CDC continues to track the evolution of SARS-CoV-2, including the Omicron variant and its descendants, using national genomic surveillance. This report summarizes U.S. trends in variant proportion estimates during May 2023-September 2024, a period when SARS-CoV-2 lineages primarily comprised descendants of Omicron variants XBB and JN.1. During summer and fall 2023, multiple descendants of XBB with immune escape substitutions emerged and reached >10% prevalence, including EG.5-like lineages by June 24, FL.1.5.1-like lineages by August 5, HV.1 lineage by September 30, and HK.3-like lineages by November 11. In winter 2023, the JN.1 variant emerged in the United States and rapidly attained predominance nationwide, representing a substantial genetic shift (>30 spike protein amino acid differences) from XBB lineages. Descendants of JN.1 subsequently circulated and reached >10% prevalence, including KQ.1-like and KP.2-like lineages by April 13, KP.3 and LB.1-like lineages by May 25, and KP.3.1.1 by July 20. Surges in COVID-19 cases occurred in winter 2024 during the shift to JN.1 predominance, as well as in summer 2023 and 2024 during circulation of multiple XBB and JN.1 descendants, respectively. The ongoing evolution of the Omicron variant highlights the importance of continued genomic surveillance to guide medical countermeasure development, including the selection of antigens for updated COVID-19 vaccines.

Nearly a century after the beginning of the antibiotic era, which has been associated with unparalleled improvements in human health and reductions in mortality associated with infection, the dwindling pipeline for new antibiotic classes coupled with the inevitable spread of antimicrobial resistance (AMR) poses a major global challenge. Historically, surveillance of bacteria with AMR typically relied on phenotypic analysis of isolates taken from infected individuals, which provides only a low-resolution view of the epidemiology behind an individual infection or wider outbreak. Recent years have seen increasing adoption of powerful new genomic technologies with the potential to revolutionise AMR surveillance by providing a high-resolution picture of the AMR profile of the bacteria causing infections and providing real-time actionable information for treating and preventing infection. However, many barriers remain to be overcome before genomic technologies can be adopted as a standard part of routine AMR surveillance around the world. Accordingly, the Surveillance and Epidemiology of Drug-resistant Infections Consortium convened an expert working group to assess the benefits and challenges of using genomics for AMR surveillance. In this Series, we detail these discussions and provide recommendations from the working group that can help to realise the massive potential benefits for genomics in surveillance of AMR.

Early detection of emerging SARS-CoV-2 variants is critical to guiding rapid risk assessments, providing clear and timely communication messages, and coordinating public health action. CDC identifies and monitors novel SARS-CoV-2 variants through diverse surveillance approaches, including genomic, wastewater, traveler-based, and digital public health surveillance (e.g., global data repositories, news, and social media). The SARS-CoV-2 variant BA.2.86 was first sequenced in Israel and reported on August 13, 2023. The first U.S. COVID-19 case caused by this variant was reported on August 17, 2023, after a patient received testing for SARS-CoV-2 at a health care facility on August 3. In the following month, eight additional U.S. states detected BA.2.86 across various surveillance systems, including specimens from health care settings, wastewater surveillance, and traveler-based genomic surveillance. As of October 23, 2023, sequences have been reported from at least 32 countries. Continued variant tracking and further evidence are needed to evaluate the full public health impact of BA.2.86. Timely genomic sequence submissions to global public databases aided early detection of BA.2.86 despite the decline in the number of specimens being sequenced during the past year. This report describes how multicomponent surveillance and genomic sequencing were used in real time to track the emergence and transmission of the BA.2.86 variant. This surveillance approach provides valuable information regarding implementing and sustaining comprehensive surveillance not only for novel SARS-CoV-2 variants but also for future pathogen threats.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

CDC has used national genomic surveillance since December 2020 to monitor SARS-CoV-2 variants that have emerged throughout the COVID-19 pandemic, including the Omicron variant. This report summarizes U.S. trends in variant proportions from national genomic surveillance during January 2022-May 2023. During this period, the Omicron variant remained predominant, with various descendant lineages reaching national predominance (>50% prevalence). During the first half of 2022, BA.1.1 reached predominance by the week ending January 8, 2022, followed by BA.2 (March 26), BA.2.12.1 (May 14), and BA.5 (July 2); the predominance of each variant coincided with surges in COVID-19 cases. The latter half of 2022 was characterized by the circulation of sublineages of BA.2, BA.4, and BA.5 (e.g., BQ.1 and BQ.1.1), some of which independently acquired similar spike protein substitutions associated with immune evasion. By the end of January 2023, XBB.1.5 became predominant. As of May 13, 2023, the most common circulating lineages were XBB.1.5 (61.5%), XBB.1.9.1 (10.0%), and XBB.1.16 (9.4%); XBB.1.16 and XBB.1.16.1 (2.4%), containing the K478R substitution, and XBB.2.3 (3.2%), containing the P521S substitution, had the fastest doubling times at that point. Analytic methods for estimating variant proportions have been updated as the availability of sequencing specimens has declined. The continued evolution of Omicron lineages highlights the importance of genomic surveillance to monitor emerging variants and help guide vaccine development and use of therapeutics.

