Disease Surveillance

Foodborne illnesses sicken 48 million and kill 3,000 Americans every year, presenting an enduring threat to the public’s health. In just the past three years alone, the United States has experienced at least four major multistate outbreaks in food. Despite this growing problem, efforts to prevent foodborne illness pose a particular public health challenge due in part to the widely variable laws governing foodborne illness surveillance and outbreak response. The recent passage of the Food Safety Modernization Act (FSMA) presents an opportunity for researchers, program managers, and policy makers to assess and correct the legal barriers that may hinder states in effectively implementing the FSMA’s vision with regard to increased state and local capacity for surveillance and outbreak response.

Objective

To document and assess the variation in state legislation relating to foodborne disease surveillance and outbreak response for all 50 states and the District of Columbia by creating a database and appendix of laws and regulations that will be made available to researchers and policymakers.

Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods [1,2]. Spatial Scan Statistics have been adapted to analyze multivariate data sources [1]. In this context, only ad hoc procedures have been devised to address the problem of selecting the most likely cluster and computing its significance. A multi-objective scan was proposed to detect clusters for a single data source [3].

Objective:

To incorporate information from multiple data streams of disease surveillance to achieve more coherent spatial cluster detection using statistical tools from multi-criteria analysis.

Los Alamos National Laboratory has been funded by the Defense Threat Reduction Agency to determine the relevance of data streams for an integrated global biosurveillance system. We used a novel method of evaluating the effectiveness of data streams called the “surveillance window”. The concept of the surveillance window is defined as the brief period of time when information gathered can be used to assist decision makers in effectively responding to an impending outbreak. We used a stepwise approach to defining disease specific surveillance windows;

1. Timeline generation through historical perspectives and epidemiological simulations.
2. Identifying the surveillance windows between changes in “epidemiological state” of an outbreak.
3. Data streams that are used or could have been used due to their availability during the generated timeline are identified. If these data streams fall within a surveillance window, and provide both actionable and non-actionable information, they are deemed to have utility.

Objective

The goal of this project is the evaluation of data stream utility in integrated, global disease surveillance. This effort is part of a larger project with the goal of developing tools to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.

Previous studies have documented significant lags in official reporting of outbreaks compared to unofficial reporting (1,2). MoH+ provides an additional tool to analyze this issue, with the unique advantage of actively gathering a wide range of streamlined official communication, including formal publications, online press releases, and social media updates.

Objective:

To introduce MoH+, HealthMap’s (HM) real-time feed of official government sources, and demonstrate its utility in comparing the timeliness of outbreak reporting between official and unofficial sources.

 

Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory (LANL) has been funded by the Defense Threat Reduction Agency to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. Specifically, this project entails the evaluation of data streams either currently in use in surveillance systems or new data streams having the potential to enable early disease detection. An overview of this project will be presented, together with results of data stream evaluation. This project will help gain an understanding of data streams relevant to early warning/monitoring of disease outbreaks.

Objective:

The overall objective of this project is to provide a robust evaluation of data streams that can be leveraged from existing and developing national and international disease surveillance systems, to create a global disease monitoring system and provide decision makers with timely information to prepare for and mitigate the spread of disease.

The Infectious Disease Society of America’s Emerging Infections Network (EIN) is a sentinel network of over 1,200 practicing infectious disease physicians, supported by the Centers for Disease Control and Prevention (CDC). In January 2012, the EIN listserv fielded a member inquiry about treatment recommendations for a complicated polymicrobial wound infection in a traveler returning to the United States from India. The posting led to a member-to-member communication that resulted in shipment of clinical microbiology isolates from one member’s hospital to another’s research laboratory. Molecular evaluation of the clinical isolates uncovered previously undetected carriage of the emerging NDM-1 enzyme in 2 of the Enterobacteriaceae species. Based on this interaction, we built a flexible online surveillance registry (CaseFinder) for infectious disease physicians to report cases of CRE.

Objective

To create a flexible online surveillance system for infectious disease experts to report cases of emerging infectious diseases.

