BACKGROUND:The UK's National Health Service Test and Trace (NHSTT) program aimed to provide the most effective and accessible SARS-CoV-2 testing approach possible. Early user feedback indicated that there were accessibility issues associated with throat swabbing. We report the results of service evaluations performed by NHSTT to assess the effectiveness and user acceptance of swabbing approaches, as well as qualitative findings of user experiences from research reports, surveys, and incident reports. Our intent is to present and summarize our findings about the application of alternative swabbing approaches during the COVID-19 pandemic in the UK. METHODS:From May 2020 to December 2021, NHSTT conducted a series of service evaluations assessing self-swabbing and assisted swabbing of the nose and throat, and nose only (anterior nares/mid-turbinate) using polymerase chain reaction (PCR) and lateral flow devices (LFDs), for diagnostic suitability within the COVID-19 National Testing Programme. Outcomes included observational user feedback on swabbing approaches and quantitative testing performance (concordance, sensitivity, and specificity). A post-hoc indirect comparison of swabbing approaches was also performed. Additionally, an analysis of existing cross-service research was conducted in April 2021 to determine user feedback regarding swabbing approaches. RESULTS:Observational data from cross-service research indicated a user preference for nose swabbing over throat swabbing. Significantly more users reported that nose swabbing was easier to perform than throat swabbing (50% vs. 12%) and there were significantly fewer reported incidents. In the service evaluations, while there was reduced sensitivity for nose-only swabbing for PCR (88%) compared with nose and throat swabbing, similar sensitivities were observed for nose-only and nose and throat swabbing for LFDs. The sensitivity of nose-only swabbing for LFDs was higher for individuals with higher viral concentrations. CONCLUSIONS:User experience analyses supported a preference for nose-only swabbing. Nose-only swabbing for LFDs provided sufficient diagnostic accuracy, supporting its use as a viable option in the COVID-19 National Testing Programme. Less invasive swabbing approaches are important to maximize testing accessibility and alongside other behavioral interventions, increase user uptake.

Bedaquiline (BDQ) has only been approved for use for just over a decade and is a key drug for treating multidrug-resistant tuberculosis; however, rising levels of resistance threaten to reduce its effectiveness. Catalogues of mutations associated with resistance to BDQ are key to detecting resistance genetically for either diagnosis or surveillance. At present, building catalogues requires considerable expert knowledge, often requires the use of complex grading rules and is an irreproducible process. We developed an automated method, catomatic, that associates genetic variants with resistance (or susceptibility) using a two-tailed binomial test with a stated background rate and applied it to a dataset of 11,867 Mycobacterium tuberculosis samples with whole-genome and BDQ susceptibility testing data. Using this framework, we investigated how to best classify variants and the phenotypic significance of minor alleles. The genes mmpS5 and mmpL5 are not directly associated with BDQ resistance, and our catalogue of Rv0678 , atpE and pepQ variants attains a cross-validated sensitivity and specificity of 79.4±1.8% and 98.5±0.3%, respectively, for 94±0.4% of samples. Identifying samples with subpopulations containing Rv0678 variants improves sensitivity, and detection thresholds in bioinformatic pipelines should therefore be lowered. By using a more permissive and deterministic algorithm trained on a sufficient number of resistant samples, we have reproducibly constructed a catalogue of BDQ resistance-associated variants that is comprehensive and accurate.

