From Implementation to Automation A Step-by-Step Approach to Developing Syndromic Surveillance Systems from a Public Health Perspective

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.

Download Full-text

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

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v7i1.5835 ◽

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.

Download Full-text

Interpreting specific and general respiratory indicators in syndromic surveillance

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7597 ◽

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.

Download Full-text

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

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7612 ◽

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

Download Full-text

Identifying and Communicating the Importance of the Variable Nature of SyS Data

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7655 ◽

2017 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Heather Rubino ◽

David Atrubin ◽

Janet J. Hamilton

Keyword(s):

Public Health ◽

Trend Analysis ◽

Subject Matter ◽

General Public ◽

Syndromic Surveillance ◽

Surveillance Systems ◽

Subject Matter Experts ◽

Variable Nature ◽

The Media ◽

Time Evaluation

ObjectiveThis roundtable will provide a forum for national, state, and localmanagers of syndromic surveillance systems to discuss how theyidentify, monitor, and respond to changes in the nature of their data.Additionally, this session will focus on the strengths and weaknessof the syndromic surveillance systems for supporting programevaluation and trend analysis. This session will also provide a forumwhere subject matter experts can discuss the ways in which this deepunderstanding of their data can be leveraged to forge and improvepartnerships with academic partners.IntroductionAs syndromic surveillance systems continue to grow, newopportunities have arisen to utilize the data in new or alternativeways for which the system was not initially designed. For example,in many jurisdictions syndromic surveillance has recently becomepopulation-based, with 100% coverage of targeted emergencydepartment encounters. This makes the data more valuable for real-time evaluation of public health and prevention programs. There hasalso been increasing pressure to make more data publicly available –to the media, academic partners, and the general public.

Download Full-text

“That was then, this is now” improving public health syndromic surveillance baselines

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7600 ◽

2017 ◽

Vol 9 (1) ◽

Author(s):

Roger Morbey ◽

Alex J. Elliot ◽

Paul Loveridge ◽

Helen Hughes ◽

Sally Harcourt ◽

...

Keyword(s):

Public Health ◽

Disease Surveillance ◽

Syndromic Surveillance ◽

Regression Models ◽

Major Change ◽

Surveillance Systems ◽

Care Activity ◽

Working Practices ◽

Available Information ◽

Traditional Surveillance

ObjectiveTo improve the ability of syndromic surveillance systems to detectunusual events.IntroductionSyndromic surveillance systems are used by Public Health England(PHE) to detect changes in health care activity that are indicative ofpotential threats to public health. By providing early warning andsituational awareness, these systems play a key role in supportinginfectious disease surveillance programmes, decision making andsupporting public health interventions.In order to improve the identification ofunusualactivity, wecreated new baselines to modelseasonally expectedactivity inthe absence of outbreaks or other incidents. Although historicaldata could be used to model seasonality, changes due to publichealth interventions or working practices affected comparability.Specific examples of these changes included a major change in theway telehealth services were provided in England and the rotavirusvaccination programme introduced in July 2013 that changed theseasonality of gastrointestinal consultations. Therefore, we needed toincorporate these temporal changes in our baselines.MethodsWe used negative binominal regression to model daily syndromicsurveillance, allowing for day of week and public holiday effects.To account for step changes in data caused by changes in healthcaresystem working practices or public health interventions we introducedspecific independent variables into the models. Finally, we smoothedthe regression models to provide short term forecasts of expectedtrends.The new baselines were applied to PHE’s four syndromicsurveillance systems for daily surveillance and public-facing weeklybulletins.ResultsWe replaced traditional surveillance baselines (based on simpleaverages of historical data) with the regression models for dailysurveillance of 53 syndromes across four syndromic surveillancesystems. The improved models captured current seasonal trends andmore closely reflected actual data outside of outbreaks.ConclusionsSyndromic surveillance baselines provide context forepidemiologists to make decisions about seasonal disease activity andemerging public health threats. The improved baselines developedhere showed whether current activity was consistent with expectedactivity, given all available information, and improved interpretationwhen trends diverged from expectations.

Download Full-text

Using State and National Surveillance Systems during World Equestrian Games in NC

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v11i1.9691 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Lana Deyneka ◽

Zachary Faigen ◽

Anne Hakenwerth ◽

Nicole Lee ◽

Amy Ising ◽

...

