ESSENCE

The 2016 U.S. Olympic Track and Field Team Trials were held July 1-10 in Eugene, OR. This mass gathering included over 1,000 athletes, 1,500 volunteers, and 175,000 spectators. The Oregon Public Health Division (PHD) and Lane County Public Health (LCPH) participated in pre-event planning and collaborated to produce a daily epidemiology report for the Incident Management Team (IMT) during the event. The state and county public health agencies had collaborated on surveillance for prior mass gatherings, including the 2012 Trials. However, 2016 was the first opportunity to use complete state and county syndromic surveillance data. 

Objective

To streamline production of a daily epidemiology report including syndromic surveillance, notifiable disease, and outbreak data during a mass gathering. 

Syndromic surveillance systems have historically focused on aggregating data into syndromes for analysis and visualization. These syndromes provide users a way to quickly filter large amounts of data into a manageable number of streams to analyze. Additionally, ESSENCE users have the ability to build their own case definitions to look for records matching particular sets of criteria. Those user- defined queries can be stored and analyzed automatically, along with the pre-defined syndromes. Aside from these predefined and user- defined syndromic categories, ESSENCE did not previously provide alerts based on individual words in the chief complaint text that had not been specified a priori. Thus, an interesting cluster of records linked only by non-syndromic keywords would likely not be brought to a user’s attention. 

Objective

The objective of this presentation is to describe the new word alert capability in ESSENCE and how it has been used by the Florida Department of Health (FDOH). Specifically, this presentation will describe how the word alert feature works to find individual chief complaint terms that are occurring at an abnormal rate. It will then provide usage statistics and first-person accounts of how the alerts have impacted public health practice for the users. Finally, the presentation will offer future enhancement possibilities and a summary of the benefits and shortcomings of this new feature. 

Oregon Public Health Division (OPHD), in collaboration with the Johns Hopkins University Applied Physics Laboratory, implemented Oregon ESSENCE in 2012. Oregon ESSENCE is an automated, electronic syndromic surveillance system that captures emergency department data. To strengthen the capabilities of Oregon ESSENCE, OPHD sought other sources of health-outcome information, including Oregon Poison Center (OPC). In the past, Oregon’s surveillance staff manually monitored OPC data on the National Poison Data Service (NPDS) website. Although functional, it was not integrated into Oregon’s syndromic surveillance system and required epidemiologists to assess alerts on individual calls. To achieve data integration, OPHD pursued an automated solution to deliver OPC data into Oregon ESSENCE. OPHD’s growing interoperability infrastructure fostered development of a low-cost, reliable solution to automate the integration of these data sources. 

Objective

To enhance Oregon ESSENCE’s surveillance capabilities by incorporating data from the Oregon Poison Center using limited resources. 

The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States PCs. PCs provide triage and treatment advice for hazardous exposures through a free national hotline. Information on demographics, health effects, implicated substance(s), medical outcome of the patient, and other variables are collected.

CDC uses automated algorithms to identify anomalies in both pure call volume and specific clinical effect volume, and to identify calls reporting exposure to high priority agents. Pure and clinical effect volume anomalies are identified when an hourly call count exceeds a threshold based on historical data using HLM.1 Clinical toxicologists and epidemiologists at the American Association of Poison Control Centers and CDC apply standardized criteria to determine if the anomaly identifies a potential incident of public health significance (IPHS) and to notify the respective health departments and local PCs as needed. Discussions with NPDS users and analysis of IPHS showed that alerting based on pure call volume yielded excessive false positives. A study using a 5-year NPDS call dataset assessed the positive predictive value (PPV) of the call volume-based approach. This study showed that less than 4% of anomalies were IPHS.2 A low PPV can cause unnecessary waste of staff time and resources analyzing false positive anomalies.

As an alternative to pure call volume-based detection where all calls to each PC are aggregated for anomaly detection, we considered separating calls by toxicologically-relevant exposure categories for more targeted anomaly detection. We hypothesized that this stratified approach would reduce the number of false positives. 

Objective

Our objective was to compare the effectiveness of applying the historical limits method (HLM) to poison center (PC) call volumes with vs without stratifying by exposure type. 

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.