Surveillance Systems

The Public Health Security and Bioterrorism Preparedness and Response Act of 2002 mandated establishing an integrated national public health surveillance system for early detection and rapid assessment of potential bioterrorism-related illness. In 2003, CDC created and launched the BioSense software program. At that time, CDC’s focus was on rapidly developing and implementing Web-based software to collect hospital emergency department data for analysis to detect and monitor syndromes of public health importance. During the ensuing decade, BioSense evolved and now is part of CDC’s renamed National Syndromic Surveillance Program (NSSP). The broader vision of NSSP aims to achieve two key goals: significantly improve technical capabilities for collecting and analyzing syndromic surveillance data, and to create and facilitate opportunities for collaboration among local, state, and national public health programs. Through NSSP, the syndromic surveillance community can be strengthened by access to improved technical capacity and to best-practices knowledge sharing among syndromic surveillance professionals. These NSSP initiatives can help the nation-wide public health community strengthen situational awareness and enhance response capability to hazardous events. NSSP encompasses people, partners, policies, information systems, standards, and resources. Session attendees will learn more about NSSP, its growing group of partners, what the program is doing now, and its future.

Objective

Inform conference attendees about the CDC National Syndromic Surveillance Program (NSSP), various program-related projects and who is working on them, what was accomplished during the past year, and NSSP-development plans for the future.

The advent of Meaningful Use (MU) has allowed for the expansion of data collected at the hospital level and received by public health for syndromic surveillance. The triage note, a free text expansion on the chief complaint, is one of the many variables that are becoming commonplace in syndromic surveillance data feeds. Triage notes are readily available in many ED information systems, including, but not limited to, Allscripts, Cerner, EPIC, HMS, MedHost, Meditech, and T-System. North Carolina’s syndromic surveillance system, NC DETECT, currently collects triage notes from 33 out of 122 hospitals in the State (27%), and this number is likely to increase.

Objective

This roundtable will provide a forum for the ISDS community to discuss the use of emergency department (ED) triage notes in syndromic surveillance. It will be an opportunity to discuss both the benefits of having this variable included in syndromic surveillance data feeds, as well as the drawbacks and challenges associated with working with such a detailed data field.

Geographic Information System (GIS) technology provides visual tools, through the creation of computerized maps, graphs, and tables of geographic data, which can assist with problem solving and inform decision-making. One of the GIS tools being developed by KFL&A Public Health is the Social Determinants of Health (SDOH) Mapper. The SDOH Mapper consists of layers of information related to deprivation and marginalization indices across Ontario. The SDOH Mapper facilitates the inclusion of information related to vulnerable populations with the use of both age and social determinants of health data into the GIS portal. This is useful for observing trends in marginalization and deprivation across dissemination areas in Ontario, and for examining health inequities in an area over time. The SDOH mapper will, in this way, improve knowledge transmission on the effects of poverty and marginalization on outcomes.

Objective

To describe how the Social Determinants of Health (SDOH) Mapper is used by KFL&A Public Health to enhance real-time situational awareness of vulnerable populations across Ontario by facilitating the inclusion of information relating to marginalization and deprivation indices.

Brucellosis is a serious disease caused by bacteria of the Brucella genus. It principally affects ruminants but may be transmitted to humans. Registration of cases in cattle farms causes considerable economic losses and creates favorable conditions for mass infection among humans. In Armenia the expansion of animal industries and urbanization are the main reasons for occurrence and development of brucellosis.

Objective

In the spring of 2014, people from vulnerable households in all marzes of Armenia were examined with the aim of active surveillance.

Influenza is a contagious disease that causes epidemics in many parts of the world. The World Health Organization estimates that influenza causes three to five million severe illnesses each year and 250,000-500,000 deaths. Predicting and characterizing outbreaks of influenza is an important public health problem and significant progress has been made in predicting single outbreaks. However, multiple temporally overlapping outbreaks are also common. These may be caused by different subtypes or outbreaks in multiple demographic groups. We describe our Multiple Outbreak Detection System (MODS) and its performance on two actual outbreaks. This work extends previous work by our group by using model-averaging and a new method to estimate non-influenza influenza-like illness (NI-ILI). We also apply MODS to a real dataset with a double outbreak.

