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Automated tools for antimicrobial resistance surveillance are critical for improving detection of drug-resistant organisms and informing prevention and control interventions. In this study, the WHONET-SaTScan software was used at a multihospital level in Tuscany, Italy, to identify case clusters consistent with hospital outbreaks caused by drug-resistant pathogens.Antimicrobial resistance surveillance data from all Tuscany hospitals between January 2018 and December 2020 were analyzed using WHONET. The SaTScan package was used to detect case clusters applying a simulated prospective approach and the space-time permutation algorithm. Clusters were identified using resistance profiles and two distinct spatial variables: single medical services ('service') or groups of related services ('metaservice').Data from eight bacterial pathogens were provided from 49 hospitals for 312,779 isolates from 158,809 patients. Single service-based analysis detected 693 hospital clusters, while metaservice-based analysis identified 635. There was no evidence for a difference between the two methods in terms of cluster length, cluster size, recurrence intervals, number of alerts, distribution across years or hospitals. Among clusters involving multiple services identified by both analyses, metaservice-detected clusters were usually larger and more statistically significant.WHONET-SaTScan proved to be a valuable multi-facility cluster detection tool that can be implemented for real-time surveillance.

Introduction Culture is the gold-standard diagnosis for Urinary Tract Infections (UTIs). However, most hospitals in poor-resource countries lack adequately equipped laboratories and relevant expertise to perform culture and, therefore, rely heavily on dipstick tests for UTI diagnosis. Research gap In many Kenyan hospitals, routine evaluations are rarely done to assess the accuracy of popular screening tests such as the dipstick test. As such, there is a substantial risk of misdiagnosis emanating from inaccuracy in proxy screening tests. This may result in misuse, under-use, or over-use of antimicrobials. Aim The present study aimed to assess the accuracy of the urine dipstick test as a proxy for the diagnosis of urinary tract infections in selected Kenyan hospitals. Methods A hospital-based cross-sectional method was used in the present study. Utility of dipstick in the diagnosis of UTI was assessed using Mid stream urine against culture as gold standard Results The dipstick test predicted 1,416 positive UTIs, but only 1,027 were confirmed positive by culture, translating to a true prevalence of 54.1%. The sensitivity of the dipstick test was better when leukocytes and nitrite tests were combined (63.1%) than when the two tests were separate (62.6% and 50.7%, respectively). Similarly, the two tests combined had a better positive predictive value (87.0%) than either test alone. The nitrite test had the best specificity (89.8%) and negative predictive value (97.4%) than leukocytes esterase (L.E) or both tests combined. In addition, sensitivity in samples from the inpatients (69.2%) was higher than outpatients (62.7%). Furthermore, the dipstick test had a better sensitivity and positive predictive value among female (66.0% and 88.6%) patients than male patients (44.3% and 73.9%). Among the various patients' age groups, the dipstick test's sensitivity and positive predictive value were exceptionally high in patients ≥75 years (87.5% and 93.3%). Conclusion The prevalence discrepancies from the urine dipstick test and culture, the gold standard, indicate dipstick test inadequacy for accurate UTI diagnosis. The finding also demonstrates the need for urine culture for accurate UTI diagnosis. However considering it’s not always possible to perform a culture, especially in low resource settings, future studies can combine specific UTI symptoms and dipstick results to assess possible increase in the test’s sensitivity. There is also need to develop readily available and affordable algorithms that can detected UTI where culture is not available.

There are few studies comparing proportion, frequency, mortality and mortality rate of antimicrobial-resistant (AMR) bacterial infections between tertiary-care hospitals (TCHs) and secondary-care hospitals (SCHs) in low and middle-income countries (LMICs) to inform infection control strategies. We evaluated bloodstream infections (BSIs) from 2012 to 2015 in 15 TCHs and 34 SCHs in Thailand. There were differences in the proportions (%) of BSI caused by AMR strains for some pathogens between TCHs and SCHs. Of 19,665 patients with AMR BSI, 6,746 (34.3%) died. Among patients with AMR BSI, there were no or minimal differences in mortality proportion for all AMR pathogens between TCHs and SCHs. However, the frequency and mortality rates of AMR BSI were considerably higher in TCHs for most pathogens. For example, the mortality rate of hospital-origin carbapenem-resistantAcinetobacter baumanniiBSI in TCHs was nearly three times higher than that in SCHs (10.2 vs. 3.6 per 100,000 patient-days at risk, mortality rate ratio 2.77; 95% confidence interval 1.71 to 4.48, p

A vast amount of antimicrobial susceptibility test (AST) data is generated from routine testing in diagnostic laboratories for the primary purpose of guiding clinicians in antimicrobial therapy decisions for their patients. However, there is additional value for these data when they are compiled at the local, regional, national, and global levels. Cumulative AST data can be used to prepare antibiograms at the individual health care facility level. These reports can be used to gain insight into appropriate empirical therapy options prior to the availability of AST results on an individual patient’s isolate. Different types of cumulative AST data reports can also be compiled at the regional, national, and global levels to estimate susceptibility rates in geographic regions, document trends in evolving microbial populations, and recognize the appearance and spread of emerging antimicrobial resistance threats. The first CLSI M39 Guideline for Analysis and Presentation of Cumulative AST Data was published in 2000. Since that time, there have been changes to AST and reporting recommendations as well as the introduction of advanced informatics technologies to analyze and present data. The 5th edition of M39 has taken into consideration these changes to assist those who analyze, present, and utilize routine antibiograms and other types of cumulative AST data reports as well as those who design information systems for the capturing and analyzing of AST data. Furthermore, antimicrobial stewardship programs (ASPs) have expanded considerably, and uses of the antibiogram by ASPs have been addressed. This minireview will remind users of the basic recommendations for analysis and presentation of antibiograms and provide new suggestions to enhance these reports.

