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BackgroundNeisseria gonorrhoeae is a major public health problem due to increasing incidence and antimicrobial resistance. Genetic markers of reduced susceptibility have been identified; the extent to which those are representative of global antimicrobial resistance is unknown. We evaluated the performance of whole-genome sequencing (WGS) used to predict susceptibility to ciprofloxacin and other antimicrobials using a global collection of N. gonorrhoeae isolates.MethodsSusceptibility testing of common antimicrobials and the recently developed zolifodacin was performed using agar dilution to determine minimum inhibitory concentrations (MICs). We identified resistance alleles at loci known to contribute to antimicrobial resistance in N. gonorrhoeae from WGS data. We tested the ability of each locus to predict antimicrobial susceptibility.ResultsA total of 481 N. gonorrhoeae isolates, collected between 2004 and 2019 and making up 457 unique genomes, were sourced from 5 countries. All isolates with demonstrated susceptibility to ciprofloxacin (MIC ≤0.06 μg/mL) had a wild-type gyrA codon 91. Multilocus approaches were needed to predict susceptibility to other antimicrobials. All isolates were susceptible to zoliflodacin, defined by an MIC ≤0.25 μg/mL.ConclusionsSingle marker prediction can be used to inform ciprofloxacin treatment of N. gonorrhoeae infection. A combination of molecular markers may be needed to determine susceptibility for other antimicrobials.

ObjectiveTo describe the genomic analysis and epidemiologic response related to a slow and prolonged methicillin-resistant Staphylococcus aureus (MRSA) outbreak.DesignProspective observational study.SettingNeonatal intensive care unit (NICU).MethodsWe conducted an epidemiologic investigation of a NICU MRSA outbreak involving serial baby and staff screening to identify opportunities for decolonization. Whole-genome sequencing was performed on MRSA isolates.ResultsA NICU with excellent hand hygiene compliance and longstanding minimal healthcare-associated infections experienced an MRSA outbreak involving 15 babies and 6 healthcare personnel (HCP). In total, 12 cases occurred slowly over a 1-year period (mean, 30.7 days apart) followed by 3 additional cases 7 months later. Multiple progressive infection prevention interventions were implemented, including contact precautions and cohorting of MRSA-positive babies, hand hygiene observers, enhanced environmental cleaning, screening of babies and staff, and decolonization of carriers. Only decolonization of HCP found to be persistent carriers of MRSA was successful in stopping transmission and ending the outbreak. Genomic analyses identified bidirectional transmission between babies and HCP during the outbreak.ConclusionsIn comparison to fast outbreaks, outbreaks that are "slow and sustained" may be more common to units with strong existing infection prevention practices such that a series of breaches have to align to result in a case. We identified a slow outbreak that persisted among staff and babies and was only stopped by identifying and decolonizing persistent MRSA carriage among staff. A repeated decolonization regimen was successful in allowing previously persistent carriers to safely continue work duties.

The CLEAR Trial recently found that decolonization reduced infections and hospitalizations in MRSA carriers in the year following hospital discharge. In this secondary analysis, we explored whether decolonization had a similar benefit in the subgroup of trial participants who harbored USA300, using two different definitions for the USA300 strain-type.

