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Introductory paragraph Infections caused by carbapenemase-producing enterobacteria (CPE) are a major concern in clinical settings worldwide. Two fundamentally different processes shape the epidemiology of CPE in hospitals: the dissemination of CPE clones from patient to patient (between-patient transfer), and the transfer of carbapenemase-encoding plasmids between enterobacteria in the gut microbiota of individual patients (within-patient transfer). The relative contribution of each process to the overall dissemination of carbapenem resistance in hospitals remains poorly understood. Here, we used mechanistic models combining epidemiological data from more than 9,000 patients with whole genome sequence information from 250 enterobacteria clones to characterise the dissemination routes of a pOXA-48-like carbapenemase-encoding plasmid in a hospital setting over a two-year period. Our results revealed frequent between-patient transmission of high-risk pOXA-48-carrying clones, mostly of Klebsiella pneumoniae and sporadically Escherichia coli. The results also identified pOXA-48 dissemination hotspots within the hospital, such as specific wards and individual rooms within wards. Using high-resolution plasmid sequence analysis, we uncovered the pervasive within-patient transfer of pOXA-48, suggesting that horizontal plasmid transfer occurs in the gut of virtually every colonised patient. The complex and multifaceted epidemiological scenario exposed by this study provides insights for the development of intervention strategies to control the in-hospital spread of CPE.

Objectives The magnitude of impact caused by low blood culture utilization on estimates of the proportions and incidence rates of antimicrobial-resistant (AMR) bacterial infections is largely unknown. Methods We used routine electronic databases of microbiology, hospital admission and drug prescription at Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand, from 2011 to 2015, and bootstrap simulations. Results The proportions of Escherichia coli and Klebsiella pneumoniae bacteraemias caused by 3rd generation cephalosporin resistant isolates (3GCREC and 3GCRKP) were estimated to increase by 13 and 24 percentage points (from 44% to 57% and from 51% to 75%), respectively, if blood culture utilization rate was reduced from 82 to 26 blood culture specimens per 1,000 patient-days. Among patients with hospital-origin bloodstream infections, the proportion of 3GCREC and 3GCRKP whose first positive blood culture was taken within ±1 calendar day of the start of a parenteral antibiotic at the study hospital was substantially lower than those whose first positive blood culture was taken later into parenteral antibiotic treatment (30% versus 79%, p Conclusion Impacts of low blood culture utilization rate on the estimated proportions and incidence rates of AMR infections could be high. We recommend that AMR surveillance reports should additionally include blood culture utilization rate and stratification by exposure to a parenteral antibiotic at the hospital.

Objectives To estimate the transmission rate of carbapenemase-producing Enterobacteriaceae (CPE) in households with recently hospitalized CPE carriers. Methods We conducted a prospective case-ascertained cohort study. We identified the presence of CPE in stool samples from index subjects, household contacts and companion animals and environmental samples at regular intervals. Linked transmissions were identified by WGS. A Markov model was constructed to estimate the household transmission potential of CPE. Results Ten recently hospitalized index patients and 14 household contacts were included. There were seven households with one contact, two households with two contacts, and one household with three contacts. Index patients were colonized with blaOXA-48-like (n = 4), blaKPC-2 (n = 3), blaIMP (n = 2), and blaNDM-1 (n = 1), distributed among divergent species of Enterobacteriaceae. After a cumulative follow-up time of 9.0 years, three family members (21.4%, 3/14) acquired four different types of CPE in the community (hazard rate of 0.22/year). The probability of CPE transmission from an index patient to a household contact was 10% (95% CI 4%–26%). Conclusions We observed limited transmission of CPE from an index patient to household contacts. Larger studies are needed to understand the factors associated with household transmission of CPE and identify preventive strategies.

Background Nosocomial spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been widely reported, but the transmission pathways among patients and healthcare workers (HCWs) are unclear. Identifying the risk factors and drivers for these nosocomial transmissions is critical for infection prevention and control interventions. The main aim of our study was to quantify the relative importance of different transmission pathways of SARS-CoV-2 in the hospital setting. Methods and findings This is an observational cohort study using data from 4 teaching hospitals in Oxfordshire, United Kingdom, from January to October 2020. Associations between infectious SARS-CoV-2 individuals and infection risk were quantified using logistic, generalised additive and linear mixed models. Cases were classified as community- or hospital-acquired using likely incubation periods of 3 to 7 days. Of 66,184 patients who were hospitalised during the study period, 920 had a positive SARS-CoV-2 PCR test within the same period (1.4%). The mean age was 67.9 (±20.7) years, 49.2% were females, and 68.5% were from the white ethnic group. Out of these, 571 patients had their first positive PCR tests while hospitalised (62.1%), and 97 of these occurred at least 7 days after admission (10.5%). Among the 5,596 HCWs, 615 (11.0%) tested positive during the study period using PCR or serological tests. The mean age was 39.5 (±11.1) years, 78.9% were females, and 49.8% were nurses. For susceptible patients, 1 day in the same ward with another patient with hospital-acquired SARS-CoV-2 was associated with an additional 7.5 infections per 1,000 susceptible patients (95% credible interval (CrI) 5.5 to 9.5/1,000 susceptible patients/day) per day. Exposure to an infectious patient with community-acquired Coronavirus Disease 2019 (COVID-19) or to an infectious HCW was associated with substantially lower infection risks (2.0/1,000 susceptible patients/day, 95% CrI 1.6 to 2.2). As for HCW infections, exposure to an infectious patient with hospital-acquired SARS-CoV-2 or to an infectious HCW were both associated with an additional 0.8 infection per 1,000 susceptible HCWs per day (95% CrI 0.3 to 1.6 and 0.6 to 1.0, respectively). Exposure to an infectious patient with community-acquired SARS-CoV-2 was associated with less than half this risk (0.2/1,000 susceptible HCWs/day, 95% CrI 0.2 to 0.2). These assumptions were tested in sensitivity analysis, which showed broadly similar results. The main limitations were that the symptom onset dates and HCW absence days were not available. Conclusions In this study, we observed that exposure to patients with hospital-acquired SARS-CoV-2 is associated with a substantial infection risk to both HCWs and other hospitalised patients. Infection control measures to limit nosocomial transmission must be optimised to protect both staff and patients from SARS-CoV-2 infection., In a cohort study, Mo Yin and colleagues investigate transmission of community- and hospital-acquired SARS-CoV-2 in hospital settings in the UK., Author summary Why was this study done? Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the hospital setting has been widely reported, but little is known about the incidence and pathways of transmission. Hospitalised patients are especially vulnerable to Coronavirus Disease 2019 (COVID-19)-associated complications, and infected patients may contribute to the further spread of SARS-CoV-2 in the community and nursing homes upon discharge. Healthcare workers (HCWs) are disproportionately infected with SARS-CoV-2, and a reduced staff workforce due to SARS-CoV-2 infection may compromise the clinical management of patients and infection prevention and control measures. Improved understanding of the drivers of hospital-acquired SARS-CoV2 infection is important to prevent and control the spread of SARS-CoV-2 in hospitals. What did the researchers do and find? We collected data from 4 teaching hospitals in Oxfordshire, United Kingdom, from January to October 2020. The data were analysed to find the associations between infectious SARS-CoV-2 individuals (classified as community- or hospital-acquired) and infection risk posed to the susceptible individuals using statistical models. For susceptible patients, 1 day in the same ward with another patient with hospital-acquired SARS-CoV-2 was associated with an additional 8 infections per 1,000 susceptible patients, while exposure to an infectious patient with community-acquired COVID-19 or to an infectious HCW was associated with substantially lower infection risks of 2 per 1,000 susceptible patients. As for HCW infections, exposure to an infectious patient with hospital-acquired SARS-CoV-2 or to an infectious HCW were both associated with an additional 1 infection per 1,000 susceptible HCWs per day, while exposure to an infectious patient with community-acquired SARS-CoV-2 was associated with less than half this risk. What do these findings mean? Our data provide strong evidence that newly infected COVID-19 patients are associated with a high risk of onward transmission to patients and HCWs in hospital. Our findings support enhanced strategies to prevent and identify early hospital-onset SARS-CoV-2 infection among hospitalised patients, for example, regular screening and prompt testing to identify these patients. Measures to ensure infected staff are not at work, including regular staff screening and adequate sick pay arrangements, are vital. The relatively low risk of transmission associated with patients with suspected community-acquired COVID-19 suggests that for these patients, the peak of their infectivity may have passed such that existing infection prevention and control policies including universal use of personal protective equipment, prompt testing, and isolation of suspected or known cases are sufficient to mitigate most of the remaining infectiousness. The main limitations were that the symptom onset dates and HCW absence days were not available, which may affect the estimation of the transmission pathways.

Background Antimicrobial resistance (AMR) in Enterobacterales is a global health threat. Capacity for individual-level surveillance remains limited in many countries, whilst population-level surveillance approaches could inform empiric antibiotic treatment guidelines. Methods In this exploratory study, a novel approach to population-level prediction of AMR in Enterobacterales clinical isolates using metagenomic (Illumina) profiling of pooled DNA extracts from human faecal samples was developed and tested. Taxonomic and AMR gene profiles were used to derive taxonomy-adjusted population-level AMR metrics. Bayesian modelling, and model comparison based on cross-validation, were used to evaluate the capacity of each metric to predict the number of resistant Enterobacterales invasive infections at a population-level, using available bloodstream/cerebrospinal fluid infection data. Findings Population metagenomes comprised samples from 177, 157, and 156 individuals in Kenya, the UK, and Cambodia, respectively, collected between September 2014 and April 2016. Clinical data from independent populations included 910, 3356 and 197 bacterial isolates from blood/cerebrospinal fluid infections in Kenya, the UK and Cambodia, respectively (samples collected between January 2010 and May 2017). Enterobacterales were common colonisers and pathogens, and faecal taxonomic/AMR gene distributions and proportions of antimicrobial-resistant Enterobacterales infections differed by setting. A model including terms reflecting the metagenomic abundance of the commonest clinical Enterobacterales species, and of AMR genes known to either increase the minimum inhibitory concentration (MIC) or confer clinically-relevant resistance, had a higher predictive performance in determining population-level resistance in clinical Enterobacterales isolates compared to models considering only AMR gene information, only taxonomic information, or an intercept-only baseline model (difference in expected log predictive density compared to best model, estimated using leave-one-out cross-validation: intercept-only model = -223 [95% credible interval (CI): -330,-116]; model considering only AMR gene information = -186 [95% CI: -281,-91]; model considering only taxonomic information = -151 [95% CI: -232,-69]). Interpretation Whilst our findings are exploratory and require validation, intermittent metagenomics of pooled samples could represent an effective approach for AMR surveillance and to predict population-level AMR in clinical isolates, complementary to ongoing development of laboratory infrastructures processing individual samples. Funding The study was funded by Bill & Melinda Gates Foundation (grant agreement OPP1160974) and was sponsored by University of Oxford. The study was also supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, and NIHR Health Protection Research Unit in Healthcare-associated Infections and Antimicrobial Resistance (a partnership between the University of Oxford and Public Health England [PHE]). Kenyan samples were collected in a study funded by the MRC/DfID/Wellcome Joint Clinical Trials scheme: MR/M007367/1.