Syndromic surveillance offers the potential for earlier detection of bioterrorism, outbreaks, and other public health emergencies than traditional disease surveillance. The Maryland Department of Health and Mental Hygiene (DHMH) Office of Preparedness and Response (OP&R) conducts syndromic surveillance using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). Since its inception, ESSENCE has been a vital tool for DHMH, providing continuous situational awareness for public health policy decision makers. It has been established in the public health community that syndromic surveillance data, including school absenteeism data, has efficacy in monitoring disease, and specifically, influenza activity. Schools have the potential to play a major role in the spread of disease during an epidemic. Therefore, having school absenteeism data in ESSENCE would provide the opportunity to monitor schools throughout the school year and take appropriate actions to mitigate infections and the spread of disease.

Objective

The state of Maryland has incorporated 100% of its public school systems into a statewide disease surveillance system. This session will discuss the process, challenges, and best practices for expanding the ESSENCE system to include school absenteeism data as part of disease surveillance. It will also discuss the plans that Maryland has for using this new data source, as well as the potential for further expansion.

The International Society for Disease Surveillance held its eleventh annual conference in San Diego on December 4th and 5th, 2012, under the theme Expanding Collaborations to Chart a New Course in Public Health Surveillance.  During these two days, practitioners and researchers across many disciplines gathered to share best practices, lessons learned and cutting edge approaches to timely disease surveillance.  A record number of abstracts were received, reviewed and presented – the schedule included 99 orals, 4 panels, 94 posters, 5 roundtables and 12 system demonstrations.  Presenters represented 24 different countries from Africa, North and South America, Europe, and Asia .  Topics covered included, but were not limited to, statistical methods for outbreak detection, border health, data quality, evaluation of novel data streams, influenza surveillance, best practices and policies for information sharing, social network analysis, data mining techniques, surveillance during weather events and mass gatherings, syndrome development, and novel uses of syndromic surveillance data.  There were also discussions on the impact of regulations and standards development on disease surveillance, including Meaningful Use and the International Health Regulations.

Monitoring laboratory test reports could aid disease surveillance by adding diagnostic specificity to early warning signals and thus improving the efficiency of public health investigation of detected signals. Laboratory data could also be employed to direct and evaluate interventions and countermeasures, while monitoring outbreak trends and progress; this would ultimately result in better outbreak response and management, and enhanced situation awareness. Since Electronic Laboratory Reporting (ELR) has the potential to be more accurate, timely, and cost-effective than reporting by other means of communication (e.g., mail, fax, etc.), ELR adoption has been systematically promoted as a public health priority.  However, the continuing use of non-standard, local codes or text to represent laboratory test type and results complicates the use of ELR data in public health practice. Use of structured, unique, and widely available coding system(s) to support the concepts represented by locally assigned laboratory test order and result information improves the computational characteristics of ELR data. Out of several coding strategies available, the Office of the U.S. National Coordinator for Health Information Technology has recently suggested incorporating Logical Observation Identifiers Names and Codes (LOINC) for laboratory orders and Systemized Nomenclature of Medicine- Clinical Terms (SNOMED CT) codes for laboratory results to standardize ELR.

Objective:

To examine the use of LOINC and SNOMED CT codes for coding laboratory orders and results in laboratory reports sent from 63 non-federal hospitals to the BioSense Program in calendar year 2011.

 

The International Health Regulations (IHR) 2005, provides a framework that supports efforts to improve global health security and requires that, member states develop and strengthen systems and capacity for disease surveillance and detection and response to public health threats. To contribute to this global agenda, an international collaborative comprising of personnel from the Health Protection Agency, West Midlands, United Kingdom (HPA); the Indian Institute of Public Health (IIPH), Hyderabad, Andhra Pradesh (AP) state, India and the Department of Community Medicine, Rajarajeswari Medical College and Hospital (RRMCH), Bangalore, Karnataka state, India was established with funding from the HPA Global Health Fund to deliver the objectives stated above.

Objective:

This project aimed to contribute to ongoing efforts to improve the capability and capacity to undertake disease surveillance and Emergency Preparedness and Response (EPR) activities in India. The main outcome measure was to empower a cadre of trainers through the inter-related streams of training & education to enhance knowledge and skills and the development of collaborative networks in the regions.