Surveillance Systems

School closure has long been proposed as a non-pharmaceutical intervention in reducing the transmission of pandemic influenza. Children are thought to have high transmission potential because of their low immunity to circulating influenza viruses and high contact rates. In the wake of pandemic (H1N1) 2009, simulation studies suggest that early and sustained school closure might be effective at reducing community-wide transmission of influenza. Determining when to close schools once an outbreak occurs has been difficult. Influenza-related absentee data from Japan were previously used to develop an algorithm to predict an outbreak of influenza-related absenteeism. However, the cause of absenteeism is frequently unavailable, making application of this model difficult in certain settings. For this study, we aimed to evaluate the potential for adapting the Japanese algorithm for use with all-cause absenteeism, using data on the rate of influenza-related nurse visits in

corresponding schools to validate our findings.

Objective

To determine the optimal pattern in school-specific all-cause absenteeism for use in informing school closure related to pandemic influenza.

Funded by the Army’s Telemedicine and Advanced Technology Research Center, we developed the BioSINE toolset to provide visualization and collaboration capabilities to improve the accessibility and utility of health surveillance data. Investigation of public health (PH) practitioners’ needs with cognitive engineering methods revealed two key objectives: 

1. To provide analysts and decision makers with an intuitive, visually driven workspace. 

2. To support a web presence to provide rapid updating and facilitate greater interaction with data analysis in the PH community.

To better serve under-resourced PH organizations, both domestic and abroad, it is necessary to minimize information technology requirements and expertise in complex analytic tools.

BioSINE provides decision makers with the ability to create customized visualizations, focus on specific aspects of the data, or conduct hypothesis testing. Users can also view or hide variables, specify data ranges, and filter data relevant to their interests. Figure 1 shows a display in which a user investigated seasonal effects by narrowing the analysis to the summer months. Intuitive filtering is a key characteristic of the application to quickly produce snapshots of local interests.

Objective

BioSINE strives to improve situational awareness by making data visualization and collaboration capabilities intuitive and readily available for a wide range of PH stakeholders.

The BioSense program’s mission is to support and improve public health surveillance infrastructure and human capacity required to monitor (with minimal lag) critical population health indicators of the scope and severity of acute health threats to the public health; and support national, state, and local responses to those threats. This mission is consistent with the 2006 Pandemic All Hazards Preparedness Act, and 2007 Homeland Security Presidential Directive (HSPD-21), both of which call for regional and nationwide public health situational awareness, through an interoperable network of systems, built on existing state and local situational awareness capability.

Objective

The objective of this study is that the Centers for Disease Control and Prevention will update the International Society for Disease Surveillance community on the latest activities for the BioSense program redesign (Centers for Disease Control and Prevention, USA).

Patient consultations recorded as voice dictations are frequently stored electronically as transcriptions in free text format. The information stored in free text is not computer tractable. Advances in artificial intelligence permit the conversion of free text into structured information that allows statistical analysis.

Objective

This paper describes DMReporter, a medical language processing system that automatically extracts information pertaining to diabetes (demography, numerical measurement values, medication list, and diagnoses) from the free text in physicians’ notes and stores it in a structured format in a MYSQL database.

Public health departments have a strong interest in monitoring the incidence, care, and complications of gestational diabetes, as it is associated with poor outcomes for infants and increased risk of diabetes type II for mothers. Gestational diabetes rates are also a possible early marker for changes in the incidence of diabetes type II in the general population. However, diabetes is not generally a reportable condition and therefore, public health surveillance is limited to periodic telephone surveys (subject to self-report inaccuracies), sponsored clinical examinations (expensive, small sample size, no information about processes of care), and occasional research studies. Automated analysis of electronic health record data is a promising method to complement existing surveillance tools with longitudinal, continually updated, clinically rich data derived from large populations. We describe a pilot project to automatically survey electronic health record data in order to identify cases of gestational diabetes, describe their patterns of care and complications, and report summary data to the state health department.

