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

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.

The epidemiology of pediatric outpatient acute respiratory tract infections in the US: a multi-facility analysis of multiplex PCR testing from 2018 to 2023

IMPORTANCE

Post-pandemic, it is essential to understand the epidemiology of pediatric acute respiratory tract infections (ARTIs). Our multi-facility study elucidates the outpatient epidemiology of pediatric ARTI using highly multiplexed PCR testing, providing critical insights into the evolving landscape of the etiological agents with a particular focus on the years following the emergence of SARS-CoV-2. Utilizing data from two different multiplex PCR panels, our research provides a comprehensive analysis of respiratory pathogen positivity from 2018 to 2023. Our findings indicate that over half of the annual test results identified at least one pathogen, primarily of viral origin. Intriguingly, despite the surge in testing during the COVID-19 pandemic, pathogen detection rates remain similar to the pre-pandemic era. These data hold significant implications for directing antimicrobial stewardship strategies, curbing unnecessary antibiotic use in pediatric respiratory diseases, and the value of multiplex PCR testing in the outpatient setting among pediatrics.

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.

Evaluation and Clinical Impact of Biofire FilmArray Pneumonia Panel Plus in ICU-Hospitalized COVID-19 Patients

Microbiological diagnosis by using commercial multiplex quantitative PCR systems provides great advantages over the conventional culture. In this work, the Biofire FilmArray Pneumonia Panel Plus (FAPP+) was used to test 144 low respiratory tract samples from 105 COVID-19 patients admitted to an Intensive Care Unit (ICU), detecting 78 pathogens in 59 (41%) samples. The molecular panel was evaluated by using the conventional culture (CC) as comparator, which isolated 42 pathogens in 40 (27.7%) samples. The overall percentage of agreement was 82.6%. Values of sensitivity (93%), specificity (62%), positive predictive value (50%), and negative predictive value (96%) were obtained. The mean time elapsed from sample extraction to modification of antibiotic treatment was 7.6 h. A change in antimicrobial treatment after the FAPP+ results was performed in 27% of patients. The FAPP+ is a highly sensitive diagnostic method that can be used to significantly reduce diagnostic time and that allows an early optimization of antimicrobial treatment.

Clinical Presentation and Severity of Adenovirus Detection Alone vs Adenovirus Co-detection With Other Respiratory Viruses in US Children With Acute Respiratory Illness from 2016 to 2018

BACKGROUND

Human adenovirus (HAdV) is commonly associated with acute respiratory illnesses (ARI) in children and is also frequently co-detected with other viral pathogens. We compared clinical presentation and outcomes in young children with HAdV detected alone vs co-detected with other respiratory viruses.

METHODS

We used data from a multicenter, prospective, viral surveillance study of children seen in the emergency department and inpatient pediatric settings at seven US sites. Children less than 18 years old with fever and/or respiratory symptoms were enrolled between 12/1/16 and 10/31/18 and tested by molecular methods for HAdV, human rhinovirus/enterovirus (HRV/EV), respiratory syncytial virus (RSV), parainfluenza (PIV, types 1-4), influenza (flu, types A-C), and human metapneumovirus (HMPV). Our primary measure of illness severity was hospitalization; among hospitalized children, secondary severity outcomes included oxygen support and length of stay (LOS).

RESULTS

Of the 18,603 children enrolled, HAdV was detected in 1,136 (6.1%), among whom 646 (56.9%) had co-detection with at least one other respiratory virus. HRV/EV (n = 293, 45.3%) and RSV (n = 123, 19.0%) were the most frequent co-detections. Children with HRV/EV (aOR = 1.61; 95% CI = [1.11-2.34]), RSV (aOR = 4.48; 95% CI = [2.81-7.14]), HMPV (aOR = 3.39; 95% CI = [1.69-6.77]), or ≥ 2 co-detections (aOR = 1.95; 95% CI = [1.14-3.36]) had higher odds of hospitalization compared to children with HAdV alone. Among hospitalized children, HAdV co-detection with RSV or HMPV was each associated with higher odds of oxygen support, while co-detection with PIV or influenza viruses was each associated with higher mean LOS.

CONCLUSIONS

HAdV co-detection with other respiratory viruses was associated with greater disease severity among children with ARI compared to HAdV detection alone.

