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.

Impact of home versus hospital dressing on bacterial contamination of surgical scrubs in the obstetric setting: A randomized controlled trial.

BACKGROUND: The impact of the site where an obstetrician dresses in their surgical scrubs, home versus hospital, on total bacterial burden remains unknown. Therefore, our objective was to quantify the effect of dressing in surgical scrubs at home versus at the hospital on the bacterial contamination at the beginning of a scheduled shift. METHODS: This was a single blind randomized controlled trial. Eligible participants were resident physicians assigned to labor and delivery at a single institution during the study period, and participants were randomized daily to 1 of 4 arms based on the site where their scrubs were laundered (A) and where the resident dressed (B) (A/B): home/home, home/hospital, hospital/home, and hospital/hospital. At the beginning of the assigned shift, microbiologic samples from the chest pocket and pants' tie were collected with a sterile culture swab. Samples were plated on trypticase soy agar with 5% sheep blood before being incubated at 35°C-37°C for 48 hours, with observation every 24 hours. The primary outcome was total bacterial burden, defined as the sum of the colony forming units (CFUs) from the 2 sampling sites. RESULTS: There were 21 residents randomized daily for 4 days to 1 of 4 study arms, resulting in 84 observations. There were no baseline differences between the home- and hospital-dressed cohorts. Overall, 68% of sampled scrubs demonstrated some bacterial growth. There was no difference between the home- and hospital-dressed cohorts in percentage of samples demonstrating any bacterial growth after 72 hours (60% vs 76%, P = .14), nor in median bacterial burden at the beginning of a shift (2 [interquartile range, 0-7] vs 1 [interquartile range, 1-5] CFUs, P = .62). Finally, there was no difference in total bacterial burden at the beginning of a shift between the home- and hospital-dressed cohorts when stratified by site where the scrubs were laundered. CONCLUSIONS: There was no significant difference in total bacterial burden of surgical scrubs at the start of a shift between cohorts who dressed at home versus at the hospital.

Microbial Contamination on Touch Surfaces in Sick- and Well-Child Waiting Rooms in Pediatric Outpatient Facilities.

BACKGROUND: Healthcare-associated infections are a significant public health burden resulting in approximately 1.7 million infections each year. Much work is done to study the contributing factors in inpatient settings; however, little has been done to study outpatient facilities and their roles in healthcare-associated infections. While many pediatric outpatient offices utilize separated waiting areas for sick and well children to decrease the spread of disease, research has not been done to determine whether this practice is of benefit. In this study, we aimed to determine whether there is a difference in microbial burden between sick- and well-child waiting areas and to identify surfaces with the highest levels of contamination. METHODS: Touch surfaces in waiting rooms were swabbed and surveyed for total microbial growth, staphylococcal growth and Gram-negative enteric bacterial growth. Selected bacteria were identified to screen for pathogenic organisms. Surfaces sampled included seats, tables, children's tables, children's seats, magazines and books. RESULTS: We found seats, children's seats and children's books to have the highest microbial burden. No conclusions can be made on the differences in microbial contamination in sick- and well-child waiting areas because of high variation. Streptococcus pyogenes was isolated as were several opportunistic pathogens. CONCLUSIONS: This study suggests the need for better cleaning practices by pediatric outpatient facilities, to include the disinfection of additional surfaces as well as more frequent and thorough cleaning.