Funding: UK National Institute for Health Research, UK Medical Research Council, and the Department of Health and Social Care. Background Poverty is a proposed driver of antimicrobial resistance, influencing inappropriate antibiotic use in low-income and middle-income countries (LMICs). However, at subnational levels, studies investigating multidimensional poverty and antibiotic misuse are sparse, and the results are inconsistent. We aimed to investigate the relationship between multidimensional poverty and antibiotic use in patient populations in Kenya, Tanzania, and Uganda. Methods In this mixed-methods study, the Holistic Approach to Unravelling Antimicrobial Resistance (HATUA) Consortium collected data from 6827 outpatients (aged 18 years and older, or aged 14–18 years and pregnant) with urinary tract infection (UTI) symptoms in health-care facilities in Kenya, Tanzania, and Uganda. We used Bayesian hierarchical modelling to investigate the association between multidimensional poverty and self-reported antibiotic self-medication and non-adherence (ie, skipping a dose and not completing the course). We analysed linked qualitative in-depth patient interviews and unlinked focus-group discussions with community members. Findings Between Feb 10, 2019, and Sept 10, 2020, we collected data on 6827 outpatients, of whom 6345 patients had complete data; most individuals were female (5034 [79·2%]), younger than 35 years (3840 [60·5%]), worked in informal employment (2621 [41·3%]), and had primary-level education (2488 [39·2%]). Antibiotic misuse was more common among those least deprived, and lowest among those living in severe multidimensional poverty. Regardless of poverty status, difficulties in affording health care, and more familiarity with antibiotics, were related to more antibiotic misuse. Qualitative data from linked qualitative in-depth patient interviews (n=82) and unlinked focus-group discussions with community members (n=44 groups) suggested that self-medication and treatment non-adherence were driven by perceived inconvenience of the health-care system, financial barriers, and ease of unregulated antibiotic access. Interpretation We should not assume that higher deprivation drives antibiotic misuse. Structural barriers such as inefficiencies in public health care, combined with time and financial constraints, fuel alternative antibiotic access points and treatment non-adherence across all levels of deprivation. In designing interventions to reduce antibiotic misuse and address antimicrobial resistance, greater attention is required to these structural barriers that discourage optimal antibiotic use at all levels of the socioeconomic hierarchy in LMICs. Publisher PDF

Background The CLUSTER trial assessed the impact of prospective identification of clusters coupled with a response protocol on the containment of hospital clusters. Methods This 82-hospital CRT in 16 states compared clusters of bacterial and fungal healthcare pathogens using a statistical outbreak detection tool (WHONET-SaTScan) coupled with a standardized response protocol (automated cluster detection arm) compared to routine surveillance with the response protocol (control arm). Trial periods: 24 mo Baseline (2/17–1/19); 5 mo Phase-in (2/19–6/19); 30 mo Intervention (7/19–1/22). The primary outcome was the number of additional cases occurring after initial cluster detection. Analyses used generalized linear mixed models to assess differences in additional cases between the intervention vs baseline periods across arms, clustering by hospital. Results were assessed overall and, to account for the effect of COVID-19 on hospital operations, stratified into pre-COVID-19 (7/19–6/20) and during COVID-19 (7/20–1/22) intervention periods. We also assessed the probability that a patient was in a cluster. Results In the baseline period, the automated cluster detection and control arms had 0.09 and 0.07 additional cluster cases/1000 admissions, respectively. The automated cluster detection arm had a 22% greater relative reduction in additional cluster cases in the intervention vs baseline period compared to control (P=0.5). Within the intervention period, the automated cluster detection arm had a significant 64% relative reduction pre-COVID-19 (P< 0.05) and a non-significant 6% relative reduction during COVID-19 (P=0.9) compared to control (Figure). When evaluating patient risk of being part of a cluster across the entire intervention period, the automated cluster detection arm had a significant 35% relative reduction vs control (P< 0.01). Conclusion A statistical automated tool coupled with a response protocol improved cluster containment by 64% pre-COVID-19 but not during COVID-19; there were no significant differences between the arms when using the entire intervention period. Automated cluster detection may substantially improve outbreak containment in non-pandemic periods when infection prevention programs are able to optimize containment protocols. Disclosures Susan S. Huang, MD, MPH, Medline: Conducted studies in which hospitals and nursing homes received contributed antiseptic and/or environmental cleaning products|Molnlyke: Conducted clinical studies in which hospitals received contributed antiseptic product|Stryker: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products|Xttrium Laboratories: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic product.