The report “Genomic Surveillance for SARS-CoV-2 Variants: Predominance of the Delta (B.1.617.2) and Omicron (B.1.1.529) Variants — United States, June 2021–January 2022” contained several errors.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases(1-3). SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing(4,5). Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Genomic surveillance is a critical tool for tracking emerging variants of SARS-CoV-2 (the virus that causes COVID-19), which can exhibit characteristics that potentially affect public health and clinical interventions, including increased transmissibility, illness severity, and capacity for immune escape. During June 2021-January 2022, CDC expanded genomic surveillance data sources to incorporate sequence data from public repositories to produce weighted estimates of variant proportions at the jurisdiction level and refined analytic methods to enhance the timeliness and accuracy of national and regional variant proportion estimates. These changes also allowed for more comprehensive variant proportion estimation at the jurisdictional level (i.e., U.S. state, district, territory, and freely associated state). The data in this report are a summary of findings of recent proportions of circulating variants that are updated weekly on CDC's COVID Data Tracker website to enable timely public health action.(†) The SARS-CoV-2 Delta (B.1.617.2 and AY sublineages) variant rose from 1% to >50% of viral lineages circulating nationally during 8 weeks, from May 1-June 26, 2021. Delta-associated infections remained predominant until being rapidly overtaken by infections associated with the Omicron (B.1.1.529 and BA sublineages) variant in December 2021, when Omicron increased from 1% to >50% of circulating viral lineages during a 2-week period. As of the week ending January 22, 2022, Omicron was estimated to account for 99.2% (95% CI = 99.0%-99.5%) of SARS-CoV-2 infections nationwide, and Delta for 0.7% (95% CI = 0.5%-1.0%). The dynamic landscape of SARS-CoV-2 variants in 2021, including Delta- and Omicron-driven resurgences of SARS-CoV-2 transmission across the United States, underscores the importance of robust genomic surveillance efforts to inform public health planning and practice.

BACKGROUND: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. RESULTS: As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. CONCLUSIONS: Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI's BioSample database.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has had high incidence rates at institutions of higher education (IHE) in the United States, but the transmission dynamics in these settings are poorly understood. It remains unclear to what extent IHE-associated outbreaks have contributed to transmission in nearby communities. METHODS: We implemented high-density prospective genomic surveillance to investigate these dynamics at the University of Michigan and the surrounding community during the Fall 2020 semester (August 16-November 24). We sequenced complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 1659 individuals, including 468 students, representing 20% of cases in students and 25% of total cases in Washtenaw County over the study interval. RESULTS: Phylogenetic analysis identified >200 introductions into the student population, most of which were not related to other student cases. There were 2 prolonged student transmission clusters, of 115 and 73 individuals, that spanned multiple on-campus residences. Remarkably, <5% of nonstudent genomes were descended from student clusters, and viral descendants of student cases were rare during a subsequent wave of infections in the community. CONCLUSIONS: The largest outbreaks among students at the University of Michigan did not significantly contribute to the rise in community cases in Fall 2020. These results provide valuable insights into SARS-CoV-2 transmission dynamics at the regional level.

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2,500 COVID-19 cases associated with this variant have been detected in the United States (US) since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid- to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

On December 14, 2020, the United Kingdom reported a SARS-CoV-2 variant of concern (VOC), lineage B.1.1.7, also referred to as VOC 202012/01 or 20I/501Y.V1.* The B.1.1.7 variant is estimated to have emerged in September 2020 and has quickly become the dominant circulating SARS-CoV-2 variant in England (1). B.1.1.7 has been detected in over 30 countries, including the United States. As of January 13, 2021, approximately 76 cases of B.1.1.7 have been detected in 12 U.S. states.(†) Multiple lines of evidence indicate that B.1.1.7 is more efficiently transmitted than are other SARS-CoV-2 variants (1-3). The modeled trajectory of this variant in the U.S. exhibits rapid growth in early 2021, becoming the predominant variant in March. Increased SARS-CoV-2 transmission might threaten strained health care resources, require extended and more rigorous implementation of public health strategies (4), and increase the percentage of population immunity required for pandemic control. Taking measures to reduce transmission now can lessen the potential impact of B.1.1.7 and allow critical time to increase vaccination coverage. Collectively, enhanced genomic surveillance combined with continued compliance with effective public health measures, including vaccination, physical distancing, use of masks, hand hygiene, and isolation and quarantine, will be essential to limiting the spread of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). Strategic testing of persons without symptoms but at higher risk of infection, such as those exposed to SARS-CoV-2 or who have frequent unavoidable contact with the public, provides another opportunity to limit ongoing spread.

Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.

Rapid advances in DNA sequencing technology ("next-generation sequencing") have inspired optimism about the potential of human genomics for "precision medicine." Meanwhile, pathogen genomics is already delivering "precision public health" through more effective investigations of outbreaks of foodborne illnesses, better-targeted tuberculosis control, and more timely and granular influenza surveillance to inform the selection of vaccine strains. In this article, we describe how public health agencies have been adopting pathogen genomics to improve their effectiveness in almost all domains of infectious disease. This momentum is likely to continue, given the ongoing development in sequencing and sequencing-related technologies.

Advances in laboratory and information technologies are transforming public health microbiology. High-throughput genome sequencing and bioinformatics are enhancing our ability to investigate and control outbreaks, detect emerging infectious diseases, develop vaccines, and combat antimicrobial resistance-all with increased accuracy, timeliness, and efficiency. The Advanced Molecular Detection (AMD) initiative has allowed the Centers for Disease Control and Prevention (CDC) to provide leadership and coordination in integrating new technologies into routine practice throughout the US public health laboratory system. Collaboration and partnerships are the key to navigating this transition and to leveraging the next generation of methods and tools most effectively for public health.

At the turn of the century, a shift in incidence of Clostridium difficile infection occurred with the emergence of a novel, more virulent strain of C. difficile. It became apparent that strain typing was critical to our understanding of the epidemiology and transmission of the disease. Over the past 25 years, C. difficile strain typing methods have developed from phenotypic to genotypic techniques, each with their own advantages and disadvantages. Unfortunately, with the advent of polymerase chain reaction for C. difficile diagnosis, many clinical laboratories no longer perform anaerobic culture of the organism, and therefore routine strain typing for surveillance or detection of hospital outbreaks is rarely performed. Even among reference laboratories that do perform strain typing of C. difficile, standardisation of methods and inter-laboratory exchange of data continues to be problematic. Herein, an overview of molecular strain typing methods for C. difficile is provided, along with specific applications and limitations of each technique. In addition, current efforts to standardise laboratory protocols and a review of recent studies are discussed.

Multidrug-resistant Klebsiella pneumoniae producing the KPC carbapenemase have rapidly spread throughout the world, causing severe healthcare-associated infections with limited antimicrobial treatment options. Dissemination of KPC-producing K. pneumoniae is largely attributed to expansion of a single dominant strain, ST258. In this study, we explore phylogenetic relationships and evolution within ST258 and its clonal group, CG258, using whole genome sequence analysis of 167 isolates from 20 countries collected over 17 years. Our results show a common ST258 ancestor emerged from its diverse parental clonal group around 1995 and likely acquired blaKPC prior to dissemination. Over the past two decades, ST258 has remained highly clonal despite diversity in accessory elements and divergence in the capsule polysaccharide synthesis locus. Apart from the large recombination event that gave rise to ST258, few mutations set it apart from its clonal group. However, one mutation occurs in a global transcription regulator. Characterization of outer membrane protein sequences revealed a profile in ST258 that includes a truncated OmpK35 and modified OmpK37. Our work illuminates potential genomic contributors to the pathogenic success of ST258, helps us better understand the global dissemination of this strain, and identifies genetic markers unique to ST258.