Providing On-Site Laboratory and Biosafety Just-In-Time Training Inside a Box-Based Laboratory during the West Africa Ebola Outbreak: Supporting Better Preparedness for Future Health Emergencies

The Biological Light Fieldable Laboratory for Emergencies (B-LiFE) is a box-based modular laboratory with the capacity to quickly deploy on-site in cases of uncontrolled spread of infectious disease. During the 2014-2015 West Africa Ebola outbreak, this tent laboratory provided diagnostic support to the N'Zerekore Ebola Treatment Center (ETC), Guinea, for three months. One of the objectives of B-LiFE deployment was to contribute, as much as possible, to national capacity building by training local scientists. Two Guinean biologists were selected according to their basic biological knowledge and laboratory skills among 50 candidate trainees, and were integrated into the team through "just-in-time training" (JiTT), which helped the biologists acquire knowledge and laboratory skills beyond their expertise. The JiTT program was conducted according to standard laboratory procedures, in line with international biosafety guidelines adapted to field conditions. Supervised acquisition of field-laboratory practices mainly focused on biochemical testing and Ebola viral load quantification using routine PCR-based detection, including the Biofire FilmArray^® system (BFA), a novel, as yet non-validated, automated assay for diagnostic testing of Ebola virus disease at the time of B-LiFE deployment. During the JiTT, the two biologists were closely involved in all laboratory activities, including BFA validation and biosafety procedures. Meanwhile, this successful JiTT enhanced the B-LiFE in-field operational capacity and contributed to national capacity building. A post-training evaluation and contacts were organised to assess the evolution and technical skills gained by the two researchers during the B-LiFE mission. At the end of the B-LiFE mission, both biologists were enrolled in follow-on programmes to curb the epidemic spreading in Africa. These results demonstrate that during infectious disease outbreaks or major crises, the JiTT approach can rapidly expand access to critical diagnostic testing and train local staff to do so.

Epidemiology of Antimicrobial Resistance Among Blood and Respiratory Specimens in the United States Using Genotypic Analysis from a Cloud-Based Population Surveillance Network

Background

Antimicrobial resistance (AMR) surveillance is critical in informing strategies for infection control in slowing the spread of resistant organisms and for antimicrobial stewardship in the care of patients. However, significant challenges exist in timely and comprehensive AMR surveillance.

Methods

Using BioFire Pneumonia and Blood Culture 2 Panels data from BioFire Syndromic Trends (Trend), a cloud-based population surveillance network, we described the detection rate of AMR among a US cohort. Data were included from 2019 to 2021 for Gram-positive and -negative organisms and their related AMR genomic-resistant determinants as well as for detections of Candida auris. Regional and between panel AMR detection rate differences were compared. In addition, AMR codetections and detection rate per organism were evaluated for Gram-negative organisms.

Results

A total of 26 912 tests were performed, primarily in the Midwest. Overall, AMR detection rate was highest in the South and more common for respiratory specimens than blood. methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus detection rates were 34.9% and 15.9%, respectively, whereas AMR for Gram-negative organisms was lower with 7.0% CTX-M and 2.9% carbapenemases. In addition, 10 mcr-1 and 4 C auris detections were observed. For Gram-negative organisms, Klebsiella pneumoniae and Escherichia coli were most likely to be detected with an AMR gene, and of Gram-negative organisms, K pneumoniae was most often associated with 2 or more AMR genes.

Conclusions

Our study provides important in-depth evaluation of the epidemiology of AMR among respiratory and blood specimens for Gram-positive and -negative organism in the United States. The Trend surveillance network allows for near real-time surveillance of AMR.

Partial ORF1ab Gene Target Failure with Omicron BA.2.12.1

Mutations in the genome of SARS-CoV-2 can affect the performance of molecular diagnostic assays. In some cases, such as S-gene target failure, the impact can serve as a unique indicator of a particular SARS-CoV-2 variant and provide a method for rapid detection. Here, we describe partial ORF1ab gene target failure (pOGTF) on the cobas SARS-CoV-2 assays, defined by a ≥2-thermocycle delay in detection of the ORF1ab gene compared to that of the E-gene. We demonstrate that pOGTF is 98.6% sensitive and 99.9% specific for SARS-CoV-2 lineage BA.2.12.1, an emerging variant in the United States with spike L452Q and S704L mutations that may affect transmission, infectivity, and/or immune evasion. Increasing rates of pOGTF closely mirrored rates of BA.2.12.1 sequences uploaded to public databases, and, importantly, increasing local rates of pOGTF also mirrored increasing overall test positivity. Use of pOGTF as a proxy for BA.2.12.1 provides faster tracking of the variant than whole-genome sequencing and can benefit laboratories without sequencing capabilities.

A Hierarchical Genotyping Framework Using DNA Melting Temperatures Applied to Adenovirus Species Typing

Known genetic variation, in conjunction with post-PCR melting curve analysis, can be leveraged to provide increased taxonomic detail for pathogen identification in commercial molecular diagnostic tests. Increased taxonomic detail may be used by clinicians and public health decision-makers to observe circulation patterns, monitor for outbreaks, and inform testing practices. We propose a method for expanding the taxonomic resolution of PCR diagnostic systems by incorporating a priori knowledge of assay design and sequence information into a genotyping classification model. For multiplexed PCR systems, this framework is generalized to incorporate information from multiple assays to increase classification accuracy. An illustrative hierarchical classification model for human adenovirus (HAdV) species was developed and demonstrated ~95% cross-validated accuracy on a labeled dataset. The model was then applied to a near-real-time surveillance dataset in which deidentified adenovirus detected patient test data from 2018 through 2021 were classified into one of six adenovirus species. These results show a marked change in both the predicted prevalence for HAdV and the species makeup with the onset of the COVID-19 pandemic. HAdV-B decreased from a pre-pandemic predicted prevalence of up to 40% to less than 5% in 2021, while HAdV-A and HAdV-F species both increased in predicted prevalence.

