Surveillance Systems

The Border Infectious Disease Surveillance (BIDS) program was established in 1999 by the Centers for Disease Control and Prevention and Mexico Secretariat of Health, following mandates from the Council of State and Territorial Epidemiologists and the U.S.-Mexico Border health association to improve border surveillance. The BIDS program is a bi-national public health collaboration to create an active sentinel-site surveillance of infectious disease among the U.S.-Mexico border. It is a collaborative effort between Local, State, Federal, and International Public Health agencies throughout both countries in the border region. This project is aimed at using the best aspects of both countries surveillance system.

Objective

To establish and maintain an active binational sentinel hospital-site surveillance system. To enhance border region epidemiology and laboratory infrastructure.

Livestock owners normally pay the full cost of disease testing. As a result the number of laboratory submissions is dependent on the owner's perception that testing is beneficial. This decreases the likelihood of an accurate diagnosis and biases the number and type of samples received by a laboratory. Despite these limitations, laboratory data are commonly used for passive disease surveillance. The Ontario Farm-call Surveillance Project (OFSP) analyzed disease-related farm call data supplied by livestock veterinarians. Project goals were to provide a new data source for livestock disease monitoring and to improve the quality of laboratory data. As an incentive for participation, veterinarians were not charged when diagnostic samples were sent to the Animal Health Laboratory (AHL), University of Guelph.

Objective

To evaluate free diagnostic testing as an incentive for compliance with a livestock disease surveillance program.

The spread of infectious diseases is facilitated by human travel. Infectious diseases are often introduced into a population by travelers and then spread among susceptible individuals. Likewise uninfected susceptible travelers can move into populations sustaining the spread of an infectious disease.

Several disease-modeling efforts have incorporated travel data (e.g., air, train, or subway traffic) as well as census data, all in an effort to better understand the spread of infectious diseases. Unfortunately, most travel data is not fine grained enough to capture individual movements over long periods and large spaces. It does not, for example, document what happens when people get off a train or airplane. Thus, other methods have been suggested to measure how people move, including both the tracking of currency and movement of individuals using cell phone data. Although these data are finer grained, they have their own limitations (e.g., sparseness) and are not generally available for research purposes.

FourSquare is a social media application that permits users to "check-in" (i.e., record their current location at stores, restaurants, etc.) via their mobile telephones in exchange for incentives (e.g., location-specific coupons). FourSquare and similar applications (Gowalla, Yelp, etc.) generally broadcast each check-in via Twitter or Facebook; in addition, some GPS-enabled mobile Twitter clients add explicit geocodes to individual tweets.

Here, we propose the use of geocoded social media data as a real-time fine-grained proxy for human travel.

Objective

To use sequential, geocoded social media data as a proxy for human movement to support both disease surveillance and disease modeling efforts.

Previous studies in developed countries showed school absenteeism data can serve as a proxy for monitoring infectious disease activities and facilitates early community outbreak detection. However, absenteeism patterns may differ in developing settings and affect the utility of the surveillance system. Despite the non-specific nature of absenteeism data, other practical challenges will need to overcome for system set up and maintenance in remote area.

Objective

We explored the feasibility and practicability of setting up an electronic school absenteeism reporting system for disease surveillance in rural area of Kampot province, Cambodia.

It is admitted that real time surveillance system permits to reduce delay of outbreak detection, and preventive measures implementation. It is usually based on pre diagnostic numeric data collection and transmission. ASTER (Alerte et surveillance en temps reel) is a real time surveillance system for French Armed Forces deployed in French Guiana and Djibouti, constituted by 2 kinds of networks : several declaration networks and one analysis network. On June 2011, an outbreak occurred among a French Army Regiment in Djibouti, which has permitted to evaluate ASTER in real conditions.

Objective

To discuss advantages of real time surveillance system within Armed Forces, using a real outbreak case.

For public health surveillance to achieve its desired purpose of reducing morbidity and mortality, surveillance data must be linked to public health response. While there is evidence of the growing popularity of syndromic surveillance (1,2), the impact or value added with its application to public health responses is not well described (3).

Objective

To describe if and how syndromic surveillance data influenced public health decisions made during the 2009 H1N1 pandemic within the context of other existing public health surveillance systems.

Imbalances in wealth, education, infrastructure, socio-political leadership, healthcare, and demographics create opportunities and challenges when implementing public health interventions. Understanding these, while embracing "smart power," one can objectively assess a country's receptivity for support. Therefore, we developed a novel conceptual framework and toolset that objectively measured opportunities and challenges to inform decision-making, specifically about future implementation of the Electronic Integrated Disease Surveillance System (EIDSS) - a computer-based system for national reporting and monitoring of reportable human and veterinary infectious diseases in East Africa and the Middle East.

An increase in tuberculosis (TB) among homeless men residing in Marion County, Indiana was noticed in the summer of 2008. The Marion County Public Health Department (MCPHD) hosted screening events at homeless shelters in hopes of finding unidentified cases. To locate men who had a presumptive positive screen, the MCPHD partnered with researchers at Regenstrief Institute (RI) to create an alert for health care providers who use the Gopher patient management system in one of the city's busiest emergency departments. A similar process was used at this facility to impact prescription behavior.[1] A similar method was also used at the New York City Department of Health and Mental Hygiene.[2]

Oregon Health Authority (OHA), in collaboration with the Johns Hopkins University Applied Physics Laboratory, recently implemented Oregon ESSENCE, an automated, electronic syndromic surveillance system. One way to strengthen syndromic surveillance is to include data from multiple sources. We are integrating data from emergency departments, state notifiable conditions and vital statistics, and the Oregon Poison Center (OPC). Implementing ESSENCE in Oregon provided the opportunity to automate poison center surveillance, which was previously done manually. In order to achieve this, OHA needed a daily data feed of OPC data to upload into Oregon ESSENCE servers. For OPC to do this directly, they would have incurred significant costs to develop the necessary electronic infrastructure to query and send the data; furthermore, OPC does not employ IT staff. OHA does not currently have funding available to support IT system interoperability with Oregon ESSENCE, so we sought a low-cost solution that would build upon existing systems that utilized the National Poison Data System (NPDS) web service.

Objective

Enhance Oregon ESSENCE by integrating data from the Oregon Poison Center (OPC) in a cost-effective manner.

In Reunion Island, the non-specific surveillance was mainly developed during A(H1N1) influenza pandemic in 2009. In March 2010, a new surveillance system was implemented from National Health Insurance data. This monitoring was based on the weekly consultation number and home visits by general practitioners.

Objective

To assess the ability to detect an unusual health event from National Health Insurance data.