Reemergence of Bordetella parapertussis, United States, 2019-2023.

To determine changes in Bordetella pertussis and B. parapertussis detection rates, we analyzed 1.43 million respiratory multiplex PCR test results from US facilities from 2019 through mid-2023. From mid-2022 through mid-2023, Bordetella spp. detection increased 8.5-fold; 95% of detections were B. parapertussis. While B. parapertussis rates increased, B. pertussis rates decreased.

Candida auris rates in blood culture on the rise: results of US surveillance.

Candida auris is an emerging pathogen that poses a significant public health risk. Its multidrug resistance has led to high mortality, making rapid detection crucial for effective treatment and prevention of transmission. Recent data from the Centers for Disease Control and Prevention indicate a substantial increase in C. auris cases in the United States, with a 95% rise in 2021. To provide an update on the detection rates of C. auris, we analyzed blood culture results from a near real-time cloud-based surveillance network, BioFire Trend. From January 2021 to April 2023, 34 C. auris detections were observed. The analysis showed a notable increase in detections in 2023 compared to previous years. The detection rate in 2023 was higher in all four US Census Regions, except for the Northeast, where it remained constant. The findings emphasize the continuous rise in C. auris cases and highlight the importance of near real-time surveillance systems in monitoring this emerging pathogen.

How Virtual Animal Anatomy facilitated a successful transition to online instruction and supported student learning during the coronavirus pandemic.

Anatomy faculty with cadaver-based laboratory courses were presented with a significant challenge in March 2020 to create equivalent learning experiences without cadaveric access. The undergraduate domestic animal anatomy course at the Colorado State University was halfway into a 16-week semester when COVID-19 lockdown orders and the transition to remote instruction began. The new course curriculum was critically evaluated using student surveys and course outcome data. Most students (92.5%) agreed that the transition to online learning was a success; however, students who valued face-to-face lectures prior to March were less likely to perceive the transition as a success. Qualitative and quantitative analyses of survey results suggest that the resources perceived as most helpful for the transition to online learning were not the same as those that helped facilitate animal anatomy learning. Most students (92.5%) agreed that the Virtual Animal Anatomy (VAA) helped them learn anatomy, and 82.2% indicated that the VAA was a valuable resource following the transition to online learning. Additional resources associated with transition success included course instructors, weekly quizzes, written descriptions of anatomical structures and open laboratory sessions. In contrast, those resources associated with facilitating learning included guided quizzes and asynchronous lecture recordings. These findings suggest that the VAA can support online anatomy learning when used in conjunction with other best practices for online teaching.

Enhancing Anatomical Instruction: Impact of a Virtual Canine Anatomy Program on Student Outcomes.

Innovative reforms in medical education will require instructional tools to support these changes and to give students more flexibility in where and how they learn. At Colorado State University, the software program Virtual Canine Anatomy (VCA) was developed to assist student learning both inside and outside the anatomical laboratory. The program includes interactive anatomical photographs of dissected canine cadavers, dissection instructions with accompanying videos and diagrams, radiographs, and three-dimensional models. There is a need to evaluate the effectiveness of instructional tools like VCA so that decisions on pedagogical delivery can be evidence-based. To measure the impact of VCA on student outcomes in a dissection laboratory, this study compared student attitudes, quiz scores, dissection quality and accuracy, and instructor reliance between students with and without access to VCA. Students with VCA needed less time with teaching assistants (P < 0.01), asked teaching assistants fewer questions (P = 0.04), felt that the dissection was easier (P = 0.02), and were in stronger agreement that they had access to adequate resources (P = 0.02). No differences were found in the dissection quality or accuracy, quiz scores, or attitudes regarding overall enjoyment of the activity between the two groups. This study shows that VCA increases student independence and can be used to enhance anatomical instruction.

Enterovirus D68 outbreak detection through a syndromic disease epidemiology network.

BACKGROUND: In 2014, enterovirus D68 (EV-D68) was responsible for an outbreak of severe respiratory illness in children, with 1,153 EV-D68 cases reported across 49 states. Despite this, there is no commercial assay for its detection in routine clinical care. BioFire® Syndromic Trends (Trend) is an epidemiological network that collects, in near real-time, deidentified. BioFire test results worldwide, including data from the BioFire® Respiratory Panel (RP). OBJECTIVES: Using the RP version 1.7 (which was not explicitly designed to differentiate EV-D68 from other picornaviruses), we formulate a model, Pathogen Extended Resolution (PER), to distinguish EV-D68 from other human rhinoviruses/enteroviruses (RV/EV) tested for in the panel. Using PER in conjunction with Trend, we survey for historical evidence of EVD68 positivity and demonstrate a method for prospective real-time outbreak monitoring within the network. STUDY DESIGN: PER incorporates real-time polymerase chain reaction metrics from the RPRV/EV assays. Six institutions in the United States and Europe contributed to the model creation, providing data from 1,619 samples spanning two years, confirmed by EV-D68 gold-standard molecular methods. We estimate outbreak periods by applying PER to over 600,000 historical Trend RP tests since 2014. Additionally, we used PER as a prospective monitoring tool during the 2018 outbreak. RESULTS: The final PER algorithm demonstrated an overall sensitivity and specificity of 87.1% and 86.1%, respectively, among the gold-standard dataset. During the 2018 outbreak monitoring period, PER alerted the research network of EV-D68 emergence in July. One of the first sites to experience a significant increase, Nationwide Children's Hospital, confirmed the outbreak and implemented EV-D68 testing at the institution in response. Applying PER to the historical Trend dataset to determine rates among RP tests, we find three potential outbreaks with predicted regional EV-D68 rates as high as 37% in 2014, 16% in 2016, and 29% in 2018. CONCLUSIONS: Using PER within the Trend network was shown to both accurately predict outbreaks of EV-D68 and to provide timely notifications of its circulation to participating clinical laboratories.

Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

BACKGROUND: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. OBJECTIVE: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. METHODS: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. RESULTS: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. CONCLUSIONS: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.