IntroductionVentilator-associated pneumonia (VAP) is the most common nosocomial infection in intensive care units (ICUs). Using short-course antibiotics to treat VAP caused by Gram-negative non-fermenting bacteria has been reported to be associated with excess pneumonia recurrences. The “REducinG Antibiotic tReatment Duration for Ventilator-Associated Pneumonia” (REGARD-VAP) trial aims to provide evidence for using a set of reproducible clinical criteria to shorten antibiotic duration for individualised treatment duration of VAP.Methods and analysisThis is a randomised controlled hierarchical non-inferiority–superiority trial being conducted in ICUs across Nepal, Thailand and Singapore. The primary outcome is a composite endpoint of death and pneumonia recurrence at day 60. Secondary outcomes include ventilator-associated events, multidrug-resistant organism infection or colonisation, total duration of antibiotic exposure, mechanical ventilation and hospitalisation. Adult patients who satisfy the US Centers for Disease Control and Prevention National Healthcare Safety Network VAP diagnostic criteria are enrolled. Participants are assessed daily until fever subsides for >48 hours and have stable blood pressure, then randomised to a short duration treatment strategy or a standard-of-care duration arm. Antibiotics may be stopped as early as day 3 if respiratory cultures are negative, and day 5 if respiratory cultures are positive in the short-course arm. Participants receiving standard-of-care will receive antibiotics for at least 8 days. Study participants are followed for 60 days after enrolment. An estimated 460 patients will be required to achieve 80% power to determine non-inferiority with a margin of 12%. All outcomes are compared by absolute risk differences. The conclusion of non-inferiority, and subsequently superiority, will be based on unadjusted and adjusted analyses in both the intention-to-treat and per-protocol populations.Ethics and disseminationThe study has received approvals from the Oxford Tropical Research Ethics Committee and the respective study sites. Results will be disseminated to patients, their caregivers, physicians, the funders, the critical care societies and other researchers.Trial registration numberNCT03382548.

Fitting stochastic transmission models to electronic patient data can offer detailed insights into the transmission of healthcare-associated infections and improve infection control. Pathogen whole-genome sequencing may improve the precision of model inferences, but computational constraints have limited modelling applications predominantly to small datasets and specific outbreaks, whereas large-scale sequencing studies have mostly relied on simple rules for identifying/excluding plausible transmission. We present a novel approach for integrating detailed epidemiological data on patient contact networks in hospitals with large-scale pathogen sequencing data. We apply our approach to study Clostridioides difficile transmission using a dataset of 1223 infections in Oxfordshire, UK, 2007–2011. 262 (21% [95% credibility interval 20–22%]) infections were estimated to have been acquired from another known case. There was heterogeneity by sequence type (ST) in the proportion of cases acquired from another case with the highest rates in ST1 (ribotype-027), ST42 (ribotype-106) and ST3 (ribotype-001). These same STs also had higher rates of transmission mediated via environmental contamination/spores persisting after patient discharge/recovery; for ST1 these persisted longer than for most other STs except ST3 and ST42. We also identified variation in transmission between hospitals, medical specialties and over time; by 2011 nearly all transmission from known cases had ceased in our hospitals. Our findings support previous work suggesting only a minority of C. difficile infections are acquired from known cases but highlight a greater role for environmental contamination than previously thought. Our approach is applicable to other healthcare-associated infections. Our findings have important implications for effective control of C. difficile., Author summary Preventing infections spreading in hospitals is a major priority for healthcare systems globally. Mathematical models can be used with electronic hospital records to reconstruct how infections spread, which in turn can help guide infection control interventions. Sequencing the genetic code (DNA) of the bacteria that cause infections can help to follow when transmission is occurring, as bacterial DNA from two patients infected from the same source will likely be very similar. Our paper describes a new statistical method for combining hospital records and sequencing data to track infections. We use our approach to study over 1200 infections of Clostridioides difficile (C. diff.), which is a common cause of diarrhoea in hospitals. We show that only a minority of infections are acquired from other unwell patients, but the amount of spread varies by the subtype of C. diff involved. We also show that different C. diff subtypes survive in the hospital environment for longer than others and may need enhanced control strategies. We also can detect differences in spread at different hospitals and show that by the end of the study we had largely eliminated transmission of C. diff from unwell patients in our hospitals.

Antibiotic-induced perturbation of the human gut flora is expected to play an important role in mediating the relationship between antibiotic use and the population prevalence of antibiotic resistance in bacteria, but little is known about how antibiotics affect within-host resistance dynamics. Here we develop a data-driven model of the within-host dynamics of extended-spectrum beta-lactamase (ESBL) producing Enterobacteriaceae. We use blaCTX-M (the most widespread ESBL gene family) and 16S rRNA (a proxy for bacterial load) abundance data from 833 rectal swabs from 133 ESBL-positive patients followed up in a prospective cohort study in three European hospitals. We find that cefuroxime and ceftriaxone are associated with increased blaCTX-M abundance during treatment (21% and 10% daily increase, respectively), while treatment with meropenem, piperacillin-tazobactam, and oral ciprofloxacin is associated with decreased blaCTX-M (8% daily decrease for all). The model predicts that typical antibiotic exposures can have substantial long-term effects on blaCTX-M carriage duration., eLife digest Bacteria that are resistant to antibiotics are a growing global health crisis. One type of antibiotic resistance arises when certain bacteria that can produce enzymes called extended-spectrum beta-lactamases (or ESBLs for short) become more common in the gut. These enzymes stop important antibiotics, like penicillin, from working. However, exactly which antibiotics and treatment durations contribute to the emergence of this antibiotic resistance remain unknown. Now, Niehus et al. find certain antibiotics that are associated with an increase in the number of gut bacteria carrying antibiotic resistance genes for ESBL enzymes. First, rectal swabs collected from 133 patients from three European hospitals were analysed to measure the total gut bacteria and the number of genes for ESBL enzymes. These samples had been collected at several time points including when the patient was first admitted to hospital, then every two to three days during their stay, and finally when they were discharged. Combining the analysis of the samples with details of the patients’ charts showed that treatment with two antibiotics: cefuroxime and ceftriaxone, was linked to an increase in ESBL genes in the gut bacteria. Other antibiotics – namely, meropenem, piperacillin-tazobactam and oral ciprofloxacin – were associated with a decrease in the number of bacteria with ESBL genes. Niehus et al. then performed further analysis to see if different treatment regimens affected how long patients were carrying gut bacteria with ESBL genes. This predicted that a longer course of meropenem, 14 days rather than 5 days, would shorten the length of time patients carried ESBL-resistant bacteria in their guts by 70%, although this effect will likely depend on the location of the hospital and the local prevalence of other types of antibiotic resistance. This analysis reveals new details about how antibiotic treatment can affect ESBL resistance genes. More studies are needed to understand how antibiotics affect other antibiotic resistance genes and how resistant bacteria spread. This will help scientists understand how much specific antibiotic regimens contribute to antibiotic resistance. It may also help scientists develop new antibiotic treatment strategies that reduce antibiotic resistance.

Non-adherence in non-inferiority trials can affect treatment effect estimates and often increases the chance of claiming non-inferiority under the standard intention-to-treat analysis. This article discusses the implications of different patterns of non-adherence in non-inferiority trials and offers practical recommendations for trial design, alternative analysis strategies, and outcome reporting to reduce bias in treatment estimates and improve transparency in reporting.

BackgroundTherapeutic options for multidrug-resistant Acinetobacter spp. are limited, and resistance to last resort antibiotics in hospitals is increasing globally. Quantifying the impact of delays in concordant antibiotic treatment on patient mortality is important for designing hospital antibiotic policies.MethodsWe included patients with Acinetobacter spp. hospital-acquired bacteremia (HAB) in a hospital in Thailand over a 13-year period. For each day of stay following the first positive blood culture we considered antibiotic treatment to be concordant if the isolated organism was susceptible to at least one antibiotic given. We used marginal structural models with inverse-probability weightings to determine the association between delays in concordant treatment and 30-day mortality.ResultsBetween January 2003 and December 2015, 1,203 patients had HAB with Acinetobacter spp., of which 682 patients (56.7%) had one or more days of delay in concordant treatment. These delays were associated with an absolute increase in 30-day mortality of 6.6% (95% CI 0.2%-13.0%), from 33.8% to 40.4%. Crude 30-day mortality was substantially lower in patients with three or more days of delays in concordant treatment compared to those with one to two days of delays. Accounting for confounders and immortal time bias resolved this paradox, and showed similar 30-day mortality for one, two and three or more days of delays.ConclusionsDelays in concordant antibiotic treatment were associated with a 6.6% absolute increase in mortality among patients with hospital-acquired Acinetobacter spp. bacteremia. If this association is causal, switching fifteen patients from discordant to concordant initial treatment would be expected to prevent one death.FundingThe Mahidol Oxford Tropical Medicine Research Unit (MORU) is funded by the Wellcome Trust [grant number 106698/Z14/Z]. CL is funded by a Wellcome Trust Research Training Fellowship [grant number 206736/Z/17/Z]. MY is supported by a Singapore National Medical Research Council Research Fellowship [grant number NMRC/Fellowship/0051/2017]. BSC is funded by the UK Medical Research Council and Department for International Development [grant number MR/K006924/1]. DL is funded by a Wellcome Trust Intermediate Training Fellowship [grant number 101103]. The funder has no role in the design and conduct of the study, data collection, or in the analysis and interpretation of the data.

