Incidence of Healthcare-Associated Influenza-Like Illness After a Primary Care Encounter Among Young Children

2018 ◽  
Vol 8 (3) ◽  
pp. 191-196 ◽  
Author(s):  
Kristen Feemster ◽  
Russell Localio ◽  
Robert Grundmeier ◽  
Joshua P Metlay ◽  
Susan E Coffin
Keyword(s):  
Primary Care ◽  
Young Children ◽  
Confidence Interval ◽  
Respiratory Virus ◽  
Virus Transmission ◽  
Clinic Visit ◽  
Electronic Health Record Data ◽  
Influenza Like Illness ◽  
Pediatric Clinic ◽  
Healthcare Associated

Abstract Background Despite potential respiratory virus transmission in pediatric clinics, little is known about the risk of healthcare-associated viral infections attributable to outpatient encounters. We evaluated whether exposure to a pediatric clinic visit was associated with subsequent influenza-like illness (ILI). Methods Using electronic health record data, we conducted a retrospective cohort study of all children aged <6 years who presented to a provider in a 29-clinic pediatric primary care network for a non–ILI-related encounter over 2 respiratory virus seasons (September 1, 2012, to April 30, 2014). We defined a risk period for potential healthcare-associated (HA) ILI of 1 to 8 days after a non-ILI clinic visit and identified all cases of ILI to compare the incidences of ILI visits 1 to 8 days after a non-ILI encounter and those of visits >8 days after a non-ILI encounter. Results Among 149987 children <6 years of age (mean age, 2.5 years) with ≥1 non-ILI visit during the study period, 531928 total encounters and 13951 (2.9%) ILI encounters were identified; 1941 (13.9%) occurred within the HA-ILI risk window. The incidence rate ratios (IRRs) for ILI 1 to 8 days after compared with ILI >8 days after a non-ILI visit during season 1 were 1.36 (95% confidence interval, 1.22–1.52) among children ≥2 years of age and 1.01 (95% confidence interval, 0.93–1.09) among children <2 years of age. Estimates remained consistent during season 2 and with a risk window of 3, 4, or 9 days. Conclusions Pediatric clinic visits during a respiratory virus season were significantly associated with an increased incidence of subsequent ILI among children aged 2 to 6 years but not among those aged <2 years. These findings support the hypothesis that respiratory virus transmission in a pediatric clinic can result in HA ILI in young children.

A Primary Care and Behavioral Health Collaboration Developed to Screen for and Address Toxic Stress in Young Children in a Large Inner City Pediatric Clinic

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 174A-174A
Author(s):  
Lucy Z. Garbus ◽  
Stephanie Carlin ◽  
Tinamarie Fioroni ◽  
Maude Aldridge ◽  
Zachary Goode ◽  
...  
Keyword(s):  
Primary Care ◽  
Young Children ◽  
Inner City ◽  
Behavioral Health ◽  
Toxic Stress ◽  
Pediatric Clinic

scholarly journals Association Between Children’s Hospital Visitor Restrictions and Healthcare-Associated Viral Respiratory Infections: A Quasi-Experimental Study

2019 ◽  
Vol 9 (2) ◽  
pp. 240-243 ◽  
Author(s):  
Hawa Forkpa ◽  
Angela H Rupp ◽  
Stanford T Shulman ◽  
Sameer J Patel ◽  
Elizabeth L Gray ◽  
...  
Keyword(s):  
Respiratory Virus ◽  
Respiratory Infections ◽  
Virus Transmission ◽  
Index Number ◽  
Transmission Index ◽  
Viral Respiratory Infection ◽  
Quasi Experimental ◽  
Viral Respiratory Infections ◽  
Healthcare Associated ◽  
Viral Respiratory

AbstractWe investigated the effect of annual winter visitor restrictions on hospital respiratory virus transmission. The healthcare-associated (HA) viral respiratory infection (VRI) transmission index (number of HA VRIs per 100 inpatient community-associated VRIs) was 59% lower during the months in which visitor restrictions were implemented. These data prompt consideration for instituting year-round visitor restrictions.

scholarly journals HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden

2020 ◽  
Vol 41 (S1) ◽  
pp. s39-s39
Author(s):  
Pontus Naucler ◽  
Suzanne D. van der Werff ◽  
John Valik ◽  
Logan Ward ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Surveillance System ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Healthcare Associated

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None

scholarly journals Consultations for Influenza-like Illness in Primary Care in The Netherlands: A Regression Approach

2020 ◽  
Author(s):  
F. Christiaan K. Dolk ◽  
Pieter T. de Boer ◽  
Lisa Nagy ◽  
Gé A. Donker ◽  
Adam Meijer ◽  
...  
Keyword(s):  
Primary Care ◽  
The Netherlands ◽  
Influenza Like Illness ◽  
Regression Approach

scholarly journals Droplets and aerosols: An artificial dichotomy in respiratory virus transmission

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Yannis Drossinos ◽  
Thomas P. Weber ◽  
Nikolaos I. Stilianakis
Keyword(s):  
Respiratory Virus ◽  
Virus Transmission

scholarly journals Use of electronic health record data from diverse primary care practices to identify and characterize patients’ prescribed common medications

2018 ◽  
Vol 26 (1) ◽  
pp. 172-180 ◽  
Author(s):  
Allison M Cole ◽  
Kari A Stephens ◽  
Imara West ◽  
Gina A Keppel ◽  
Ken Thummel ◽  
...  
Keyword(s):  
Primary Care ◽  
Adverse Events ◽  
Electronic Health Record ◽  
Research Network ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Care Practices ◽  
Electronic Health ◽  
Primary Care Practices

We use prescription of statin medications and prescription of warfarin to explore the capacity of electronic health record data to (1) describe cohorts of patients prescribed these medications and (2) identify cohorts of patients with evidence of adverse events related to prescription of these medications. This study was conducted in the WWAMI region Practice and Research Network (WPRN)., a network of primary care practices across Washington, Wyoming, Alaska, Montana and Idaho DataQUEST, an electronic data-sharing infrastructure. We used electronic health record data to describe cohorts of patients prescribed statin or warfarin medications and reported the proportions of patients with adverse events. Among the 35,445 active patients, 1745 received at least one statin prescription and 301 received at least one warfarin prescription. Only 3 percent of statin patients had evidence of myopathy; 51 patients (17% of those prescribed warfarin) had a bleeding complication. Primary-care electronic health record data can effectively be used to identify patients prescribed specific medications and patients potentially experiencing medication adverse events.

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