In 2012, Canada and other World Health Organization Member States endorsed the Rio Political Declaration on Social Determinants of Health, a global commitment to address health inequities by acting on the social, economic, environmental, and other factors that shape health. The Public Health Informatics team at KFL&A Public Health works on various surveillance projects to better support vulnerable populations, and prepare for emergency situations.

Objective

This roundtable will provide a hands-on workshop to learn about three surveillance systems developed and used by the Emergency Department Syndromic Surveillance Team at KFL&A Public Health. It will be an opportunity to address issues relevant to syndromic surveillance including: equity, emergency response, health preparedness, and health systems management. Additionally, participants will be able to apply new knowledge on improving health equity, and its relationship to social determinants of health, in their own jurisdictions.

One of the greatest hurdles for BioSense Onboarding is the process of validating data received to ensure it contains Data Elements of Interest (DEOI) needed for syndromic surveillance. Efforts to automate this process are critical to meet existing and future demands for facility onboarding requests as well as provide a foundation for data quality assurance efforts. By automating the validation process, BioSense hopes to:

1. Reduce costs associated with the iterative validation process.

2. Improve BioSense response times for assistance with onboarding.

3. Improve documentation to partners about requirements and communicate changes to DEOI.

4. Provide a better foundation for data quality initiatives.

Efforts to improve data validation are being developed in alignment with BioSense future initiatives and will apply to both BioSense, Essence and other BioSense program applications.

BioSense Onboarding identified critical success factors by participating in ISDS workgroup initiatives for Onboarding and Data Quality and soliciting feedback from key jurisdictional partners. These critical success factors include; improved documentation, access to raw data, and faster validation response time.

Objective

This session will inform the BioSense Community about data validation advancements implemented this past year as well as future plans to improve the BioSense validation process to achieve emergency department representativeness goals.

Influenza-like illness (ILI) remains a significant public health burden to both the general public and the U.S. Department of Defense. Military personnel are especially susceptible to disease outbreaks owing to the often-crowded living quarters, substantial geographic movement, and physical stress placed upon them. Currently, the military employs syndromic surveillance on electronic reporting of clinical diagnoses. While faster than traditional, biologically-focused monitoring techniques, the military surveillance system proved inadequate at detecting outbreaks quickly enough in a recent study conducted by the CDC. Recently, research has included novel data sources, like social media, to conduct disease detection in real-time and capture communities not traditionally accounted for in current surveillance systems. Data-mining techniques are used to identify influenza-related social media posts and train a model against validated medical data. By integrating social media data and a medical dataset of all ILI-related laboratory specimens and doctor visits for the entire military cohort, a more comprehensive model than presently exists for disease identification and transmission will be possible.

Objective

To integrate existing influenza surveillance data sources and social media data into an accurate and timely outbreak detection model embedded into dashboard biosuveillance analytics for the Department of Defense.

Obesity and related chronic diseases cost Canadians several billion dollars annually. Dietary intake, and in particular consumption of carbonated sweetened drinks (soda), has a strong effect on the incidence of obesity and other illness. Marketing research suggests that in-store promotion, and more specifically price discounting, has a strong effect on the purchase of energy-dense products such as soda. Attempts by public health authorities to monitor price discounts are currently limited by a lack of data and methods. Although rarely used in public health surveillance, electronic retail sales data collected around the world by marketing companies such as the Nielsen Corporation have an immense potential to measure dietary choices at high geographical resolution. These scanned sales data are recorded in real-time and they include a detailed product description, price, purchased quantity, store location, and product-specific advertising activities.

Objective

To assess the influence of in-store price discounts on soda purchasing by neighborhood socio-economic status in Montreal, Canada using digital grocery store-level sales data.

Brucella spp., Coxiella burnetii, and tick-borne encephalitis virus (TBEV) are believed to be enzootic in the Republic of Kazakhstan, and pose a particular public health risk due to their transmissibility in unpasteurized milk and dairy products. We established a milk surveillance methodology employing immuno and molecular assays to identify these agents, and applied this methodology to milk samples collected in western Kazakhstan in winter 2014-2015.