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.

Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes, especially in non-inferiority trials. We performed simulations of a non-inferiority trial with a time-fixed treatment and a binary endpoint in order to: i) explore the impact of various patterns of non-adherence and analysis methods on treatment effect estimates; ii) quantify the probability of claiming non-inferiority when the experimental treatment effect is actually inferior; and iii) evaluate alternative methods such as inverse probability weighting and instrumental variable estimation. We found that the probability of concluding non-inferiority when the experimental treatment is actually inferior depends on whether non-adherence is due to confounding or non-confounding factors, and the actual treatments received by the non-adherent participants. With non-adherence, intention-to-treat analysis has a higher tendency to conclude non-inferiority when the experimental treatment is actually inferior under most patterns of non-adherence. This probability of concluding non-inferiority can be increased to as high as 0.1 from 0.025 when the adherence is relatively high at 90%. The direction of bias for the per-protocol analysis depends on the directions of influence the confounders have on adherence and probability of outcome. The inverse probability weighting approach can reduce bias but will only eliminate it if all confounders can be measured without error and are appropriately adjusted for. Instrumental variable estimation overcomes this limitation and gives unbiased estimates even when confounders are not known, but typically requires large sample sizes to achieve acceptable power. Investigators need to consider patterns of non-adherence and potential confounders in trial designs. Adjusted analysis of the per-protocol population with sensitivity analyses on confounders and other approaches, such as instrumental variable estimation, should be considered when non-compliance is anticipated. We provide an online power calculator allowing for various patterns of non-adherence using the above methods.

Antimicrobial resistance (AMR) is a global health threat, especially in low-/middle-income countries (LMICs), where there is limited surveillance to inform empiric antibiotic treatment guidelines. Enterobacterales are amongst the most important causes of drug-resistant bacterial infections. We developed a novel AMR surveillance approach for Enterobacterales by profiling pooled human faecal metagenomes from three sites (n=563 individuals; Cambodia, Kenya, UK) to derive a taxonomy-adjusted AMR metric (“resistance potential”) which could be used to predict the aggregate percentage of resistant invasive Enterobacterales infections within each setting. Samples were sequenced (Illumina); taxonomic and resistance gene profiling was performed using ResPipe. Data on organisms causing bacteraemia and meningitis and antibiotic susceptibility test results from 2010-2017 were collated for each site. Bayesian generalised linear models with a binomial likelihood were fitted to determine the capacity of the resistance potential to predict AMR in Enterobacterales infections in each setting. The most informative model accurately predicted the numbers of resistant infections in the target populations for 14/14 of antibiotics in the UK, 12/12 in Kenya, and 9/12 in Cambodia. Intermittent metagenomics of pooled human samples could represent a powerful pragmatic and economical approach for determining and monitoring AMR in clinical infections, especially in resource-limited settings.

Background: Multi-modal interventions are effective in increasing hand hygiene (HH) compliance among healthcare workers, but it is not known whether such interventions are cost-effective outside high-income countries. Aim: To evaluate the cost-effectiveness of multi-modal hospital interventions to improve HH compliance in a middle-income country. Methods: Using a conservative approach, a model was developed to determine whether reductions in meticillin-resistant Staphylococcus aureus bloodstream infections (MRSA-BSIs) alone would make HH interventions cost-effective in intensive care units (ICUs). Transmission dynamic and decision analytic models were combined to determine the expected impact of HH interventions on MRSA-BSI incidence and evaluate their cost-effectiveness. A series of sensitivity analyses and hypothetical scenarios making different assumptions about transmissibility were explored to generalize the findings. Findings: Interventions increasing HH compliance from a 10% baseline to ≥20% are likely to be cost-effective solely through reduced MRSA-BSI. Increasing compliance from 10% to 40% was estimated to cost US$2515 per 10,000 bed-days with 3.8 quality-adjusted life-years (QALYs) gained in a paediatric ICU (PICU) and US$1743 per 10,000 bed-days with 3.7 QALYs gained in an adult ICU. If baseline compliance is not >20%, the intervention is always cost-effective even with only a 10% compliance improvement. Conclusion: Effective multi-modal HH interventions are likely to be cost-effective due to preventing MRSA-BSI alone in ICU settings in middle-income countries where baseline compliance is typically low. Where compliance is higher, the cost-effectiveness of interventions to improve it further will depend on the impact on hospital-acquired infections other than MRSA-BSI.

To determine trends, mortality rates, and costs of antimicrobial resistance in invasive bacterial infections in hospitalized children, we analyzed data from Angkor Hospital for Children, Siem Reap, Cambodia, for 2007-2016. A total of 39,050 cultures yielded 1,341 target pathogens. Resistance rates were high; 82% each of Escherichia coli and Klebsiella pneumoniae isolates were multidrug resistant. Hospital-acquired isolates were more often resistant than community-acquired isolates; resistance trends over time were heterogeneous. K. pneumoniae isolates from neonates were more likely than those from nonneonates to be resistant to ampicillin-gentamicin and third-generation cephalosporins. In patients with community-acquired gram-negative bacteremia, third-generation cephalosporin resistance was associated with increased mortality rates, increased intensive care unit admissions, and 2.26-fold increased healthcare costs among survivors. High antimicrobial resistance in this setting is a threat to human life and the economy. In similar low-resource settings, our methods could be reproduced as a robust surveillance model for antimicrobial resistance.

Laboratory-based respiratory pathogen (RP) results are often available too late to influence clinical decisions such as hospitalisation or antibiotic treatment due to time delay in transport of specimens and testing schedules. Ward-based i.e. point of care (POC) testing providing rapid results may alter the clinical management pathway. FilmArray® RP polymerase chain reaction (PCR) systems were placed in three in-patient and out-patient medical areas. Patients presenting with influenza-like illness /upper respiratory tract infection +/− lower RTI were recruited between January–July 2015. FilmArray® POC testing occurred on even days of the month (intervention) or routine, laboratory-based RP PCR testing +/− atypical serology on odd days (control). The primary outcome was length of hospital stay. The secondary outcomes were impact on the use of antimicrobials, readmissions, all-cause mortality, length of ward stay and turn-around time (TAT) (time to result from admission). Of 606 eligible patients, 545 (89.9%) were included; 211 in the control arm and 334 in the intervention arm. 20% of control arm patients and 24% of intervention arm patients had an RP detected. POC testing was not associated with the primary outcome measure, length of stay, but reduced the TAT from 39.5 h to 19.0 h, p

To determine the duration of carbapenemase-producing Enterobacteriaceae (CPE) carriage, we studied 21 CPE carriers for »1 year. Mean carriage duration was 86 days; probability of decolonization in 1 year was 98.5%, suggesting that CPE-carriers' status can be reviewed yearly. Prolonged carriage was associated with use of antimicrobial drugs. Ministry of Education (MOE) National Medical Research Council (NMRC) Published version The study is primarily supported by the Singapore Biomedical Research Council–Economic Development Board Industry Alignment Fund (grant no. IAF311018). M.Y. is supported by the Singapore National Medical Research Council (NMRC) Research Fellowship (grant no. NMRC/Fellowship/0051/2017). B.S.C. is supported by the UK Medical Research Council/Department for International Development (grant no. MR/K006924/1). P.M. is supported by the Joint Programming Initiativeon Antimicrobial Resistance (MODERN) (grant no. C-17-0014). A.H. is supported by Wellcome Trust (grant no. 212630/Z/18/Z). The Mahidol-Oxford Tropical Medicine Research Unit is part of the Wellcome-Trust Major Overseas Programme in Southeast Asia (grant no. 106698/Z/14/Z).Additional grant support was provided by the NMRC Clinician-Scientist Individual Research Grant (grant no. NMRC/CIRG/1463/2016 and NMRC/CIRG/1467/2017); the Singapore Ministry of Education Academic Research Fund Tier 2 grant, New Delhi Metallo-Beta-Lactamase, a global multi-centre, whole-genome study (grant no. MOE2015-T2-2-096); an NMRC collaborative grant, Collaborative Solutions Targeting Antimicrobial Resistance Threats in Health Systems (grant no. NMRC CGAug16C005); and an NMRC Clinician Scientist Award (grant no. NMRC/CSA-INV/0002/2016).

Multidrug-resistant Klebsiella pneumoniae is an increasing cause of infant mortality in developing countries. We aimed to develop a quantitative understanding of the drivers of this epidemic by estimating the effects of antibiotics on nosocomial transmission risk, comparing competing hypotheses about mechanisms of spread, and quantifying the impact of potential interventions. Using a sequence of dynamic models, we analysed data from a one-year prospective carriage study in a Cambodian neonatal intensive care unit with hyperendemic third-generation cephalosporin-resistant K. pneumoniae. All widely-used antibiotics except imipenem were associated with an increased daily acquisition risk, with an odds ratio for the most common combination (ampicillin + gentamicin) of 1.96 (95% CrI 1.18, 3.36). Models incorporating genomic data found that colonisation pressure was associated with a higher transmission risk, indicated sequence type heterogeneity in transmissibility, and showed that within-ward transmission was insufficient to maintain endemicity. Simulations indicated that increasing the nurse-patient ratio could be an effective intervention.

Background For many infectious conditions, the optimal antibiotic course length remains unclear. The estimation of course length must consider the important trade-off between maximising short- and long-term efficacy and minimising antibiotic resistance and toxicity. Main body Evidence on optimal treatment durations should come from randomised controlled trials. However, most antibiotic randomised controlled trials compare two arbitrarily chosen durations. We argue that alternative trial designs, which allow allocation of patients to multiple different treatment durations, are needed to better identify optimal antibiotic durations. There are important considerations when deciding which design is most useful in identifying optimal treatment durations, including the ability to model the duration–response relationship (or duration–response ‘curve’), the risk of allocation concealment bias, statistical efficiency, the possibility to rapidly drop arms that are clearly inferior, and the possibility of modelling the trade-off between multiple competing outcomes. Conclusion Multi-arm designs modelling duration–response curves with the possibility to drop inferior arms during the trial could provide more information about the optimal duration of antibiotic therapies than traditional head-to-head comparisons of limited numbers of durations, while minimising the probability of assigning trial participants to an ineffective treatment regimen.

