The National Syndromic Surveillance Program (NSSP) Team hosted the 2nd webinar of its Fall 2020 New Site Onboarding Window on October 27, 2020. The webinar focuses on data ingestion into the NSSP BioSense Platform and data quality checks and reports.
View the recording of the webinar here.
The Council of State and Territorial Epidemiology (CSTE), in collaboration with Thought Bridge, LLC, recently developed the Improving the Quality of Completeness and Electronic Health Record Data Used in Syndromic Surveillance Final Report which aimed to identify data quality issues and develop short- (6 months or less) and long-term (>6 months) recommendations.
The DQ Dashboard is an interactive tool developed to help you identify potential data processing issues and to ensure useful syndromic data by measuring the timeliness, completeness, and validity of data being processed on the BioSense Platform.
Held on June 19, 2019.
During this 90-minute session, Aaron Kite-Powell, M.S., from CDC and Wayne Loschen, M.S., from JHU-APL provided updates on the NSSP ESSENCE platform and answered the community's questions on ESSENCE functions and features.
The public health problem identified by Alabama Department of Public Health Syndromic Surveillance (AlaSyS) was that the data reflected in the user application of ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) was underestimating occurrences of syndromic alerts preventing Alabama Department of Public Health (ADPH) from timely recognition of potential public health threats. Syndromic surveillance (SyS) data in ESSENCE were not reliable for up to a week after the visit date due to slow processing, server downtime, and untimely data submission from the facilities. For AlaSyS, 95 percent of data should be submitted within 24 hours from time of visit, for near real time results. The slow data processing caused latency in the data deeming it less useful for surveillance purposes, consequently the data was not meaningful for daily alerts. For example, if a user ran a report to assess the number of Emergency Department (ED) visits that mentioned heroin in the chief complaint (CC), depending on the status of the data coming from the facility (processing, sending, or offline), the number of visits visible to the user could vary from one to several days. With the opioid epidemic Alabama is currently facing, this delay poses a major public health problem.
Objective: To monitor and improve the data quality captured in syndromic surveillance for Alabama Department of Public Health Syndromic Surveillance (AlaSyS).
Routine childhood administration of varicella-containing vaccine has resulted in the number of varicella (chickenpox) cases in Pennsylvania falling from nearly 3,000 cases in 2007 to less than 400 cases in 2017. Prior to 2018, the completeness of varicella case investigation data documented in Pennsylvania's electronic disease surveillance system (PA-NEDSS) was not routinely monitored by Department of Health (DOH) staff. A pilot project was initiated in April 2018 to monitor and improve completeness of select varicella case investigation variables.
Objective: The objective of this study was to evaluate the impact of efforts made to improve the completeness of select varicella (chickenpox) case investigation variables.
On November 20, 2017, several sites participating in the NSSP reported anomalies in their syndromic data. Upon review, it was found that between November 17-18, an EHR vendorâÂÂs syndromic product experienced an outage and errors in processing data. The ISDS DQC, NSSP, a large EHR vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire NSSP CoP.
Objective: The National Syndromic Surveillance Program (NSSP) Community of Practice (CoP) works to support syndromic surveillance by providing guidance and assistance to help resolve data issues and foster relationships between jurisdictions, stakeholders, and vendors. During this presentation, we will highlight the value of collaboration through the International Society for Disease Surveillance (ISDS) Data Quality Committee (DQC) between jurisdictional sites conducting syndromic surveillance, the Centers for Disease Control and PreventionâÂÂs (CDC) NSSP, and electronic health record (EHR) vendors when vendor-specific errors are identified, using a recent incident to illustrate and discuss how this collaboration can work to address suspected data anomalies.
Real-Time Biosurveillance Program (RTBP) introduces modern surveillance technology to health departments in Sri Lanka and Tamil Nadu, India. Triage data from each patient visit (basic demographics, signs, symptoms, preliminary diagnoses) is recorded on paper at health facilities. Case records are transmitted daily to a central database using the RTBP mobile phone application. It is done by medical professionals in India, but in Sri Lanka, due to staffing constraints, the same duty is performed by lower cost personnel with limited domain knowledge. That results in noticeable differences in data entry error rates between the two locations. Most of such issues are due to systematic and subjectivemisinterpretations of the handwritten doctor notes by the data entry personnel. If not identified and remedied quickly, these errors can adversely affect accuracy and timeliness of health events detection. There is a need to support system managers in their efforts to maintain high reliability of data used for public health surveillance.
Objective
We present a method for automated detection of systematic data entry errors in real time biosurveillance.
In Montreal, notifiable diseases are reported to the Public Health Department (PHD). Of 44, 250 disease notifications received in 2009, up to 25% had potential address errors. These can be introduced during transcription, handwriting interpretation and typing at various stages of the process, from patients, labs and/or physicians, and at the PHD. Reports received by the PHD are entered manually (initial entry) into a database. The archive personnel attempts to correct omissions by calling reporting laboratories or physicians. Investigators verify real addresses with patients or physicians for investigated episodes (40–60%).
The Dracones qualite (DQ) address verification algorithm compares the number, street and postal code against the 2009 Canada Post database. If the reported address is not consistent with a valid address in the Canada Post database, DQ suggests a valid alternative address.
Objective
To (1) validate DQ developed to improve data quality for public health mapping and (2) identify the origin of address errors.
In June 2009, the CDC defined a confirmed case of H1N1 as a person with an ILI and laboratory confirmed novel influenza A H1N1 virus infection. ILI is defined by the CDC as fever and cough and/or sore throat, in the absence of a known cause other than influenza. ILI cases are usually reported without accounting for alternate diagnoses (that is, pneumonia). Therefore, evaluation is needed to determine the impact of alternate diagnoses on the accuracy of the ILI case definition.
Objective
This study investigates the impact of alternate diagnoses on the accuracy of the Centers for Disease Control and Prevention’s (CDC) case definition for influenza-like illness (ILI) when used as a screening tool for influenza A (H1N1) virus during the 2009 pandemic, and the implications for public health surveillance.