The objective of this study was to develop a canonical, parsimoniously-informative SNP panel for subtyping Shiga-toxin producing Escherichia coli (STEC) O157:H7 that would be consistent with epidemiological, PFGE, and MLVA clustering of human specimens. Our group had previously identified 906 putative discriminatory SNPs, which were pared down to 391 SNPs based on their prevalence in a test set. The 391 SNPs were screened using a high-throughput form of TaqMan PCR against a set of clinical isolates that represent the most diverse collection of O157:H7 isolates from outbreaks and sporadic cases examined to date. Another 30 SNPs identified by others were also screened using the same method. Two additional targets were tested using standard TaqMan PCR endpoint analysis. These 423 SNPs were reduced to a 32 SNP panel with the almost the same discriminatory value. While the panel partitioned our diverse set of isolates in a manner that was consistent with epidemiological data and PFGE and MLVA phylogenies, it resulted in fewer subtypes than either existing method and insufficient epidemiological resolution in 10 of 47 clusters. Therefore, another round of SNP discovery was undertaken using comparative genomic resequencing of pooled DNA from the 10 clusters with insufficient resolution. This process identified 4,040 potential SNPs and suggested one of the ten clusters was incorrectly grouped. After its removal, there were 2,878 SNPs, of which only 63 were previously identified and 438 occurred across multiple clusters. Among highly clonal bacteria like STEC O157:H7, linkage disequilibrium greatly limits the number of parsimoniously informative SNPs. Therefore, it is perhaps unsurprising that our panel accounted for the potential discriminatory value of numerous other SNPs reported in the literature. We concluded published O157:H7 SNPs are insufficient for effective epidemiological subtyping. However, the 438 multi-cluster SNPs we identified may provide the additional information required.

PCR-ribotyping has been adopted in many laboratories as the method of choice for C. difficile typing and surveillance. However, issues with the conventional agarose gel-based technique, including inter-laboratory variation and interpretation of banding patterns have impeded progress. The method has recently been adapted to incorporate high-resolution capillary gel-based electrophoresis (CE-ribotyping), so improving discrimination, accuracy and reproducibility. However, reports to date have all represented single-centre studies and inter-laboratory variability has not been formally measured or assessed. Here, we achieved in a multi-centre setting a high level of reproducibility, accuracy and portability associated with a consensus CE-ribotyping protocol. Local databases were built at four participating laboratories using a distributed set of 70 known PCR-ribotypes. A panel of 50 isolates and 60 electronic profiles (blinded and randomized) were distributed to each testing centre for PCR-ribotype identification based on local databases generated using the standard set of 70 PCR-ribotypes, and the performance of the consensus protocol assessed. A maximum standard deviation of only +/-3.8bp was recorded in individual fragment sizes, and PCR-ribotypes from 98.2% of anonymised strains were successfully discriminated across four ribotyping centres spanning Europe and North America (98.8% after analysing discrepancies). Consensus CE-ribotyping increases comparability of typing data between centres and thereby facilitates the rapid and accurate transfer of standardized typing data to support future national and international C. difficile surveillance programs.

Exserohilum rostratum was the cause of most cases of fungal meningitis and other infections associated with the injection of contaminated methylprednisolone acetate produced by the New England Compounding Center (NECC). Until this outbreak, very few human cases of Exserohilum had been reported and very little was known about this dematiaceous fungus, which usually infects plants. Here we report using whole genome sequencing (WGS) for detection of single nucleotide polymorphisms (SNP) and phylogenetic analysis to investigate molecular origin of the outbreak using 22 isolates of E. rostratum isolated from 19 case-patients with meningitis or epidural/spinal abscesses, six isolates isolated from contaminated NECC vials, and seven isolates that are unrelated to the outbreak. Our analysis indicates that all 28 isolates associated with the outbreak had nearly identical genomes of 33.8 Mb. A total of eight SNPs were detected among the outbreak genomes, with no more than two SNPs separating any two of the 28 genomes. The outbreak genomes were separated from the next most closely related control strain by approximately 136,000 SNPs. We also observed significant genomic variability among strains unrelated to the outbreak, which may suggest a possibility of cryptic speciation in E. rostratum.

OBJECTIVE: To investigate an outbreak of New Delhi metallo-beta-lactamase (NDM)-producing carbapenem-resistant Enterobacteriaceae (CRE) and determine interventions to interrupt transmission. Design, Setting, and Patients. Epidemiologic investigation of an outbreak of NDM-producing CRE among patients at a Colorado acute care hospital. METHODS: Case patients had NDM-producing CRE isolated from clinical or rectal surveillance cultures (SCs) collected during the period January 1, 2012, through October 20, 2012. Case patients were identified through microbiology records and 6 rounds of SCs in hospital units where they had resided. CRE isolates were tested by real-time polymerase chain reaction for blaNDM. Medical records were reviewed for epidemiologic links; relatedness of isolates was evaluated by pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS). Infection control (IC) was assessed through staff interviews and direct observations. RESULTS: Two patients were initially identified with NDM-producing CRE during July-August 2012. A third case patient, admitted in May, was identified through microbiology records review. SC identified 5 additional case patients. Patients had resided in 11 different units before identification. All isolates were highly related by PFGE. WGS suggested 3 clusters of CRE. Combining WGS with epidemiology identified 4 units as likely transmission sites. NDM-producing CRE positivity in certain patients was not explained by direct epidemiologic overlap, which suggests that undetected colonized patients were involved in transmission. CONCLUSIONS: A 4-month outbreak of NDM-producing CRE occurred at a single hospital, highlighting the risk for spread of these organisms. Combined WGS and epidemiologic data suggested transmission primarily occurred on 4 units. Timely SC, combined with targeted IC measures, were likely responsible for controlling transmission.