Antibiotic exposure can perturb the human gut microbiome and cause changes in the within-host abundance of the genetic determinants of drug-resistance in bacteria. Such within-host dynamics are expected to play an important role in mediating the relationship between antibiotic use and persistence of drug-resistance within a host and its prevalence within a population. Developing a quantitative representation of these within-host dynamics is an important step towards a detailed mechanistic understanding of the population-level processes by which antibiotics select for resistance. Here we study extended-spectrum beta-lactamase (ESBL) producing organisms of the Enterobacteriaceae bacterial family. These have been identified as a global public health priority and are resistant to most first-line antibiotics for treatment of Enterobacteriaceae infections.We analyse data from 833 rectal swabs from a prospective longitudinal study in three European countries including 133 ESBL-positive hospitalised patients. Quantitative polymerase chain reaction was used to quantify the abundance of the CTX-M gene family – the most wide-spread ESBL gene family – and the 16S rRNA gene as a proxy for bacterial load. We find strong dynamic heterogeneity in CTX-M abundance that is largely explained by the variable nature of the swab sampling. Using information on time-varying antibiotic treatments, we develop a dynamic Bayesian model to decompose the serial data into observational variation and ecological signal and to quantify the potentially causal antibiotic effects.We find an association of treatment with cefuroxime or ceftriaxone with increased CTX-M abundance (approximately 21% and 10% daily increase, respectively), while treatment with meropenem or piperacillin-tazobactam is associated with decreased CTX-M (approximately 8% daily decrease for both). Despite a potential risk for indirect selection, oral ciprofloxacin is also associated with decreasing CTX-M (approximately 8% decrease per day). Using our dynamic model to make forward stochastic simulations of CTX-M dynamics, we generate testable predictions about antibiotic impacts on duration of carriage. We find that a typical course of cefuroxime or ceftriaxone is expected to more than double a patient’s carriage duration of CTX-M. A typical course of piperacillin-tazobactam or of meropenem – both options to treat hospital acquired infections (HAI) like pneumonia – would reduce CTX-M carriage time relative to ceftriaxone plus amikacin (also an option to treat HAIs) by about 70%. While most antibiotics showed little association with changes in total bacterial abundance, meropenem and piperacillin-tazobactam were associated with decrease in 16S rRNA abundance (3% and 4% daily decrease, respectively).Our study quantifies antibiotic impacts on within-host resistance abundance and resistance carriage, and informs our understanding of how changes in patterns of antibiotic use will affect the prevalence of resistance. This work also provides an analytical framework that can be used more generally to quantify the antibiotic treatment effects on within-host dynamics of determinants of antibiotic resistance using clinical data.

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.

Importance: The effects of chlorhexidine (CHX) mouthwash, selective oropharyngeal decontamination (SOD), and selective digestive tract decontamination (SDD) on patient outcomes in ICUs with moderate to high levels of antibiotic resistance are unknown. Objective: To determine associations between CHX 2%, SOD, and SDD and the occurrence of ICU-acquired bloodstream infections with multidrug-resistant gram-negative bacteria (MDRGNB) and 28-day mortality in ICUs with moderate to high levels of antibiotic resistance. Design, Setting, and Participants: Randomized trial conducted from December 1, 2013, to May 31, 2017, in 13 European ICUs where at least 5% of bloodstream infections are caused by extended-spectrum β-lactamase–producing Enterobacteriaceae. Patients with anticipated mechanical ventilation of more than 24 hours were eligible. The final date of follow-up was September 20, 2017. Interventions: Standard care was daily CHX 2% body washings and a hand hygiene improvement program. Following a baseline period from 6 to 14 months, each ICU was assigned in random order to 3 separate 6-month intervention periods with either CHX 2% mouthwash, SOD (mouthpaste with colistin, tobramycin, and nystatin), or SDD (the same mouthpaste and gastrointestinal suspension with the same antibiotics), all applied 4 times daily. Main Outcomes and Measures: The occurrence of ICU-acquired bloodstream infection with MDRGNB (primary outcome) and 28-day mortality (secondary outcome) during each intervention period compared with the baseline period. Results: A total of 8665 patients (median age, 64.1 years; 5561 men [64.2%]) were included in the study (2251, 2108, 2224, and 2082 in the baseline, CHX, SOD, and SDD periods, respectively). ICU-acquired bloodstream infection with MDRGNB occurred among 144 patients (154 episodes) in 2.1%, 1.8%, 1.5%, and 1.2% of included patients during the baseline, CHX, SOD, and SDD periods, respectively. Absolute risk reductions were 0.3% (95% CI, −0.6% to 1.1%), 0.6% (95% CI, −0.2% to 1.4%), and 0.8% (95% CI, 0.1% to 1.6%) for CHX, SOD, and SDD, respectively, compared with baseline. Adjusted hazard ratios were 1.13 (95% CI, 0.68-1.88), 0.89 (95% CI, 0.55-1.45), and 0.70 (95% CI, 0.43-1.14) during the CHX, SOD, and SDD periods, respectively, vs baseline. Crude mortality risks on day 28 were 31.9%, 32.9%, 32.4%, and 34.1% during the baseline, CHX, SOD, and SDD periods, respectively. Adjusted odds ratios for 28-day mortality were 1.07 (95% CI, 0.86-1.32), 1.05 (95% CI, 0.85-1.29), and 1.03 (95% CI, 0.80-1.32) for CHX, SOD, and SDD, respectively, vs baseline. Conclusions and Relevance: Among patients receiving mechanical ventilation in ICUs with moderate to high antibiotic resistance prevalence, use of CHX mouthwash, SOD, or SDD was not associated with reductions in ICU-acquired bloodstream infections caused by MDRGNB compared with standard care. Trial Registration: ClinicalTrials.gov NCT02208154

Background Hepatitis E Virus (HEV) is the leading cause of acute viral hepatitis globally. Symptomatic infection is associated with case fatality rates of ~20% in pregnant women and it is estimated to account for ~10,000 annual pregnancy-related deaths in southern Asia alone. Recently, large and well-documented outbreaks with high mortality have occurred in displaced population camps in Sudan, Uganda and South Sudan. However, the epidemiology of HEV is poorly defined, and the value of different immunisation strategies in outbreak settings uncertain. We aimed to estimate the critical epidemiological parameters for HEV and to evaluate the potential impact of both reactive vaccination (initiated in response to an epidemic) and pre-emptive vaccination. Methods We analysed data from one of the world's largest recorded HEV epidemics, which occurred in internally-displaced persons camps in Uganda (2007-2009), using transmission dynamic models to estimate epidemiological parameters and assess the potential impact of reactive and pre-emptive vaccination strategies. Results Under baseline assumptions we estimated the basic reproduction number of HEV in three separate camps to range from 3.7 (95% Credible Interval [CrI] 2.8, 5.1) to 8.5 (5.3, 11.4). Mean latent and infectious periods were estimated to be 34 (95% CrI 28, 39) and 40 (95% CrI 23, 71) days respectively. Assuming 90% vaccine coverage, reactive two-dose vaccination of those aged 16-65 years excluding pregnant women (for whom vaccine is not licensed), if initiated after 50 reported cases, led to mean camp-specific reductions in mortality of 10 to 29%. Pre-emptive vaccination with two doses reduced mortality by 35 to 65%. Both strategies were more effective if coverage was extended to groups for whom the vaccine is not currently licensed. For example, two dose pre-emptive vaccination, if extended to include pregnant women, led to mean reductions in mortality of 66 to 82%. Conclusions HEV has a high transmission potential in displaced population settings. Substantial reductions in mortality through vaccination are expected, even if used reactively. There is potential for greater impact if vaccine safety and effectiveness can be established in pregnant women.

Background– Antimicrobial resistance (AMR) poses a colossal threat to global health and incurs high economic costs to society. Economic evaluations of antimicrobials and interventions such as diagnostics and vaccines that affect their consumption rarely include the costs of AMR, resulting in sub-optimal policy recommendations. We estimate the economic cost of AMR per antibiotic consumed, stratified by drug class and national income level.Methods– The model is comprised of three components: correlation coefficients between human antibiotic consumption and subsequent resistance; the economic costs of AMR for five key pathogens; and consumption data for antibiotic classes driving resistance in these organisms. These were used to calculate the economic cost of AMR per antibiotic consumed for different drug classes, using data from Thailand and the United States (US) to represent low/middle and high-income countries.Results– The correlation coefficients between consumption of antibiotics that drive resistance in S. aureus, E. coli, K. pneumoniae, A. baumanii, and P. aeruginosa and resistance rates were 0.37, 0.27, 0.35, 0.45, and 0.52, respectively. The total economic cost of AMR due to resistance in these five pathogens was $0.5 billion and $2.8 billion in Thailand and the US, respectively. The cost of AMR associated with the consumption of one standard unit (SU) of antibiotics ranged from $0.1 for macrolides to $0.7 for quinolones, cephalosporins and broad-spectrum penicillins in the Thai context. In the US context, the cost of AMR per SU of antibiotic consumed ranged from $0.1 for carbapenems to $0.6 for quinolones, cephalosporins and broad spectrum penicillins.Conclusion– The economic costs of AMR per antibiotic consumed were considerable, often exceeding their purchase cost. Differences between Thailand and the US were apparent, corresponding with variation in the overall burden of AMR and relative prevalence of different pathogens. Notwithstanding their limitations, use of these estimates in economic evaluations can make better-informed policy recommendations regarding interventions that affect antimicrobial consumption and those aimed specifically at reducing the burden of AMR.