Increasing antimicrobial resistance (AMR) in Salmonella has been observed in the Philippines. This study aims to utilize whole genome sequencing (WGS) to characterize the population and AMR mechanisms of Salmonella captured by the Philippine Antimicrobial Resistance Surveillance Program (ARSP) and contrast to traditional laboratory methods.We sequenced the whole genomes of 148 Salmonella Typhi (S. Typhi) and 65 non-typhoidal Salmonella (NTS) collected in the Philippines in 2013–2014. From the genome sequences, we determined the serotype, multilocus sequence type, presence of determinants of antimicrobial resistance and relatedness between isolates. We also compared the genotypic predictions of serotype and AMR to the phenotypic data.AMR rates in S. Typhi were low, with sparse acquisition of mutations associated with reduced susceptibility to fluoroquinolones or extended-spectrum beta-lactamases (ESBL) genes. In contrast, three quarters of NTS isolates were insusceptible to at least one antimicrobial, with more than half carrying mutations and/or genes linked to resistance to fluoroquinolones. ESBL genes were detected in five genomes that also carried other AMR determinants. The population of S. Typhi was dominated by the likely endemic genotype 3.0, which also caused of a putative local outbreak susceptible to antibiotics. The main NTS clades were global epidemic S. Enteritidis ST11 and the monophasic variant of S. Typhimurium (I 4,[5],12:i:-) ST34, which had frequently been serotyped as S. Typhimurium in the laboratory.This was the first time that Salmonella isolated from the Philippines were characterized by WGS and we provide evidence of its utility for ongoing surveillance at the ARSP.

BackgroundKlebsiella pneumoniae is a critically important pathogen in the Philippines. Isolates are commonly resistant to at least two classes of antibiotics, yet mechanisms and spread of its resistance are not well studied.MethodsA retrospective sequencing survey was performed on carbapenem-, extended spectrum beta-lactam- and cephalosporin-resistant Klebsiella pneumoniae isolated at 20 antimicrobial resistance (AMR) surveillance sentinel sites from 2015-2017. We characterized 259 isolates using biochemical methods, antimicrobial susceptibility testing, and whole genome sequencing (WGS). Known AMR mechanisms were identified. Potential outbreaks were investigated by detecting clusters from epidemiologic, phenotypic and genome-derived data.ResultsPrevalent AMR mechanisms detected include blaCTX-M-15 (76.8%) and blaNDM-1 (37.5%). An epidemic IncFII(Yp) plasmid carrying blaNDM-1 was also detected in 46 isolates from 6 sentinel sites and 14 different sequence types (ST). This plasmid was also identified as the main vehicle of carbapenem resistance in 2 previously unrecognized local outbreaks of ST348 and ST283 at 2 different sentinel sites. A third local outbreak of ST397 was also identified but without the IncFII(Yp) plasmid. Isolates in each outbreak site showed identical STs, K- and O-loci, and similar resistance profiles and AMR genes. All outbreak isolates were collected from blood of children aged ConclusionWGS provided an in-depth understanding of the epidemiology of AMR in the Philippines, which was not possible with only phenotypic and epidemiologic data. The identification of three previously unrecognized Klebsiella outbreaks highlights the utility of WGS in outbreak detection, as well as its importance in public health and in implementing infection control programs.summaryWhole genome sequencing identified three distinct previously unrecognized local outbreaks in a retrospective study in the Philippines, along with an epidemic plasmid carrying antimicrobial resistance genes, highlighting its importance in antimicrobial resistance surveillance, outbreak detection and infection control.

Performing whole genome sequencing (WGS) for the surveillance of antimicrobial resistance offers the ability to determine not only the antimicrobials to which rates of resistance are increasing, but also the evolutionary mechanisms and transmission routes responsible for the increase at local, national, and global scales. To derive WGS-based outputs, a series of processes are required, beginning with sample and metadata collection, followed by nucleic acid extraction, library preparation, sequencing, and analysis. Throughout this pathway there are many data-related operations required (informatics) combined with more biologically focused procedures (bioinformatics). For a laboratory aiming to implement pathogen genomics, the informatics and bioinformatics activities can be a barrier to starting on the journey; for a laboratory that has already started, these activities may become overwhelming. Here we describe these data bottlenecks and how they have been addressed in laboratories in India, Colombia, Nigeria, and the Philippines, as part of the National Institute for Health Research Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance. The approaches taken include the use of reproducible data parsing pipelines and genome sequence analysis workflows, using technologies such as Data-flo, the Nextflow workflow manager, and containerization of software dependencies. By overcoming barriers to WGS implementation in countries where genome sampling for some species may be underrepresented, a body of evidence can be built to determine the concordance of antimicrobial sensitivity testing and genome-derived resistance, and novel high-risk clones and unknown mechanisms of resistance can be discovered.

