Syndromic Surveillance

Public health practitioners endeavor to expand and refine their syndromic and other advanced surveillance systems which are designed to supplement their existing laboratory testing and disease surveillance toolkit. While much of the development and widespread implementation of these systems was previously supported by public health preparedness funding, the reduction of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. The appearance of highly-pathogenic avian influenza (HPAI) H3N2v, and other novel influenza A viruses required agencies to carefully identify systems which provide the most cost-effective data to support their public health practice. The global emergence of influenza A (H7N9), Ebola virus strains, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and other viruses associated with high mortality, emphasize the importance of maintaining vigilance for the presence of emerging diseases.

Objective

To continue efforts in characterizing the challenges experienced by influenza surveillance coordinators and other practitioners conducting surveillance for the presence of avian influenza, novel respiratory diseases, and other globally emerging viruses in an era of limited resources among public health agencies.

Data submitted to ILINet from ambulatory practices are a primary feature of influenza-like illness (ILI) surveillance in the United States. Practices count relevant patient records and submit this data manually to ILINet. The ongoing data collection is useful for surveillance, and a significant amount of historical data has accumulated which is useful for research purposes and comparisons of the present season to the past. However, the tabulation of this data is costly, and retention of sentinel practices can be challenging as there is no mandate to submit data. Increasingly, the EpiCenter syndromic surveillance system is receiving data from ambulatory practices. Syndromic surveillance data is sent automatically in near-realtime. Meaningful Use requirements incentivize practices to participate in ongoing data transmission. Syndromic surveillance data from ambulatory practices is thus a possible substitute for the current, more labor-intensive surveillance of ambulatory practices.

Objective

To investigate the viability of using prediagnostic syndromic surveillance data from ambulatory practices for influenza-like illness surveillance

Norovirus, commonly referred to as the winter vomiting disease, is the most common cause of gastroenteritis worldwide, with the total number of cases reported per year in Ontario second only to the common cold. The disease is highly infectious, requires a low infectious dose, and is well-known to cause large outbreaks in closely confined populations. While deaths are rare, hospitalization and longterm sequelae are more likely to occur in at-risk populations, such as the elderly or immunocompromised. Action to reduce the number of norovirus infections per year is required due to its health and economic burden. It is estimated that norovirus infections cost the United States 2.5 billion CAD and the United Kingdom close to 200 million CAD per year in health care costs alone. While laboratory surveillance is practiced in Ontario to detect norovirus outbreaks, early detection remains a challenge. This project aims to utilize syndromic surveillance with TeleHealth Ontario data in order to develop an early warning system mitigating the impact of norovirus outbreaks.

Syndromic surveillance requires reliable, accurate, and complete healthcare encounter data to assess patterns of illness and respond to public health events. Illinois implemented syndromic surveillance statewide in response to Meaningful Use reporting objectives. To address the need for continuous, automated assessment following initial on-boarding of facility Emergency Department data, we developed an R script to assess the quality of data in the private BioSense locker database.

This script builds upon and adapts from scripts previously developed for syndromic surveillance and data quality assessment.

Objective

To describe an R script developed to assess and produce reports on data quality in the BioSense locker database.

Public health in Ontario, Canada has no standardized system for carrying out syndromic surveillance. Previous research had demonstrated wide variation in the implementation of syndromic surveillance.

Objective:

To describe results of a prospective study to assess the impact of using a standard process by which public health units (PHUs) investigate syndromic surveillance alerts for respiratory illness.

Traditional influenza surveillance relies on reports of influenzalike illness (ILI) by healthcare providers, capturing individuals who seek medical care and missing those who may search, post, and tweet about their illnesses instead. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia for influenza surveillance, but with conflicting findings, studies have only evaluated these web-based sources individually or dually without comparing all three of them1-5. A comparative analysis of all three web-based sources is needed to know which of the web-based sources performs best in order to be considered to complement traditional methods.

Objective

To comparatively analyze Google, Twitter, and Wikipedia by evaluating how well change points detected in each web-based source correspond to change points detected in CDC ILI data.