Background:Self-testing for SARS-CoV-2 infection using lateral flow devices (LFDs) was a key component of the COVID-19 pandemic response; however, LFD performance has shown a high degree of variability. Between August 2020 and July 2023, the UK Health Security Agency (UKHSA), including predecessor organisations, undertook a three-phase SARS-CoV-2 test development and evaluation programme to independently evaluate commercially available SARS-CoV-2 LFDs, incorporating standardised laboratory assessment of test sensitivity. Here we describe results from a comparison of UKHSA laboratory assessment findings with manufacturer-reported LFD sensitivity data. Methods:The UKHSA assessed the sensitivity of LFDs, by laboratory testing of surplus clinical samples from a secondary healthcare setting. These data were compared with manufacturer-reported clinical sensitivity data and analytical sensitivity (limit of detection [LOD; 50% tissue culture infectious dose [TCID50]/mL]) from LFD instructions for use (IFU). Findings:UKHSA-determined LFD sensitivity ranged from 32 to 83%. Of 86 LFDs assessed, 73 included device sensitivity data in the manufacturers' IFU that claimed clinical sensitivity ≥85%, and 49 claimed clinical sensitivity ≥95%. No evidence of correlation was observed between manufacturer-reported test sensitivity and UKHSA determined test sensitivity, and no evidence of correlation was observed between manufacturer-reported test LOD and UKHSA-determined test sensitivity. Interpretation:Laboratory evaluation found no evidence of correlation between manufacturer-reported SARS-CoV-2 LFD sensitivity data and UKHSA laboratory-determined sensitivity, supporting previous reports of discrepancies. Our findings suggest that manufacturer-reported performance data and claims for SARS-CoV-2 LFDs should be interpreted with caution and support the need for independent monitoring and testing, and standardisation of analysis methodologies. Funding:This study was funded by UK Department of Health and Social Care; UK Health Security Agency (formerly Public Health England and the National Health Service Test and Trace); and the University of Oxford NIHR Biomedical Research Centre.

Background:In August 2020, Public Health England and Oxford University were commissioned to design and deliver (with NHS Test and Trace, NHSTT) a rapid evaluation programme of antigen Lateral Flow Devices (LFDs) for SARS-CoV-2 for mass community testing. Methods:A three-phase evaluation process was established: 1) desktop review of kits including claimed performance and supply; 2) laboratory testing with laboratory-grown SARS-CoV-2 virus and SARS-CoV-2 virus PCR negative volunteer samples; and 3) larger-scale laboratory testing of SARS-CoV-2 PCR positive and negative clinical samples. Variant of Concern (VOC) identification in the UK (December 2020), expanded laboratory methodology. Processes also evolved to improve workflow (irradiated viral stocks, dilution matrices, sample volumes, and replicates). Findings:Overall, 1017 kits were screened at phase 1, 185 kits tested at phase 2 and 91 at phase 3. Sixteen kits failed phase 3 due to poor performance and eight more failed to detect VOC satisfactorily. Sixty-four kits were redesigns of previously failed kits. The overall pass rate for the laboratory evaluation was 35% and 5 kits were procured for the UK National Covid 19 Testing Programme. Interpretation:The evaluation results had potential, time limited commercially sensitive aspects, and public sharing was limited to kits passing phase 3. Until now, the full data set has not been published. Over 2.5 billion self-test kits were deployed by the UK government following purchasing decisions informed by this work. We offer a potential blueprint for future evaluation programmes that might be required to assess LFDs to detect cases of a pandemic novel pathogen. Funding:This study was funded by UK Department of Health and Social Care (UKHSA); UK Health Security Agency (formerly Public Health England and the National Health Service Test and Trace); and the University of OxfordNIHRBiomedical Research Centre.

PURPOSE:The Omicron variant of SARS-CoV-2 raised concerns about the best sampling sites for PCR testing, with early indications suggesting throat swab samples were better than nasal swab samples. Our study evaluated the sensitivity of detecting SARS-CoV-2 across different swabbing sites. METHODS:Participants undergoing testing at NHS Test and Trace sites in England provided self-collected samples using nose only, throat only, and combined nose and throat swabs, which were analysed by realtime PCR. RESULTS:Among 815 participants, combined swabs had higher viral concentrations than nose only or throat only swabs. Sensitivity for detecting SARS-CoV-2 by PCR was 91 % for nose only and 97 % for throat only, relative to the combined approach. VC remained stable in nose swabs but declined in throat swabs with time. CONCLUSIONS:Combined nose and throat swabbing remains the most effective method for SARS-CoV-2 detection. If a single swab is used, a throat swab has a higher sensitivity than nose swabs, although VC in the throat decreases faster in later infection stages. The variations in VC over time and intra-person variation between sampling sites underscore the complexity of viral dynamics, highlighting the importance of considering both nose and throat samples for comprehensive testing.

