Key Recommendations for Antimicrobial Resistance Surveillance: Takeaways From the CAPTURA Project.

Antimicrobial resistance (AMR) is a growing global public health challenge associated with 4.95 million deaths in 2019 and an estimated 10 million deaths per year by 2050 in the absence of coordinated action. A robust AMR surveillance system is therefore required to avert such a scenario. Based on an analysis of country-level AMR data in 8 Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) countries, we present a list of key recommendations to strengthen AMR surveillance. We propose 10 primary considerations under 3 broad categories, including recommendations on (1) laboratory and testing practices, (2) data management and analysis, and (3) data use.

Effective Stakeholder Engagement for Collation, Analysis and Expansion of Antimicrobial Resistance (AMR) Data: A CAPTURA Experience.

BACKGROUND: An effective implementation of antimicrobial resistance (AMR) surveillance projects requires sustainable and multidisciplinary engagement with stakeholders from various backgrounds, interests and aims. The "Capturing Data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia" (CAPTURA) project, funded by the Fleming Fund, initially targeted 12 countries in South Asia (SA) and Southeast Asia (SEA) to "expand the volume of historical and current data on AMR and antimicrobial usage" and support local agencies through capacity building activities. METHODS: In this article, we focus on early stakeholder engagement activities and present overall statistics on AMR data collated from 72 laboratories across seven countries. This included 2.3 million records of antimicrobial susceptibility testing (AST) data, which were curated, analyzed, and shared back to the facilities for informed decision making. RESULTS: Approximately 98% of the data collated by CAPTURA originated from laboratories based in SA countries. Furthermore, country-wide data were analyzed to identify commonly reported pathogens in each country, followed by descriptions of AST practices and multidrug-resistant (MDR) pathogens. Overall, we found meager adherence to standard guidelines to perform and record AST results, and a significant number of MDR pathogens were reported. CONCLUSIONS: We conclude that close collaboration with the existing national mechanisms for identifying AMR data sources was crucial for the project's success. Although we show a vast retrospective dataset on AMR is available for data sharing in Asia, there remain critical gaps in data generation/management practice and analysis capacity for AMR data at most facilities.

CAPTURA Regional Workshop Proceedings (28-30 June 2022, Virtual).

In response to the global threat of antimicrobial resistance (AMR), the Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project worked with microbiology laboratories, pharmacies, and local governments in South Asia and Southeast Asia to expand the volume of historical and current data available on AMR and antimicrobial use and to identify gaps in data and areas for quality improvement. When the CAPTURA project completed its country-level engagement in the first half of 2022, the consortium brought together local, regional, and global AMR stakeholders for a virtual regional workshop to review data outputs from the project and share strategies to inform national and regional efforts to combat AMR. This paper summarizes the main topics presented in the workshop held from 28 to 30 June 2022. As such, it highlights lessons learned from the project and strategies to fight AMR. Although CAPTURA has been invaluable to countries and information from the project is already being used, barriers concerning data quality and sharing remain. Regional-level initiatives should continue to build on the momentum gained from the CAPTURA project in supporting national-level surveillance and data quality improvements to inform critical decisions around planning, policies, and clinical care. Project findings have highlighted that issues with antimicrobial resistance and use are wide ranging across countries. Going forward, building on the current foundations and tailoring approaches to meet local needs and capacities will be fundamental in combatting AMR.

Recording and Reporting of Antimicrobial Resistance (AMR) Priority Variables and Its Implication on Expanding Surveillance Sites in Nepal: A CAPTURA Experience.

Data on antimicrobial resistance (AMR) from sites not participating in the National AMR surveillance network, conducted by National Public Health Laboratory (NPHL), remain largely unknown in Nepal. The "Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia" (CAPTURA) assessed AMR data from previously untapped data sources in Nepal. A retrospective cross-sectional data review was carried out for the AMR data recorded between January 2017 and December 2019 to analyze AMR data from 26 hospital-based laboratories and 2 diagnostic laboratories in Nepal. Of the 56 health facilities initially contacted to participate in this project activity, 50.0% (28/56) signed a data-sharing agreement with CAPTURA. Eleven of the 28 hospitals were AMR surveillance sites, whereas the other 17, although not part of the National AMR surveillance network, recorded AMR-related data. Data for 663 602 isolates obtained from 580 038 patients were analyzed. A complete record of the 11 CAPTURA priority variables was obtained from 45.5% (5/11) of government hospitals, 63.6% (7/11) of private hospitals, and 54.6% (6/11) of public-private hospitals networked with NPHL for AMR surveillance. Similarly, 80% (8/10) of clinics and 54.6% (6/11) of laboratories outside the NPHL network recorded complete data for the 10 Global Antimicrobial Resistance and Use Surveillance System (GLASS) priority variables and 11/14 CAPTURA priority variables. Retrospective review of the data identified areas requiring additional resources and interventions to improve the quality of data on AMR in Nepal. Furthermore, we observed no difference in the priority variables reported by sites within or outside the NPHL network, thus suggesting that policies could be made to expand the surveillance system to include these sites without substantially affecting the government's budget.