Antimicrobial resistance (AMR) is considered a global threat, and novel drug discovery needs to be complemented with systematic and standardized epidemiological surveillance. Surveillance data are currently generated using phenotypic characterization. However, due to poor scalability, this approach does little for true epidemiological investigations. There is a strong case for whole-genome sequencing (WGS) to enhance the phenotypic data. To establish global AMR surveillance using WGS, we developed a laboratory implementation approach that we applied within the NIHR Global Health Research Unit (GHRU) on Genomic Surveillance of Antimicrobial Resistance. In this paper, we outline the laboratory implementation at 4 units: Colombia, India, Nigeria, and the Philippines. The journey to embedding WGS capacity was split into 4 phases: Assessment, Assembly, Optimization, and Reassessment. We show that on-boarding WGS capabilities can greatly enhance the real-time processing power within regional and national AMR surveillance initiatives, despite the high initial investment in laboratory infrastructure and maintenance. Countries looking to introduce WGS as a surveillance tool could begin by sequencing select Global Antimicrobial Resistance Surveillance System (GLASS) priority pathogens that can demonstrate the standardization and impact genome sequencing has in tackling AMR.

Objectives The COVID-19 pandemic has had a substantial impact on health systems. The WHO Antimicrobial Resistance (AMR) Surveillance and Quality Assessment Collaborating Centres Network conducted a survey to assess the effects of COVID-19 on AMR surveillance, prevention and control. Methods From October to December 2020, WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) national focal points completed a questionnaire, including Likert scales and open-ended questions. Data were descriptively analysed, income/regional differences were assessed and free-text questions were thematically analysed. Results Seventy-three countries across income levels participated. During the COVID-19 pandemic, 67% reported limited ability to work with AMR partnerships; decreases in funding were frequently reported by low- and middle-income countries (LMICs; P Conclusions This was the first survey to explore the global impact of COVID-19 on AMR among GLASS countries. Responses highlight important actions to help ensure that AMR remains a global health priority, including engaging with GLASS to facilitate reliable AMR surveillance data, seizing the opportunity to develop more sustainable IPC programmes, promoting integrated antibiotic stewardship guidance, leveraging increased laboratory capabilities and other system-strengthening efforts.

1.0AbstractBackgroundEvidence-based empirical antibiotic prescribing requires knowledge of local antimicrobial resistance patterns. The spectrum of pathogens and their susceptibility strongly influences guidelines for empirical therapies for urinary tract infections (UTI) management.ObjectiveThis study aimed to determine the prevalence of UTI causative bacteria and their corresponding antibiotic resistance profiles in in three counties of Kenya. Such data could be used to determine the optimal empirical therapy.MethodsIn this cross-sectional study, urine samples were collected from patients who presented with symptoms suggestive of UTI in the following healthcare facilities; Kenyatta National Hospital, Kiambu Hospital, Mbagathi, Makueni, Nanyuki, Centre for Microbiology Research, and Mukuru Health Centres. Urine cultures were done on Cystine Lactose Electrolyte Deficient (CLED) to isolate UTI bacterial etiologies, while antibiotic sensitivity testing was done using the Kirby-Bauer disk diffusion using CLSI guidelines and interpretive criteria.ResultsA total of 1,027(54%) uropathogens were isolated from the urine samples of 1898 participants. Staphylococcus spp. and Escherichia coli were the main uropathogens at 37.6% and 30.9 %, respectively. The percentage resistance to commonly used drugs for the treatment of UTI were as follows: trimethoprim (64%), sulfamethoxazole (57%), nalidixic acid(57%), ciprofloxacin (27%), amoxicillin-clavulanic acid (5%), and nitrofurantoin (9%) and cefixime (9%). Resistance rates to broad-spectrum antimicrobials, such as ceftazidime, gentamicin, and ceftriaxone, were 15%, 14%, and 11%, respectively. Additionally, the proportion of Multidrug-resistant (MDR) bacteria was 66%.ConclusionHigh resistance rates toward fluoroquinolones, sulfamethoxazole, and trimethoprim were reported. These antibiotics are commonly used drugs as they are inexpensive and readily available. Based on these findings, more robust standardised surveillance is needed to confirm the patterns observed while recognizing the potential impact of sampling biases on observed resistance rates.

This work was supported by a Newton Fund award from the Medical Research Council (United Kingdom) MR/N019296/1 and the Philippine Council for Health Research and Development. Additional support provided by National Institute for Health Research (UK) Global Health Research Unit on genomic Surveillance of AMR (16/136/111) and by research grant U01CA207167 from the U.S. National Institutes of Health. Methicillin-resistant Staphylococcus aureus (MRSA) remains one of the leading causes of both nosocomial and community infections worldwide. In the Philippines, MRSA rates have remained above 50% since 2010, but resistance to other antibiotics, including vancomycin, is low. The MRSA burden can be partially attributed to pathogen-specific characteristics of the circulating clones, but little was known about the S. aureus clones circulating in the Philippines. We sequenced the whole genomes of 116 S. aureus isolates collected in 2013-2014 within the Antimicrobial Resistance Surveillance Program. The multilocus sequence type, spa type, SCCmec type, presence of antimicrobial resistance (AMR) determinants and virulence genes and relatedness between the isolates were all derived from the sequence data. The concordance between phenotypic and genotypic resistance was also determined. The MRSA population in the Philippines comprised a limited number of genetic clones, including several international epidemic clones, such as CC30-spa-t019-SCCmec-IV-PVL+, CC5-SCCmec-typeIV and ST239-spa-t030-SCCmec-typeIII. The CC30 genomes were related to the South-West Pacific clone but formed a distinct, diverse lineage, with evidence of global dissemination. We showed independent acquisition of resistance to sulfamethoxazole/trimethoprim in various locations and genetic clones but mostly in paediatric patients with invasive infections. The concordance between phenotypic and genotypic resistance was 99.68% overall for eight antibiotics in seven classes. We have made the first comprehensive genomic survey of S. aureus in the Philippines, which bridges the gap in genomic data from the Western Pacific Region and will constitute the genetic background for contextualizing prospective surveillance. Publisher PDF

