Walsh Andrew

In the summer of 2013, the New Jersey Department of Health (NJDOH) began planning for Super Bowl XLVIII to be held on February 2, 2014, in Met Life Stadium, located in the Meadowlands of Bergen County. Surveillance and epidemiology staff in the Communicable Disease Service (CDS) provided expertise in planning for disease surveillance activities leading up to, during, and after the game. A principal component of NJDOH’s Super Bowl surveillance activities included the utilization of an existing online syndromic surveillance system, EpiCenter. EpiCenter is a system developed by Health Monitoring Systems, Inc. (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. As of February, 2014, 75 of New Jersey’s 81 acute care and satellite emergency departments (EDs) were connected to this system. CDS staff primarily used EpiCenter to monitor ED visits for unusual activity and disease outbreaks during this event. In addition, NJDOH and HMS implemented enhanced reports and expanded monitoring of visit complaints.

Objective

To describe the surveillance planning and activities for a largescale event (Super Bowl XLVIII) using New Jersey’s syndromic surveillance system (EpiCenter).

 

The EpiCenter syndromic surveillance platform currently uses Java libraries for time series analysis. Expanding the data quality capabilities of EpiCenter requires new analysis methods. While the Java ecosystem has a number of resources for general software engineering, it has lagged behind on numerical tools. As a result, including additional analytics requires implementing the methods de novo.

The R language and ecosystem has emerged as one of the leading platforms for statistical analysis. A wide range of standard time series analysis methods are available in either the base system or contributed packages, and new techniques are regularly implemented in R. Previous attempts to integrate R with EpiCenter were hampered by the limitations of available R/Java interfaces, which were not actively developed for a long time.

An alternative bridge is via the PostgreSQL database used by EpiCenter on the backend. An R extension for PostgreSQL exists, which can expose the entire R ecosystem to EpiCenter with minimal development effort.

Objective To demonstrate the broader analytical capabilities available by making the R language available to EpiCenter reporting

Data submitted to ILINet from ambulatory practices are a primary feature of influenza-like illness (ILI) surveillance in the United States. Practices count relevant patient records and submit this data manually to ILINet. The ongoing data collection is useful for surveillance, and a significant amount of historical data has accumulated which is useful for research purposes and comparisons of the present season to the past. However, the tabulation of this data is costly, and retention of sentinel practices can be challenging as there is no mandate to submit data. Increasingly, the EpiCenter syndromic surveillance system is receiving data from ambulatory practices. Syndromic surveillance data is sent automatically in near-realtime. Meaningful Use requirements incentivize practices to participate in ongoing data transmission. Syndromic surveillance data from ambulatory practices is thus a possible substitute for the current, more labor-intensive surveillance of ambulatory practices.

Objective

To investigate the viability of using prediagnostic syndromic surveillance data from ambulatory practices for influenza-like illness surveillance

Shootings with multiple victims are a concern for public safety and public health. The precise impact of such events and the trends associated with them is dependent on which events are counted. Some reports only consider events with multiple deaths, typically four or more, while other reports also include events with multiple victims and at least one death. Underreporting is also a concern. Some commonly cited databases for these events are based on media reports of shootings which may or may not capture the complete set of events that meet whatever criteria are being considered. Many gunshot wounds are treated in the emergency department setting. Emergency department registrations routinely collected for syndromic surveillance will capture all of those visits. Analysis of that data may be useful as a supplement to mass shooting databases by identifying unreported events. In addition, clusters of gunshot wound incidents which are not the result of a single shooting event but still represent significant public safety and public health concerns may also be identified.

Objective

To determine whether mass casualty shooting events are captured via syndromic surveillance data.

According to the Center for Disease Control (CDC), binge drinking causes over half of the 88,000 excessive alcohol use deaths and costs approximately $149 billion dollars annually in the United States. Additionally, excessive alcohol use can increase the risk of many other health problems, including injuries and cancer, placing a large burden on public health. In Franklin County, Ohio, The Ohio State University (OSU) football games are an occasion of binge drinking for the student body and Columbus population alike. The purpose of this study is to determine if the binge drinking population is significantly different during football games.

Objective

Identify any relationship between alcohol-related emergency department visits in Franklin County, Ohio and Ohio State University football games.

Overdoses of heroin and prescription opioids are a growing cause of mortality in the United States. Deaths from opioids have contributed to a rise in the overall mortality rate of middle-aged white males during an era when other demographics are experiencing life expectancy gains. A successful public health intervention to reverse this mortality trend requires a detailed understanding of which populations are most affected and where those populations live. While mortality is the most relevant metric for this emerging challenge, increased burden on laboratory facilities can create significant delays in obtaining confirmation of which patients died from opioid overdoses.

Emergency department visits for opioid overdoses can provide a more timely proxy measure of overall opioid use. Unfortunately, chief complaints do not always contain an indication of opioid involvement. Overdose patients are not always conscious at registration which limits the amount of information they can provide. Menu-driven registration systems can lump all overdoses together regardless of substance. A more complete record of the emergency department interaction, such as that provided by triage notes, could provide the information necessary to differentiate opioid-related visits from other overdoses. 

Objective

To identify heroin- and opioid-related emergency department visits using pre-diagnositc data. To demonstrate the value of clinical notes to public health surveillance and situational awareness.