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Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs). Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies. Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest.
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Background: Antimicrobial resistance (AMR) is becoming a critical public health issue globally. The World Health Organization launched the Global Antimicrobial Resistance and Use Surveillance System (GLASS) to support the strengthening of the AMR evidence base. Objective: The article describes the evolution of national AMR surveillance systems and AMR data reporting of countries in the African continent between 2017 and 2019, and the constraints, perceived impact and value of the participation in GLASS. Methods: Data on implementation of national surveillance systems and AMR rates were submitted to GLASS between 2017 and 2019 and summarised though descriptive statistics. The information on constraints and perceived impact and value in GLASS participation was collected though a set of questionnaires. Results: Between 2017 and 2019, Egypt, Ethiopia, Madagascar, Malawi, Mali, Mozambique, Nigeria, South Africa, Sudan, Tunisia, Uganda and Zambia submitted data to GLASS. The main constraints listed are linked to scarce laboratory capacity and capability, limited staffing, budget issues, and data management. Moreover, while the data are not yet nationally representative, high resistance rates were reported to commonly-used antibiotics, as the emerging resistance to last treatment options. Conclusion: Despite the limitations, more and more countries in the African continent are working towards reaching a status that will enable them to report AMR data in a complete and systematic manner. Future improvements involve the expansion of routine surveillance capacity for several countries and the implementation of surveys that allow to effectively define the magnitude of AMR in the continent.
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BACKGROUND: The health impact of antimicrobial resistance (AMR) has not been included in the Global Burden of Disease (GBD) report, as reliable data have been lacking. AMR burden estimates have been derived from models combining incidence and/or prevalence data from national and/or international surveillance systems and mortality estimates from clinical studies. Depending on utilized empirical data, statistical methodology and applied endpoints, the validity and reliability of results can differ substantially. OBJECTIVES: We assessed comprehensiveness, and internal and external validity of studies estimating the clinical impact of infections caused by the priority antibiotic resistant pathogens monitored by the WHO Global Antimicrobial Resistance Surveillance System. DATA SOURCES: Ovid MEDLINE, January 1950 to March 2019, In-Process and other non-indexed citations were searched. STUDY ELIGIBILITY CRITERIA: Studies reporting mortality, length of hospital stay, duration of the disease until remission and/or death, complications, hospital re-admissions, and follow-up beyond hospital discharge were eligible. METHODS: The literature was searched according to the Cochrane recommendations and reported according to Preferred Reporting Items for Systematic Reviews. RESULTS: Two-hundred and eighty-six studies out of 3529 were eligible. Studies derived mainly from high-income countries (215, 75%) and relied on data from retrospective (226, 79%), single-centre (201, 70%), cohort studies (243, 85%). The health impact was mostly limited to all-cause mortality (128, 45%) with heterogeneity in timing of assessment; attributable length of hospital stay was seldom adjusted for pre-infection admission time and a few studies had enough follow-up for assessing long-term sequelae. Overall, adjustment for confounding has shown a substantial improvement. Data on health state definitions and duration of diseases are generally lacking, precluding calculation of disability-adjusted life years, critical for application of the GBD study methodology. CONCLUSION: Efforts to improve harmonization, representativeness, quality of AMR surveillance data and cohort studies to determine AMR attributable mortality and morbidity are urgently required. Policy makers need accurate and detailed burden estimates to inform prioritization of resource allocation, and to select the most effective intervention strategies to halt the AMR crisis.
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A major issue in the surveillance of antimicrobial resistance (AMR) is "de-duplication" or removal of repeated isolates, for which there exist multiple methods. The World Health Organization (WHO) Global Antimicrobial Resistance Surveillance System (GLASS) requires de-duplication by selecting only the first isolate of a given bacterial species per patient per surveillance period per specimen type per age group, gender, and infection origin stratification. However, no study on the comparative application of this method has been reported. The objective of this study was to evaluate differences in data tabulation between the WHO GLASS and the Japan Nosocomial Infections Surveillance (JANIS) system, which counts both patients and isolates after removing repeated isolates of the same bacterial species isolated from a patient within 30 days, regardless of specimen type, but distinguishing isolates with change of antimicrobial resistance phenotype. All bacterial data, consisting of approximately 8 million samples from 1795 Japanese hospitals in 2017 were exported from the JANIS database, and were tabulated using either the de-duplication algorithm of GLASS, or JANIS. We compared the tabulated results of the total number of patients whose blood and urine cultures were taken and of the percentage of resistant isolates of Escherichia coli for each priority antibiotic. The number of patients per specimen type tabulated by the JANIS method was always smaller than that of GLASS. There was a small (< 3%) difference in the percentage of resistance of E. coli for any antibiotic between the two methods in both out- and inpatient settings and blood and urine isolates. The two tabulation methods did not show considerable differences in terms of the tabulated percentages of resistance for E. coli. We further discuss how the use of GLASS tabulations to create a public software and website that could help to facilitate the understanding of and treatment against AMR.