Antimicrobial Resistance Surveillance Methods in Bangladesh: Present and Way Forward.

The Institute of Epidemiology, Disease Control and Research (IEDCR) conducts active, case-based national antimicrobial resistance (AMR) surveillance in Bangladesh. The Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project accessed aggregated retrospective data from non-IEDCR study sites and 9 IEDCR sites to understand the pattern and extent of AMR and to use analyzed data to guide ongoing and future national AMR surveillance in both public and private laboratories. Record-keeping practices, data completeness, quality control, and antimicrobial susceptibility test practices were investigated in all laboratories participating in case-based IEDCR surveillance and laboratory-based CAPTURA sites. All 9 IEDCR laboratories recorded detailed case-based data (n = 16 816) in electronic format for a priority subset of processed laboratory samples. In contrast, most CAPTURA sites (n = 18/33 [54.5%]) used handwritten registers to store data. The CAPTURA sites were characterized by fewer recorded variables (such as patient demographics, clinical history, and laboratory findings) with 1 020 197 individual data, less integration of patient records with the laboratory information system, and nonuniform practice of data recording; however, data were collected from all available clinical samples. The analyses conducted on AMR data collected by IEDCR and CAPTURA in Bangladesh provide current data collection status and highlight opportunities to improve ongoing data collection to strengthen current AMR surveillance system initiatives. We recommend a tailored approach to conduct AMR surveillance in high-burden, resource-limited settings.

Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA).

BACKGROUND: In 2015, the UK government established the Fleming Fund with the aim to address critical gaps in surveillance of antimicrobial resistance (AMR) in low- and middle-income countries in Asia and Africa. Among a large portfolio of grants, the Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was awarded with the specific objective of expanding the volume of historical data on AMR, consumption (AMC), and use (AMU) in the human healthcare sector across 12 countries in South and Southeast Asia. METHODS: Starting in early 2019, the CAPTURA consortium began working with local governments and >100 relevant data-holding facilities across the region to identify, assess for quality, prioritize, and subsequently retrieve data on AMR, AMC, and AMU. Relevant and shared data were collated and analyzed to provide local overviews for national stakeholders as well as regional context, wherever possible. RESULTS: From the vast information resource generated on current surveillance capacity and data availability, the project has highlighted gaps and areas for quality improvement and supported comprehensive capacity-building activities to optimize local data-collection and -management practices. CONCLUSIONS: The project has paved the way for expansion of surveillance networks to include both the academic and private sector in several countries and has actively engaged in discussions to promote data sharing at the local, national, and regional levels. This paper describes the overarching approach to, and emerging lessons from, the CAPTURA project, and how it contributes to other ongoing efforts to strengthen national AMR surveillance in the region and globally.

Methodological Approach to Identify and Expand the Volume of Antimicrobial Resistance (AMR) Data in the Human Health Sector in Low- and Middle-Income Countries in Asia: Implications for Local and Regional AMR Surveillance Systems Strengthening.

Antimicrobial resistance (AMR) is a multifaceted global health problem disproportionately affecting low- and middle-income countries (LMICs). The Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was tasked to expand the volume of AMR and antimicrobial use data in Asia. The CAPTURA project used 2 data-collection streams: facility data and project metadata. Project metadata constituted information collected to map out data sources and assess data quality, while facility data referred to the retrospective data collected from healthcare facilities. A down-selection process, labelled "the funnel approach" by the project, was adopted to use the project metadata in prioritizing and selecting laboratories for retrospective AMR data collection. Moreover, the metadata served as a guide for understanding the AMR data once they were collected. The findings from CAPTURA's metadata add to the current discourse on the limitation of AMR data in LMICs. There is generally a low volume of AMR data generated as there is a lack of microbiology laboratories with sufficient antimicrobial susceptibility testing capacity. Many laboratories in Asia are still capturing data on paper, resulting in scattered or unused data not readily accessible or shareable for analyses. There is also a lack of clinical and epidemiological data captured, impeding interpretation and in-depth understanding of the AMR data. CAPTURA's experience in Asia suggests that there is a wide spectrum of capacity and capability of microbiology laboratories within a country and region. As local AMR surveillance is a crucial instrument to inform context-specific measures to combat AMR, it is important to understand and assess current capacity-building needs while implementing activities to enhance surveillance systems.