Background Since common diagnostic tests for gonorrhea do not provide information about susceptibility to antibiotics, treatment of gonorrhea remains empiric. Antibiotics used for empiric therapy are usually changed once resistance prevalence exceeds a certain threshold (e.g., 5%). A low switch threshold is intended to increase the probability that an infection is successfully treated with the first-line antibiotic, but it could also increase the pace at which recommendations are switched to newer antibiotics. Little is known about the impact of changing the switch threshold on the incidence of gonorrhea, the rate of treatment failure, and the overall cost and quality-adjusted life-years (QALYs) associated with gonorrhea. Methods and findings We developed a transmission model of gonococcal infection with multiple resistant strains to project gonorrhea-associated costs and loss in QALYs under different switch thresholds among men who have sex with men (MSM) in the United States. We accounted for the costs and disutilities associated with symptoms, diagnosis, treatment, and sequelae, and combined costs and QALYs in a measure of net health benefit (NHB). Our results suggest that under a scenario where 3 antibiotics are available over the next 50 years (2 suitable for the first-line therapy of gonorrhea and 1 suitable only for the retreatment of resistant infections), changing the switch threshold between 1% and 10% does not meaningfully impact the annual number of gonorrhea cases, total costs, or total QALY losses associated with gonorrhea. However, if a new antibiotic is to become available in the future, choosing a lower switch threshold could improve the population NHB. If in addition, drug-susceptibility testing (DST) is available to inform retreatment regimens after unsuccessful first-line therapy, setting the switch threshold at 1% to 2% is expected to maximize the population NHB. A limitation of our study is that our analysis only focuses on the MSM population and does not consider the influence of interventions such as vaccine and common use of rapid drugs susceptibility tests to inform first-line therapy. Conclusions Changing the switch threshold for first-line antibiotics may not substantially change the health and financial outcomes associated with gonorrhea. However, the switch threshold could be reduced when newer antibiotics are expected to become available soon or when in addition to future novel antibiotics, DST is also available to inform retreatment regimens., Author(s): Xuecheng Yin 1,2,3,*, Yunfei Li 4, Minttu M. Rönn 4, Song Li 5, Yue Yuan 6, Thomas L. Gift 7, Katherine Hsu 8, Joshua A. Salomon 9, Yonatan H. [...]

Methicillin-resistant Staphylococcus aureus (MRSA) can colonize multiple body sites, and carriage is a risk factor for infection. Successful decolonization protocols reduce disease incidence; however, multiple protocols exist, comprising diverse therapies targeting multiple body sites, and the optimal protocol is unclear. Standard methods cannot infer the impact of site-specific components on successful decolonization. Here, we formulate a Bayesian coupled hidden Markov model, which estimates interactions between body sites, quantifies the contribution of each therapy to successful decolonization, and enables predictions of the efficacy of therapy combinations. We applied the model to longitudinal data from a randomized controlled trial (RCT) of an MRSA decolonization protocol consisting of chlorhexidine body and mouthwash and nasal mupirocin. Our findings (i) confirmed nares as a central hub for MRSA colonization and nasal mupirocin as the most crucial therapy and (ii) demonstrated all components contributed significantly to the efficacy of the protocol and the protocol reduced self-inoculation. Finally, we assessed the impact of hypothetical therapy improvements in silico and found that enhancing MRSA clearance at the skin would yield the largest gains. This study demonstrates the use of advanced modelling to go beyond what is typically achieved by RCTs, enabling evidence-based decision-making to streamline clinical protocols.

Unusually large outbreaks of mumps across the United States in 2016 and 2017 raised questions about the extent of mumps circulation and the relationship between these and prior outbreaks. We paired epidemiological data from public health investigations with analysis of mumps virus whole genome sequences from 201 infected individuals, focusing on Massachusetts university communities. Our analysis suggests continuous, undetected circulation of mumps locally and nationally, including multiple independent introductions into Massachusetts and into individual communities. Despite the presence of these multiple mumps virus lineages, the genomic data show that one lineage has dominated in the US since at least 2006. Widespread transmission was surprising given high vaccination rates, but we found no genetic evidence that variants arising during this outbreak contributed to vaccine escape. Viral genomic data allowed us to reconstruct mumps transmission links not evident from epidemiological data or standard single-gene surveillance efforts and also revealed connections between apparently unrelated mumps outbreaks.

Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.

The goal of the Sexually Transmitted Infection Clinical Trial Group's Antimicrobial Resistance (AMR) in Neisseria gonorrhoeae (NG) meeting was to assemble experts from academia, government, nonprofit and industry to discuss the current state of research, gaps and challenges in research and technology and priorities and new directions to address the continued emergence of multidrug-resistant NG infections. Topics discussed at the meeting, which will be the focus of this article, include AMR NG global surveillance initiatives, the use of whole genome sequencing and bioinformatics to understand mutations associated with AMR, mechanisms of AMR, and novel antibiotics, vaccines and other methods to treat AMR NG. Key points highlighted during the meeting include: (i) US and International surveillance programs to understand AMR in NG; (ii) the US National Strategy for combating antimicrobial-resistant bacteria; (iii) surveillance needs, challenges, and novel technologies; (iv) plasmid-mediated and chromosomally mediated mechanisms of AMR in NG; (v) novel therapeutic (eg, sialic acid analogs, factor H [FH]/Fc fusion molecule, monoclonal antibodies, topoisomerase inhibitors, fluoroketolides, LpxC inhibitors) and preventative (eg, peptide mimic) strategies to combat infection. The way forward will require renewed political will, new funding initiatives, and collaborations across academic and commercial research and public health programs.