BackgroundEarly and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. The increasing prevalence of antimicrobial resistance risks eroding the benefits of such empiric therapy. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collected patient data, it would be possible to obtain actionable and patient-specific predictions for antibiotic-susceptibility. If sufficient discriminatory power can be achieved, such predictions could lead to substantial improvements in the chances of choosing an appropriate antibiotic for empiric therapy, while minimizing the risk of increased selection for resistance due to use of antibiotics usually held in reserve.Methods and FindingsWe analyzed blood culture data collected from a 100-bed children’s hospital in North-West Cambodia between February 2013 and January 2016. Clinical, demographic and living condition information for each child was captured with 35 independent variables. Using these variables, we used a suite of machine learning algorithms to predict Gram stains and whether bacterial pathogens could be treated with standard empiric antibiotic therapies: i) ampicillin and gentamicin; ii) ceftriaxone; iii) at least one of the above.243 cases of bloodstream infection were available for analysis. We used 195 (80%) to train the algorithms, and 48 (20%) for evaluation. We found that the random forest method had the best predictive performance overall as assessed by the area under the receiver operating characteristic curve (AUC), though support vector machine with radial kernel had similar performance for predicting Gram stain and ceftriaxone susceptibility. Predictive performance of logistic regression, simple and boosted decision trees and k-nearest neighbors were poor in comparison. The random forest method gave an AUC of 0.91 (95%CI 0.81-1.00) for predicting susceptibility to ceftriaxone, 0.75 (0.60-0.90) for susceptibility to ampicillin and gentamicin, 0.76 (0.59-0.93) for susceptibility to neither, and 0.69 (0.53-0.85) for Gram stain result. The most important variables for predicting susceptibility were time from admission to blood culture, patient age, hospital versus community-acquired infection, and age-adjusted weight score.ConclusionsApplying machine learning algorithms to patient data that are readily available even in resource-limited hospital settings can provide highly informative predictions on susceptibilities of pathogens to guide appropriate empiric antibiotic therapy. Used as a decision support tool, such approaches have the potential to lead to better targeting of empiric therapy, improve patient outcomes and reduce the burden of antimicrobial resistance.Author summaryWhy was this study done?Early and appropriate antibiotic treatment of patients with life-threatening bacterial infections is thought to reduce the risk of mortality.In hospitals that have a microbiology laboratory, it takes 3-4 days to get results which indicate which antibiotics are likely to be effective; before this information is available antibiotics have to be prescribed empirically i.e. without knowledge of the causative organism.Increasing resistance to antibiotics amongst bacteria makes finding an appropriate antibiotic to use empirically difficult; this problem is particularly severe for children in developing country settings.If we could predict which antibiotics were likely to be effective at the time of starting antibiotic therapy, we might be able to improve patient outcomes and reduce resistance.What Did the Researchers Do and Find?We evaluated the ability of a number of different algorithms (i.e. sets of step-by-step instructions) to predict susceptibility to commonly-used antibiotics using routinely available patient data from a children’s hospital in Cambodia.We found that an algorithm called random forests enabled surprisingly accurate predictions, particularly for predicting whether the infection was likely to be treatable with ceftriaxone, the most commonly used empiric antibiotic at the study hospital.Using this approach it would be possible to correctly predict when a different antibiotic would be needed for empiric treatment over 80% of the time, while recommending a different antibiotic when ceftriaxone would suffice less than 20% of the time.What Do These Findings Mean?Using readily available patient information, sophisticated algorithms can enable good predictions of whether antibiotics are likely to be effective several days before laboratory tests are available.Algorithms would need to be trained with local hospital data, but our study shows that even with relatively limited data from a small hospital, good predictions can be obtained.Used as part of a decision support system such algorithms could help choose appropriate antibiotics for empiric therapy; this would be expected to translate into better patient outcomes and may help to reduce resistance.Such as a decision support system would have very low costs and be easy to implement in low- and middle-income countries.

Background Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods. Discussion Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects. Conclusion Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials.

Background Klebsiella pneumoniae is an important and increasing cause of life-threatening disease in hospitalized neonates. Third generation cephalosporin resistance (3GC-R) is frequently a marker of multi-drug resistance, and can complicate management of infections. 3GC-R K. pneumoniae is hyper-endemic in many developing country settings, but its epidemiology is poorly understood and prospective studies of endemic transmission are lacking. We aimed to determine the transmission dynamics of 3GC-R K. pneumoniae in a newly opened neonatal unit (NU) in Cambodia and to address the following questions: what is the diversity of 3GC-R K. pneumoniae both within- and between-host; to what extent is high carriage prevalence driven by ward-based transmission; and to what extent can environmental contamination explain patterns of patient acquisition. Methods We performed a prospective longitudinal study between September and November 2013. Rectal swabs from consented patients were collected upon NU admission and every 3 days thereafter. Morphologically different colonies from swabs growing cefpodoxime-resistant K. pneumoniae were selected for whole-genome sequencing (WGS). Results One hundred and fifty-eight samples from 37 patients and 7 environmental sites were collected. 32/37 (86%) patients screened positive for 3GC-R K. pneumoniae and 93 colonies from 119 swabs were successfully sequenced. Isolates were resistant to a median of six (range 3–9) antimicrobials. WGS revealed high diversity; pairwise distances between isolates from the same patient were either 0–1 SNV or >1,000 SNVs; 19/32 colonized patients harbored K. pneumoniae colonies differing by >1000 SNVs. Diverse lineages accounted for 18 probable importations to the NU and nine probable transmission clusters involving 19/37 (51%) of screened patients. Median cluster size was five patients (range 3–9). Seven out of 46 environmental swabs (15%) were positive for 3GC-R K. pneumoniae. Environmental sources were plausible sources for acquisitions in 2/9 transmission clusters, though in both cases other patients were also plausible sources. Conclusion The epidemiology of 3GC-R K. pneumoniae was characterized by multiple introductions, high within- and between host diversity and a dense network of crossinfection, with half of screened neonates part of a transmission cluster. We found no evidence to suggest that environmental contamination was playing a dominant role in transmission.

Nosocomial pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) are the cause of significant morbidity and mortality among hospital patients. It is important to be able to assess the efficacy of control measures using data on patient outcomes. In this paper, we describe methods for analysing such data using patient-level stochastic models which seek to describe the underlying unobserved process of transmission. The methods are applied to detailed longitudinal patient-level data on vancomycin-resistant Enterococci from a study in a US hospital with eight intensive care units (ICUs). The data comprise admission and discharge dates, dates and results of screening tests, and dates during which precautionary measures were in place for each patient during the study period. Results include estimates of the efficacy of the control measures, the proportion of unobserved patients colonized with vancomycin-resistant Enterococci, and the proportion of patients colonized on admission.

BackgroundKlebsiella pneumoniae is an important and increasing cause of life-threatening disease in hospitalised neonates. Third generation cephalosporin resistance (3GC-R) is frequently a marker of multi-drug resistance, and can complicate management of infections. 3GC-R K. pneumoniae is hyper-endemic in many developing country settings, but its epidemiology is poorly understood and prospective studies of endemic transmission are lacking. We aimed to determine the transmission dynamics of 3GC-R K. pneumoniae in a newly opened neonatal unit (NU) in Cambodia.MethodsWe performed a prospective longitudinal study between September and November 2013. Rectal swabs from 37 consented patients were collected upon NU admission and every three days thereafter. Morphologically different colonies from swabs growing cefpodoxime-resistant K. pneumoniae were selected for whole-genome sequencing (WGS).Results32/37 (86%) patients screened positive for 3GC-R K. pneumoniae and 93 colonies from 119 swabs were sequenced. Isolates were resistant to a median of six (range 3-9) antimicrobials. WGS revealed high diversity; pairwise distances between isolates from the same patient were either 0-1 SNV or >1,000 SNVs; 19/32 colonized patients harboured K. pneumoniae colonies differing by >1000 SNVs. Diverse lineages accounted for 18 probable importations to the NU and nine probable transmission clusters involving 19/37 (51%) of screened patients. Median cluster size was 5 patients (range 3-9).ConclusionsThe epidemiology of 3GC-R K. pneumoniae was characterised by multiple introductions and a dense network of cross-infection, with half of screened neonates part of a transmission cluster. Efforts to reduce the 3GC-R K. pneumoniae disease burden should consider targeting both processes.

Dingle and colleagues1 provide compelling evidence that the substantial decline in Clostridium difficile infection in England since 2006 resulted from the disappearance of fluoroquinolone-resistant isolates. However, attribution of this decline to specific control measures rests on a false premise. The authors state that “if decreases in Clostridium difficile infection were driven by improvements in hospital infection control, then transmitted (secondary) cases should decline regardless of susceptibility”.1 In fact, non-specific hospital infection control measures such as hand hygiene, environmental cleaning, and patient isolation will have a disproportionate effect on resistant strains, provided these strains spread preferentially in the hospital setting.

Outbreaks of bacterial species occur for the majority of bacteria commonly identified in the intensive care unit. This study provides evidence for frequent temporal clustering of resistance outbreaks consistent with interspecies transmission of resistance elements., Background There are frequent reports of intensive care unit (ICU) outbreaks due to transmission of particular antibiotic-resistant bacteria. Less is known about the burden of outbreaks of resistance due to horizontal transfer of mobile genetic elements between species. Moreover, the potential of existing statistical software as a preliminary means for detecting such events has never been assessed. This study uses a software package to determine the burden of species and resistance outbreaks in 2 adjacent ICUs and to look for evidence of clustering of resistance outbreaks consistent with interspecies transmission of resistance elements. Methods A retrospective analysis of data from 2 adjacent 15-bed adult ICUs between 2002 and 2009 was undertaken. Detection of bacterial species-groups and resistance outbreaks was conducted using SaTScan and WHONet-SaTScan software. Resampling and permutation methods were applied to investigate temporal clustering of outbreaks. Results Outbreaks occurred for 69% of bacterial species-groups (18/26), and resistance outbreaks were detected against 63% of antibiotics (10/16). Resistance outbreaks against 7 of 10 antibiotics were observed in multiple species-groups simultaneously and there was evidence of inter–species-group dependence for 4 of 7 antibiotics; background temporal changes in resistance did not explain the temporal aggregation of outbreaks in 3 of 7 antibiotics. Conclusions Species outbreaks occurred for the majority of bacteria commonly identified in the ICU. There was evidence for frequent temporal clustering of resistance outbreaks consistent with interspecies transmission of resistance elements. Wider application of outbreak detection software combined with targeted sequencing of bacterial genomes is needed to understand the contribution of interspecies gene transfer to resistance emergence.