BACKGROUND:Surveillance and prediction of antibiotic resistance in Escherichia coli relies on curated databases of genes and mutations. We aimed to quantify the effect of acquiring specific genetic elements on minimum inhibitory concentrations (MICs) for particular antibiotic-species combinations, addressing the current scarcity of such data in existing databases. METHODS:For this observational study, we evaluated a collection of E coli isolates with linked whole-genome sequencing and MIC data, originating from human urinary or bloodstream infections obtained from the Oxford University Hospitals National Health Service Foundation Trust in Oxfordshire, UK. We used multivariable interval regression models to estimate the change in MIC (with 95% CIs) for specific antibiotics associated with the acquisition of antibiotic resistance genes and associated mutations in the National Center for Biotechnology Information AMRFinder database, with and without an adjustment for population structure. We then tested the ability of these models to predict MIC and binary resistance or susceptibility using leave-one-out cross-validation. FINDINGS:We evaluated 2875 E coli isolates obtained during 2013-2018 and 2020. Although most ARGs and resistance mutations (89 [80%] of 111) were associated with an increased MIC, a much smaller number (27 [24%] of 111) was found to be putatively independently resistance-conferring (ie, associated with an MIC above the European Committee on Antimicrobial Susceptibility Testing breakpoint) when acquired in isolation. We found evidence of differential effects of acquired ARGs and resistance mutations between different generations of cephalosporin antibiotics and showed that sub-breakpoint variation in MIC can be linked to genetic mechanisms of resistance. 20 697 (83·3%; range 52·9-97·7 across all antibiotics) of 24 858 MICs were correctly exactly predicted and 23 677 (95·2%; 87·3-97·7) of 24 858 MICs were predicted to within one doubling dilution. INTERPRETATION:Quantitative estimates of the independent effect of the acquisition of ARGs on MIC add to the interpretability and utility of existing databases. Compared with approaches using machine learning models, the use of these estimates yields similar or better performance in the prediction of antibiotic resistance phenotype with more readily interpretable results. The methods outlined here could be readily applied to other antibiotic-pathogen combinations. FUNDING:The National Institute for Health and Care Research (NIHR) and the Medical Research Council (MRC).

Better metrics to compare the impact of different antimicrobials on the gut microbiome would aid efforts to control antimicrobial resistance (AMR). The Antibiotic Resistance in the Microbiome – Oxford (ARMORD) study recruited inpatients, outpatients, and healthy volunteers in Oxfordshire, UK, who provided stool samples for metagenomic sequencing. Data on previous antimicrobial use and potential confounders were recorded. Exposures to each antimicrobial were considered as factors in a multivariable linear regression, also adjusted for demographics, with separate analyses for those contributing samples cross-sectionally or longitudinally. Outcomes were Shannon diversity and relative abundance of specific bacterial taxa (Enterobacteriaceae, Enterococcus, and major anaerobic groups) and antimicrobial resistance genes (targeting beta-lactams, tetracyclines, aminoglycosides, macrolides, and glycopeptides). 225 adults were included in the cross-sectional analysis, and a subset of 79 patients undergoing haematopoietic cell transplant provided serial samples for longitudinal analysis. Results were largely consistent between the two sampling frames. Recent use of piperacillin-tazobactam, meropenem, intravenous co-amoxiclav, and clindamycin was associated with large reductions in microbiome diversity and reduced abundance of anaerobes. Exposure to piperacillin-tazobactam and meropenem was associated with a decreased abundance of Enterobacteriaceae and an increased abundance of Enterococcus and major AMR genes, but there was no evidence that these antibiotics had a greater impact on microbiome diversity than iv co-amoxiclav or oral clindamycin. In contrast, co-trimoxazole, doxycycline, antifungals, and antivirals had less impact on microbiome diversity and selection of AMR genes. Simultaneous estimation of the impact of over 20 antimicrobials on the gut microbiome and AMR gene abundance highlighted important differences between individual drugs. Some drugs in the WHO Access group (co-amoxiclav, clindamycin) had similar magnitude impact on microbiome diversity to those in the Watch group (meropenem, piperacillin-tazobactam) with potential implications for acquisition of resistant organisms. Metagenomic sequencing can be used to compare the impact of different antimicrobial agents and treatment strategies on the commensal flora.

