Surveillance

Understanding how exposure to hazards in our environment (air, water, food and surroundings) affects our health is critical to understanding causes of many chronic and acute diseases and to planning and implementing appropriate response and prevention efforts. Centers of Disease Control and Prevention (CDC) established the congressionally mandated National Environmental Public Health Tracking Program (Tracking Program) to facilitate the analysis and interpretation of both environmental and health outcome data through the building of a national tracking network which integrates data from environmental hazard monitoring, human exposure and health effects surveillance. This network of standardized electronic data provides valid scientific information on environmental exposures and adverse health conditions in a practical format to explore plausible spatial and temporal relations between these factors. The program funds and provides guidance to 24 state and local health departments to develop local tracking networks that feed data into the National Tracking Network, enabling enhanced public health actions.

Objective

The National Environmental Public Health Tracking Program facilitates the linkage of environmental information to health outcomes through development of a national standards-based public health surveillance system that provides useful information to help improve where we live and work. The purpose of this summary is to report how state tracking programs have used their tracking networks to save lives and protect people from health threats.

This project was established through the Border Infectious Disease Surveillance (BIDS) program in Arizona (AZ) to monitor infecting respiratory pathogens among hospitalized patients with Severe Acute Respiratory Infections (SARI) in the AZ border region from September 2010 to the present.

Objective

To present the epidemiology, clinical aspects, and laboratory results of AZ SARI case patients and to describe respiratory viruses in the AZ border region.

ESSENCE is a web-based syndromic surveillance system utilized by DHMH to detect and track outbreaks, suspicious patterns of illness, public health emergencies, and biological threats. ESSENCE ED chief complaint data is collected daily from 47 emergency departments in Maryland (all 45 acute care hospitals and 2 freestanding emergency medical facilities). A chief complaint in ESSENCE is a free text field that lists the patient’s reason for the ED visit upon arrival at the hospital. Chief complaint data may be comprehensive or abbreviated and may include a single reason or multiple reasons for the ED visit. Medical history may be included in chief complaint data, which can create low specificity (false positive cases). Chief complaint data alone may yield less accurate modeling and lower outbreak detection sensitivity. This analysis evaluates whether counts of chief complaints are appropriate indicators of disease burden for several specific illnesses, by comparing chief complaints to their corresponding discharge diagnoses.

Objective

The state of Maryland has incorporated chief complaint data from 100% of its acute care emergency departments (ED) into the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). In 2012, the Maryland Department of Health and Mental Hygiene (DHMH) began using this statewide disease surveillance system to track several specific disease measures including certain chronic diseases. The validity of using ESSENCE ED data to track and analyze these health outcomes was evaluated.

Surveillance systems utilizing early indicator of disease activity would be useful for monitoring community disease pattern and facilitating timely decision making on public health interventions in an evidence-based manner. School absenteeism has been previously considered as a possible syndromic approach for monitoring influenza activity. We explored the feasibility and practicability of establishing an electronic school absenteeism surveillance system in Hong Kong for monitoring influenza-like illness (ILI) and other diseases using automatically captured data employing smart card technology.

Objective

We examined the utility of an electronic school absenteeism system for monitoring multiple types of diseases.

The International Health Regulations were revised in 2005 to adapt to increasing diversity of emerging health threats, globalization, changing trends in trade and travel, and the need for collective effort to address the international spread of disease. States Parties to the Regulations have committed to meeting their obligations within a certain timeframe, including achieving the minimum IHR core capacities throughout their territories. By May 2013, over 100 WHO Member States (MS) reported not having met their national IHR core capacity requirements. Many MS need support in making realistic estimates of their activities, plans and associated costs in a standardized way to support planning and advocacy for building capacity to meet IHR (2005) requirements.

Objective

To support national decision makers in estimating the costs for closing identified gaps in meeting the national core capacity building requirements of the International Health Regulations (IHR [2005]), including start-up and operating costs.

We previously experimented with tracking influenza in ER chief complaint data using existing syndromic surveillance tools. We identified several deficiencies in these tools: poor natural language processing, inefficient user interfaces, frequent (thus costly) false alarms, and one-size-fits-all approaches to syndromes. Furthermore, we were surprised that some epidemiologists we spoke with had relatively little faith in existing surveillance tools, and so we set out to build one that would address their concerns: DADAR (Data Analysis, Detection, And Response).

Objective

To develop an adaptable platform for periodically loading semi-structured medical text, extracting syndromic information using advanced natural language processing, detecting outbreaks in the data (including the ability to tune sensitivity vs. specificity on a syndrome-by-syndrome basis so as to reduce the rate of false alarms), generating timely cartographic surveillance reports, and providing tools to quickly validate or rule out syndromic alerts.

Currently over 18 million students are enrolled in USA institutions of higher education (IHEs), representing more than one-third of the young adult population. In a national survey, about 16% of students reported living at home. SHCs are therefore an important resource for the majority of college students. College communities are unique settings that are geographically diverse, highly mobile, and densely populated with congregate living and learning conditions. IHEs, therefore, are highly vulnerable to the introduction of contagious diseases with subsequent transmission to surrounding communities. Thousands of counseling and student health centers exist, funded by billions of dollars. Despite these facts, there was no national database on the health care utilization of this population. In an era in which health policies and plans are typically guided by data, we were relatively blind to information about the diagnoses, epidemiologic trends and health care needs of young adults attending colleges and universities.

Objective

We received CDC funding to create and maintain a multi-institutional de-identified medical records database from student health centers (SHCs) for a nationally representative sample of colleges and universities.

The Johns Hopkins Applied Physics Laboratory and the Armed Forces Health Surveillance Center have developed a hybrid processing engine that alerts monitors when a severe health condition exists based on corroboration among several sources of data. The system was designed to ingest a day's worth of recent data and provide results to monitors daily. In some theaters, the health of the US Forces must be determined at near-real time rates requiring a reassessment of current surveillance practices. Challenges exist in both acquiring data in real-time and in modifying automated alerting processes to re-evaluate as a new piece of evidence is received.

Objective

To develop a real-time surveillance capability that processes, fuses and assesses when data is received using a new fusion processing methodology and multiple sources health indicator data.

Reporting of binational cases has been enacted in Arizona systematically since 2007. Investigating binational cases and providing binational counterparts with timely information can help to enact interventions, as well as strengthens communication between binational, federal, and local health partners. This has allowed a better understanding of the burden of disease among binational/border populations. The process of systematically sharing these cases has allowed for appropriate and timely public health interventions to be implemented, crossing an international boundary in both states.

Objective

This will provide an overview of the process by which Arizona and Sonora identify and share binational cases of public health importance.

Absenteeism has great advantages in promoting the early detection of epidemics. School absenteeism surveillance could timely detect the aggregations of absentees in time and space, so as to provide effective early warning and prevention and control of infectious diseases outbreaks in schools. Since April 1, 2012, an integrated syndromic surveillance system (ISSC) has been implemented in rural Hubei Province, China. With school absence data, finding the optimal model and related appropriate parameters for early warning of epidemics is necessary and practical.

Objective

To explore the optimal model and its related parameters via EWMA and CUSUM (C1, C2, C3) models in school absenteeism surveillance for early detection of infectious disease outbreaks in rural China.