OBJECTIVE: To assess the impact on ICU performance of a modular training program in three resource-limited general adult ICUs in India, Bangladesh, and Nepal. METHOD: A modular ICU training programme was evaluated using performance indicators from June 2009 to June 2012 using an interrupted time series design with an 8 to 15 month pre-intervention and 18 to 24 month post-intervention period. ICU physicians and nurses trained in Europe and the USA provided training for ICU doctors and nurses. The training program consisted of six modules on basic intensive care practices of 2-3 weeks each over 20 months. The performance indicators consisting of ICU mortality, time to ICU discharge, rate at which patients were discharged alive from the ICU, discontinuation of mechanical ventilation or vasoactive drugs and duration of antibiotic use were extracted. Stepwise changes and changes in trends associated with the intervention were analysed. RESULTS: Pre-Training ICU mortality in Rourkela (India), and Patan (Nepal) Chittagong (Bangladesh), was 28%, 41% and 62%, respectively, compared to 30%, 18% and 51% post-intervention. The intervention was associated with a stepwise reduction in cumulative incidence of in-ICU mortality in Chittagong (adjusted subdistribution hazard ratio [aSHR] (95% CI): 0.62 (0.40, 0.97), p = 0.03) and Patan (aSHR 0.16 (0.06, 0.41), p

Summary Background Meticillin-resistant Staphylococcus aureus (MRSA) is frequently endemic in healthcare settings and may be transmitted by person-to-person spread. Asymptomatic MRSA carriers are potential, unsuspected sources for transmission and some of them may be identified by admission screening. Aim To assess whether rapid point-of-care screening (POCS) for MRSA at hospital admission may be associated with a reduction in MRSA acquisition rates when compared with slower laboratory-based methods. Methods A cluster-randomized cross-over trial was conducted in four admission wards of an acute London tertiary care hospital. Polymerase chain reaction-based POCS screening was compared with conventional culture screening. Patients were screened on ward admission and discharge, and the MRSA acquisition rate on the admission wards was calculated as the primary outcome measure. Results In all, 10,017 patients were included; 4978 in the control arm, 5039 in the POCS arm. The MRSA carriage rate on admission was 1.7%. POCS reduced the median reporting time from 40.4 to 3.7 h (P

Infection control for hospital pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) often takes the form of a package of interventions, including the use of patient isolation and decolonization treatment. Such interventions, though widely used, have generated controversy because of their significant resource implications and the lack of robust evidence with regard to their effectiveness at reducing transmission. The aim of this study was to estimate the effectiveness of isolation and decolonization measures in reducing MRSA transmission in hospital general wards. Prospectively collected MRSA surveillance data from 10 general wards at Guy's and St. Thomas' hospitals, London, United Kingdom, in 2006-2007 were used, comprising 14,035 patient episodes. Data were analyzed with a Markov chain Monte Carlo algorithm to model transmission dynamics. The combined effect of isolation and decolonization was estimated to reduce transmission by 64% (95% confidence interval: 37, 79). Undetected MRSA-positive patients were estimated to be the source of 75% (95% confidence interval: 67, 86) of total transmission events. Isolation measures combined with decolonization treatment were strongly associated with a reduction in MRSA transmission in hospital general wards. These findings provide support for active methods of MRSA control, but further research is needed to determine the relative importance of isolation and decolonization in preventing transmission.

Influenza epidemiology differs substantially in tropical and temperate zones, but estimates of seasonal influenza mortality in developing countries in the tropics are lacking. We aimed to quantify mortality due to seasonal influenza in Thailand, a tropical middle-income country. Time series of polymerase chain reaction-confirmed influenza infections between 2005 and 2009 were constructed from a sentinel surveillance network. These were combined with influenza-like illness data to derive measures of influenza activity and relationships to mortality by using a Bayesian regression framework. We estimated 6.1 (95% credible interval: 0.5, 12.4) annual deaths per 100,000 population attributable to influenza A and B, predominantly in those aged ≥60 years, with the largest contribution from influenza A(H1N1) in 3 out of 4 years. For A(H3N2), the relationship between influenza activity and mortality varied over time. Influenza was associated with increases in deaths classified as resulting from respiratory disease (posterior probability of positive association, 99.8%), cancer (98.6%), renal disease (98.0%), and liver disease (99.2%). No association with circulatory disease mortality was found. Seasonal influenza infections are associated with substantial mortality in Thailand, but evidence for the strong relationship between influenza activity and circulatory disease mortality reported in temperate countries is lacking.

Supplemental Digital Content is available in the text., Background: Antimicrobial-resistant Gram-negative infections are a significant cause of mortality in young infants. We aimed to determine characteristics of, and risk factors for, colonization and invasive infection caused by 3rd generation cephalosporin (3GC) or carbapenem-resistant organisms in outborn infants admitted to a neonatal unit (NU) in Cambodia. Methods: During the first year of operation, patients admitted to the Angkor Hospital for Children NU, Siem Reap, Cambodia, underwent rectal swabbing on admission and twice weekly until discharge. Swabs were taken also from 7 environmental sites. Swabs were cultured to identify 3GC or carbapenem-resistant Acinetobacter sp., Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. Results: The study included 333 infants with a median age at NU admission of 10 days (range, 0–43). Colonization by ≥1 3GC-resistant organism was detected in 85.9% (286/333). Admission swabs were collected in 289 infants: 61.9% were colonized by a 3GC-resistant organism at the time of admission, and a further 23.2% were colonized during hospitalization, at a median of 4 days [95% confidence interval: 3–5]. Probiotic treatment (hazard ratio: 0.58; 95% confidence interval: 0.35–0.98) was associated with delayed colonization. Colonization by a carbapenem-resistant organism occurred in 25 (7.5%) infants. Six infants had NU-associated K. pneumoniae bacteremia; phenotypically identical colonizing strains were found in 3 infants. Environmental colonization occurred early. Conclusions: Colonization by antimicrobial-resistant Gram-negative organisms occurred early in hospitalized Cambodian infants and was associated with subsequent invasive infection. Trials of potential interventions such as probiotics are needed.

We hypothesized that the gold standard for diagnosing leptospirosis is imperfect. We used Bayesian latent class models and random-effects meta-analysis to test this hypothesis and to determine the true accuracy of a range of alternative tests for leptospirosis diagnosis., Background. We observed that some patients with clinical leptospirosis supported by positive results of rapid tests were negative for leptospirosis on the basis of our diagnostic gold standard, which involves isolation of Leptospira species from blood culture and/or a positive result of a microscopic agglutination test (MAT). We hypothesized that our reference standard was imperfect and used statistical modeling to investigate this hypothesis. Methods. Data for 1652 patients with suspected leptospirosis recruited during three observational studies and one randomized control trial that described the application of culture, MAT, immunofluorescence assay (IFA), lateral flow (LF) and/or PCR targeting the 16S rRNA gene were reevaluated using Bayesian latent class models and random-effects meta-analysis. Results. The estimated sensitivities of culture alone, MAT alone, and culture plus MAT (for which the result was considered positive if one or both tests had a positive result) were 10.5% (95% credible interval [CrI], 2.7%–27.5%), 49.8% (95% CrI, 37.6%–60.8%), and 55.5% (95% CrI, 42.9%–67.7%), respectively. These low sensitivities were present across all 4 studies. The estimated specificity of MAT alone (and of culture plus MAT) was 98.8% (95% CrI, 92.8%–100.0%). The estimated sensitivities and specificities of PCR (52.7% [95% CrI, 45.2%–60.6%] and 97.2% [95% CrI, 92.0%–99.8%], respectively), lateral flow test (85.6% [95% CrI, 77.5%–93.2%] and 96.2% [95% CrI, 87.7%–99.8%], respectively), and immunofluorescence assay (45.5% [95% CrI, 33.3%–60.9%] and 96.8% [95% CrI, 92.8%–99.8%], respectively) were considerably different from estimates in which culture plus MAT was considered a perfect gold standard test. Conclusions. Our findings show that culture plus MAT is an imperfect gold standard against which to compare alterative tests for the diagnosis of leptospirosis. Rapid point-of-care tests for this infection would bring an important improvement in patient care, but their future evaluation will require careful consideration of the reference test(s) used and the inclusion of appropriate statistical models.

Norovirus is the most common cause of outbreaks of acute gastroenteritis in National Health Service hospitals in the United Kingdom. Wards (units) are often closed to new admissions to stop the spread of the virus, but there is limited evidence describing the cost-effectiveness of ward closure. : An economic analysis based on the results from a large, prospective, active-surveillance study of gastroenteritis outbreaks in hospitals and from an epidemic simulation study compared alternative ward closure options evaluated at different time points since first infection, assuming different efficacies of ward closure. : A total of 232 gastroenteritis outbreaks occurring in 14 hospitals over a 1-year period were analyzed. The risk of a new outbreak in a hospital is significantly associated with the number of admission, general medical, and long-stay wards that are concurrently affected but is less affected by the level of community transmission. Ward closure leads to higher costs but reduces the number of new outbreaks by 6%-56% and the number of clinical cases by 1%-55%, depending on the efficacy of the intervention. The incremental cost per outbreak averted varies from £10 000 ($14 000) to £306 000 ($428 000), and the cost per case averted varies from £500 ($700) to £61 000 ($85 000). The cost-effectiveness of ward closure decreases as the efficacy of the intervention increases, and the cost-effectiveness increases with the timing of the intervention. The efficacy of ward closure is critical from a cost-effectiveness perspective. : Ward closure may be cost-effective, particularly if targeted to high-throughput units.