Impact of COVID-19 Social Distancing Mandates on Gastrointestinal Pathogen Positivity: Secondary Data Analysis

Background

Acute gastrointestinal (GI) illnesses are of the most common problems evaluated by physicians and some of the most preventable. There is evidence of GI pathogen transmission when people are in close contact. The COVID-19 pandemic led to the sudden implementation of widespread social distancing measures in the United States. There is strong evidence that social distancing measures impact the spread of SARS-CoV-2, and a growing body of research indicates that these measures also decrease the transmission of other respiratory pathogens.

Objective

This study aims to investigate the impact of COVID-19 social distancing mandates on the GI pathogen positivity rates.

Methods

Deidentified GI Panel polymerase chain reaction test results from a routinely collected diagnostic database from January 1, 2019, through August 31, 2020, were analyzed for the GI pathogen positivity percentage. An interrupted time series analysis was performed, using social distancing mandate issue dates as the intervention date. The following 3 target organisms were chosen for the final analysis to represent different primary transmission routes: adenovirus F40 and 41, norovirus GI and GII, and Escherichia coli O157.

Results

In total, 84,223 test results from 9 states were included in the final data set. With the exception of E coli O157 in Kansas, Michigan, and Nebraska, we observed an immediate decrease in positivity percentage during the week of social distancing mandates for all other targets and states. Norovirus GI and GII showed the most notable drop in positivity, whereas E coli O157 appeared to be least impacted by social distancing mandates. Although we acknowledge the analysis has a multiple testing problem, the majority of our significant results showed significance even below the .01 level.

Conclusions

This study aimed to investigate the impact of social distancing mandates for COVID-19 on GI pathogen positivity, and we discovered that social distancing measures in fact decreased GI pathogen positivity initially. The use of similar measures may prove useful in GI pathogen outbreaks. The use of a unique diagnostic database in this study exhibits the potential for its use as a public health surveillance tool.

Partial ORF1ab Gene Target Failure with Omicron BA.2.12.1

Mutations in the viral genome of SARS-CoV-2 can impact the performance of molecular diagnostic assays. In some cases, such as S gene target failure, the impact can serve as a unique indicator of a particular SARS-CoV-2 variant and provide a method for rapid detection. Here we describe partial ORF1ab gene target failure (pOGTF) on the cobas ^® SARS-CoV-2 assays, defined by a ≥2 thermocycles delay in detection of the ORF1ab gene compared to the E gene. We demonstrate that pOGTF is 97% sensitive and 99% specific for SARS-CoV-2 lineage BA.2.12.1, an emerging variant in the United States with spike L452Q and S704L mutations that may impact transmission, infectivity, and/or immune evasion. Increasing rates of pOGTF closely mirrored rates of BA.2.12.1 sequences uploaded to public databases, and, importantly increasing local rates of pOGTF also mirrored increasing overall test positivity. Use of pOGTF as a proxy for BA.2.12.1 provides faster tracking of the variant than whole-genome sequencing and can benefit laboratories without sequencing capabilities.

Clinical features and epidemiological analysis of respiratory human adenovirus infection in hospitalized children: a cross-sectional study in Zhejiang

Background

HAdV is one of the common pathogens in hospitalized children with acute respiratory infections (ARIs). We aim to describe the clinical and laboratory features, epidemiological characteristics, and HAdV species and/or types of inpatients with HAdV respiratory infections.

Methods

Respiratory samples were gathered from inpatients diagnosed ARIs in Children’s Hospital, Zhejiang University School of Medicine, and were detected by using Direct Immunofluorescence Assay from 2018 to 2019. PCR amplification and sequencing of the hypervariable zone of hexon gene were used for genotyping. The clinical and laboratory features, and HAdV genotyping, and epidemiological characteristic analysis were retrospectively performed.

Results

Of 7072 samples collected, 488 were identified as HAdV-positive. The overall detection rate was 6.9%. The peaked detection rate was 14.1% in January 2019. HAdV-positive cases with ARIs mainly appeared in winter. The detection rate was highest among children between 6 months and 2 years (8.7%, 123/1408). Clinical diagnosis included pneumonia (70.3%, 343/488), bronchitis (7.0%, 34/488) and acute upper respiratory tract infection (22.7%, 111/488). The common clinical manifestations were fever (93.4%, 456/488), cough (94.7%, 462/488), wheezing (26.2%, 128/488), and shortness of breath (14.8%, 72/488). 213 (43.6%) cases had co-infection and 138 (28.3%) cases had extrapulmonary symptoms. 96(19.7%) cases had intrapulmonary and intrathoracic complications.78 (16.0%) had an underlying condition, most of which were congenital heart diseases (20.5%, 16/78). The proportions of hyperpyrexia, duration of fever > 10 days, severe pneumonia, and wheezing in the co-infection group were remarkably higher than those in HAdV single-infection group (all p < 0.05). The proportions of duration of hospitalization, duration of fever > 10 days, wheezing, shortness of breath, change in level of consciousness, serosal fluids, extrapulmonary symptoms, co-infections and underlying diseases were significantly higher in severe pneumonia group than those in the mild pneumonia group (all p < 0.05). Four HAdV species were successfully identified in 155 cases and presented by 8 genotypes. HAdV-B3 (56.1%, 87/155) and HAdV -B7 (31.0%, 48/155) were the most predominant detected types and occurred commonly in different severity groups (p = 0.000), while, HAdV-B55 was detected only in the severe group. HAdV-B7’s detection rate in the severe pneumonia group was significantly higher than the non-severe pneumonia group.

