Surveillance Systems

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.

The CDC provides data on incidences of diseases on its website (https://data.cdc.gov/). Data is available at national, regional, and state levels, and is uploaded to the CDC’s website on a weekly basis. The CDCPlot web application (available at https://michaud.shinyapps.io/ CDCPlot/), built using the Shiny package in R, provides a quick and user-friendly method of visualizing this data. Users are able to the select timeframes, locations, and diseases which they wish to view, and plots are produced. There is an optional alert threshold, which will alert users when a disease increases significantly from one week to the next. In addition, CDCPlot provides visualizations of CDC data on Pneumonia and Influenza mortality.

Objective

To demonstrate the current features and functionality of the CDCPlot application, and to introduce potential new features of the application. 

Emerging disease clusters must be detected in a timely manner so that necessary remedial action can be taken to prevent the spread of an outbreak. The Exponentially Weighted Moving Average method (EWMA) is a particularly popular method, and has been utilized for disease surveillance in the United States.

A spatio-temporal EWMA statistic is proposed for on-line disease surveillance over multiple geographic regions. To capture spatial association, disease counts of neighboring regions are pooled together, similar to a method originally proposed by Raubertas for a different control chart. Also to increase statistical power in testing multiple EWMA statistics simultaneously, false discovery rate (FDR) is used instead of the traditional family-wise error rate (FWER).

Objective

To propose a computationally simple and a fast algorithm to detect disease outbreaks in multiple regions

In recent years, the use of social media has increased at an unprecedented rate. For example, the popular social media platform Reddit (http://www.reddit.com) had 83 billion page views from over 88,000 active sub-communities (subreddits) in 2015. Members of Reddit made over 73 million individual posts and over 725 million associated comments in the same year [1]. We use Reddit to track opium related discussions, because Reddit allows for throwaway and unidentifiable accounts that are suitable for stigmatized discussions that may not be appropriate for identifiable accounts. Reddit members exchange conversation via a forum like platform, and members who have achieved a certain status within the community are able to create new topically focused group called subreddits.

Objective

We aim to develop an automated method to track opium related discussions that are made in the social media platform called Reddit. As a first step towards this goal, we use a keyword-based approach to track how often Reddit members discuss opium related issues.

The test-negative design is a variation of the case-control study, in which patients are enrolled in outpatient clinics (and/or hospitals) based on a clinical case definition such as influenza-like illness (ILI). Patients are then tested for influenza virus, and VE is estimated from the odds ratio comparing the odds of vaccination among patients testing positive for influenza versus those testing negative, adjusting for potential confounding factors. The design leverages existing disease surveillance networks and as a result, studies using it are increasingly being reported.

Objective

We aimed to describe the theoretical basis and the potential applications of the test-negative design for estimating influenza vaccination effectiveness in sentinel influenza surveillance.

Syndromic surveillance systems are used by Public Health England (PHE) to detect changes in health care activity that are indicative of potential threats to public health. By providing early warning and situational awareness, these systems play a key role in supporting infectious disease surveillance programmes, decision making and supporting public health interventions. In order to improve the identification of unusual activity, we created new baselines to model seasonally expected activity in the absence of outbreaks or other incidents. Although historical data could be used to model seasonality, changes due to public health interventions or working practices affected comparability. Specific examples of these changes included a major change in the way telehealth services were provided in England and the rotavirus vaccination programme introduced in July 2013 that changed the seasonality of gastrointestinal consultations. Therefore, we needed to incorporate these temporal changes in our baselines.

Objective

To improve the ability of syndromic surveillance systems to detect unusual events.