Objectives: The provides a framework to improve antibiotic use, but cost-effectiveness data on implementation of outpatient antibiotic stewardship interventions are limited. We evaluated the cost-effectiveness of Core Element implementation in the outpatient setting.
Methods: An economic simulation model from the health-system perspective was developed for patients presenting to outpatient settings with uncomplicated acute respiratory tract infections (ARI). Effectiveness was measured as quality-adjusted life years (QALYs). Cost and utility parameters for antibiotic treatment, adverse drug events (ADEs), and healthcare utilization were obtained from the literature. Probabilities for antibiotic treatment and appropriateness, ADEs, hospitalization, and return ARI visits were estimated from 16,712 and 51,275 patient visits in intervention and control sites during the pre- and post-implementation periods, respectively. Data for materials and labor to perform the stewardship activities were used to estimate intervention cost. We performed a one-way and probabilistic sensitivity analysis (PSA) using 1,000,000 second-order Monte Carlo simulations on input parameters.
Results: The proportion of ARI patient-visits with antibiotics prescribed in intervention sites was lower (62% vs 74%) and appropriate treatment higher (51% vs 41%) after implementation, compared to control sites. The estimated intervention cost over a 2-year period was $133,604 (2018 US dollars). The intervention had lower mean costs ($528 vs $565) and similar mean QALYs (0.869 vs 0.868) per patient compared to usual care. In the PSA, the intervention was dominant in 63% of iterations.
Conclusions: Implementation of the CDC Core Elements in the outpatient setting was a cost-effective strategy.
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http://dx.doi.org/10.1017/ice.2021.393 | DOI Listing |
Pediatr Infect Dis J
January 2025
Department of Paediatrics, University of Melbourne.
Background: Lower respiratory tract infections (LRTIs) remain a leading cause of community-acquired and nosocomial infection in children and a common indication for antimicrobial use and intensive care admission. Determining the causative pathogen for LRTIs is difficult and traditional culture-based methods are labor- and time-intensive. Emerging molecular diagnostic tools may identify pathogens and detect antimicrobial resistance more quickly, to enable earlier targeted antimicrobial therapy.
View Article and Find Full Text PDFBackground: has recently been categorized as low-risk for AmpC β-lactamase inducible production, but research on outcomes in bacteremia by antibiotic choice is limited.
Objectives: This study examined the clinical characteristics and outcomes of patients with ceftriaxone-susceptible bacteremia who received AmpC-directed β-lactam therapy vs. narrower spectrum therapies.
Front Vet Sci
December 2024
Faculty of Veterinary Medicine, Helsinki One Health, University of Helsinki, Helsinki, Finland.
Background: Canine gastroenteritis (CGE) is a common cause for seeking veterinary care in companion animal medicine and an area where antibiotics have been reported to be widely used. Therefore, creating relevant benchmarks for antibiotic use in CGE is important when implementing and analyzing antibiotic stewardship interventions. The aim of this paper was to describe the level and temporal trend of systemic antibiotic use for CGE in Sweden between 2020 and 2023.
View Article and Find Full Text PDFFront Public Health
December 2024
Institute of Pharmacy, Faculty of Pharmaceutical and Allied Health Sciences, Lahore College for Women University, Lahore, Pakistan.
J Infect
December 2024
Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address:
Background: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
Methods: We used XGBoost machine learning models to predict antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021.
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