Surveillance systems that monitor pathogen genome sequences are critical for rapidly detecting the introduction and emergence of pathogen variants. To evaluate how interactions between surveillance capacity, variant properties, and the epidemiological context influence the timeliness of pathogen variant detection, we developed a geographically explicit stochastic compartmental model to simulate the transmission of a novel SARS-CoV-2 variant in New York City. We measured the impact of (1) testing and sequencing volume, (2) geographic targeting of testing, (3) the timing and location of variant emergence, and (4) the relative variant transmissibility on detection speed and on the undetected disease burden. Improvements in detection times and reduction of undetected infections were driven primarily by increases in the number of sequenced samples. The relative transmissibility of the new variant and the epidemic context of variant emergence also influenced detection times, showing that individual surveillance strategies can result in a wide range of detection outcomes, depending on the underlying dynamics of the circulating variants. These findings help contextualize the design, interpretation, and trade-offs of genomic surveillance strategies of pandemic respiratory pathogens. Author summary: To prevent the spread of infections that are more transmissible, evade immunity, or cause more serious illness, public health agencies must quickly detect changes in pathogens such as the virus responsible for COVID-19, which is done by testing the population to identify infections and then sequencing the positive cases to determine which virus variants caused the infections. However, it is unclear how different factors, such as the volume of testing and sequencing, the timing in the outbreak, or the transmissibility of the new variants affect our ability to quickly detect new variants of concern. In our study, we used mathematical simulations of disease spread in New York City to better understand how these factors influence the time it takes to detect a new variant and how many people have been infected by the time it is detected. In our simulations, the greatest improvement in detection speed was achieved by increasing the number of positive cases that are sampled for sequencing. However, factors beyond policymakers' control also influenced the time it took to detect the new variant, meaning that a wide range of detection outcomes was possible even under an ideal public health strategy. These findings help guide decision making for future outbreaks. [ABSTRACT FROM AUTHOR]

After decades of declining mumps incidence amid widespread vaccination, the United States and other developed countries have experienced a resurgence in mumps cases over the last decade. Outbreaks affecting vaccinated individuals and communities with high vaccine coverage have prompted concerns about the effectiveness of the live attenuated vaccine currently in use. It is unclear whether immune protection wanes or whether the vaccine protects inadequately against currently circulating mumps virus lineages. Synthesizing data from six studies of mumps vaccine effectiveness, we estimated that vaccine-derived immune protection against mumps wanes on average 27 years (95% confidence interval, 16 to 51 years) after vaccination. After accounting for this waning, we found no evidence that the emergence of heterologous virus genotypes contributed to changes in vaccine effectiveness over time. A mathematical model of mumps transmission confirmed the central role of waning immunity to the vaccine in the re-emergence of mumps cases. Outbreaks from 2006 to the present among young adults, and outbreaks in the late 1980s and early 1990s among adolescents, aligned with peaks in mumps susceptibility of these age groups predicted to be due to loss of vaccine-derived protection. In contrast, evolution of mumps virus strains escaping immune pressure would be expected to cause a higher proportion of cases among children, not adolescents and young adults as observed. Routine use of a third vaccine dose at 18 years of age, or booster dosing throughout adulthood, may be a strategy to prevent mumps re-emergence and should be assessed in clinical trials.

