ABSTRACT
BACKGROUND: Antibiotics are commonly prescribed for respiratory tract infections (RTIs) among older adults in long-term care facilities (LTCFs), and this contributes to the emergence of antimicrobial resistance. The objective of this study was to determine the antibiotic prescribing rate for RTIs among LTCF residents, and to analyse the antibiotic consumption patterns with the AwaRe monitoring tool, developed by the World Health Organization. METHODS: MEDLINE, EMBASE and CINAHL were searched from inception to March 2022. Original articles reporting antibiotic use for RTIs in LTCFs were included in this review. Study quality was assessed using the Joanna Briggs Institute's Critical Appraisal Checklist for Prevalence Data. A random-effects meta-analysis was employed to calculate the pooled estimates. Subgroup analysis was conducted by type of RTI, country, and study start year. RESULTS: In total, 47 articles consisting of 50 studies were included. The antibiotic prescribing rate ranged from 21.5% to 100% (pooled estimate 69.8%, 95% confidence interval 55.2-82.6%). The antibiotic prescribing rate for lower respiratory tract infections (LRTIs) was higher than the rates for viral and general RTIs. Compared with Italy, France and the USA, the Netherlands had lower antibiotic use for LRTIs. A proportion of viral RTIs were treated with antibiotics, and all the antibiotics were from the Watch group. Use of antibiotics in the Access group was higher in the Netherlands, Norway, Switzerland and Slovenia compared with the USA and Australia. CONCLUSION: The antibiotic prescribing rate for RTIs in LTCFs was high, and AWaRe antibiotic use patterns varied by type of RTI and country. Improving antibiotic use may require coordination efforts.
ABSTRACT
Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine whether an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R0) and the dispersion factor (k) from empirical data on clusters of epidemiologically linked coronavirus disease 2019 (COVID-19) cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.