During the COVID-19 pandemic, numerous SARS-CoV-2 infections remained undetected. We combined results from routine monthly nose and throat swabs, and self-reported positive swab tests, from a UK household survey, linked to national swab testing programme data from England and Wales, together with Nucleocapsid (N-)antibody trajectories clustered using a longitudinal variation of K-means (N = 185,646) to estimate the number of infections undetected by either approach. Using N-antibody (hypothetical) infections and swab-positivity, we estimated that 7.4% (95%CI: 7.0-7.8%) of all true infections (detected and undetected) were undetected by both approaches, 25.8% (25.5-26.1%) by swab-positivity-only and 28.6% (28.4-28.9%) by trajectory-based N-antibody-classifications-only. Congruence with swab-positivity was respectively much poorer and slightly better with N-antibody classifications based on fixed thresholds or fourfold increases. Using multivariable logistic regression N-antibody seroconversion was more likely as age increased between 30-60 years, in non-white participants, those less (recently/frequently) vaccinated, for lower cycle threshold values in the range above 30, and in symptomatic and Delta (vs. BA.1) infections. Comparing swab-positivity data sources showed that routine monthly swabs were insufficient to detect infections and incorporating national testing programme/self-reported data substantially increased detection. Overall, whilst N-antibody serosurveillance can identify infections undetected by swab-positivity, optimal use requires fourfold-increase-based or trajectory-based analysis.

BACKGROUND:The advent of lateral flow devices (LFDs) for SARS-CoV-2 detection enabled widespread use of rapid self-tests during the pandemic. While self-testing using LFDs is now common, whether self-testing provides comparable performance to professional testing was a key question that remained important for pandemic planning.METHODS:Three prospective multi-centre studies were conducted to compare the performance of self- and professional testing using LFDs. Participants tested themselves or were tested by trained (professional) testers at community testing sites in the UK. Corresponding qRT-PCR test results served as reference standard. The performance of Innova, Orient Gene and SureScreen LFDs by users (self) and professional testers was assessed in terms of sensitivity, specificity, and kit failure (void) rates. Impact of age, sex and symptom status was analysed using logistic regression modelling.RESULTS:16,617 participants provided paired tests, of which 15,418 were included in the analysis. Self-testing with Innova, Orient Gene or SureScreen LFDs achieved sensitivities of 50 %, 53 % or 72 %, respectively, compared to qRT-PCR. Self and professional LFD testing showed no statistically different sensitivity with respect to corresponding qRT-PCR testing. Specificity was consistently equal to or higher than 99 %. Sex and age had no or only marginal impact on LFD performance while sensitivity was significantly higher for symptomatic individuals. Sensitivity of LFDs increased strongly to up to 90 % with higher levels of viral RNA measured by qRT-PCR.CONCLUSIONS:Our results support SARS-CoV-2 self-testing with LFDs, especially for the detection of individuals whose qRT-PCR tests showed high viral concentrations.

Detecting and quantifying changes in the growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers' rationale for implementing or continuing interventions aimed at reducing their impact. Substantial changes in SARS-CoV-2 prevalence with the emergence of variants have provided an opportunity to investigate different methods for doing this. We collected polymerase chain reaction (PCR) results from all participants in the United Kingdom's COVID-19 Infection Survey between August 1, 2020, and June 30, 2022. Change points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalized additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8 799 079 study visits, 147 278 (1.7%) were PCR-positive. Change points associated with the emergence of major variants were estimated to occur a median of 4 days earlier (IQR, 0-8) when using GAMs versus ISR. When estimating recent change points using successive data periods, 4 change points (4/96) identified by GAMs were not found when adding later data or by ISR. Change points were detected 3-5 weeks after they occurred under both methods but could be detected earlier within specific subgroups. Change points in growth rates of SARS-CoV-2 can be detected in near real time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories, both methods could be used in parallel.

ObjectivesTo identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery.MethodsWe included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 01-January-2016 to 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method.ResultsIn 88,348 suspected BSI episodes; 6,908(7.8%) were culture-positive with a probable pathogen, 4,309(4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1–2 days after blood culture collection, with varying responses for different pathogens and infection sources (p<0.0001).We identified five CRP trajectory subgroups: peak on day-1 (36,091;46.3%) or 2 (4,529;5.8%), slow recovery (10,666;13.7%), peak on day-6 (743;1.0%), and low response (25,928;33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day-1/2.ConclusionsCRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.