Unravelling patient pathways in the context of antibacterial resistance in East Africa.

BACKGROUND: A key factor driving the development and maintenance of antibacterial resistance (ABR) is individuals' use of antibiotics (ABs) to treat illness. To better understand motivations and context for antibiotic use we use the concept of a patient treatment-seeking pathway: a treatment journey encompassing where patients go when they are unwell, what motivates their choices, and how they obtain antibiotics. This paper investigates patterns and determinants of patient treatment-seeking pathways, and how they intersect with AB use in East Africa, a region where ABR-attributable deaths are exceptionally high. METHODS: The Holistic Approach to Unravelling Antibacterial Resistance (HATUA) Consortium collected quantitative data from 6,827 adult outpatients presenting with urinary tract infection (UTI) symptoms in Kenya, Tanzania, and Uganda between February 2019- September 2020, and conducted qualitative in-depth patient interviews with a subset (n = 116). We described patterns of treatment-seeking visually using Sankey plots and explored explanations and motivations using mixed-methods. Using Bayesian hierarchical regression modelling, we investigated the associations between socio-demographic, economic, healthcare, and attitudinal factors and three factors related to ABR: self-treatment as a first step, having a multi-step treatment pathway, and consuming ABs. RESULTS: Although most patients (86%) sought help from medical facilities in the first instance, many (56%) described multi-step, repetitive treatment-seeking pathways, which further increased the likelihood of consuming ABs. Higher socio-economic status patients were more likely to consume ABs and have multi-step pathways. Reasons for choosing providers (e.g., cost, location, time) were conditioned by wider structural factors such as hybrid healthcare systems and AB availability. CONCLUSION: There is likely to be a reinforcing cycle between complex, repetitive treatment pathways, AB consumption and ABR. A focus on individual antibiotic use as the key intervention point in this cycle ignores the contextual challenges patients face when treatment seeking, which include inadequate access to diagnostics, perceived inefficient public healthcare and ease of purchasing antibiotics without prescription. Pluralistic healthcare landscapes may promote more complex treatment seeking and therefore inappropriate AB use. We recommend further attention to healthcare system factors, focussing on medical facilities (e.g., accessible diagnostics, patient-doctor interactions, information flows), and community AB access points (e.g., drug sellers).

The Impact of Coronavirus Disease 2019 (COVID-19) on Healthcare-Associated Infections.

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.

What's New in Antibiograms? Updating CLSI M39 Guidance with Current Trends.

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.

Genome Sequencing Identifies Previously Unrecognized Klebsiella pneumoniae Outbreaks in Neonatal Intensive Care Units in the Philippines.

BACKGROUND: Klebsiella pneumoniae is a critically important pathogen in the Philippines. Isolates are commonly resistant to at least 2 classes of antibiotics, yet mechanisms and spread of its resistance are not well studied. METHODS: A 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 through 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. RESULTS: Prevalent 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 (STs). 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 and K- and O-loci, and similar resistance profiles and AMR genes. All outbreak isolates were collected from blood of children aged < 1 year. CONCLUSION: WGS provided a better understanding of the epidemiology of multidrug resistant Klebsiella in the Philippines, which was not possible with only phenotypic and epidemiologic data. The identification of 3 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.

Impact of the COVID-19 pandemic on the surveillance, prevention and control of antimicrobial resistance: a global survey.

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 < 0.01). Reduced availability of nursing, medical and public health staff for AMR was reported by 71%, 69% and 64%, respectively, whereas 67% reported stable cleaning staff availability. The majority (58%) reported reduced reagents/consumables, particularly LMICs (P < 0.01). Decreased numbers of cultures, elective procedures, chronically ill admissions and outpatients and increased ICU admissions reported could bias AMR data. Reported overall infection prevention and control (IPC) improvement could decrease AMR rates, whereas increases in selected inappropriate IPC practices and antimicrobial prescribing could increase rates. Most did not yet have complete data on changing AMR rates due to COVID-19. 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.

Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries.

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.

Use of WHONET-SaTScan system for simulated real-time detection of antimicrobial resistance clusters in a hospital in Italy, 2012 to 2014.