We sequenced the whole genomes of 117Carbapenem resistance was mainly explained by acquisition of the class-D β-lactamase gene blaThis is the first extensive genomic study of carbapenem-resistant

Advanced genomics and sequencing technologies are increasingly becoming critical for global health applications such as pathogen and antimicrobial resistance (AMR) surveillance. Limited resources challenge capacity development in low- and middle-income countries (LMICs), with few countries having genomics facilities and adequately trained staff. Training research and public health experts who are directly involved in the establishment of such facilities offers an effective, but limited, solution to a growing need. Instead, training them to impart their knowledge and skills to others provides a sustainable model for scaling up the much needed capacity and capability for genomic sequencing and analysis locally with global impact. We designed and developed a Train-the-Trainer course integrating pedagogical aspects with genomic and bioinformatics activities. The course was delivered to 18 participants from 12 countries in Africa, Asia, and Latin America. A combination of teaching strategies culminating in a group project created a foundation for continued development at home institutions. Upon follow-up after 6 months, at least 40% of trainees had initiated training programs and collaborations to build capacity at local, national, and regional level. This work provides a framework for implementing a training and capacity building program for the application of genomics tools and resources in AMR surveillance.

Background The profound changes wrought by coronavirus disease 2019 (COVID-19) on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). We aimed to evaluate the association between COVID-19 surges and HAI and cluster rates. Methods In 148 HCA Healthcare-affiliated hospitals, from 1 March 2020 to 30 September 2020, and a subset of hospitals with microbiology and cluster data through 31 December 2020, we evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month. Results Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased. There were 60% (95% confidence interval [CI]: 23–108%) more CLABSI, 43% (95% CI: 8–90%) more CAUTI, and 44% (95% CI: 10–88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus, and Gram-negative organisms, were each significantly associated with COVID-19 surges. Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased. Conclusions COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention.

BACKGROUND: Hospital outbreaks of antimicrobial-resistant (AMR) bacteria should be detected and controlled as early as possible. AIM: To develop a framework for automatic detection of AMR outbreaks in hospitals. METHODS: Japan Nosocomial Infections Surveillance (JANIS) is one of the largest national AMR surveillance systems in the world. For this study, all bacterial data in the JANIS database were extracted between 2011 and 2016. WHONET, a free software for the management of microbiology data, and SaTScan, a free cluster detection tool embedded in WHONET, were used to analyse 2015–2016 data of eligible hospitals. Manual evaluation and validation of 10 representative hospitals around Japan were then performed using 2011–2016 data. FINDINGS: Data from 1,031 hospitals were studied; mid-sized (200–499 bed) hospitals accounted for 60%, followed by large (≥ 500 beds) hospitals (24%) and small (< 200 beds) hospitals (16%). Large hospitals resulted in more clusters detected. Most of the clusters included five or fewer patients. From the in-depth analysis of 10 hospitals, approximately 80% of the detected clusters were unrecognised by infection control staff because the bacterial species involved were not included in the priority pathogen list for routine surveillance. In particular, in two hospitals, clusters of more susceptible isolates were detected before outbreaks of more resistant pathogens. CONCLUSION: WHONET-SaTScan can automatically detect clusters of epidemiologically-related patients based on isolate resistance profiles beyond lists of high-priority AMR pathogens. If clusters of more susceptible isolates can be detected, it may allow early intervention in infection control practices before outbreaks of more resistant pathogens occur.

Background The profound changes wrought by COVID-19 on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). Objective Evaluate the association between COVID-19 surges and HAI or cluster rates Methods Design: Prospective cohort study Setting 148 HCA Healthcare-affiliated hospitals, 3/1/2020-9/30/2020, and a subset of hospitals with microbiology and cluster data through 12/31/2020 Patients All inpatients Measurements We evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month at each hospital. Results Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased (P ≤ 0.001 for all), with 60% (95% CI, 23 to 108%) more CLABSI, 43% (95% CI, 8 to 90%) more CAUTI, and 44% (95% CI, 10 to 88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection (CDI) was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus and Gram-negative organisms were each significantly associated with COVID-19 surges (P < 0.05 for all). Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased (P = 0.02). Limitations Variations in surveillance and reporting may affect HAI data. Table 1. Effect of an increase in number of COVID-19 discharges on HAIs and hospital-onset pathogens Figure 1. Predicted mean HAI rates as COVID-19 discharges increase Predicted mean HAI rate by increasing monthly COVID-19 discharges. Panel a. CLABSI, Panel b, CAUTI Panel c. MRSA Bacteremia, Panel d. CDI. Data are stratified by small, medium and large hospitals. Figure 2. Monthly comparison of COVID discharges to clusters COVID-19 discharges and the number of clusters of hospital-onset pathogens are correlated throughout the pandemic. Conclusion COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention. Disclosures Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Eunice J. Blanchard, MSN RN, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Micaela H. Coady, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Deborah S. Yokoe, MD, MPH, Nothing to disclose Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)