Population-representative estimates of SARS-CoV-2 infection prevalence and antibody levels in specific geographic areas at different time points are needed to optimise policy responses. However, even population-wide surveys are potentially impacted by biases arising from differences in participation rates across key groups. Here, we used spatio-temporal regression and post-stratification models to UK’s national COVID-19 Infection Survey (CIS) to obtain representative estimates of PCR positivity (6,496,052 tests) and antibody prevalence (1,941,333 tests) for different regions, ages and ethnicities (7-December-2020 to 4-May-2022). Not accounting for vaccination status through post-stratification led to small underestimation of PCR positivity, but more substantial overestimations of antibody levels in the population (up to 21 percentage points), particularly in groups with low vaccine uptake in the general population. There was marked variation in the relative contribution of different areas and age-groups to each wave. Future analyses of infectious disease surveys should take into account major drivers of outcomes of interest that may also influence participation, with vaccination being an important factor to consider.

Background Surveillance and prediction of antibiotic resistance in Escherichia coli relies on curated databases of genes and mutations that are variably associated with resistance. Such databases currently lack quantitative data on the effect on MIC caused by the acquisition of any given element for a given antibiotic-species combination. Methods Using a collection of 2875 E. coli isolates with linked whole genome sequencing and MIC data, we used multivariable interval regression models to estimate the change in MIC for specific antibiotics associated with the acquisition of genes and mutations in the AMRFinder database with and without an adjustment for population structure. We then tested the ability of these models to predict MIC and binary resistance/susceptibility using leave-one-out cross validation. Findings We provide quantitative estimates (with confidence intervals) of the change in MIC associated with the acquisition of genes/mutations in the NCBI-AMRFinder database. Whilst the majority of genes and mutations (89/111 (80.2%) were associated with an increased MIC, a much smaller number (27/111, 24.3%) were found to be putatively independently resistance conferring (i.e. caused the MIC to be above the EUCAST breakpoint) when acquired in isolation. We found evidence of differential effects of acquired genes and mutations between different antibiotics within the cephalosporin class of antibiotics and demonstrated that sub-breakpoint variation in MIC can be linked to genetic mechanisms of resistance. 20,697/24,858 (83.3%, range 52.9-97.7 across all antibiotics) of MICs were correctly exactly predicted and 23,677/24,858 (95.2%, range 87.3-97.7) to within +/-1 doubling dilution. Interpretation Quantitative estimates of the independent effect on MIC of the acquisition of antibiotic resistance genes add to the interpretability and utility of existing databases such as the NCBI AMRFinder tool. Using these estimates to predict antibiotic resistance phenotype demonstrates performance that is comparable to or better than approaches utilising machine learning models and crucially more readily interpretable. The approach outlined here could be readily applied to other antibiotic/pathogen combinations. Funding This work was funded by the NIHR and the MRC.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThe computational aspects of this research were funded from the NIHR Oxford BRC with additional support from the Wellcome Trust Core Award Grant Number 203141/Z/16/Z. SL was funded by an MRC Clinical Research Training Fellowship MR/T001151/1. ASW and TEAP are also supported by the NIHR Oxford Biomedical Research Centre. ASW is an NIHR Senior Investigator. NS is an NIHR Oxford BRC Senior Fellow. This research is supported by the National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (NIHR200915), a partnership between the UK Health Security Agency (UKHSA) and the University of Oxford. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. This research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. ### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Routinely collected healthcare data, including microbiological data, were acquired via pseudonymised linkage in the Infections in Oxfordshire Research Database (IORD). IORD has generic Research Ethics Committee, Health Research Authority and Confidentiality Advisory Group approvals (19/SC/0403, 19/CAG/0144) as a de-identified electronic research database. The use of bacterial isolates obtained from clinical infections for the development of methods for genomic antimicrobial resistance prediction were covered by a separate approval (London - Queen Square Research Ethics Committee ; REC ref: 17/LO/1420)I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesRaw reads for all isolates used in the study are available in NCBI under project accession numbers PRJNA604975 and PRJNA1007570.