Conclusion

HAdV detection rate is related to age and season. Bronchopneumonia accounts for about 70% HAdV-positive inpatients. The common clinical manifestations include hyperpyrexia, cough, wheezing, and shortness of breath. HAdV-B3 and HAdV-B7 are the most common types in children diagnosed with respiration infections.

Front-Line Emergency Department Clinician Acceptability and Use of a Prototype Real-Time Cloud-Based Influenza Surveillance System

Objectives: To assess emergency department (ED) clinicians' perceptions of a novel real-time influenza surveillance system using a pre- and post-implementation structured survey.

Methods: We created and implemented a laboratory-based real-time influenza surveillance system at two EDs at the beginning of the 2013-2014 influenza season. Patients with acute respiratory illness were tested for influenza using rapid PCR-based Cepheid Xpert Flu assay. Results were instantaneously uploaded to a cloud-based data aggregation system made available to clinicians via a web-based dashboard. Clinicians received bimonthly email updates summating year-to-date results. Clinicians were surveyed prior to, and after the influenza season, to assess their views regarding acceptability and utility of the surveillance system data which were shared via dashboard and email updates.

Results: The pre-implementation survey revealed that the majority (82%) of the 151 ED clinicians responded that they “sporadically” or “don't,” actively seek influenza-related information during the season. However, most (75%) reported that they would find additional information regarding influenza prevalence useful. Following implementation, there was an overall increase in the frequency of clinician self-reporting increased access to surveillance information from 50 to 63%, with the majority (75%) indicating that the surveillance emails impacted their general awareness of influenza. Clinicians reported that the additional real-time surveillance data impacted their testing (65%) and treatment (51%) practices.

Conclusions: The majority of ED clinicians found surveillance data useful and indicated the additional information impacted their clinical practice. Accurate and timely surveillance information, distributed in a provider-friendly format could impact ED clinician management of patients with suspected influenza.

Spotlight influenza: Influenza surveillance before and after the introduction of point-of-care testing in Denmark, season 2014/15 to 2018/19

Background

In Denmark, influenza surveillance is ensured by data capturing from existing population-based registers. Since 2017, point-of-care (POC) testing has been implemented outside the regional clinical microbiology departments (CMD).

Aim

We aimed to assess influenza laboratory results in view of the introduction of POC testing.

Methods

We retrospectively observed routine surveillance data on national influenza tests before and after the introduction of POC testing as available in the Danish Microbiological Database. Also, we conducted a questionnaire study among Danish CMD about influenza diagnostics.

Results

Between the seasons 2014/15 and 2018/19, 199,744 influenza tests were performed in Denmark of which 44,161 were positive (22%). After the introduction of POC testing, the overall percentage of positive influenza tests per season did not decrease. The seasonal influenza test incidence was higher in all observed age groups. The number of operating testing platforms placed outside a CMD and with an instrument analytical time ≤ 3 h increased after 2017. Regionally, the number of tests registered as POC in the Danish Microbiological Database and the number of tests performed with an instrument analytical time ≤ 3 h or outside a CMD partially differed. Where comparable (71% of tests), the relative proportion of POC tests out of all tests increased from season 2017/18 to 2018/19. In both seasons, the percentage of positive POC tests resulted slightly lower than for non-POC tests.

Conclusion

POC testing integrated seamlessly into national influenza surveillance. We propose the use of POC results in the routine surveillance of seasonal influenza.

Accurate influenza forecasts using type-specific incidence data for small geographic units

Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.