Objective

To develop an electronic, prospective surveillance system to describe the incidence, care, and complications of gestational diabetes using live electronic health record data from a large defined population.

During the 2009 H1N1 influenza pandemic, the Washington State Department of Health (DOH) temporarily made lab-confirmed influenza hospitalizations and deaths reportable. As reporting influenza hospitalizations is resource intensive for hospitals, electronic sources of inpatient influenza surveillance data are being explored. A large Health Information Exchange (WA-HIE) currently sends DOH the following data elements on patients admitted to 14 hospitals throughout eastern Washington: hospital, admission date, age, gender, patient zip code, chief complaint, final diagnoses, discharge disposition, and unique identifiers. WA-HIE inpatient data may be valuable for monitoring influenza activity, influenza morbidity, and the basic epidemiology of hospitalized influenza cases in Washington.

Objective

To evaluate the timeliness, completeness, and representativeness of influenza hospitalization data from an inpatient health information exchange.

The burden of asthma on the youngest children in Boston is largely characterized through hospitalizations and self-report surveys. Hospitalization rates are highest in Black and Hispanic populations under age five. A study of children living in Boston public housing showed significant risk factors, including obesity and pest infestation, with less than half of the study population being prescribed daily medication.

Information on asthma visits for children 5 years old or younger was requested by the Boston Public Health Commission Community Initiatives Bureau. The information is being used to establish a baseline for an integrated Healthy Homes Program that includes pest management and lead abatement. There is limited experience in using syndromic surveillance data for chronic disease program planning.

Objective

The objective of this study is to report on the use of syndromic surveillance data to describe seasonal patterns of asthma and health inequities among Boston residents, age five and under.

Public health surveillance using death data is critical for tracking the impact of diseases such as influenza. However, utility of such systems is compromised by delayed reporting, particularly when it is paper based. In Nebraska, funeral directors are encouraged to initiate death certificates electronically by an electronic death registration system (EDRS). Although paper-based or mixed (electronic followed by paper) registration is still accepted statewide, EDRS usage has gradually increased over time. Fact of death (FOD) data that includes time and place of death, and a deceased person’s identity are usually recorded by a funeral director. Cause of death data in the medical portion are provided by physicians or medical examiners at a later date. FOD data entered into EDRS are immediately available, whereas paper-based data must first be mailed to vital records whereupon staff enter it into EDRS. Although implemented in 2006, epidemiology surveillance staff did not have realtime access to EDRS data until early 2009, when a collaboration was formed between the Office of vital records and the Office of epidemiology within the Nebraska Department of Health and Human Services. Daily electronic access by surveillance staff to death certificate data was established enabling the conductance of public health death surveillance.

Objective

This report describes use and evaluation of a near real time, novel electronic influenza mortality surveillance system developed in Nebraska.

Argus is an event-based, multi-lingual, biosurveillance system, which captures and analyzes information from publicly available internet media. Argus produces reports that summarize and contextualize direct, indirect, and enviroclimatic indications and warning (I&W) of human, animal, and plant disease events, and makes these reports available to the system’s users. Early warning of highly infectious animal diseases, like foot-and-mouth disease (FMD), is critical for the enactment of containment and/or prevention measures aiming to curb disease spread and reduce the potential for devastating trade and economic implications.

Objective

Our objective is to demonstrate how biosurveillance, using direct and indirect I&W of disease within vernacular internet news media, provides early warning and situational awareness for infectious animal diseases that have the potential for trade and economic implications in addition to detecting social disruption. Tracking of I&W during the 2010 Japan FMD epidemic and outbreaks in other Asian countries was selected to illustrate this methodology.

As the electronic medical record (EMR) market matures, long-term time series of EMR-based surveillance data are becoming available. In this work, we hypothesized that statistical aberrancy-detection methods that incorporate seasonality and other long-term data trends reduce the time required to discover an influenza outbreak compared with methods that only consider the most recent past.