PURPOSE:In April 2020, the UK Government implemented NHS Test and Trace to provide SARS-CoV-2 quantitative reverse transcription polymerase chain reaction (qRT-PCR) testing for the public, with nose-and-throat swabbing for samples performed by trained staff. Self-swabbing (SS) would allow rapid scale-up of testing capacity and access. Six studies were undertaken to determine whether SS was as effective for detecting SARS-CoV-2 as swabbing performed by trained staff. METHODS:Six prospective studies were conducted between April-October 2020, using six swab/media combinations. Differences between assisted swabbing (AS) and SS were evaluated for concordance, positivity, sensitivity, cycle threshold (Ct) values and void rates. Statistical analysis was performed using 95% confidence intervals (CIs), paired t-tests and model-based methods. RESULTS:Overall, 3,253 individuals were recruited (median age 37 years, 49% female), with 2,933 having valid paired qRT-PCR results. Pooled concordance rate was 98% (95% CI: 96%, 99%). Positivity rate differences for SS (8.1%) and AS (8.4%) and differences in pooled sensitivities between SS (86%; 95% CI: 78%, 92%) and AS (91%; 95% CI: 78%, 96%) were nonsignificant. Both types of swabbing led to pooled void rates below 2% and strongly correlated Ct values. Age, sex and previous swabbing experience did not have a significant impact on concordance or sensitivity. CONCLUSION:The UK adopted a policy to promote self-testing for SARS-CoV-2 based on data demonstrating equivalence of SS versus AS. Positive outcomes with SS are likely generalisable to testing for other respiratory pathogens, and we consider self-sampling and self-testing essential for future pandemic preparedness.

Tuberculosis remains a global health problem. Making it easier and quicker to identify which antibiotics an infection is likely to be susceptible to will be a key part of the solution. Whilst whole-genome sequencing offers many advantages, the processing of the genetic reads to produce the relevant public health and clinical information is, surprisingly, often the responsibility of the end user which inhibits uptake. Here we describe our Mycobacterial genetics processing pipeline and its deployment in a cloud-based platform. For antibiotic resistance prediction we have implemented the second edition of the WHO catalogue of resistance-associated variants. We validate the resistance prediction performance by constructing and processing a diverse dataset of 2,663 publicly-available M. tuberculosis samples with published drug susceptibility testing (DST) data and find that identifying a sample as resistant if it contains a minor allele known to be associated with resistance increases sensitivity. By only considering high confidence DST results we are able to show that both isoniazid and rifampicin achieve sensitivities and specificities in excess of 95%. ### Competing Interest Statement SS and KG are employed by the Ellison Institute of Technology, Oxford. TEAP, DWC, and PWF receive consultancy fees from the Ellison Institute of Technology, Oxford.

BACKGROUND/OBJECTIVES:We investigated if performing two lateral flow device (LFD) tests, LFD2 immediately after LFD1, could improve diagnostic sensitivity or specificity for detecting severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) antigen. STUDY DESIGN:Individuals aged ≥16 years attending UK community testing sites (February-May 2021) performed two successive LFD tests and provided a nose-and-throat sample for a polymerase chain reaction (PCR) test. Using the PCR result as the reference diagnosis, we assessed whether improvements could be achieved in sensitivity (by counting a positive result in either LFD as a positive overall test result) or specificity (by using LFD2 as confirmatory test). RESULTS:Overall, 2231 participants were included with 159 (7 %) having a positive PCR test. Of 2223 participants who completed both LFD tests, LFD results were highly concordant both with each other and with PCR tests (>97 %). The proportion of discord LFD results decreased significantly over the study period. Combined LFD usage achieved a sensitivity of 68.6 %, versus 67.1 % for either LFD individually. The specificity increased from 99.5 % to 99.8 % when using LFD2 as confirmatory test. Observed increases in sensitivity and specificity were not statistically significant. Void results were recorded for 31 (1.4 %) LFD1s, 19 (0.9 %) LFD2s and 6 (0.3 %) combined LFD tests. CONCLUSIONS:LFD tests were highly reproducible even when they were performed by untrained users following only written instructions and without supervision. While performing two LFD tests of the same type in quick succession marginally increased sensitivity or specificity, statistically significant improvements were not detected in our study.