Carbapenem-resistant Enterobacteriaceae (CRE) are among the most severe threats to the antibiotic era. Multiple different species can exhibit resistance due to many different mechanisms, and many different mobile elements are capable of transferring resistance between lineages. We prospectively sampled CRE from hospitalized patients from three Boston-area hospitals, together with a collection of CRE from a single California hospital, to define the frequency and characteristics of outbreaks and determine whether there is evidence for transfer of strains within and between hospitals and the frequency with which resistance is transferred between lineages or species. We found eight species exhibiting resistance, with the majority of our sample being the sequence type 258 (ST258) lineage of Klebsiella pneumoniae There was very little evidence of extensive hospital outbreaks, but a great deal of variation in resistance mechanisms and the genomic backgrounds carrying these mechanisms. Local transmission was evident in clear phylogeographic structure between the samples from the two coasts. The most common resistance mechanisms were KPC (K. pneumoniae carbapenemases) beta-lactamases encoded by blaKPC2, blaKPC3, and blaKPC4, which were transferred between strains and species by seven distinct subgroups of the Tn4401 element. We also found evidence for previously unrecognized resistance mechanisms that produced resistance when transformed into a susceptible genomic background. The extensive variation, together with evidence of transmission beyond limited clonal outbreaks, points to multiple unsampled transmission chains throughout the continuum of care, including asymptomatic carriage and transmission of CRE. This finding suggests that to control this threat, we need an aggressive approach to surveillance and isolation.

In a retrospective, ecological analysis of US medical claims, visit rates explained more of the geographic variation in outpatient antibiotic prescribing rates than per-visit prescribing. Efforts to reduce antibiotic use may benefit from addressing the factors that drive higher rates of outpatient visits, in addition to continued focus on stewardship. [ABSTRACT FROM AUTHOR]

Background Group A Streptococcus (GAS) causes an estimated 5.2 million outpatient visits for pharyngitis annually in the United States, with incidence peaking in winter, but the annual spatiotemporal pattern of GAS pharyngitis across the United States is poorly characterized. Methods We used outpatient claims data from individuals with private medical insurance between 2010 and 2018 to quantify GAS pharyngitis visit rates across U.S. census regions, subregions, and states. We evaluated seasonal and age-based patterns of geographic spread and the association between school start dates and the summertime upward inflection in GAS visits. Results The South had the most visits per person (yearly average, 39.11 visits per 1000 people; 95% confidence interval, 36.21–42.01) and the West had the fewest (yearly average, 17.63 visits per 1000 people; 95% confidence interval, 16.76–18.49). Visits increased earliest in the South and in school-age children. Differences in visits between the South and other regions were most pronounced in the late summer through early winter. Visits peaked earliest in central southern states, in December to January, and latest on the coasts, in March. The onset of the rise in GAS pharyngitis visits correlated with, but preceded, average school start times. Conclusions The burden and timing of GAS pharyngitis varied across the continental United States, with the South experiencing the highest overall rates and earliest onset and peak in outpatient visits. Understanding the drivers of these regional differences in GAS pharyngitis will help in identifying and targeting prevention measures. [ABSTRACT FROM AUTHOR]

SARS‐CoV‐2 antibody levels may serve as a correlate for immunity and could inform optimal booster timing. The relationship between antibody levels and protection from infection was evaluated in vaccinated individuals from the US National Basketball Association who had antibody levels measured at a single time point from September 12, 2021, to December 31, 2021. Cox proportional hazards models were used to estimate the risk of infection within 90 days of serologic testing by antibody level (<250, 250–800, and >800 AU/mL1), adjusting for age, time since last vaccine dose, and history of SARS‐CoV‐2 infection. Individuals were censored on date of booster receipt. The analytic cohort comprised 2323 individuals and was 78.2% male, 68.1% aged ≤40 years, and 56.4% vaccinated (primary series) with the Pfizer‐BioNTech mRNA vaccine. Among the 2248 (96.8%) individuals not yet boosted at antibody testing, 77% completed their primary vaccine series 4–6 months before testing and the median (interquartile range) antibody level was 293.5 (interquartile range: 121.0–740.5) AU/mL. Those with levels <250 AU/mL (adj hazard ratio [HR]: 2.4; 95% confidence interval [CI]: 1.5–3.7) and 250–800 AU/mL (adj HR: 1.5; 95% CI: 0.98–2.4) had greater infection risk compared to those with levels >800 AU/mL. Antibody levels could inform individual COVID‐19 risk and booster scheduling. [ABSTRACT FROM AUTHOR]