IMPORTANCE Clostridium difficile infection (CDI) has been increasingly reported among healthy individuals in the community. Recent data suggest that community-associated CDI represents one-third of all C difficile cases. The epidemiology and potential sources of C difficile in the community are not fully understood. OBJECTIVES To determine epidemiological and clinical characteristics of community-associated CDI and to explore potential sources of C difficile acquisition in the community. DESIGN AND SETTING Active population-based and laboratory-based CDI surveillance in 8 US states. PARTICIPANTS Medical records were reviewed and interviews performed to assess outpatient, household, and food exposures among patients with community-associated CDI (ie, toxin or molecular assay positive for C difficile and no overnight stay in a health care facility within 12 weeks). Molecular characterization of C difficile isolates was performed. Outpatient health care exposure in the prior 12 weeks among patients with community-associated CDI was a priori categorized into the following 3 levels: no exposure, low-level exposure (ie, outpatient visit with physician or dentist), or high-level exposure (ie, surgery, dialysis, emergency or urgent care visit, inpatient care with no overnight stay, or health care personnel with direct patient care). MAIN OUTCOMES AND MEASURES Prevalence of outpatient health care exposure among patients with community-associated CDI and identification of potential sources of C difficile by level of outpatient health care exposure. RESULTS Of 984 patients with community-associated CDI, 353 (35.9%) did not receive antibiotics, 177 (18.0%) had no outpatient health care exposure, and 400 (40.7%) had low-level outpatient health care exposure. Thirty-one percent of patients without antibiotic exposure received proton pump inhibitors. Patients having CDI with no or low-level outpatient health care exposure were more likely to be exposed to infants younger than 1 year (P = .04) and to household members with active CDI (P = .05) compared with those having high-level outpatient health care exposure. No association between food exposure or animal exposure and level of outpatient health care exposure was observed. North American pulsed-field gel electrophoresis (NAP) 1 was the most common (21.7%) strain isolated; NAP7 and NAP8 were uncommon (6.7%). CONCLUSIONS AND RELEVANCE Most patients with community-associated CDI had recent outpatient health care exposure, and up to 36% would not be prevented by reduction of antibiotic use only. Our data support evaluation of additional strategies, including further examination of C difficile transmission in outpatient and household settings and reduction of proton pump inhibitor use.

BACKGROUND: Of the 13 US vancomycin-resistant Staphylococcus aureus (VRSA) cases, 8 were identified in southeastern Michigan, primarily in patients with chronic lower-extremity wounds. VRSA infections develop when the vanA gene from vancomycin-resistant enterococcus (VRE) transfers to S. aureus. Inc18-like plasmids in VRE and pSK41-like plasmids in S. aureus appear to be important precursors to this transfer. OBJECTIVE: Identify the prevalence of VRSA precursor organisms. DESIGN: Prospective cohort with embedded case-control study. PARTICIPANTS: Southeastern Michigan adults with chronic lower-extremity wounds. METHODS: Adults presenting to 3 southeastern Michigan medical centers during the period February 15 through March 4, 2011, with chronic lower-extremity wounds had wound, nares, and perirectal swab specimens cultured for S. aureus and VRE, which were tested for pSK41-like and Inc18-like plasmids by polymerase chain reaction. We interviewed participants and reviewed clinical records. Risk factors for pSK41-positive S. aureus were assessed among all study participants (cohort analysis) and among only S. aureus-colonized participants (case-control analysis). RESULTS: Of 179 participants with wound cultures, 26% were colonized with methicillin-susceptible S. aureus, 27% were colonized with methicillin-resistant S. aureus, and 4% were colonized with VRE, although only 17% consented to perirectal culture. Six participants (3%) had pSK41-positive S. aureus, and none had Inc18-positive VRE. Having chronic wounds for over 2 years was associated with pSK41-positive S. aureus colonization in both analyses. CONCLUSIONS: Colonization with VRSA precursor organisms was rare. Having long-standing chronic wounds was a risk factor for pSK41-positive S. aureus colonization. Additional investigation into the prevalence of VRSA precursors among a larger cohort of patients is warranted.