[Clinical Infectious Diseases, 1 August 2023]

Background: Antibiotics are frequently prescribed unnecessarily in outpatients with coronavirus disease 2019 (COVID-19). We sought to evaluate factors associated with antibiotic prescribing in outpatients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Methods: We performed a population-wide cohort study of outpatients aged ≥66 years with polymerase chain reaction–confirmed SARS-CoV-2 from 1 January 2020 to 31 December 2021 in Ontario, Canada. We determined rates of antibiotic prescribing within 1 week before (prediagnosis) and 1 week after (postdiagnosis) reporting of the positive SARS-CoV-2 result, compared to a self-controlled period (baseline). We evaluated predictors of prescribing, including a primary-series COVID-19 vaccination, in univariate and multivariable analyses.

Results: We identified 13 529 eligible nursing home residents and 50 885 eligible community-dwelling adults with SARS-CoV-2 infection. Of the nursing home and community residents, 3020 (22%) and 6372 (13%), respectively, received at least 1 antibiotic prescription within 1 week of a SARS-CoV-2 positive result. Antibiotic prescribing in nursing home and community residents occurred, respectively, at 15.0 and 10.5 prescriptions per 1000 person-days prediagnosis and 20.9 and 9.8 per 1000 person-days postdiagnosis, higher than the baseline rates of 4.3 and 2.5 prescriptions per 1000 person-days. COVID-19 vaccination was associated with reduced prescribing in nursing home and community residents, with adjusted postdiagnosis incidence rate ratios (95% confidence interval) of 0.7 (0.4–1) and 0.3 (0.3–0.4), respectively.

Conclusions: Antibiotic prescribing was high and with little or no decline following SARS-CoV-2 diagnosis but was reduced in COVID-19–vaccinated individuals, highlighting the importance of vaccination and antibiotic stewardship in older adults with COVID-19.

[PLoS ONE, 23 February 2023]

Background: Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to fill gaps in knowledge for areas of the system where quantitative data does not yet exist or are hard to quantify. Therefore, the objective of this study was to identify quantifiable data about the current state of the factors that drive AMR and the strengths and directions of relationships between the factors from statements made by a group of experts from the One Health system that drives AMR development and transmission in a European context.

Methods: This study builds upon previous work that developed a causal loop diagram of AMR using input from two workshops conducted in 2019 in Sweden with experts within the European food system context. A secondary analysis of the workshop transcripts was conducted to identify semi-quantitative data to parameterize drivers in a model of AMR.

Main findings: Participants spoke about AMR by combining their personal experiences with professional expertise within their fields. The analysis of participants’ statements provided semi-quantitative data that can help inform a future of AMR emergence and transmission based on a causal loop diagram of AMR in a Swedish One Health system context.

Conclusion: Using transcripts of a workshop including participants with diverse expertise across the system that drives AMR, we gained invaluable insight into the past, current, and potential future states of the major drivers of AMR, particularly where quantitative data are lacking.

[Preventive Veterinary Medicine, 30 May 2023]

Antimicrobial resistance (AMR) in animals, including dairy cattle, is a significant concern for animal and public health worldwide. In this study, we used data collected through the Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR) to: (1) describe the proportions of AMR in fecal E. coli, and (2) investigate the relationship between antimicrobial use (AMU) (intramammary and systemic routes, while accounting for confounding by other variables) and AMR/multidrug resistance (MDR – resistance to ≥ 3 antimicrobial classes) in fecal E. coli from Canadian dairy farms. We hypothesized that an increase of the AMU was associated with an increase in AMR in E. coli isolates. A total of 140 dairy farms across five provinces in Canada were included in the study. Fecal samples from pre-weaned calves, post-weaned heifers, lactating cows, and farm manure storage were cultured, and E. coli isolates were identified using MALDI-TOF MS. The minimum inhibitory concentrations (MIC) to 14 antimicrobials were evaluated using a microbroth dilution methodology. AMU was quantified in Defined Course Dose (DCD - the dose for a standardized complete treatment course on a standard size animal) and converted to a rate indicator - DCD/100 animal-years. Of 1134 fecal samples collected, the proportion of samples positive for E. coli in 2019 and 2020 was 97.1% (544/560) and 94.4% (542/574), respectively. Overall, 24.5% (266/1086) of the E. coli isolates were resistant to at least one antimicrobial. Resistance towards tetracycline was commonly observed (20.7%), whereas resistance to third-generation cephalosporins, fluoroquinolones, and carbapenems was found in 2.2%, 1.4%, and 0.1% of E. coli isolates, respectively. E. coli isolates resistant to two or ≥ 3 antimicrobial classes (MDR) was 2.7% and 15%, respectively. Two multilevel models were built to explore risk factors associated with AMR with AMU being the main exposure. Systemic AMU was associated with increased E. coli resistance. For an increase in systemic AMU equivalent to its IQR, the odds of resistance to any antimicrobial in the model increased by 18%. Fecal samples from calves had higher odds of being resistant to any antimicrobial when compared to other production ages and farm manure storage. The samples collected in 2020 were less likely to be resistant when compared to samples collected in 2019. Compared to previous studies in dairy cattle in North America, AMR in E. coli was lower.

[BMJ Open, 16 March 2023]

Introduction: Antimicrobial resistance (AMR) is a complex problem that requires the One Health approach, that is, a collaboration among various disciplines working in different sectors (animal, human and environment) to resolve it. Mathematical and statistical models have been used to understand AMR development, emergence, dissemination, prediction and forecasting. A review of the published models of AMR will help consolidate our knowledge of the dynamics of AMR and will also facilitate decision-makers and researchers in evaluating the credibility, generalisability and interpretation of the results and aspects of AMR models. The study objective is to identify and synthesise knowledge on mathematical and statistical models of AMR among bacteria in animals, humans and environmental compartments.

Methods and analysis: Eligibility criteria: Original research studies reporting mathematical and statistical models of AMR among bacteria in animal, human and environmental compartments that were published until 2022 in English, French and Spanish will be included in this study. Sources of evidence: Database of PubMed, Agricola (Ovid), Centre for Agriculture and Bioscience Direct (CABI), Web of Science (Clarivate), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and MathScinet. Data charting: Metadata of the study, the context of the study, model structure, model process and reporting quality will be extracted. A narrative summary of this information, gaps and recommendations will be prepared and reported in One Health decision-making context.

Ethics and dissemination: Research ethics board approval was not obtained for this study as neither human participation nor unpublished human data were used in this study. The study findings will be widely disseminated among the One Health Modelling Network for Emerging Infections network and stakeholders by means of conferences, and publication in open-access peer-reviewed journals.

[arXiv, 8 June 2023]

Antimicrobial resistance (AMR) poses a significant threat to public health by increasing mortality, extending hospital stays, and increasing healthcare costs. It affects people of all ages and affects health services, veterinary medicine, and agriculture, making it a pressing global issue. Mathematical models are required to predict the behaviour of AMR and to develop control measures to eliminate resistant bacteria or reduce their prevalence. This study presents a simple deterministic mathematical model in which sensitive and resistant bacteria interact in the environment, and mobile genetic elements (MGEs) are functions that depend on resistant bacteria. We analyze the qualitative properties of the model and propose an optimal control problem in which avoiding mutations and horizontal gene transfer (HGT) are the primary control strategies. We also provide a case study of the resistance and multidrug resistance (MDR) percentages of Escherichia coli to gentamicin and amoxicillin in some European countries using data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). Our theoretical results and numerical experiments indicate that controlling the spread of resistance in southern European regions through the supply of amoxicillin is challenging. However, the host immune system is also critical for controlling AMR.