Antimicrobial resistance (AMR) is a tremendous contemporary challenge, described by the former secretary-general of the World Health Organization (WHO), M Chan as a “slow-moving tsunami” threatening “the end of modern medicine as we know it” [1]. AMR has multi-faceted adverse consequences spanning human and animal health, economics, climate, and environment, endangering the future of societies […]

This paper introduces a comprehensive framework that elucidates the microfoundations of data-driven antimicrobial stewardship programs (ASPs), specifically focusing on resource-constrained settings. Such settings necessitate the utilization of available resources and engagement among multiple stakeholders. The microfoundations are conceptualized as interlinked components: input, process, output, and outcome. Collectively, these components provide a comprehensive framework for understanding the development and implementation of data-driven ASPs in resource-constrained settings. It underscores the importance of considering both the social and material dimensions when evaluating microbiological, clinical, and social impacts. By harmonizing technology, practices, values, and behaviors, this framework offers valuable insights for the development, implementation, and assessment of ASPs tailored to resource-constrained environments.

Background: Antimicrobial resistance (AMR) poses a significant threat to public health, especially in low- and middle-income countries (LMICs), where surveillance infrastructure is underdeveloped. Bihar, India’s third most populous state and one of its least-resourced states, has remained largely absent from national AMR monitoring initiatives. Methods: This study aimed to characterize the AMR infection landscape across five public tertiary care hospitals in Bihar over three years (2022–2024) and to assess the feasibility of integrating digital workflows for real-time microbiological reporting. Standardized antimicrobial susceptibility testing (AST) was performed on >48,000 urine, pus, and blood samples using CLSI guidelines. Facility-level data were digitized into an open-source AMR reporting system, enabling automated antibiogram generation. Results: The findings revealed substantial resistance: high resistance to beta-lactams, carbapenems, and fluoroquinolones across pathogens. For instance, E. coli sensitivity to nitrofurantoin varied from 86.5% at NMCH (Patna) to 44.7% at JLNMCH (Bhagalpur), while cephalosporin sensitivity in Klebsiella spp. dropped below 2% in several hospitals. MRSA prevalence exceeded 65% in two facilities, far above the national average of 47.8%. Digital integration led to a four-fold increase in culture testing in all facilities and improved data completeness and turnaround times. Spatial analysis and microbiology laboratory assessment revealed significant geographic disparities in diagnostic access, with facilities in remote districts facing delays of over four hours for basic testing. Conclusions: Our study is the first study from India to create such a broad, facility-associated AMR picture over time at a state level. Policy implications include the need for a state-level AMR surveillance dashboard, alignment of procurement with facility-specific resistance patterns, and routine stewardship audits. Clinically, this study demonstrates the utility of localized antibiograms for guiding empirical therapy in resource-limited settings. This study provides a scalable framework for embedding AMR surveillance into routine health system workflows in LMICs.

Background: It is well established by research that large-scale and indiscriminate prescribing, dispensing, and use of antimicrobials drive antimicrobial resistance (AMR) endangering the health and well-being of people, animals, and the environment. In the context of low- and middle-income countries (LMICs), the prescribing of antimicrobials is often not based on biomedical rationality but involves alternative logic driven by social, cultural, and institutional factors. This paper seeks to develop a “biosocial” perspective, reflecting a unified perspective that treats the biomedical and social conditions as two sides of the same coin. Methods: This analysis is based on an empirical investigation of prescription slips that patients carry to buy drugs from the pharmacy following an outpatient department encounter with the clinician. Data collection involves mixed methods, including the quantitative analysis of the antimicrobials prescribed and a qualitative analysis of the underlying reasons for these prescriptions, as described by doctors, pharmacists, and patients. Data analysis involved triangulating quantitative and qualitative data, to develop a “biosocial” perspective, which can provide implications for the development of antimicrobial stewardship policies, particularly relevant for health institutions in low- and middle-income countries. Results: Our analysis of 1175 prescription slips showed that 98% contained antimicrobials, with 74% being broad-spectrum antimicrobials. Only 9% of cases were advised antimicrobial sensitivity testing (AST) before initiating treatment. Qualitative findings indicated that patients had poor awareness of antimicrobials and pharmacists played a crucial role in counseling. Conclusions: This study highlights that antimicrobial prescriptions in public health settings are influenced by both biomedical and social factors, supporting a biosocial perspective. Although AMS interventions are predominantly biomedical, adhering to clinical standards and best practices, this study underscores the necessity of integrating a biosocial viewpoint by incorporating the experiences of pharmacists and patient groups. Strengthening diagnostic support, patient education, and interprofessional collaboration could improve rational antimicrobial uses in low-resource settings.

This work examines stabilization and reference tracking problems for uncertain deterministic as well as switched stochastic nonlinear systems. The concept of input state stability is employed here in the tracking problems. Additionally, an attempt is made to improve the transient performance of deterministic nonlinear systems by employing the notion of return time which is applicable to a broader class of nonlinearities. The transient performances are compared using the Integral Square Error (ISE). The control law is linear in states and thus is easy to implement. The optimization problem is formulated in a framework of Linear Matrix Inequalities (LMIs). The proposed design is tested on simulation examples. The advantages are established on benchmark problems from the literature like Control of the Wing Rocket Aircraft and the Continuously Stirred Tank Chemical Reactor (CSTR). As per the author's knowledge, not much work is reported on these problems.