scholarly journals Delay between Discharge and Admit Time Delay in ADT-A03 messages via LEEDS

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jose Serrano
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Surveillance Systems ◽  
Hospital Emergency ◽  
Timely Manner ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
The Difference ◽  
Disease Specific

ObjectiveTo explore the difference between the reported date of admissionand discharge date using discharge messages (A03), from hospitalemergency departments participating in the Louisiana Early EventDetection System (LEEDS.IntroductionThe Infectious Disease Epidemiology Section (IDEpi) within theOffice of Public Health (LaOPH) conducts syndromic surveillanceof emergency departments by means of the Louisiana Early EventDetection System (LEEDS). LEEDS accepts ADT (admit-discharge-transfer) messages from participating hospitals, predominately A04(registration) and A03 (discharge), to obtain symptom or syndromeinformation on patients reporting to hospital emergency departments.Capturing the data using discharge messages (A03) only could resultin a delay in receipt of data by LaOPH, considering the variability inthe length of stay of a patient in the ED.MethodsEmergency department data from participating hospitals isimported daily to LEEDS and processed for syndrome classification.IDEpi syndromic surveillance messages received for the period ofCDC week 1632 and 1636 (8/8/16-9/8/16) using MS Access andExcel to calculate the difference (in days) between the reported admitdate and discharge date in A03 messages.Results88.1% of the A03 messages submitted in the 4 week analysisperiod exhibited no delay (delay=0 days) between the admit date andthe reported discharge date, compared to only 10.7% showing a delayof one day (delay = 1 day) and 1.06% showing a delay of 2 days ormore (delay≥2 days). Less than 0.2% of the messages had missinginformation regarding discharge date (Table 1).ConclusionsSyndromic surveillance systems operate under a constant need forimprovement and enhancement. The quality of the data, independentof the quality of the system, should always strive to be of the highestpedigree in order to inform disease-specific programs and detectpublic health aberrations. In order to identify these potential concerns,it is imperative that the data be submitted to public health agenciesin a timely manner. Based on this analysis, the lapse in time betweenadmit and discharge results in little to no patient syndromic data delayfor those hospital ED’s that exclusively send A03 messages. Thisstatement is supported by the finding that close to 99% of messagesdemonstrated a delay between admit date and discharge date of oneday or less.Table 1. Delay between reported Admit and Discharge date in A03 messagessubmitted to LEEDS

scholarly journals Implementing Mandatory Testing and a Public Health Commitment to Control COVID-19 on a College Campus

2021 ◽  
Author(s):  
Cindy Liu ◽  
Amita Vyas ◽  
Amanda D Castel ◽  
Karen A McDonnell ◽  
Lynn R Goldman
Keyword(s):  
Public Health ◽  
College Campus ◽  
Surveillance Program ◽  
Surveillance Systems ◽  
Health And Wellness ◽  
George Washington ◽  
Behavioral Measures ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
Daily Risk

The COVID-19 pandemic has greatly impacted US colleges and universities. As The George Washington University (GWU), a large urban university, prepared to reopen for the Fall 2020 semester, GWU established protocols to protect the health and wellness of all members of campus community. Reopening efforts included a cadre of COVID-19 surveillance systems including development of a public health COVID-19 laboratory, weekly and symptomatic SARS-CoV-2 testing and daily risk screening and symptom monitoring. Other activities included completion of a mandatory COVID-19 training and influenza vaccination for the on-campus population, quarantining of students returning to campus, campus-focused case investigations and quarantining of suspected close contacts, clinical follow-up of infected persons, and regular communication and monitoring. A smaller on-campus population of 4,435 students, faculty and staff returned to campus with later expansion of testing to accommodate GWU students living in the surrounding area. Between August 17 and December 4, 2020, 38,288 tests were performed; 220 were positive. The surveillance program demonstrated a relatively low positivity rate, with temporal clustering of infected persons mirroring community spread, and little evidence for transmission among the GWU on-campus population. These efforts demonstrate the feasibility of safely partially reopening a large urban college campus by applying core principles of public health surveillance, infectious disease epidemiology, behavioral measures, and increased testing capacity, while continuing to promote educational and research opportunities. GWU will continue to monitor the program as the pandemic evolves and periodically reassess to determine if these strategies will be successful upon a full return to in-person learning.

scholarly journals The Centennial of the Department of Epidemiology at Johns Hopkins Bloomberg School of Public Health: A Century of Epidemiologic Discovery and Education

2019 ◽  
Vol 188 (12) ◽  
pp. 2043-2048
Author(s):  
David D Celentano ◽  
Elizabeth Platz ◽  
Shruti H Mehta
Keyword(s):  
Public Health ◽  
New Technologies ◽  
Immune Deficiency Syndrome ◽  
Deficiency Syndrome ◽  
Early Years ◽  
Epidemiologic Transition ◽  
Disease Etiology ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
New Methods