SynopsisObjectivesThe COVID-19 pandemic has had a substantial impact on health systems. The WHO Antimicrobial Resistance (AMR) Collaborating Centres Network conducted a survey to assess the effects of COVID-19 on AMR surveillance, prevention and control.MethodsFrom October-December 2020, WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) national focal points completed a questionnaire including Likert-scales and open-ended questions. Data were descriptively analysed, income/regional differences were assessed, and free-text questions were thematically analysed.ResultsSeventy-three countries across income levels participated. During the COVID-19 pandemic, 67% reported limited ability to work with AMR partnerships; decreases in funding were frequently reported by low- and middle-income countries (LMICs; pConclusionsThis was the first survey to explore the global impact of COVID-19 on AMR among GLASS countries. Responses revealed universal patterns but also captured country variability. Although focus is understandably on COVID-19, gains in detecting and controlling AMR, a global health priority, cannot afford to be lost.

Acinetobacter baumannii is an opportunistic nosocomial pathogen that has increasingly become resistant to carbapenems worldwide. In the Philippines, carbapenem resistance and multi-drug resistance (MDR) rates are above 50%. We undertook a genomic study of carbapenem resistant A. baumannii in the Philippines to characterize the population diversity and antimicrobial resistance (AMR) mechanisms.We sequenced the whole genomes of 117 A. baumannii isolates recovered by 16 hospitals in the Philippines between 2013 and 2014. We determined the multi-locus sequence type (MLST), presence of acquired AMR determinants and relatedness between isolates from the genome sequences. We also compared the phenotypic and genotypic resistance results.Carbapenem resistance was mainly explained by the acquisition of class-D beta-lactamase gene blaOXA-23. The concordance between phenotypic and genotypic resistance to imipenem was 98.15% and 94.97% overall for the seven antibiotics analysed. Twenty-two different sequence types (ST) were identified, including 7 novel STs. The population was dominated by high-risk international clone 2 (i.e., clonal complex 92), in particular by ST195 and ST208 and their single locus variants. With WGS we identified local clusters representing potential undetected nosocomial outbreaks, as well as multi-hospital clusters indicating inter-hospital transmission. Comparison with global genomes suggested that the establishment of carbapenem-resistant IC2 clones in the Philippines is likely the result of clonal expansion and geographical dissemination and at least partly explained by inadequate hospital infection control and prevention.This study is the first extensive genomic study of carbapenem-resistant A. baumannii in the Philippines and underscores the importance of hospital infection control and prevention to contain high-risk clones.

Background: Poverty is a proposed driver of antimicrobial resistance (AMR), influencing inappropriate antibiotic (AB) use in low and middle-income countries (LMICs). However, at sub-national levels, studies investigating poverty and AB use are sparse and the results inconsistent. Methods: The Holistic Approach to Unravelling Antimicrobial Resistance (HATUA) Consortium collected data from 6,827 patients presenting with urinary tract infection (UTI) symptoms in Kenya, Uganda, and Tanzania. Using Bayesian hierarchical modelling, we investigated the association between multidimensional poverty and self-reported AB self-medication and treatment non-adherence (skipping a dose and not completing the course). We also analysed linked qualitative in-depth patient interviews (IDIs) (n = 82) and unlinked focus group discussions (FGDs) with community members (n = 44 groups). Findings: AB self-medication and non-adherence to treatment courses was significantly more common in the least deprived group compared with those in severe poverty. Adjustment for AB ‘knowledge’, attitudes and socio-demographics diminished the association with self-medication, but not non-adherence. IDIs and FGDs suggested that self-medication and non-adherence are driven by perceived inconvenience of the healthcare system, financial barriers, and ease of unregulated AB access. Interpretation: Structural barriers to optimal AB use exist at all levels of the socioeconomic hierarchy. Inefficiencies in public healthcare may be fuelling alternative antibiotic access points, for those who can afford it. In designing interventions to tackle AMR and reduce AB misuse, the behaviours and needs of wealthier population groups should not be neglected. Funding Information: UK National Institute for Health Research, Medical Research Council and the Department of Health and Social Care. Declaration of Interests: None. Ethics Approval Statement: Ethical approval for this project was obtained from the University of St Andrews, UK (No. MD14548, 10/09/19); National Institute for Medical Research, Tanzania (No. 2831, updated 26/07/19), CUHAS/BMC research ethics and review committee (No. CREC/266/2018, updated on 02/2019), Mbeya Medical Research and Ethics Committee (No. SZEC-2439/R. A/V.1/303030), Kilimanjaro Christian Medical College, Tanzania (No. 2293, updated 14/08/19). Uganda National Council for Science and Technology (number HS2406, 18/06/18); Makerere University, Uganda (number 514, 25/04/18); and Kenya Medical Research Institute (04/06/19, Scientific and Ethics Review Committee (SERU) number KEMRI/SERU/CMR/P00112/3865 V.1.2). For Uganda, administrative letters of support were obtained from the district health officers to allow the research to be conducted in the respective hospitals and health centres.