Real-time gastrointestinal infection surveillance through a cloud-based network of clinical laboratories

Acute gastrointestinal infection (AGI) represents a significant public health concern. To control and treat AGI, it is critical to quickly and accurately identify its causes. The use of novel multiplex molecular assays for pathogen detection and identification provides a unique opportunity to improve pathogen detection, and better understand risk factors and burden associated with AGI in the community. In this study, de-identified results from BioFire^® FilmArray^® Gastrointestinal (GI) Panel were obtained from January 01, 2016 to October 31, 2018 through BioFire^® Syndromic Trends (Trend), a cloud database. Data was analyzed to describe the occurrence of pathogens causing AGI across United States sites and the relative rankings of pathogens monitored by FoodNet, a CDC surveillance system were compared. During the period of the study, the number of tests performed increased 10-fold and overall, 42.6% were positive for one or more pathogens. Seventy percent of the detections were bacteria, 25% viruses, and 4% parasites. Clostridium difficile, enteropathogenic Escherichia coli (EPEC) and norovirus were the most frequently detected pathogens. Seasonality was observed for several pathogens including astrovirus, rotavirus, and norovirus, EPEC, and Campylobacter. The co-detection rate was 10.2%. Enterotoxigenic E. coli (ETEC), Plesiomonas shigelloides, enteroaggregative E. coli (EAEC), and Entamoeba histolytica were detected with another pathogen over 60% of the time, while less than 30% of C. difficile and Cyclospora cayetanensis were detected with another pathogen. Positive correlations among co-detections were found between Shigella/Enteroinvasive E. coli with E. histolytica, and ETEC with EAEC. Overall, the relative ranking of detections for the eight GI pathogens monitored by FoodNet and BioFire Trend were similar for five of them. AGI data from BioFire Trend is available in near real-time and represents a rich data source for the study of disease burden and GI pathogen circulation in the community, especially for those pathogens not often targeted by surveillance.

Epidemiological dynamics of enterovirus D68 in the United States and implications for acute flaccid myelitis

Acute flaccid myelitis (AFM) recently emerged in the United States as a rare but serious neurological condition since 2012. Enterovirus D68 (EV-D68) is thought to be a main causative agent, but limited surveillance of EV-D68 in the United States has hampered the ability to assess their causal relationship. Using surveillance data from the BioFire Syndromic Trends epidemiology network in the United States from January 2014 to September 2019, we characterized the epidemiological dynamics of EV-D68 and found latitudinal gradient in the mean timing of EV-D68 cases, which are likely climate driven. We also demonstrated a strong spatiotemporal association of EV-D68 with AFM. Mathematical modeling suggested that the recent dominant biennial cycles of EV-D68 dynamics may not be stable. Nonetheless, we predicted that a major EV-D68 outbreak, and hence an AFM outbreak, would have still been possible in 2020 under normal epidemiological conditions. Nonpharmaceutical intervention efforts due to the ongoing COVID-19 pandemic are likely to have reduced the sizes of EV-D68 and AFM outbreaks in 2020, illustrating the broader epidemiological impact of the pandemic.

The Effects of Social Distancing Policies on Non-SARS-CoV-2 Respiratory Pathogens

Background

The initial focus of the US public health response to coronavirus disease 2019 (COVID-19) was the implementation of numerous social distancing policies. While COVID-19 was the impetus for imposing these policies, it is not the only respiratory disease affected by their implementation. This study aimed to assess the impact of social distancing policies on non-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) respiratory pathogens typically circulating across multiple US states.

Methods

Linear mixed-effect models were implemented to explore the effects of 5 social distancing policies on non-SARS-CoV-2 respiratory pathogens across 9 states from January 1 through May 1, 2020. The observed 2020 pathogen detection rates were compared week by week with historical rates to determine when the detection rates were different.

Results

Model results indicate that several social distancing policies were associated with a reduction in total detection rate, by nearly 15%. Policies were associated with decreases in pathogen circulation of human rhinovirus/enterovirus and human metapneumovirus, as well as influenza A, which typically decrease after winter. Parainfluenza viruses failed to circulate at historical levels during the spring. The total detection rate in April 2020 was 35% less than the historical average. Many of the pathogens driving this difference fell below the historical detection rate ranges within 2 weeks of initial policy implementation.

Conclusions

This analysis investigated the effect of multiple social distancing policies implemented to reduce transmission of SARS-CoV-2 on non-SARS-CoV-2 respiratory pathogens. These findings suggest that social distancing policies may be used as an impactful public health tool to reduce communicable respiratory illness.

Epidemiological dynamics of enterovirus D68 in the US: implications for acute flaccid myelitis

The lack of active surveillance for enterovirus D68 (EV-D68) in the US has hampered the ability to assess the relationship with predominantly biennial epidemics of acute flaccid myelitis (AFM), a rare but serious neurological condition. Using novel surveillance data from the BioFire® Syndromic Trends (Trend) epidemiology network, we characterize the epidemiological dynamics of EV-D68 and demonstrate strong spatiotemporal association with AFM. Although the recent dominant biennial cycles of EV-D68 dynamics may not be stable, we show that a major EV-D68 epidemic, and hence an AFM outbreak, would still be possible in 2020 under normal epidemiological conditions. Significant social distancing due to the ongoing COVID-19 pandemic could reduce the size of an EV-D68 epidemic in 2020, illustrating the potential broader epidemiological impact of the pandemic.

Human adenovirus species in children with acute respiratory illnesses

BACKGROUND

Human adenovirus (HAdV) species B, C, and E are commonly associated with acute respiratory illnesses (ARI). We sought to determine the association between HAdV species and ARI severity in children over one respiratory season at Monroe Carell Jr. Children's Hospital at Vanderbilt.