Background Experimental treatments for Ebola virus disease (EVD) might reduce EVD mortality. There is uncertainty about the ability of different clinical trial designs to identify effective treatments, and about the feasibility of implementing individually randomised controlled trials during an Ebola epidemic. Methods and Findings A treatment evaluation programme for use in EVD was devised using a multi-stage approach (MSA) with two or three stages, including both non-randomised and randomised elements. The probabilities of rightly or wrongly recommending the experimental treatment, the required sample size, and the consequences for epidemic outcomes over 100 d under two epidemic scenarios were compared for the MSA, a sequential randomised controlled trial (SRCT) with up to 20 interim analyses, and, as a reference case, a conventional randomised controlled trial (RCT) without interim analyses. Assuming 50% 14-d survival in the population treated with the current standard of supportive care, all designs had similar probabilities of identifying effective treatments correctly, while the MSA was less likely to recommend treatments that were ineffective. The MSA led to a smaller number of cases receiving ineffective treatments and faster roll-out of highly effective treatments. For less effective treatments, the MSA had a high probability of including an RCT component, leading to a somewhat longer time to roll-out or rejection. Assuming 100 new EVD cases per day, the MSA led to between 6% and 15% greater reductions in epidemic mortality over the first 100 d for highly effective treatments compared to the SRCT. Both the MSA and SRCT led to substantially fewer deaths than a conventional RCT if the tested interventions were either highly effective or harmful. In the proposed MSA, the major threat to the validity of the results of the non-randomised components is that referral patterns, standard of care, or the virus itself may change during the study period in ways that affect mortality. Adverse events are also harder to quantify without a concurrent control group. Conclusions The MSA discards ineffective treatments quickly, while reliably providing evidence concerning effective treatments. The MSA is appropriate for the clinical evaluation of EVD treatments., Ben Cooper and colleagues model different trial designs, including a multi-stage approach that contains non-randomized and randomized elements., Editors' Summary Background The current outbreak of Ebola virus disease (EVD)—a frequently fatal disease that first appeared in human populations in 1976 in remote villages in central Africa—has infected more than 24,000 people and killed more than 10,000 people in Guinea, Sierra Leone, and Liberia since early 2014. Ebola virus is transmitted to people from wild animals and spreads in human populations through direct contact with the bodily fluids (including blood, saliva, and urine) or with the organs of infected people or through contact with bedding and other materials contaminated with bodily fluids. The symptoms of EVD which start 2–21 days after infection, include fever, headache, vomiting, diarrhea, and internal and external bleeding. Infected individuals are not infectious until they develop symptoms but remain infectious as long as their bodily fluids contain virus. There is no proven treatment or vaccine for EVD, but supportive care—given under strict isolation conditions to prevent the spread of the disease to other patients or to healthcare workers—improves survival. Why Was This Study Done? Potential treatments for EVD include several antiviral drugs and injections of antibodies against Ebola virus from patients who have survived EVD. Before such therapies can be used clinically, their safety and effectiveness need to be evaluated, but experts disagree about how to undertake this evaluation. Drugs for clinical use are usually evaluated by undertaking a series of clinical trials. A phase I trial establishes the safety of the treatment and how the human body copes with it by giving several healthy volunteers the drug. Next, a phase II trial provides early indications of the drug’s efficacy by giving a few patients the drug. Finally, a large-scale multi-arm phase III randomized controlled trial (RCT) confirms the drug’s efficacy by comparing outcomes in patients randomly chosen to receive the investigational drug or standard care. This evaluation process is very lengthy. Moreover, it is hard to ethically justify undertaking an RCT in which only some patients receive a potentially life-saving drug during an Ebola epidemic. Here, the researchers evaluate a multi-stage approach (MSA) to EVD drug evaluation that comprises a single-arm phase II study followed by one or two phase III trials, one of which may be a sequential RCT (SRCT), a type of RCT that allows for multiple interim analyses, each of which may lead to study termination. What Did the Researchers Do and Find? The researchers used analytic methods and computer simulations to compare EVD drug evaluation using the MSA, an SRCT, and a conventional RCT without interim analyses. Specifically, they estimated the probabilities of rightly or wrongly recommending the experimental treatment and the consequences for epidemic outcomes over 100 days for the three approaches. Assuming 50% survival at 14 days after symptom development in patients treated with supportive care only, all three trial designs were equally likely to identify effective treatments, but the MSA was less likely than the other designs to incorrectly recommend an ineffective treatment. Notably, the MSA led to fewer patients receiving ineffective treatments and faster roll-out of highly effective treatments. In an epidemic where 100 new cases occurred per day, for highly effective treatments, the MSA led to between 6% and 15% larger reductions in epidemic mortality over the first 100 days of the epidemic than the SRCT did. Finally, both the MSA and the SRCT led to fewer deaths than the conventional RCT if the tested interventions were either highly effective or harmful. What Do These Findings Mean? These findings suggest that for experimental treatments that offer either no clinically significant benefit or large reductions in mortality the MSA can provide useful information about drug effectiveness faster than the other approaches tested. Thus, the MSA has the potential to reduce patient harm and the time to roll-out of an effective treatment for EVD. Although alternative evaluation designs are possible, the researchers suggest that including a non-randomized design in phase II is the quickest way to triage potential treatments and to decide how to test them further. For treatments that show strong evidence of benefit, it might even be possible to recommend the treatment without undertaking an RCT, they suggest. Moreover, for treatments that show only modest benefit in phase II, it should be easier (and more ethical) to set up RCTs to test the treatment further. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001815. The World Health Organization (WHO) provides information about EVD, information about potential EVD therapies, and regular updates on the current EVD epidemic; a summary of the discussion of a WHO Ethics Working Group Meeting on the ethical issues related to study design for EVD drug trials is available; the WHO website also provides information about efforts to control Ebola in the field and personal stories from people who have survived EVD The UK National Health Service Choices website provides detailed information on EVD The US Centers for Disease Control and Prevention also provides information about EVD Wikipedia provides information about adaptive clinical trial design; a Lancet Global Health blog argues why adaptive trial designs should be used to evaluate drugs for the treatment of EVD

Background The indirect immunofluorescence assay (IFA) is considered a reference test for scrub typhus. Recently, the Scrub Typhus Infection Criteria (STIC; a combination of culture, PCR assays and IFA IgM) were proposed as a reference standard for evaluating alternative diagnostic tests. Here, we use Bayesian latent class models (LCMs) to estimate the true accuracy of each diagnostic test, and of STIC, for diagnosing scrub typhus. Methods/Principal Findings Data from 161 patients with undifferentiated fever were re-evaluated using Bayesian LCMs. Every patient was evaluated for the presence of an eschar, and tested with blood culture for Orientia tsutsugamushi, three different PCR assays, IFA IgM, and the Panbio IgM immunochromatographic test (ICT). True sensitivity and specificity of culture (24.4% and 100%), 56kDa PCR assay (56.8% and 98.4%), 47kDa PCR assay (63.2% and 96.1%), groEL PCR assay (71.4% and 93.0%), IFA IgM (70.0% and 83.8%), PanBio IgM ICT (72.8% and 96.8%), presence of eschar (42.7% and 98.9%) and STIC (90.5% and 82.5%) estimated by Bayesian LCM were considerably different from those obtained when using STIC as a reference standard. The IgM ICT had comparable sensitivity and significantly higher specificity compared to IFA (p=0.34 and p

Background: Large reductions in the incidence of antibiotic-resistant strains of Staphylococcus aureus and Clostridium difficile have been observed in response to multifaceted hospital-based interventions. Reductions in antibiotic-sensitive strains have been smaller or non-existent. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed changes must have largely resulted from other factors, including changes in antibiotic use. We used a mathematical model to test the validity of this reasoning. Methods: We developed a mechanistic model of resistant and sensitive strains in a hospital and its catchment area. We assumed the resistant strain had a competitive advantage in the hospital and the sensitive strain an advantage in the community. We simulated a hospital hand hygiene intervention that directly affected resistant and sensitive strains equally. The annual incidence rate ratio (IRR) associated with the intervention was calculated for hospital- and community-acquired infections of both strains. Results: For the resistant strain, there were large reductions in hospital-acquired infections (0.1 ≤ IRR ≤ 0.6) and smaller reductions in community-acquired infections (0.2 ≤ IRR ≤ 0.9). These reductions increased in line with increasing importance of nosocomial transmission of the strain. For the sensitive strain, reductions in hospital acquisitions were much smaller (0.6 ≤ IRR ≤ 0.9), while communityacquisitions could increase or decrease (0.9 ≤ IRR ≤ 1.2). The greater the importance of the community environment for the transmission of the sensitive strain, the smaller the reductions. Conclusions: Counter-intuitively, infection control interventions, including hand hygiene, can have strikingly discordant effects on resistant and sensitive strains even though they target them equally, following differences in their adaptation to hospital and community-based transmission. Observed lack of effectiveness of control measures for sensitive strains does not provide evidence that infection control interventions have been ineffective in reducing resistant strains.

BackgroundLittle is known about the epidemiology of nosocomial bloodstream infections in public hospitals in developing countries. We evaluated trends in incidence of hospital-acquired bacteremia (HAB) and healthcare-associated bacteremia (HCAB) and associated mortality in a developing country using routinely available databases.MethodsInformation from the microbiology and hospital databases of 10 provincial hospitals in northeast Thailand was linked with the national death registry for 2004-2010. Bacteremia was considered hospital-acquired if detected after the first two days of hospital admission, and healthcare-associated if detected within two days of hospital admission with a prior inpatient episode in the preceding 30 days.ResultsA total of 3,424 patients out of 1,069,443 at risk developed HAB and 2,184 out of 119,286 at risk had HCAB. Of these 1,559 (45.5%) and 913 (41.8%) died within 30 days, respectively. Between 2004 and 2010, the incidence rate of HAB increased from 0.6 to 0.8 per 1,000 patient-days at risk (pConclusionsThis study demonstrates a high and increasing incidence of HAB and HCAB in provincial hospitals in northeast Thailand, increasing proportions of ESBL-producing isolates, and very high associated mortality.

BACKGROUND: In many parts of the world, livestock production is undergoing a process of rapid intensification. The health implications of this development are uncertain. Intensification creates cheaper products, allowing more people to access animal-based foods. However, some practices associated with intensification may contribute to zoonotic disease emergence and spread: for example, the sustained use of antibiotics, concentration of animals in confined units, and long distances and frequent movement of livestock. OBJECTIVES: Here we present the diverse range of ecological, biological, and socioeconomic factors likely to enhance or reduce zoonotic risk, and identify ways in which a comprehensive risk analysis may be conducted by using an interdisciplinary approach. We also offer a conceptual framework to guide systematic research on this problem. DISCUSSION: We recommend that interdisciplinary work on zoonotic risk should take into account the complexity of risk environments, rather than limiting studies to simple linear causal relations between risk drivers and disease emergence and/or spread. In addition, interdisciplinary integration is needed at different levels of analysis, from the study of risk environments to the identification of policy options for risk management. CONCLUSION: Given rapid changes in livestock production systems and their potential health implications at the local and global level, the problem we analyze here is of great importance for environmental health and development. Although we offer a systematic interdisciplinary approach to understand and address these implications, we recognize that further research is needed to clarify methodological and practical questions arising from the integration of the natural and social sciences.