This work was supported by a Newton Fund award from the Medical Research Council (United Kingdom) MR/N019296/1 and the Philippine Council for Health Research and Development. Additional support was provided by the National Institute for Health Research (United Kingdom), the Global Health Research Unit on genomic surveillance of AMR (16/136/111) and by research grant U01CA207167 from the US National Institutes of Health. Antimicrobial-resistant Neisseria gonorrhoeae is a major threat to public health and is of particular concern in the Western Pacific Region, where the incidence of gonorrhoea is high. The Antimicrobial Resistance Surveillance Program (ARSP) has been capturing information on resistant gonorrhoea since 1996, but genomic epidemiology studies on this pathogen are lacking in the Philippines. We sequenced the whole genomes of 21 N. gonorrhoeae isolates collected in 2013-2014 by ARSP. The multilocus sequence type, multiantigen sequence type, presence of determinants of antimicrobial resistance and relatedness among the isolates were all derived from the sequence data. The concordance between phenotypic and genotypic resistance was also determined. Ten of 21 isolates were resistant to penicillin, ciprofloxacin and tetracycline, due mainly to the presence of the blaTEM gene, the S91F mutation in the gyrA gene and the tetM gene, respectively. None of the isolates was resistant to ceftriaxone or cefixime. The concordance between phenotypic and genotypic resistance was 92.38% overall for five antibiotics in four classes. Despite the small number of isolates studied, they were genetically diverse, as shown by the sequence types, the N. gonorrhoeae multiantigen sequence typing types and the tree. Comparison with global genomes placed the Philippine genomes within global lineage A and led to the identification of an international transmission route. This first genomic survey of N. gonorrhoeae isolates collected by ARSP will be used to contextualize prospective surveillance. It highlights the importance of genomic surveillance in the Western Pacific and other endemic regions for understanding the spread of drug-resistant gonorrhoea worldwide. Publisher PDF

IntroductionAntimicrobial resistance (AMR) is a global health threat that requires urgent research using a multidisciplinary approach. The biological drivers of AMR are well understood, but factors related to treatment-seeking and the social contexts of antibiotic (AB) use behaviours are less understood. Here we describe the Holistic Approach to Unravelling Antibacterial Resistance in East Africa (HATUA), a multi-centre consortium that investigates the diverse drivers of drug-resistance in urinary tract infections (UTIs) in East Africa.Methods and AnalysisThis study will take place in Uganda, Kenya and Tanzania. We will conduct geospatial mapping of AB sellers, and conduct mystery client studies and in-depth interviews (IDI) with drug sellers to investigate AB provision practices. In parallel, we will conduct IDIs with doctors, alongside community focus groups. Clinically diagnosed UTI patients will be recruited from healthcare centres, provide urine samples, and complete a questionnaire capturing retrospective treatment pathways, socio-demographic characteristics, attitudes and knowledge. Bacterial isolates from urine and stool samples will be subject to culture and antibiotic susceptibility testing (C&AST). Genomic DNA from bacterial isolates will be extracted with a subset being sequenced. A follow-up household interview will be conducted with 1800 UTI-positive patients, where further environmental samples will be collected. A sub-sample of patients will be interviewed using qualitative tools. Questionnaire data, microbiological analysis and qualitative data will be linked at the individual level. Quantitative data will be analysed using statistical modelling including Bayesian network analysis, and all forms of qualitative data analysed through iterative thematic content analysis.Ethics and DisseminationApprovals have been obtained from all national and local ethical review bodies in East Africa and the UK. Results will be disseminated in communities, with local and global policy stakeholders, and in academic circles. They will have great potential to inform policy, improve clinical practice and build regional pathogen surveillance capacity.

Background Molecular genetics has risen in both output and affordability to become the gold standard in diagnosis, however it is not yet available for most routine clinical microbiology due to cost and the level of skill it requires. Matrix assisted laser desorption/ionisation – time of flight mass spectrometry (MALDI-TOF MS) approaches may be useful in bridging the gap between low-resolution phenotypic methods and bulky genotypic methods in the goal of epidemiological source-typing of microbes. Burkholderia has been shown to be identifiable at the subspecies level using MALDI-TOF MS. There have not yet been studies assessing the ability of MALDI-TOF MS to source-type Burkholderia contaminans isolates into epidemiologically relevant outbreak clusters. Methods 55 well-characterised B. contaminans isolates were used to create a panel for analysis of MALDI-TOF MS biomarker peaks and their relation to outbreak strains, location, source, patient, diagnosis and isolate genetics. Unsupervised clustering was performed and classification models were generated using biostatistical analysis software. Results B. contaminans spectra derived from MALDI-TOF MS were of sufficiently high resolution to identify 100% of isolates. Unsupervised clustering methods showed poor evidence of spectra clustering by all characteristics measured. Classification algorithms were discriminatory, with Genetic Algorithm models showing 100% recognition capability for all outbreaks, the pulsed-field gel electrophoresis (PFGE) typeability model, and 96.63% recognition for the location model. A consistent peak at m/z of approximately 6943 was identified in all non-typeable strains but in none of the typeable strains. Conclusions MALDI-TOF MS successfully discriminates B. contaminans isolates into clonal, epidemiological clusters, and can recognise isolates non-typeable by PFGE. Further work should investigate this capability, and include peptide studies and genomic sequencing to identify individual proteins or genes responsible for this non-typeablity, particularly at the peak weight identified.