Abstract The Department of Epidemiology at Johns Hopkins School of Hygiene and Public Health was founded in 1919, with Wade Hampton Frost as inaugural chair. In our Centennial Year, we review how our research and educational programs have changed. Early years focused on doctoral education in epidemiology and some limited undergraduate training for practice. Foundational work on concepts and methods linked to the infectious diseases of the day made major contributions to study designs and analytical methodologies, largely still in use. With the epidemiologic transition from infectious to chronic disease, new methods were developed. The Department of Chronic Diseases merged with the Department of Epidemiology in 1970, under the leadership of Abraham Lilienfeld. Leon Gordis became chair in 1975, and multiple educational tracks were developed. Genetic epidemiology began in 1979, followed by advances in infectious disease epidemiology spurred by the human immunodeficiency virus/acquired immune deficiency syndrome epidemic. Collaborations with the Department of Medicine led to development of the Welch Center for Prevention, Epidemiology, and Clinical Research in 1989. Between 1994 and 2008, the department experienced rapid growth in faculty and students. A new methods curriculum was instituted for upper-level epidemiologic training in 2006. Today’s research projects are increasingly collaborative, taking advantage of new technologies and methods of data collection, responding to “big data” analysis challenges. In our second century, the department continues to address issues of disease etiology and epidemiologic practice.

Lessons Learnt From Exercise Celestial Navigation: The Application of a Geographic Information System to Inform Legionnaires’ Disease Control Activity

2018 ◽  
Vol 13 (02) ◽  
pp. 372-374 ◽  
Author(s):  
Emma Quinn ◽  
Kai Hsiao ◽  
George Truman ◽  
Nectarios Rose ◽  
Richard Broome
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Outbreaks ◽  
Field Investigation ◽  
Geographic Information ◽  
Local Health ◽  
Control Activity ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
Legionnaires Disease

AbstractGeographic information systems (GIS) have emerged in the past few decades as a technology capable of assisting in the control of infectious disease outbreaks. A Legionnaires’ disease cluster investigation in May 2016 in Sydney, New South Wales (NSW), Australia, demonstrated the importance of using GIS to identify at-risk water sources in real-time for field investigation to help control any immediate environmental health risk, as well as the need for more staff trained in the use of this technology. Sydney Local Health District Public Health Unit (PHU) subsequently ran an exercise (based on this investigation) with 11 staff members from 4 PHUs across Sydney to further test staff capability to use GIS across NSW. At least 80% of exercise participants reported that the scenario progression was realistic, assigned tasks were clear, and sufficient data were provided to complete tasks. The exercise highlighted the multitude of geocoding applications and need for inter-operability of systems, as well as the need for trained staff with specific expertise in spatial analysis to help assist in outbreak control activity across NSW. Evaluation data demonstrated the need for a common GIS, regular education and training, and guidelines to support the collaborative use of GIS for infectious disease epidemiology in NSW. (Disaster Med Public Health Preparedness. 2019;13:372–374)

scholarly journals Developing Syndromic Surveillance to Monitor and Respond to Adverse Health Events Related to Psychoactive Substance Use: Methods and Applications

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 65S-72S ◽  
Author(s):  
Michelle L. Nolan ◽  
Hillary V. Kunins ◽  
Ramona Lall ◽  
Denise Paone
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
York City ◽  
Syndromic Surveillance ◽  
Psychoactive Drug ◽  
Surveillance Systems ◽  
Chief Complaints ◽  
Level Information ◽  
Ed Visits

Introduction: Recent increases in drug overdose deaths, both in New York City and nationally, highlight the need for timely data on psychoactive drug-related morbidity. We developed drug syndrome definitions for syndromic surveillance to monitor drug-related emergency department (ED) visits in real time. Materials and Methods: We used 2012 archived syndromic surveillance data from New York City hospitals to develop definitions for psychoactive drug-related syndromes. The dataset contained ED visit-level information that included patients’ chief complaints, dates of visits, ZIP codes of residence, discharge diagnoses, and dispositions. After manually reviewing chief complaints, we developed a classification scheme comprising 3 categories (overdose, drug mention, and drug abuse/misuse), which we used to define 25 psychoactive drug syndromes. From July 2013 through December 2015, the New York City Department of Health and Mental Hygiene performed daily syndromic surveillance of psychoactive drug-related ED visits using the 25 syndrome definitions. Results: Syndromic surveillance triggered 4 public health investigations, supported 8 other public health investigations that had been triggered by other mechanisms, and resulted in the identification of 5 psychoactive drug-related outbreaks. Syndromic surveillance also identified a substantial increase in synthetic cannabinoid-related visits (from an average of 3 per week in January 2014 to >300 per week in July 2015) and an increase in heroin overdose visits (from 80 to 171 in the first 3 quarters of 2012 and 2014, respectively) in a single neighborhood. Practice Implications: Syndromic surveillance using these novel definitions enabled monitoring of trends in psychoactive drug-related morbidity, initiation and support of public health investigations, and targeting of interventions. Health departments can refine these definitions for their jurisdictions using the described methods and integrate them into existing syndromic surveillance systems.

