부산대학교 도서관

Background: The emergence of antimicrobial-resistant bacteria represents a considerable threat to human health, particularly for vulnerable populations such as those living in residential aged care. However, antimicrobial resistance carriage and modes of transmission remain incompletely understood. The Generating evidence on antimicrobial Resistance in the Aged Care Environment (GRACE) study was established to determine principal risk factors of antimicrobial resistance carriage and transmission in residential aged care facilities (RACFs). This article describes the cohort characteristics, national representation, and planned analyses for this study.Methods: Between March 2019 and March 2020, 279 participants were recruited from five South Australian RACFs. The median age was 88.6 years, the median period in residence was 681 days, and 71.7% were female. A dementia diagnosis was recorded in 54.5% and more than two thirds had moderate to severe cognitive impairment (68.8%). 61% had received at least one course of antibiotics in the 12 months prior to enrolment.Results: To investigate the representation of the GRACE cohort to Australians in residential aged care, its characteristics were compared to a subset of the historical cohort of the Registry of Senior Australians (ROSA). This included 142,923 individuals who were permanent residents of RACFs on June 30th, 2017. GRACE and ROSA cohorts were similar in age, sex, and duration of residential care, prevalence of health conditions, and recorded dementia diagnoses. Differences were observed in care requirements and antibiotic exposure (both higher for GRACE participants). GRACE participants had fewer hospital visits compared to the ROSA cohort, and a smaller proportion were prescribed psycholeptic medications.Conclusions: We have assembled a cohort of aged care residents that is representative of the Australian aged care population, and which provides a basis for future analyses. Metagenomic data isolated from participants and built environments will be used to determine microbiome and resistome characteristics of an individual and the facility. Individual and facility risk exposures will be aligned with metagenomic data to identify principal determinants for antimicrobial resistance carriage. Ultimately, this analysis will inform measures aimed at reducing the emergence and spread of antimicrobial resistant pathogens in this high-risk population.