Resistant pathogens infections cause in healthcare settings, higher patient mortality, longer hospitalisation times and higher costs for treatments. Strengthening and coordinating local, national and international surveillance systems is the cornerstone for the control of antimicrobial resistance (AMR). In this study, the WHONET-SaTScan software was applied in a hospital in Italy to identify potential outbreaks of AMR. Data from San Filippo Neri Hospital in Rome between 2012 and 2014 were extracted from the national surveillance system for antimicrobial resistance (AR-ISS) and analysed using the simulated prospective analysis for real-time cluster detection included in the WHONET-SaTScan software. Results were compared with the hospital infection prevention and control system. The WHONET-SaTScan identified 71 statistically significant clusters, some involving pathogens carrying multiple resistance phenotypes. Of these 71, three were also detected by the hospital system, while a further 15, detected by WHONET-SaTScan only, were considered of relevant importance and worth further investigation by the hospital infection control team. In this study, the WHONET-SaTScan system was applied for the first time to the surveillance of AMR in Italy as a tool to strengthen this surveillance to allow more timely intervention strategies both at local and national level, using data regularly collected by the Italian national surveillance system.

World Health Organization Ranking of Antimicrobials According to Their Importance in Human Medicine: A Critical Step for Developing Risk Management Strategies to Control Antimicrobial Resistance From Food Animal Production.

Antimicrobial use in food animals selects for antimicrobial resistance in bacteria, which can spread to people. Reducing use of antimicrobials-particularly those deemed to be critically important for human medicine-in food production animals continues to be an important step for preserving the benefits of these antimicrobials for people. The World Health Organization ranking of antimicrobials according to their relative importance in human medicine was recently updated. Antimicrobials considered the highest priority among the critically important antimicrobials were quinolones, third- and fourth-generation cephalosporins, macrolides and ketolides, and glycopeptides. The updated ranking allows stakeholders in the agriculture sector and regulatory agencies to focus risk management efforts on drugs used in food animals that are the most important to human medicine. In particular, the current large-scale use of fluoroquinolones, macrolides, and third-generation cephalosporins and any potential use of glycopeptides and carbapenems need to be addressed urgently.

Predominance of multidrug-resistant bacteria causing urinary tract infections among symptomatic patients in East Africa: a call for action.

Background: In low- and middle-income countries, antibiotics are often prescribed for patients with symptoms of urinary tract infections (UTIs) without microbiological confirmation. Inappropriate antibiotic use can contribute to antimicrobial resistance (AMR) and the selection of MDR bacteria. Data on antibiotic susceptibility of cultured bacteria are important in drafting empirical treatment guidelines and monitoring resistance trends, which can prevent the spread of AMR. In East Africa, antibiotic susceptibility data are sparse. To fill the gap, this study reports common microorganisms and their susceptibility patterns isolated from patients with UTI-like symptoms in Kenya, Tanzania and Uganda. Within each country, patients were recruited from three sites that were sociodemographically distinct and representative of different populations. Methods: UTI was defined by the presence of >104 cfu/mL of one or two uropathogens in mid-stream urine samples. Identification of microorganisms was done using biochemical methods. Antimicrobial susceptibility testing was performed by the Kirby-Bauer disc diffusion assay. MDR bacteria were defined as isolates resistant to at least one agent in three or more classes of antimicrobial agents. Results: Microbiologically confirmed UTI was observed in 2653 (35.0%) of the 7583 patients studied. The predominant bacteria were Escherichia coli (37.0%), Staphylococcus spp. (26.3%), Klebsiella spp. (5.8%) and Enterococcus spp. (5.5%). E. coli contributed 982 of the isolates, with an MDR proportion of 52.2%. Staphylococcus spp. contributed 697 of the isolates, with an MDR rate of 60.3%. The overall proportion of MDR bacteria (n = 1153) was 50.9%. Conclusions: MDR bacteria are common causes of UTI in patients attending healthcare centres in East African countries, which emphasizes the need for investment in laboratory culture capacity and diagnostic algorithms to improve accuracy of diagnosis that will lead to appropriate antibiotic use to prevent and control AMR.

Frequency and mortality rate following antimicrobial-resistant bloodstream infections in tertiary-care hospitals compared with secondary-care hospitals.