scholarly journals What did Syndromic Surveillance Show During London 2012? Lessons for Mass Gatherings

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Dan Todkill ◽  
Helen Hughes ◽  
Alex Elliot ◽  
Roger Morbey ◽  
Obaghe Edeghere ◽  
...  
Keyword(s):  
Public Health ◽  
General Practitioner ◽  
Syndromic Surveillance ◽  
Lessons Learned ◽  
Surveillance Systems ◽  
Mass Gathering ◽  
Daily Number ◽  
Mass Gatherings ◽  
London 2012 ◽  
The Impact

This paper investigates the impact of the London 2012 Olympic and Paralympic Games on syndromic surveillance systems coordinated by Public Health England. The Games had very little obvious impact on the daily number of ED attendances and general practitioner consultations both nationally, and within London. These results provide valuable lessons learned for future mass gathering events.

scholarly journals Interpreting specific and general respiratory indicators in syndromic surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Roger Morbey ◽  
Alex J. Elliot ◽  
Maria Zambon ◽  
Richard Pebody ◽  
Gillian E. Smith
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Respiratory Illness ◽  
Surveillance Systems ◽  
Respiratory Pathogens ◽  
Family Doctors ◽  
Influenza Like Illness ◽  
Public Health Decision ◽  
Syncytial Virus ◽  
To Receive

ObjectiveTo improve understanding of the relative burden of differentcausative respiratory pathogens on respiratory syndromic indicatorsmonitored using syndromic surveillance systems in England.IntroductionPublic Health England (PHE) uses syndromic surveillance systemsto monitor for seasonal increases in respiratory illness. Respiratoryillnesses create a considerable burden on health care services andtherefore identifying the timing and intensity of peaks of activity isimportant for public health decision-making. Furthermore, identifyingthe incidence of specific respiratory pathogens circulating in thecommunity is essential for targeting public health interventionse.g. vaccination. Syndromic surveillance can provide early warningof increases, but cannot explicitly identify the pathogens responsiblefor such increases.PHE uses a range of general and specific respiratory syndromicindicators in their syndromic surveillance systems, e.g. “allrespiratory disease”, “influenza-like illness”, “bronchitis” and“cough”. Previous research has shown that “influenza-like illness”is associated with influenza circulating in the community1whilst“cough” and “bronchitis” syndromic indicators in children under 5are associated with respiratory syncytial virus (RSV)2, 3. However, therelative burden of other pathogens, e.g. rhinovirus and parainfluenzais less well understood. We have sought to further understand therelationship between specific pathogens and syndromic indicators andto improve estimates of disease burden. Therefore, we modelled theassociation between pathogen incidence, using laboratory reports andhealth care presentations, using syndromic data.MethodsWe used positive laboratory reports for the following pathogens as aproxy for community incidence in England: human metapneumovirus(HMPV), RSV, coronavirus, influenza strains, invasivehaemophilusinfluenzae, invasivestreptococcus pneumoniae, mycoplasmapneumoniae, parainfluenza and rhinovirus. Organisms were chosenthat were found to be important in previous work2and were availablefrom routine laboratory testing. Syndromic data included consultationswith family doctors (called General Practitioners or GPs), calls to anational telephone helpline “NHS 111” and attendances at emergencydepartments (EDs). Associations between laboratory reports andsyndromic data were examined over four winter seasons (weeks40 to 20), between 2011 and 2015. Multiple linear regression was usedto model correlations and to estimate the proportion of syndromicconsultations associated with specific pathogens. Finally, burdenestimates were used to infer the proportion of patients affected byspecific pathogens that would be diagnosed with different symptoms.ResultsInfluenza and RSV exhibited the greatest seasonal variation andwere responsible for the strongest associated burden on generalrespiratory infections. However, associations were found with theother pathogens and the burden ofstreptococcus pneumoniaewasimportant in adult age groups (25 years and over).The model estimates suggested that only a small proportion ofpatients with influenza receive a specific diagnosis that is coded toan “influenza-like illness” syndromic indicator, (6% for both GPin-hours consultations and for emergency department attendances),compared to a more general respiratory diagnosis. Also, patients withinfluenza calling NHS 111 were more likely to receive a diagnosisof fever or cough than cold/flu. Despite these findings, the specificsyndromic indicators remained more sensitive to changes in influenzaincidence than the general indicators.ConclusionsThe majority of patients affected by a seasonal respiratory pathogenare likely to receive a non-specific respiratory diagnosis. Therefore,estimates of community burden using more specific syndromicindicators such as “influenza-like illness” are likely to be a severeunderestimate. However, these specific indicators remain importantfor detecting changes in incidence and providing added intelligenceon likely causative pathogens.Specific syndromic indicators were associated with multiplepathogens and we were unable to identify indicators that were goodmarkers for pathogens other than influenza or RSV. However, futurework focusing on differences between ages and the relative levels ofa range of pathogens may be able to provide estimates for the mix ofpathogens present in the community in real-time.

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