Improving Syndromic Data Quality through Implementation of Error Capture Module

Oregon Public Health Division (OPHD), in collaboration with The Johns Hopkins University Applied Physics Laboratory, implemented Oregon ESSENCE in 2011. ESSENCE is an automated, electronic syndromic surveillance system that captures emergency department data from hospitals across Oregon. While each hospital system sends HL7 2.5.1-formatted messages, each uses a uniquely configured interface to capture, extract, and send data. Consequently, ESSENCE receives messages that vary greatly in content and structure.

January 25, 2018

Investigating Other Syndrome in ESSENCE from a Data Quality Perspective

The Louisiana Office of Public Health (OPH) Infectious Disease Epidemiology Section (IDEpi) conducts syndromic surveillance of Emergency Department (ED) visits through the Louisiana Early Event Detection System (LEEDS) and submits the collected data to ESSENCE. There are currently 86 syndromes defined in LEEDS including infectious disease, injury and environmental exposure syndromes, among others. LEEDS uses chief complaint, admit reason, and/or diagnosis fields to tag visits to relevant syndromes.

January 25, 2018

Data Quality

Problem Summary

Data collection across a growing stream of contributing facilities and variables requires automated, consistent, and efficient monitoring of quality. Epidemiologists tasked with analyzing syndromic data need to be confident in the overall quality of their data, and aware of the effects of poor data quality when interpreting data. Data quality is also increasingly important as data are shared across jurisdictions and combined for analysis.

October 30, 2017

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

The Infectious Disease Epidemiology Section (IDEpi) within the Office of Public Health (LaOPH) conducts syndromic surveillance of emergency departments by means of the Louisiana Early Event Detection System (LEEDS). LEEDS accepts ADT (admit-discharge transfer) messages from participating hospitals, predominately A04 (registration) and A03 (discharge), to obtain symptom or syndrome information on patients reporting to hospital emergency departments.

September 07, 2017

Data quality monitoring for syndromic surveillance using R: A tidy approach

Presented July 27, 2017.

The inferences we make from data can only be as good as the quality of the data; making sure that we are receiving timely, quality data is important. In this presentation, Mark White will describe a number of functions that he has written to perform data quality checks on Kansas emergency department records from NSSP’s BioSense Platform.

September 21, 2017

Data Quality Committee (DQC)

Our mission as the Data Quality Committee is to engage the NSSP Community of Practice to identify and attempt to address syndromic surveillance data quality challenges with thoughtful discussion and the inclusion of outside stakeholders. We strive to foster relationships between all groups with a hand in syndromic messaging in order to better syndromic surveillance practice for everyone. 


October 31, 2018

Importance of Continued Data Quality Assessment of Syndromic Production Data

Once a facility meets data quality standards and is approved for production, an assumption is made that the quality of data received remains at the same level. When looking at production data quality reports from various states generated using a SAS data quality program, a need for production data quality assessment was identified. By implementing a periodic data quality update on all production facilities, data quality has improved for production data as a whole and for individual facility data.

July 06, 2017

Comparative Analysis of Methods of Molecular Detection of Avian Influenza Virus

As part of this surveillance study for Avian Influenza both active and passive surveillance samples were tested using PCR and also utilized to validate the LAMP method. Active surveillance samples include pathological material and tracheal and cloacal swabs from ill poultry, which were subsequently assessed for avian influenza during diagnosis, and birds collected by hunters. Passive surveillance included environmental samples such as sand and bird faeces.

August 15, 2017

HIV Bio-behavioral Risk Study Implementation in Resource-poor Military Settings

Circumstances within the military environment may place military personnel at increased risk of contracting sexually transmitted infections (STI) including HIV. HIV bio-behavioral risk studies provide a critical source of data to estimate HIV/STI prevalence and identify risk factors, allowing programs to maximize impact by focusing on the drivers of the epidemic. 


We present lessons learned from over a decade of HIV bio- behavioral risk study implementation and capacity-building in African militaries. 

July 06, 2017

The Canadian Chronic Disease Surveillance System: A Distributed Surveillance Model

The Public Health Agency of Canada (PHAC) established the Canadian Chronic Disease Surveillance System (CCDSS) in 2009 to facilitate national estimates of chronic disease prevalence, incidence, and health outcomes. The CCDSS uses population-based linked health administrative databases from all provinces/territories (P/Ts) and a distributed analytic protocol to produce standardized disease estimates.


August 20, 2017


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