There are few studies comparing proportion, frequency, mortality and mortality rate following antimicrobial-resistant (AMR) infections between tertiary-care hospitals (TCHs) and secondary-care hospitals (SCHs) in low and middle-income countries (LMICs) to inform intervention strategies. The aim of this study is to demonstrate the utility of an offline tool to generate AMR reports and data for a secondary data analysis. We conducted a secondary-data analysis on a retrospective, multicentre data of hospitalised patients in Thailand. Routinely collected microbiology and hospital admission data of 2012 to 2015, from 15 TCHs and 34 SCHs were analysed using the AMASS v2.0 (www.amass.website). We then compared the burden of AMR bloodstream infections (BSI) between those TCHs and SCHs. Of 19,665 patients with AMR BSI caused by pathogens under evaluation, 10,858 (55.2%) and 8,807 (44.8%) were classified as community-origin and hospital-origin BSI, respectively. The burden of AMR BSI was considerably different between TCHs and SCHs, particularly of hospital-origin AMR BSI. The frequencies of hospital-origin AMR BSI per 100,000 patient-days at risk in TCHs were about twice that in SCHs for most pathogens under evaluation (for carbapenem-resistant Acinetobacter baumannii [CRAB]: 18.6 vs. 7.0, incidence rate ratio 2.77; 95%CI 1.72-4.43, p<0.001; for carbapenem-resistant Pseudomonas aeruginosa [CRPA]: 3.8 vs. 2.0, p = 0.0073; third-generation cephalosporin resistant Escherichia coli [3GCREC]: 12.1 vs. 7.0, p<0.001; third-generation cephalosporin resistant Klebsiella pneumoniae [3GCRKP]: 12.2 vs. 5.4, p<0.001; carbapenem-resistant K. pneumoniae [CRKP]: 1.6 vs. 0.7, p = 0.045; and methicillin-resistant Staphylococcus aureus [MRSA]: 5.1 vs. 2.5, p = 0.0091). All-cause in-hospital mortality (%) following hospital-origin AMR BSI was not significantly different between TCHs and SCHs (all p>0.20). Due to the higher frequencies, all-cause in-hospital mortality rates following hospital-origin AMR BSI per 100,000 patient-days at risk were considerably higher in TCHs for most pathogens (for CRAB: 10.2 vs. 3.6,mortality rate ratio 2.77; 95%CI 1.71 to 4.48, p<0.001; CRPA: 1.6 vs. 0.8; p = 0.020; 3GCREC: 4.0 vs. 2.4, p = 0.009; 3GCRKP, 4.0 vs. 1.8, p<0.001; CRKP: 0.8 vs. 0.3, p = 0.042; and MRSA: 2.3 vs. 1.1, p = 0.023). In conclusion, the burden of AMR infections in some LMICs might differ by hospital type and size. In those countries, activities and resources for antimicrobial stewardship and infection control programs might need to be tailored based on hospital setting. The frequency and in-hospital mortality rate of hospital-origin AMR BSI are important indicators and should be routinely measured to monitor the burden of AMR in every hospital with microbiology laboratories in LMICs.

A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.

BACKGROUND: Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown. METHODS: We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods. RESULTS: Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92). CONCLUSIONS: Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

Integrated Multilevel Surveillance of the World's Infecting Microbes and Their Resistance to Antimicrobial Agents.

Microbial surveillance systems have varied in their source of support; type of laboratory reporting (patient care or reference); inclusiveness of reports filed; extent of microbial typing; whether single hospital, multihospital, or multicountry; proportion of total medical centers participating; and types, levels, integration across levels, and automation of analyses performed. These surveillance systems variably support the diagnosis and treatment of patients, local or regional infection control, local or national policies and guidelines, laboratory capacity building, sentinel surveillance, and patient safety. Overall, however, only a small fraction of available data are under any surveillance, and very few data are fully integrated and analyzed. Advancing informatics and genomics can make microbial surveillance far more efficient and effective at preventing infections and improving their outcomes. The world's microbiology laboratories should upload their reports each day to programs that detect events, trends, and epidemics in communities, hospitals, countries, and the world.

Laboratory Surveillance, Quality Management, and Its Role in Addressing Antimicrobial Resistance in Africa: A Narrative Review.

AMR is a major public health concern that calls for extensive work and a multidisciplinary team approach. The high prevalence of infectious diseases in African nations leads to widespread antibiotic usage and eventual antimicrobial resistance, which has significant negative effects on people's health, the economy, and society. Additionally, inadequate or nonexistent antimicrobial drug regulations, inappropriate prescription practices, and restrictions on public health prevention initiatives such as immunization, water and sanitation, and sexual health may all contribute to the emergence of AMR. Despite the need for laboratory quality assurance, many African laboratories confront substantial difficulties in implementing efficient quality assurance programs. AMR surveillance in Africa is hampered by a lack of laboratory capacity, insufficient data collection and analysis, and poor stakeholder collaboration. Several initiatives and programs, including the World Health Organization's Global Antimicrobial Resistance and Use Surveillance System (GLASS), the Africa Centres for Disease Control and Prevention (Africa CDC) Antimicrobial Resistance Surveillance Network (AMRSNET), and the Fleming Fund, a UK government initiative aimed at tackling AMR in low- and middle-income countries, have been established to strengthen AMR surveillance in Africa and globally.