Keyword(s):

Public Health ◽

Syndromic Surveillance ◽

Urgent Care ◽

Poison Center ◽

Surveillance Systems ◽

Mass Gathering ◽

Mass Gatherings ◽

Epidemic Intelligence ◽

Polk County ◽

Ed Visits

ObjectiveTo describe surveillance activities and use of existing state (NC DETECT) and national (NSSP) syndromic surveillance systems during the International Federation for Equestrian Sports (FEI) World Equestrian Games (WEG), in Mill Spring, NC from September 11 to September 23, 2018MethodsNC DETECT collects statewide data from hospital emergency department (ED) visits and Carolinas Poison Center (CPC) calls. NC DETECT also collects data from select Urgent Care Centers (UCC) in the Charlotte area. CPC data are updated hourly, while ED data are updated twice a day. NC DETECT data were monitored daily for census (total ED visits), communicable disease syndromes, injury syndromes, and other occurrences of public health significance related to the event. The geographic areas monitored were Polk County (the location of the main event), the counties where the guests were lodging in the Western NC Region (Henderson, Transylvania, Buncombe, Rutherford, McDowell, and Cleveland), the Charlotte Metropolitan area, and statewide. Because of the large number of people from other states and countries who attended, ED surveillance was mainly conducted by hospitals so that visits were captured for all patients and not just NC residents. WEG dashboards containing ED data were created prior to the event using NC DETECT and NSSP ESSENCE systems, and were accessible to epidemiologists at the state level. NSSP syndrome queries were shared with the neighboring state (SC) public health agency. Surveillance began two weeks prior to the event to establish baseline levels for all ED visits for hospitals in Polk County and the Western NC Region. Surveillance occurred daily before the event, during the event, and for two weeks following the event to account for incubation periods of potential diseases.ResultsThe 2018 Equestrian games in Western NC were affected by heavy rain and heat. The weather led to low attendance and cancellation of a few competitions. During the observation period, ED admissions and most of the mass gathering related syndromes in both NC DETECT and NSSP systems were at baseline. ED admissions for motor vehicle collisions and dehydration syndromes were above baseline for 09/19 and 09/21/18 (Figures 3-4). CPC calls and UC admissions for selected UC centers in the Charlotte area were also monitored, and were at baseline.ConclusionsNC DETECT and NSSP Dashboards provided effective and timely surveillance for the WEG event to assist local public health in the rural NC area with epidemiologic investigations and appropriate response. NC DETECT’s CPC and UC data provided additional valuable information, and complemented ED surveillance during the mass gathering event. Syndromic surveillance became essential during WEG, as NC DPH deployment plans and resource availability changed when Hurricane Florence bore down on the region.References1. Joseph S. Lombardo, Carol A. Sniegoski, Wayne A. Loschen, Matthew Westercamp, Michael Wade, Shandy Dearth, and Guoyan Zhang Public Health Surveillance for Mass Gatherings Johns Hopkins APL Technical Digest , Volume 27, Number 4 (2008)2. Kaiser R, Coulombier D. Epidemic intelligence during mass gatherings. Euro Surveill. 2006;113. Ising A, Li M, Deyneka L, Vaughan-Batten H, Waller A. Improving syndromic surveillance for nonpower users: NC DETECT dashboards. Emerging Health Threats Journal 2011, 4: 11702 - DOI: 10.3402/ehtj.v4i0.11702

Download Full-text

The COVID-19 pandemic: a new challenge for syndromic surveillance

Epidemiology and Infection ◽

10.1017/s0950268820001314 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 7

Author(s):

Alex J. Elliot ◽

Sally E. Harcourt ◽

Helen E. Hughes ◽

Paul Loveridge ◽

Roger A. Morbey ◽

...

Keyword(s):

Public Health ◽

Health System ◽

Syndromic Surveillance ◽

Surveillance Data ◽

Surveillance Systems ◽

Clinical Coding ◽

Public Health Response ◽

Specific Symptom ◽

Health And Social Care ◽

The Impact

Abstract The COVID-19 pandemic is exerting major pressures on society, health and social care services and science. Understanding the progression and current impact of the pandemic is fundamental to planning, management and mitigation of future impact on the population. Surveillance is the core function of any public health system, and a multi-component surveillance system for COVID-19 is essential to understand the burden across the different strata of any health system and the population. Many countries and public health bodies utilise ‘syndromic surveillance’ (using real-time, often non-specific symptom/preliminary diagnosis information collected during routine healthcare provision) to supplement public health surveillance programmes. The current COVID-19 pandemic has revealed a series of unprecedented challenges to syndromic surveillance including: the impact of media reporting during early stages of the pandemic; changes in healthcare-seeking behaviour resulting from government guidance on social distancing and accessing healthcare services; and changes in clinical coding and patient management systems. These have impacted on the presentation of syndromic outputs, with changes in denominators creating challenges for the interpretation of surveillance data. Monitoring changes in healthcare utilisation is key to interpreting COVID-19 surveillance data, which can then be used to better understand the impact of the pandemic on the population. Syndromic surveillance systems have had to adapt to encompass these changes, whilst also innovating by taking opportunities to work with data providers to establish new data feeds and develop new COVID-19 indicators. These developments are supporting the current public health response to COVID-19, and will also be instrumental in the continued and future fight against the disease.

Download Full-text