INTRODUCTION: Economic evaluations of interventions in the hospital setting often rely on the estimated long-term impact on patient survival. Estimates of mortality rates and long-term outcomes among patients discharged alive from the intensive care unit (ICU) are lacking from lower- and middle-income countries. This study aimed to assess the long-term survival and life expectancy (LE) amongst post-ICU patients in Thailand, a middle-income country. METHODS: In this retrospective cohort study, data from a regional tertiary hospital in northeast Thailand and the regional death registry were linked and used to assess patient survival time after ICU discharge. Adult ICU patients aged at least 15 years who had been discharged alive from an ICU between 1 January 2004 and 31 December 2005 were included in the study, and the death registry was used to determine deaths occurring in this cohort up to 31st December 2010. These data were used in conjunction with standard mortality life tables to estimate annual mortality and life expectancy. RESULTS: This analysis included 10,321 ICU patients. During ICU admission, 3,251 patients (31.5%) died. Of 7,070 patients discharged alive, 2,527 (35.7%) were known to have died within the five-year follow-up period, a mortality rate 2.5 times higher than that in the Thai general population (age and sex matched). The mean LE was estimated as 18.3 years compared with 25.2 years in the general population. CONCLUSIONS: Post-ICU patients experienced much higher rates of mortality than members of the general population over the five-year follow-up period, particularly in the first year after discharge. Further work assessing Health Related Quality of Life (HRQOL) in both post-ICU patients and in the general population in developing countries is needed.

Background Estimates of the sensitivity and specificity for new diagnostic tests based on evaluation against a known gold standard are imprecise when the accuracy of the gold standard is imperfect. Bayesian latent class models (LCMs) can be helpful under these circumstances, but the necessary analysis requires expertise in computational programming. Here, we describe open-access web-based applications that allow non-experts to apply Bayesian LCMs to their own data sets via a user-friendly interface. Methods/Principal Findings Applications for Bayesian LCMs were constructed on a web server using R and WinBUGS programs. The models provided (http://mice.tropmedres.ac) include two Bayesian LCMs: the two-tests in two-population model (Hui and Walter model) and the three-tests in one-population model (Walter and Irwig model). Both models are available with simplified and advanced interfaces. In the former, all settings for Bayesian statistics are fixed as defaults. Users input their data set into a table provided on the webpage. Disease prevalence and accuracy of diagnostic tests are then estimated using the Bayesian LCM, and provided on the web page within a few minutes. With the advanced interfaces, experienced researchers can modify all settings in the models as needed. These settings include correlation among diagnostic test results and prior distributions for all unknown parameters. The web pages provide worked examples with both models using the original data sets presented by Hui and Walter in 1980, and by Walter and Irwig in 1988. We also illustrate the utility of the advanced interface using the Walter and Irwig model on a data set from a recent melioidosis study. The results obtained from the web-based applications were comparable to those published previously. Conclusions The newly developed web-based applications are open-access and provide an important new resource for researchers worldwide to evaluate new diagnostic tests.

Summary Background The benefits of universal meticillin-resistant Staphylococcus aureus (MRSA) admission screening, compared with screening targeted patient groups and the additional impact of discharge screening, are uncertain. Aims To quantify the impact of MRSA screening plus decolonization treatment on MRSA infection rates. To compare universal with targeted screening policies, and to evaluate the additional impact of screening and decolonization on discharge. Methods A stochastic, individual-based model of MRSA transmission was developed that included patient movements between general medical and intensive care unit (ICU) wards, and between the hospital and community, informed by 18 months of individual patient data from a 900-bed tertiary care hospital. We simulated the impact of universal and targeted [for ICU, acute care of the elderly (ACE) or readmitted patients] MRSA screening and decolonization policies, both on admission and discharge. Findings Universal admission screening plus decolonization resulted in 77% (95% confidence interval: 76–78) reduction in MRSA infections over 10 years. Screening only ACE specialty or ICU patients yielded 62% (61–63) and 66% (65–67) reductions, respectively. Targeted policies reduced the number of screens by up to 95% and courses of decolonization by 96%. In addition to screening on admission, screening on discharge had little impact, with a maximum 7% additional reduction in infection. Conclusions Compared with universal screening, targeted screening substantially reduced the amount of screening and decolonization required to achieve only 12% lower reduction in infection. Targeted screening and decolonization could lower the risk of resistance emerging as well as offer a more efficient use of resources.

An important determinant of a pathogen's success is the rate at which it is transmitted from infected to susceptible hosts. Although there are anecdotal reports that methicillin-resistant Staphylococcus aureus (MRSA) clones vary in their transmissibility in hospital settings, attempts to quantify such variation are lacking for common subtypes, as are methods for addressing this question using routinely-collected MRSA screening data in endemic settings. Here we present a method to quantify the time-varying transmissibility of different subtypes of common bacterial nosocomial pathogens using routine surveillance data. The method adapts approaches for estimating reproduction numbers based on the probabilistic reconstruction of epidemic trees, but uses relative hazards rather than serial intervals to assign probabilities to different sources for observed transmission events. The method is applied to data collected as part of a retrospective observational study of a concurrent MRSA outbreak in the United Kingdom with dominant endemic MRSA clones (ST22 and ST36) and an Asian ST239 MRSA strain (ST239-TW) in two linked adult intensive care units, and compared with an approach based on a fully parametric transmission model. The results provide support for the hypothesis that the clones responded differently to an infection control measure based on the use of topical antiseptics, which was more effective at reducing transmission of endemic clones. They also suggest that in one of the two ICUs patients colonized or infected with the ST239-TW MRSA clone had consistently higher risks of transmitting MRSA to patients free of MRSA. These findings represent some of the first quantitative evidence of enhanced transmissibility of a pandemic MRSA lineage, and highlight the potential value of tailoring hospital infection control measures to specific pathogen subtypes., Author Summary Different strains of hospital pathogens may differ in their ability to spread between patients and respond differently to control measures. Attempts to quantify such between-strain variation are lacking in high prevalence settings. We analysed data from concurrent outbreaks with different MRSA strains in two adult intensive care units. MRSA is usually carried by patients asymptomatically, and most of our data came from routine screening swabs used to detect such carriage. We divided strains into two groups: common United Kingdom strains and strains from a type often found in Southeast Asia. We developed a new method to estimate how transmission changes over time and compared results with those from an adaptation of a previously described approach. An advantage of the new method is that it makes weaker assumptions about the process generating the data. The methods gave broadly similar results: the introduction of daily antiseptic bodywashes for all patients was the only intervention associated with a substantial fall in transmission, but this intervention was less effective for the Asian strain. This work should be useful for assessing the between-strain variation in the transmission of other hospital pathogens, and for assessing the impact of interventions on patient-to-patient transmission.

Background Accurate measures of the severity of pandemic influenza A/H1N1 (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely. Methods and Findings We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data: medically attended cases in Milwaukee or self-reported influenza-like illness in New York, were used to estimate ratios of symptomatic cases:hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic cases that died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated sCFR of 0.048% (95% credible interval, CI 0.026%-0.096%), sCIR of 0.239% (0.134%-0.458%), and sCHR of 1.44% (0.83%-2.64%). Using self-reported ILI, we obtained estimates approximately 7-9x lower. sCFR and sCIR appear to be highest in persons 18 and older, and lowest in children 5-17. sCHR appears to be lowest in persons 5-17; our data were too sparse to allow us to determine the group in which it was the highest. Conclusions These estimates suggest that an autumn-winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with greatest impact in young children and non-elderly adults. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the proportion infected or symptomatic were lower.

Background: The effectiveness of single-drug antiviral interventions to reduce morbidity and mortality during the next influenza pandemic will be substantially weakened if transmissible strains emerge which are resistant to the stockpiled antiviral drugs. We developed a mathematical model to test the hypothesis that a small stockpile of a secondary antiviral drug could be used to mitigate the adverse consequences of the emergence of resistant strains. Methods and Findings: We used a multistrain stochastic transmission model of influenza to show that the spread of antiviral resistance can be significantly reduced by deploying a small stockpile (1% population coverage) of a secondary drug during the early phase of local epidemics. We considered two strategies for the use of the secondary stockpile: early combination chemotherapy (ECC; individuals are treated with both drugs in combination while both are available); and sequential multidrug chemotherapy (SMC; individuals are treated only with the secondary drug until it is exhausted, then treated with the primary drug). We investigated all potentially important regions of unknown parameter space and found that both ECC and SMC reduced the cumulative attack rate (AR) and the resistant attack rate (RAR) unless the probability of emergence of resistance to the primary drug pA was so low (less than 1 in 10,000) that resistance was unlikely to be a problem or so high (more than 1 in 20) that resistance emerged as soon as primary drug monotherapy began. For example, when the basic reproductive number was 1.8 and 40% of symptomatic individuals were treated with antivirals, AR and RAR were 67% and 38% under monotherapy if pA = 0.01. If the probability of resistance emergence for the secondary drug was also 0.01, then SMC reduced AR and RAR to 57% and 2%. The effectiveness of ECC was similar if combination chemotherapy reduced the probabilities of resistance emergence by at least ten times. We extended our model using travel data between 105 large cities to investigate the robustness of these resistance-limiting strategies at a global scale. We found that as long as populations that were the main source of resistant strains employed these strategies (SMC or ECC), then those same strategies were also effective for populations far from the source even when some intermediate populations failed to control resistance. In essence, through the existence of many wild-type epidemics, the interconnectedness of the global network dampened the international spread of resistant strains. Conclusions:Our results indicate that the augmentation of existing stockpiles of a single anti-influenza drug with smaller stockpiles of a second drug could be an effective and inexpensive epidemiological hedge against antiviral resistance if either SMC or ECC were used. Choosing between these strategies will require additional empirical studies. Specifically, the choice will depend on the safety of combination therapy and the synergistic effect of one antiviral in suppressing the emergence of resistance to the other antiviral when both are taken in combination. © 2009 Wu et al., link_to_subscribed_fulltext