METHODS

We conducted a retrospective cohort study of children with HAdV from a provider-ordered BioFire® FilmArray Respiratory Pathogen Panel 2.0 (RPP) from 05/2018-06/2019. Type-specific PCR assays for HAdV-B3, B7, B11, B14, B16, B21, HAdV-C1, C2, C5, C6, and HAdV-E4 were performed. Demographics, clinical characteristics, and outcome data were compared between HAdV species.

RESULTS

Of 4514 respiratory specimens collected, 2644 (59 %) had at least one pathogen detected by RPP, and 384 (15 %) were HAdV-positive; 342 (89 %) were available for research testing with 306 (89 %) specimens from unique symptomatic individuals; 237 (77 %) were positive for the following species: 104 (44 %) HAdV-B, 114 (48 %) HAdV-C, 9 (4%) HAdV-E, and 10 (4%) with co-detection between species. The majority with identified HAdV species were seen in the ED (62 %), and approximately one-third were hospitalized. Patients with HAdV-C were more likely to be younger, hospitalized, and have a higher frequency of seizures compared to HAdV-B.

CONCLUSION

HAdV-C and HAdV-B were the most common species detected, with differences in clinical characteristics and outcomes noted. Additional studies with larger sample sizes focusing on a high-risk pediatric population are necessary to determine if differences in illness severity across individual HAdV types exist to guide further type-specific HAdV vaccine development.

Socioeconomic bias in influenza surveillance

Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America’s primary national influenza surveillance system (ILINet) monitors outpatient healthcare providers, which may be largely inaccessible to lower socioeconomic populations. Recent initiatives to incorporate Internet-source and hospital electronic medical records data into surveillance systems seek to improve the timeliness, coverage, and accuracy of outbreak detection and situational awareness. Here, we use a flexible statistical framework for integrating multiple surveillance data sources to evaluate the adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google Flu Trends) data for situational awareness of influenza across poverty levels. We find that ZIP Codes in the highest poverty quartile are a critical vulnerability for ILINet that the integration of next generation data fails to ameliorate.

Public health agencies maintain increasingly sophisticated surveillance systems, which integrate diverse data streams within limited budgets. Here we develop a method to design robust and efficient forecasting systems for influenza hospitalizations. With these forecasting models, we find support for a key data gap namely that the USA’s public health surveillance data sets are much more representative of higher socioeconomic sub-populations and perform poorly for the most at-risk communities. Thus, our study highlights another related socioeconomic inequity—a reduced capability to monitor outbreaks in at-risk populations—which impedes effective public health interventions.

The Epidemiology of Admission-Requiring Pediatric Respiratory Infections in a Japanese Community Hospital Using Multiplex PCR

Respiratory tract infections (RTIs) are the most common diseases globally among children. This study aimed to assess the epidemiology of admission-requiring pediatric RTI cases and evaluate the effect of the pathogen type on the length of hospital stay (LOS) using the FilmArray^® respiratory panel, a multiplex PCR test. The age-specific distribution and seasonality of viruses were investigated between March 26, 2018 and April 12, 2019. Multivariable linear regression analyses were performed to evaluate the effect of pathogen type and coinfection on LOS. Among 153 hospitalized RTI patients, respiratory syncytial virus was the leading cause of hospitalization in infants < 12 months of age (27.7%). Human metapneumovirus and parainfluenza virus were also major causes of hospitalization in patients aged 2-3 years (22.6% and 22.6%, respectively). In the multivariable linear regression model excluding rhinovirus/enterovirus, there was a significant association between viral coinfection and longer LOS (p = 0.012), while single viral infection of any type was not positively correlated with LOS. This study revealed the epidemiology of admission-requiring pediatric RTIs.

In vivo antiviral host response to SARS-CoV-2 by viral load, sex, and age

Despite limited genomic diversity, SARS-CoV-2 has shown a wide range of clinical manifestations in different patient populations. The mechanisms behind these host differences are still unclear. Here, we examined host response gene expression across infection status, viral load, age, and sex among shotgun RNA-sequencing profiles of nasopharyngeal swabs from 430 individuals with PCR-confirmed SARS-CoV-2 and 54 negative controls. SARS-CoV-2 induced a strong antiviral response with upregulation of antiviral factors such as OAS1-3 and IFIT1-3 , and Th1 chemokines CXCL9/10/11 , as well as a reduction in transcription of ribosomal proteins. SARS-CoV-2 culture in human airway epithelial cultures replicated the in vivo antiviral host response. Patient-matched longitudinal specimens (mean elapsed time = 6.3 days) demonstrated reduction in interferon-induced transcription, recovery of transcription of ribosomal proteins, and initiation of wound healing and humoral immune responses. Expression of interferon-responsive genes, including ACE2 , increased as a function of viral load, while transcripts for B cell-specific proteins and neutrophil chemokines were elevated in patients with lower viral load. Older individuals had reduced expression of Th1 chemokines CXCL9/10/11 and their cognate receptor, CXCR3 , as well as CD8A and granzyme B, suggesting deficiencies in trafficking and/or function of cytotoxic T cells and natural killer (NK) cells. Relative to females, males had reduced B and NK cell-specific transcripts and an increase in inhibitors of NF-κB signaling, possibly inappropriately throttling antiviral responses. Collectively, our data demonstrate that host responses to SARS-CoV-2 are dependent on viral load and infection time course, with observed differences due to age and sex that may contribute to disease severity.

