Estimating the impact of health systems factors on antimicrobial resistance in priority pathogens. (September 2022)

• Record Type: Journal Article
• Authors: Awasthi, Raghav; Rakholia, Vaidehi; Agrawal, Samprati; Dhingra, Lovedeep Singh; Nagori, Aditya; Kaur, Harleen; Sethi, Tavpritesh
• Is Part Of: Journal of global antimicrobial resistance. Volume 30(2022)
• Page Start: 133
• Page End: 142
• Publication Date: 2022-09
• Subjects: Antimicrobial resistance -- Bayesian network -- Counterfactual analysis -- Machine learning; Drug resistance -- Periodicals; Drug resistance -- Periodicals; Drug resistance; Periodicals; 616.9041
• DOI: 10.1016/j.jgar.2022.04.021
• Languages: English
• ISSNs: 2213-7165

Abstract

Highlights: We analyzed AMR longitudinal data with 633 820 isolates from 2004 to 2017. We proposed a robust longitudinal artificial intelligence–based association analysis to assess the impact of AMR drivers. Our analysis revealed that Access to immunisation and obstetric care and government effectiveness are strong, actionable factors in reducing AMR. Our supervised machine learning predicted antibiotic susceptibility with AUROC >90%. ABSTRACT: Objectives: Antimicrobial resistance (AMR) is the next big pandemic that threatens humanity. The One Health approach to AMR requires quantification of interactions between health, demographic, socioeconomic, environmental, and geopolitical factors to design interventions. This study is focused on learning health system factors on global AMR. Methods: This study analysed longitudinal data (2004–2017) of AMR having 6 33 820 isolates from 70 middle and high-income countries. We integrated AMR data with the Global Burden of Disease (GBD), Governance (WGI), and Finance data sets to find AMR's unbiased and actionable determinants. We chose a Bayesian decision network (BDN) approach within the causal modelling framework to quantify determinants of AMR. Further, we integrated Bayesian networks' global knowledge discovery approach with discriminative machine learning to predict individual-level antibiotic susceptibility in patients. Results: From MAR (multiple antibiotic resistance) scores, we found a non-uniform spread pattern of AMR. Components-level analysis revealed that governance, finance, and disease burden variables strongly correlate with AMR. From the Bayesian network analysis, we found that access to immunization, obstetric care, and government effectiveness are strong, actionable factors in reducing AMR, confirmed by what-if analysis. Finally, our discriminative machine learning models achieved an individual-level AUROC (Area under receiver operating characteristic curve) of 0.94 (SE = 0.01) and 0.89 (SE = 0.002) to predict Staphylococcus aureus resistance to ceftaroline and oxacillin, respectively. Conclusion: Causal machine learning revealed that immunisation strategies and quality of governance are vital, actionable interventions to reduce AMR.