ISDS Conference

Legionellosis is a respiratory disease that can lead to serious illness such as pneumonia, and can even result in death. Since 2010, increased reports of legionellosis have been received in Toronto during the summer months and led to a five-fold increase by 2012. This underscored the need to rule out common sources through a rapid assessment of exposure data (i.e., locations visited) for any spatio-temporal links. Legionella bacteria from a single source can affect individuals at distances as great as 10 km (1) but dispersion of Legionella bacteria is generally within 1 km of the source (2). This information was used to describe an area of potential risk around each exposure location. Adding temporal information from dates of potential exposures could provide a useful tool for outbreak detection. An automated tool was developed to link spatial and temporal data to assess need for further follow up.

Objective:

To develop an outbreak detection tool which uses spatial information related to temporally clustered legionellosis cases reported in Toronto, Canada.

OSS is rapidly becoming part of more public health applications. Mobile health (mHealth) initiatives and the need for electronic processes to support healthcare (eHealth) provide particularly good examples of government use of open source software. The growth of global and national mHealth and eHealth needs has spurred innovation in software development. In resource limited areas that do not have the infrastructure for sophisticated computing tools but where cellular technology is prevalent, mHealth solutions are able to move such communities into the digital age. Monetary costs of licensing and maintaining proprietary software systems have been common challenges to these end users, but OSS helps solve these problems. OSS has already been used to further certain global public health initiatives, but more needs to be done. For instance, the passage of the World Health Organization (WHO) International Health Regulations (IHR) in 2005 required member countries to implement certain core public health capacities by June 2012. The adoption more broadly of OSS has the potential to improve the efficiency of IHR implementation, and therefore global public health initiatives in general, because it provides a free, modifiable software option which can be altered to meet specific requirements.

Objective

Provide an overview of common open source software (OSS) licenses used in public health applications, and discuss how OSS can help improve global public health security.

Symptoms of influenza-like illness (ILI) were reported daily by health workers via the integrated syndromic surveillance system in rural China (ISSC)(1).

Objective

To analyze the growth and variation of ILI reports using individual growth curve model.

The Influenza Division (ID) in the Centers for Disease Control and Prevention (CDC) maintains the WHO/NREVSS surveillance system, a network of laboratories in the U.S. that report influenza testing results. This system has seen many changes during the past 40 years, especially since the 2009 H1N1 pandemic. This was due in large part to increased adoption of HL7 messaging via PHLIP. PHLIP data is detailed, standardized influenza testing information, reported in near real-time. The data received through this and other report methods is published online in national and regional aggregate form in FluView, a weekly surveillance report prepared by CDC.

Objective

Describe the changes to the World Health Organization/National Respiratory and Enteric Virus Surveillance System (WHO/NREVSS) influenza surveillance system over time, with a focus on the Public Health Laboratory Interoperability Project (PHLIP) and how it has influenced the system

Schools inherently foster the transmission of infections from person to person because they are a group setting in which people are in close contact and share supplies and equipment. Surveillance is important in schools and actions that can help control the spread of infections are the key to effective disease control in the community [1]. School health physicians should play an important role in surveillance. Their training on data collection, analysis, reporting and importance of feedback is recommended in order to improve the disease surveillance system and therefore the prevention and control of diseases.

Objective

We assessed the effect of a training program on the knowledge of school physicians regarding surveillance. The purpose of evaluation is to improve the information provided and thereby help improve service provision and delivery.

Despite the number of infections, hospitalizations, and deaths from influenza each year, developing the ability to predict the timing of these outbreaks has remained elusive. Public health practitioners have lacked a reliable, easy-to-implement method for predicting the onset of a period of elevated influenza incidence in a community. We (a team of statisticians, epidemiologists, and clinicians) have developed a model to help public health practitioners develop simple, adaptable, data-driven rules to define a period of increased disease incidence in a given location. We call this method the Above Local Elevated Respiratory illness Threshold (ALERT) algorithm. The ALERT algorithm is a simple method that defines a period of elevated disease incidence in a community or hospital that systematically collects surveillance data on a particular disease.

Objective

Our objective was to develop a simple, easy-to-use algorithm to predict the onset of a period of elevated influenza incidence in a community using surveillance data.

Adoption of electronic medical records is on the rise, due to the Health Information Technology for Economic and Clinical Health Act and meaningful use incentives. Simultaneously, numerous HIE initiatives provide data sharing flexibility to streamline clinical care. Due to the consolidated data availability in centralized HIE models, conducting syndromic surveillance using locally developed systems, such as GUARDIAN, is becoming feasible. During the past year, Chicago has embarked on a city-wide HIE deployment campaign. Perhaps the most unique aspect of this endeavor is that the data warehouse for the HIE is intricately tied to the GUARDIAN syndromic surveillance system.

Objective

The objective is to describe the technical process, challenges, and lessons learned in scaling up from a local to regional syndromic surveillance system using the MetroChicago Health Information Exchange (HIE) and Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification (GUARDIAN) collaborative initiative.

The ASTER system aims at providing an integrated real time epidemiological status of all the French Forces deployed abroad1. But, unlike usual surveillance systems, ASTER must cover several target populations exposed to different biological and chemical threats, and the surveillance of each population must be tailored to meet its specific risk profile2. Moreover, a surveillance may change at any moment, depending on the evolution of the nature of the threats. For coping with these highly varying surveillance profiles within a same surveillance system, we have developed a formal surveillance system model we have used for designing the collaborations of the system components and allowing the required surveillance versatility.

Objective

This paper briefly describes the model for surveillance system design that is used by the ASTER system, which is progressively deployed within the French Forces.

Syndromic surveillance is usually presented as relevant for event detection. As the data collected automatically from data sources is detailed enough (e.g. ICD10 codes), it may contribute to assess and quantify the burden of health events and describe their main epidemiological features. In France, besides the national liver transplant data, no surveillance data are available for ALF. Since ALF is severe, threatens the vital prognosis in absence of intensive care, may require liver transplantation and is quite well characterized clinically, patients are very likely to be diagnosed with ALF in ED at the onset phase. ALF is caused by viral infections (hepatitis A, B, C, D or E viruses), drug or toxic exposures, autoimmune or metabolic disorders (Wilson's disease), some of which have public health implications (viral hepatitis, drug or toxicological adverse effects). We therefore hypothesized that surveillance of ALF through an ED syndromic surveillance system would be feasible. The aim of our work was to explore the relevance of ED data to describe the main features and assess the burden of ALF.

Objective

The objective of this study was to assess the interest and feasibility of using syndromic surveillance data from emergency departments (ED) for the description of clinical and epidemiological characteristics of patients with acute liver failure (ALF) during the 2010-2012 period in France.

During March-May 2013, 14 overdose deaths occurred in RI that were caused by acetyl fentanyl, a novel synthetic opioid about five times more potent than heroin1. Ten of these deaths were clustered in March, causing a significant increase over baseline of monthly illicit drug overdose deaths in RI1. Overdose deaths are well described in RI by forensic toxicology testing results. However, the overall number of ED visits associated with this event was unknown. We used RODS data retrospectively to characterize overdose related ED visits in RI and to analyze trends.

Objective

Determine if the Rhode Island (RI) Real-time Outbreak and Disease Surveillance (RODS) system (a syndromic surveillance system) identified an increase in overdoses during a known cluster of illicit drug overdose deaths in RI and characterize emergency department (ED) overdose visits during the 15 month period prior to and including the known cluster.