Dynamic LTR retrotransposon transcriptome landscape in septic shock patients

BACKGROUND

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. Numerous studies have explored the complex and dynamic transcriptome modulations observed in sepsis patients, but a large fraction of the transcriptome remains unexplored. This fraction could provide information to better understand sepsis pathophysiology. Multiple levels of interaction between human endogenous retroviruses (HERV) and the immune response have led us to hypothesize that sepsis is associated with HERV transcription and that HERVs may contribute to a signature among septic patients allowing stratification and personalized management.

METHODS

We used a high-density microarray and RT-qPCR to evaluate the HERV and Mammalian Apparent Long Terminal Repeat retrotransposons (MaLR) transcriptome in a pilot study that included 20 selected septic shock patients, stratified on mHLA-DR expression, with samples collected on day 1 and day 3 after inclusion. We validated the results in an unselected, independent cohort that included 100 septic shock patients on day 3 after inclusion. We compared septic shock patients, according to their immune status, to describe the transcriptional HERV/MaLR and conventional gene expression. For differential expression analyses, moderated t tests were performed and Wilcoxon signed-rank tests were used to analyze RT-qPCR results.

RESULTS

We showed that 6.9% of the HERV/MaLR repertoire was transcribed in the whole blood, and septic shock was associated with an early modulation of a few thousand of these loci, in comparison to healthy volunteers. We provided evidence that a subset of HERV/MaLR and conventional genes were differentially expressed in septic shock patients, according to their immune status, using monocyte HLA-DR (mHLA-DR) expression as a proxy. A group of 193 differentially expressed HERV/MaLR probesets, tested in an independent septic shock cohort, identified two groups of patients with different immune status and severity features.

CONCLUSION

We demonstrated that a large, unexplored part of our genome, which codes for HERV/MaLR, may be linked to the host immune response. The identified set of HERV/MaLR probesets should be evaluated on a large scale to assess the relevance of these loci in the stratification of septic shock patients. This may help to address the heterogeneity of these patients.

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.

Comparison of Multiplex Gastrointestinal Pathogen Panel and Conventional Stool Testing for Evaluation of Patients With HIV Infection

BACKGROUND

Gastrointestinal pathogen panels (GPPs) are increasingly used to identify stool pathogens, but their impact in people with HIV (PWH) is unknown. We performed a retrospective cohort study comparing GPP and conventional stool evaluation in PWH.

METHODS

We included all PWH who underwent GPP (Biofire Diagnostics; implemented September 15, 2015) or conventional testing, including stool culture, Clostridium difficile polymerase chain reaction testing, fluorescent smears for Cryptosporidium or Giardia, and ova and parasite exams (O&P) from 2013 to 2017. A total of 1941 specimens were tested, with 169 positive specimens detected in 144 patients. We compared result turnaround time, pathogen co-infection, antibiotic treatment, and treatment outcomes between positive specimens detected by conventional testing vs GPP.

RESULTS

Overall, 124 patient samples tested positive by GPP, compared with 45 patient specimens by conventional testing. The GPP group demonstrated a higher co-infection rate (48.4% vs 13.3%; P < .001) and quicker turnaround time (23.4 vs 71.4 hours; P < .001). The GPP identified 29 potential viral infections that were undetectable by conventional stool tests. Unnecessary anti-infective therapy was avoided in 9 of 11 exclusively viral infections. Exclusively nonpathogenic parasites (n = 13) were detected by conventional stool tests, the majority of which were treated with metronidazole. There were no significant differences in clinical outcomes between groups.

CONCLUSIONS

In PWH, GPP implementation improved antibiotic stewardship through shorter turnaround times and detection of enteric viral pathogens.

One Health and Zoonotic Uncertainty in Singapore and Australia: Examining Different Regimes of Precaution in Outbreak Decision-Making

A One Health approach holds great promise for attenuating the risk and burdens of emerging infectious diseases (EIDs) in both human and animal populations. Because the course and costs of EID outbreaks are difficult to predict, One Health policies must deal with scientific uncertainty, whilst addressing the political, economic and ethical dimensions of communication and intervention strategies. Drawing on the outcomes of parallel Delphi surveys conducted with policymakers in Singapore and Australia, we explore the normative dimensions of two different precautionary approaches to EID decision-making—which we call regimes of risk management and organizing uncertainty, respectively. The imperative to act cautiously can be seen as either an epistemic rule or as a decision rule, which has implications for how EID uncertainty is managed. The normative features of each regime, and their implications for One Health approaches to infectious disease risks and outbreaks, are described. As One Health attempts to move upstream to prevent rather than react to emergence of EIDs in humans, we show how the approaches to uncertainty, taken by experts and decision-makers, and their choices about the content and quality of evidence, have implications for who pays the price of precaution, and, thereby, social and global justice.