Plasmids carry genes conferring antimicrobial resistance and other clinically important traits, and contribute to the rapid dissemination of such genes. Previous studies using complete plasmid assemblies, which are essential for reliable inference, have been small and/or limited to plasmids carrying antimicrobial resistance genes (ARGs). In this study, we sequenced 1,880 complete plasmids from 738 isolates from bloodstream infections in Oxfordshire, UK. The bacteria had been originally isolated in 2009 (194 isolates) and 2018 (368 isolates), plus a stratified selection from intervening years (176 isolates). We demonstrate that plasmids are largely, but not entirely, constrained to a single host species, although there is substantial overlap between species of plasmid gene-repertoire. Most ARGs are carried by a relatively small number of plasmid groups with biological features that are predictable. Plasmids carrying ARGs (including those encoding carbapenemases) share a putative ‘backbone’ of core genes with those carrying no such genes. These findings suggest that future surveillance should, in addition to tracking plasmids currently associated with clinically important genes, focus on identifying and monitoring the dissemination of high-risk plasmid groups with the potential to rapidly acquire and disseminate these genes.

ABSTRACTBackgroundAntimicrobial resistance (AMR) inE. coliis a global problem associated with substantial morbidity and mortality. AMR-associated genes are typically annotated based on similarity to a variants in a curated reference database with an implicit assumption that uncatalogued genetic variation within these is phenotypically unimportant. In this study we evaluated the potential for discovering new AMR-associated gene families and characterising variation within existing ones to improve genotype-to-susceptibility-phenotype prediction inE. coli.MethodsWe assembled a global dataset of 9001E. colisequences of which 8586 had linked antibiotic susceptibility data. Raw reads were assembled using Shovill and AMR genes extracted using the NCBI AMRFinder tool. Mash was used to calculate the similarity between extracted genes using Jaccard distances. We empirically reclustered extracted gene sequences into AMR-associated gene families (70% match) and alleles (ARGs, 100% match).ResultsThe performance of the AMRFinder database for genotype-to-phenotype predictions using strict 100% identity and coverage thresholds did not meet FDA thresholds for any of the eight antibiotics evaluated. Relaxing filters to default settings improved sensitivity with a specificity cost. For all antibiotics, a small number of genes explained most resistance although a proportion could not be explained by known ARGs; this ranged from 75.1% for co-amoxiclav to 3.4% for ciprofloxacin. Only 17,177/36,637 (47%) of ARGs detected had a 100% identity and coverage match in the AMRFinder database. After empirically reclassifying genes at 100% nucleotide sequence identity, we identified 1292 unique ARGs of which 158 (12%) were present ≥10 times, 374 (29%) were present 2-9 times and 760 (59%) only once. Simulated accumulation curves revealed that discovery of new (100%-match) ARGs present more than once in the dataset plateaued relatively quickly whereas new singleton ARGs were discovered even after many thousands of isolates had been included. We identified a strong correlation (Spearman coefficient 0.76 (95% CI 0.72-0.79, p<0.001)) between the number of times an ARG was observed in Oxfordshire and the number of times it was seen internationally, with ARGs that were observed 7 times in Oxfordshire always being found elsewhere. Finally, using the example ofblaTEM-1, we demonstrated that uncatalogued variation, including synonymous variation, is associated with potentially important phenotypic differences (e.g. two common, uncataloguedblaTEM-1alleles with only synonymous mutations compared to the known reference were associated with reduced resistance to co-amoxiclav [aOR 0.57, 95%CI 0.34-0.93, p=0.03] and piperacillin-tazobactam [aOR 0.54, 95%CI 0.32-0.87, p=0.01]).ConclusionsOverall we highlight substantial uncatalogued genetic variation with respect to known ARGs, although a relatively small proportion of these alleles are repeatedly observed in a large international dataset suggesting strong selection pressures. The current approach of using fuzzy matching for ARG detection, ignoring the unknown effects of uncatalogued variation, is unlikely to be acceptable for future clinical deployment. The association of synonymous mutations with potentially important phenotypic differences suggests that relying solely on amino acid-based gene detection to predict resistance is unlikely to be sufficient. Finally, the inability to explain all resistance using existing knowledge highlights the importance of new target gene discovery.