Antibiotic use for respiratory tract infections among older adults living in long-term care facilities: a systematic review and meta-analysis.

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.

The race between contact tracing and transmission: A framework for resource allocation during an emerging pathogen epidemic

B Background: b The epidemics of severe acute respiratory syndrome (SARS) in Hong Kong generates the need to evaluate the effectiveness of control measures. B Methods and materials: b An individual-based mathematical model was developed alongside a resource-constrained contact tracing process for SARS outbreak. B Conclusion: b An improved understanding of the transmission dynamics of the SARS outbreak under different scenarios of contact tracing approach helps design the optimal control strategies with the given resources to control new emerging disease in the future. [Extracted from the article] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome

The emergence and reemergence of coronavirus epidemics sparked renewed concerns from global epidemiology researchers and public health administrators. Mathematical models that represented how contact tracing and follow-up may control Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions were developed for evaluating different infection control interventions, estimating likely number of infections as well as facilitating understanding of their likely epidemiology. We reviewed mathematical models for contact tracing and follow-up control measures of SARS and MERS transmission. Model characteristics, epidemiological parameters and intervention parameters used in the mathematical models from seven studies were summarized. A major concern identified in future epidemics is whether public health administrators can collect all the required data for building epidemiological models in a short period of time during the early phase of an outbreak. Also, currently available models do not explicitly model constrained resources. We urge for closed-loop communication between public health administrators and modelling researchers to come up with guidelines to delineate the collection of the required data in the midst of an outbreak and the inclusion of additional logistical details in future similar models. FAU - Kwok, Kin On

Inferring super-spreading from transmission clusters of COVID-19 in Hong Kong, Japan, and Singapore.

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.