The French syndromic surveillance system SursaUD® has been set up by Santé publique France, the national public health agency (formerly French institute for public health - InVS) in 2004. In 2016, the system is based on three main data sources: the attendances in about 650 emergency departments (ED), the consultations to

 62 emergency general practitioners’ (GPs) associations SOS Médecins and the mortality data from 3,000 civil status offices [1]. Daily, about 60,000 attendances in ED (88% of the national attendances), 8,000 visits in SOS Médecins associations (95% of the national visits) and 1,200 deaths (80% of the national mortality) are recorded all over the territory and transmitted to Santé publique France. About 100 syndromic groupings of interest are constructed from the reported diagnostic codes, and monitored daily or weekly, for different age groups and geographical scales, to characterize trends, detect expected or unexpected events (outbreaks) and assess potential impact of both environmental and infectious events. All-causes mortality is also monitored in similar objectives. Two user-friendly interactive web applications have been developed using the R shiny package [2] to provide a homogeneous framework for all the epidemiologists involved in the syndromic surveillance at the national and the regional levels.

Objective

The presentation describes the design and the main functionalities of two user-friendly applications developed using R-shiny to support the statistical analysis of morbidity and mortality data from the French syndromic surveillance system SurSaUD.

The CMS EHR Incentive Programs include a measure for meaningful use of EHR systems for submitting syndromic surveillance messages to public health. The Stage 2 measure defines the standard for transmission to be HL7 v2.5.1 Admit/Discharge/Transfer messages according to the PHIN Messaging Guide for Syndromic Surveillance and Conformance Clarification for EHR Certification of Electronic Syndromic Surveillance, Addendum to PHIN Messaging Guide for Syndrome Surveillance. The National Institute of Standards and Technology (NIST) provides an online testing tool for validating messages. While some jurisdictions use the Biosense platform for receiving, managing, and analyzing syndromic surveillance data, there is no consistent tool that is available to jurisdictions to assess the quality and conformance of data submissions both at the time of on-boarding a new reporting facility and on an ongoing basis during production operations.

The New York City Citywide Immunization Registry (CIR), the immunization information system for NYC that has been operational since 1997, has as part of its software suite an Open Source, webbased data quality assurance (QA) tool used by its research scientists to qualify new sites for reporting data electronically via HL7 v2 messages, and for monitoring the ongoing quality of data submissions over time. A validation process evaluates incoming messages against the rules established by an implementation guide (IG) and stores the result of the evaluation in a CIR database table that is accessible by the QA Tool which displays the data to an administrative user. This project served as a proof-of-concept for implementing a similar process for syndromic surveillance.

Objective

To leverage an existing open source quality assurance software tool created for the immunization domain and modify it to serve as a quality assurance tool for syndromic surveillance messages.

Kansas storms can occur without warning and have potential to cause a multitude of health issues. Extreme weather preparedness and event monitoring for public health effects is being developed as a function of syndromic surveillance at the Kansas Department of Health and Environment (KDHE). The Syndromic Surveillance Program at KDHE utilized emergency department (ED) data to detect direct health effects of the weather events in the first 9 months of 2016. Current results show injuries directly related to the storms and also some unexpected health effects that warrant further exploration.

Objective

To evaluate syndrome definitions capturing storm- and extreme weather-related emergency department visits in Kansas hospitals participating in the National Syndromic Surveillance Program (NSSP).

Kansas storms can occur without warning and have potential to cause a multitude of health issues. Extreme weather preparedness and event monitoring for public health effects is being developed as a function of syndromic surveillance at the Kansas Department of Health and Environment (KDHE). The Syndromic Surveillance Program at KDHE utilized emergency department (ED) data to detect direct health effects of the weather events in the first 9 months of 2016. Current results show injuries directly related to the storms and also some unexpected health effects that warrant further exploration.

Objective

To evaluate syndrome definitions capturing storm- and extreme weather-related emergency department visits in Kansas hospitals participating in the National Syndromic Surveillance Program (NSSP).