This repository contains code and data related to the research article titled "Unveiling the dynamics of antimicrobial utilization and resistance in a large hospital network over five years: Insights from health record data analysis" DOI: 10.21203/rs.3.rs-3014899/v1. The study explores the impact of the COVID-19 pandemic on antimicrobial resistance (AMR) using population-level data from clinical, laboratory, and prescription records. We are adding the data resulting from the analysis to this repository (File folder). Original EHR data may be directly accessed with the permission from Dubai Health Authority.
Antimicrobial Resistance (AMR) poses a significant global public health challenge, which has been further compounded by the COVID-19 pandemic. However, there is a lack of comprehensive population-level data integrating clinical, laboratory, and prescription data to understand the impact of the pandemic on AMR evolution. In this study, we present an analysis of data extracted from a centralized electronic platform that captures health records of 60,551 patients across a network of public healthcare facilities in Dubai, United Arab Emirates. Our analysis employs various analytical methods, including time-series analysis, natural language processing (NLP), and unsupervised clustering algorithms, to investigate trends in antimicrobial usage and resistance over time, assess the impact of prescription practices on resistance rates, and explore the effects of COVID-19 on antimicrobial usage and resistance.
Data was extracted from the centralized electronic platform, encompassing inpatient and outpatient records of patients diagnosed with bacterial infections between 01/01/2017 and 31/05/2022. The dataset includes structured and unstructured Electronic Health Record data, microbiological laboratory data (antibiogram, molecular typing, and COVID-19 testing information), as well as antibiotic prescribing data. We utilized various analytical techniques, such as time-series analysis, NLP, and unsupervised clustering algorithms, to analyze the data and derive insights into antimicrobial utilization and resistance patterns.
Our findings identified a significant impact of COVID-19 on antimicrobial prescription practices, with short-term and long-lasting over-prescription of these drugs. Resistance to antimicrobials increased the odds ratio of all mortality to an average of 2.18 (95% CI: 1.87-2.49) for the most commonly prescribed antimicrobials. Moreover, the effects of antimicrobial prescription practices on resistance were observed within one week of initiation. Significant trends in antimicrobial resistance, exhibiting fluctuations for various drugs and organisms, with an overall increasing trend in resistance levels, particularly post-COVID-19 were identified.
Please refer to the research article for detailed findings and discussion.
The Code file contains R codes for predocing the figures for causal impact analysis and natural language processing. The repository contains the following files:
If you use the code or findings from this study, please cite the following research article:
This paper is still under review
For any questions or inquiries, please contact the authors.
This paper is still under review