Feasibility of Point-of-Care Testing for Influenza Within a National Primary Care Sentinel Surveillance Network in England: Protocol for a Mixed Methods Study

Background

Point-of-care testing (POCT) for influenza promises to provide real-time information to influence clinical decision making and improve patient outcomes. Public Health England has published a toolkit to assist implementation of these tests in the UK National Health Service.

Objective

A feasibility study will be undertaken to assess the implementation of influenza POCT in primary care as part of a sentinel surveillance network.

Methods

We will conduct a mixed methods study to compare the sampling rates in practices using POCT and current virology swabbing practices not using POCT, and to understand the issues and barriers to implementation of influenza POCT in primary care workflows. The study will take place between March and May 2019. It will be nested in general practices that are part of the English national sentinel surveillance network run by the Royal College of General Practitioners Research and Surveillance Centre. The primary outcome is the number of valid influenza swabs taken and tested by the practices involved in the study using the new POCT.

Results

A total of 6 practices were recruited, and data collection commenced on March 11, 2019. Moreover, 312 swab samples had been collected at the time of submission of the protocol, which was 32.5% (312/960) of the expected sample size. In addition, 68 samples were positive for influenza, which was 20.1% (68/338) of the expected sample size.

Conclusions

To the best of our knowledge, this is the first time an evaluation study has been undertaken on POCT for influenza in general practice in the United Kingdom. This proposed study promises to shed light on the feasibility of implementation of POCT in primary care and on the views of practitioners about the use of influenza POCT in primary care, including its impact on primary care workflows.

International Registered Report Identifier (IRRID)

DERR1-10.2196/14186

Technology to advance infectious disease forecasting for outbreak management

Forecasting is beginning to be integrated into decision-making processes for infectious disease outbreak response. We discuss how technologies could accelerate the adoption of forecasting among public health practitioners, improve epidemic management, save lives, and reduce the economic impact of outbreaks.

[Meningitis/Encephalitis diagnosis in ICU using Multiplex PCR system: Is it time of change?]

OBJECTIVE

To evaluate the clinical impact of Meningitis/Encephalitis FilmArray® panel for the diagnosis of cerebral nervous system infection and to compare the results (including time for diagnosis) with those obtained by conventional microbiological techniques.

METHODS

A prospective observational study in an Intensive Care Unit of adults from a tertiary hospital was carried out. Cerebrospinal fluid from all patients was taken by lumbar puncture and assessed by the meningitis/encephalitis FilmArray® panel ME, cytochemical study, Gram, and conventional microbiological cultures.

RESULTS

A total of 21 patients admitted with suspicion of Meningitis/Encephalitis. Median age of patients was 58.4 years (RIQ 38.1-67.3), median APACHE II 18 (RIQ 12-24). Median stay in ICU and median hospital stay was 4 (RIQ 2-6) and 17 days (RIQ 14-28), respectively. The overall mortality was 14.3%. A final clinical diagnosis of meningitis or encephalitis was established in 16 patients, obtaining the etiological diagnosis in 12 of them (75%). The most frequent etiology was Streptococcus pneumoniae (8 cases). FilmArray® allowed etiological diagnosis in 3 cases in which the culture had been negative, and the results led to changes in the empirical antimicrobial therapy in 7 of 16 cases (43.8%). FilmArray® yielded a global sensitivity and specificity of 100% and 90%, respectively. The median time to obtain results from the latter and conventional culture (including antibiogram) was 2.9 hours (RIQ 2.1-3.8) and 45.1 hours (RIQ 38.9-58.7), respectively.

CONCLUSIONS

The Meningitis/Encephalitis FilmArray® panel was able to establish the etiologic diagnosis faster than conventional methods. Also, it achieved a better sensitivity and led to prompt targeted antimicrobial therapy.

Multiplex PCR for Detection and Identification of Microbial Pathogens

 Multiplexed nucleic acid-based tests for infectious disease have become a standard part of clinical laboratory practice. These tests provide a comprehensive syndrome-based approach to determine the etiological agent of disease. The technology underlying these different systems is reviewed here with a special focus on the BioFire FilmArray® platform. The literature on the clinical utility and cost-effectiveness of these platforms for respiratory, blood culture, and gastrointestinal infections is discussed. Although there are reports showing a clear benefit to the patient or to the healthcare system from adopting a syndromic molecular approach, it is also apparent that clinical laboratories and healthcare providers are still learning how to take full advantage of the new systems. Finally, some improvements to this technology that should appear in the next few years are discussed. These include automated pathogen-specific surveillance based on aggregating the data from these systems, a move toward point-of-care syndromic testing, and further decreases in time to result of the tests.