Objectives: This study presents demographic and temporal trends in the isolation of Staphylococcus aureus in Vermont clinical microbiology laboratories and explores the use of statistical algorithms and multi-resistance phenotypes to improve outbreak detection.Methods: Routine microbiology test results downloaded from Vermont clinical laboratory information systems were used to monitor S. aureus antimicrobial resistance trends. The integrated WHONET-SaTScan software used multi-resistance phenotypes to identify possible acute outbreaks with the space-time permutation model and slowly emerging geographic clusters using the spatial-only multinomial model.Results: Data were provided from seven hospital laboratories from 2012 to 2018 for 19,224 S. aureus isolates from 14,939 patients. Statistically significant differences (p ≤ 0.05) in methicillin-resistant S. aureus (MRSA) isolation were seen by age group, specimen type, and health-care setting. Among MRSA, multi-resistance profiles permitted the recognition and tracking of 6 common and 21 rare 'phenotypic clones.' We identified 43 acute MRSA clusters and 7 significant geographic clusters (p ≤ 0.05).Conclusions: There was significant heterogeneity in MRSA strains between facilities and the use of multi-resistance phenotypes facilitated the recognition of possible outbreaks. Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging resistance threats.

OBJECTIVE: This study presents trends in organism isolation and antimicrobial resistance in routine microbiology test results from acute-care hospital microbiology laboratories in Vermont. METHODS: The WHO Collaborating Centre for Surveillance of Antimicrobial Resistance captured microbiology test results from acute-care Vermont hospital laboratories to monitor geographic and temporal trends in antimicrobial resistance and emerging microbial threats with the free WHONET software. RESULTS: Data were provided from 12 acute care hospital laboratories from 2011 through 2018 for 318,833 isolates from 148,994 patients (70% female, 74% outpatient, and 63% urine). Significant differences (p

Objective:To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.Design:Multicenter retrospective cohort study.Setting:The study included 43 hospitals using a common infection prevention surveillance system.Methods:A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.Results:We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.Conclusions:Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

National networks of laboratory-based surveillance of antimicrobial resistance (AMR) monitor resistance trends and disseminate these data to AMR stakeholders. Whole-genome sequencing (WGS) can support surveillance by pinpointing resistance mechanisms and uncovering transmission patterns. However, genomic surveillance is rare in low- and middle-income countries. Here, we implement WGS within the established Antimicrobial Resistance Surveillance Program of the Philippines via a binational collaboration. In parallel, we characterize bacterial populations of key bug-drug combinations via a retrospective sequencing survey. By linking the resistance phenotypes to genomic data, we reveal the interplay of genetic lineages (strains), AMR mechanisms, and AMR vehicles underlying the expansion of specific resistance phenotypes that coincide with the growing carbapenem resistance rates observed since 2010. Our results enhance our understanding of the drivers of carbapenem resistance in the Philippines, while also serving as the genetic background to contextualize ongoing local prospective surveillance., Whole-genome sequencing (WGS) can support drug resistance surveillance, but is rare in low- and middle-income countries. Here, the authors integrate WGS within the national antimicrobial resistance surveillance program in the Philippines and conduct a retrospective sequencing survey to characterize bacterial populations and dissect resistance phenotypes.

BACKGROUND Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel. OBJECTIVE This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly. METHODS An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People’s Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam. RESULTS We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository. CONCLUSIONS The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.

BackgroundReporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national and global levels. However, analysing data and generating a report are time-consuming and often require trained personnel. We illustrate the development and utility of an offline, open-access and automated tool that can support the generation of AMR surveillance reports promptly at the local level.MethodsAn offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language. The application can be run by a double-click on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested in Microsoft Windows 10 and 7 using open-access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People’s Democratic Republic (PDR), Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam.FindingsWe developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyse their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and Excel formats). The data files could be those exported from WHONET and/or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from www.amass.website. The participating hospitals tested the application and deposited their AMR surveillance reports in an open-access data repository.InterpretationThe AMASS application can be a useful tool to support the generation and sharing of AMR surveillance reports.FundingMahidol Oxford Tropical Medicine Research Unit (MORU) is funded by the Wellcome Trust (Grant no. 106698/Z/14/Z). Oxford University Clinical Research Unit (OUCRU) is funded by the Wellcome Trust (Grant no. 106680/B/14/Z). The investigators are funded by the Wellcome Trust (CL is funded by a Training Research Fellowship [Grant no. 206736] and DL is funded by an Intermediate Training Fellowship [Grant no. 101103]). BSC is funded by the UK Medical Research Council and Department for International Development (Grant no. MR/K006924/1). The funder has no role in the design and conduct of the study, data collection, or analysis and interpretation of the data.