesis. Together, these objectives address a central actionability gap in AMR surveillance. The thesis argues that a major barrier to effective AMR stewardship is not only the absence of data, but the limited conversion of existing and emerging data streams into dynamic, interpretable, and actionable intelligence. The thesis connects resistance trend analysis, candidate temporal indicators and trajectory-based prioritisation with a pathway for contextual surveillance, providing an empirical and methodological foundation for more informed stewardship.