COVID-19 has exacted a disproportionate toll on the health of persons living in nursing homes. Healthcare providers and other decision-makers in those settings must refer to multiple evolving sources of guidance to coordinate care delivery in such a way as to minimize the introduction and spread of the causal virus, SARS-CoV-2. It is essential that guidance be presented in an accessible and usable format to facilitate its translation into evidence-based best practice. In this article, we propose the Haddon matrix as a tool wellsuited to this task. The Haddon matrix is a conceptual model that organizes influencing factors into pre-event, event, and post-event phases, and into host, agent, and environment domains akin to the components of the epidemiologic triad. The Haddon matrix has previously been applied to topics relevant to the care of older persons, such as fall prevention, as well as to pandemic planning and response. Presented here is a novel application of the Haddon matrix to pandemic response in nursing homes, with practical applications for nursing home decision-makers in their efforts to prevent and contain COVID-19.
Objective: This study presents trends in organism isolation and antimicrobial resistance in routine microbiology test results from acute-care hospital microbiology laboratories in Vermont.
Methods:The WHO Collaborating Centre for Surveillance of Antimicrobial Resistance captured microbiology test results from acute-care Vermont hospital laboratories to monitor geographic and temporal trends in antimicrobial resistance and emerging microbial threats with the free WHONET software.Results: Data were provided from 12 acute care hospital laboratories from 2011 through 2018 for 318,833 isolates from 148,994 patients (70% female, 74% outpatient, and 63% urine). Significant differences (p<0.05) in age, gender, and antimicrobial susceptibility results (e.g. Escherichia coli and levofloxacin) between outpatient and inpatient isolates were identified with temporal increases in certain species (e.g. Aerococcus urinae) and resistance (e.g. Streptococcus pneumoniae and erythromycin). The use of multi-resistance phenotypes demonstrated significant heterogeneity (p<0.05) in MRSA strains between facilities, for example Staphylococcus aureus resistant to six priority antimicrobials were found in no critical access hospitals (fewer than 25 inpatient beds) but in all non-critical access hospitals.
Conclusions:Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging community and healthcare resistance threats at the local and state level.
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