Poison Control

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. 

The Centers for Disease Control and Prevention (CDC) uses the National Poison Data System (NPDS) to conduct surveillance of calls to United States poison centers (PCs) to identify clusters of reports of hazardous exposures and illnesses. NPDS stores basic information from PC calls including call type (information request only or call reporting a possible chemical exposure), exposure agent, demographics, clinical, and other variables.

CDC looks for anomalies in PC data by using automated algorithms to analyze call and clinical effect volume, and by identifying calls reporting exposures to pre-specified high priority agents. Algorithms analyzing call and clinical effect volume identify anomalies when the number of calls exceeds a threshold using the historical limits method (HLM). Clinical toxicologists and epidemiologists at the American Association of Poison Control Centers and CDC apply standardized criteria to determine if the anomaly is a potential incident of public health significance (IPHS) and then notify the respective health departments and PCs as needed. Discussions with surveillance system users and analysis of past IPHS determined that call volume-based surveillance results in a high proportion of false positive anomalies. A study assessing the positive predictive value (PPV) of this approach determined that fewer than four percent of anomalies over a five-year period were IPHS.1 A low PPV can cause an unnecessary waste of staff time and resources. We hypothesized that first stratifying call volume by exposure category would reduce the number of false positives. With the help of medical toxicologists, we created 20 toxicologically-relevant exposure categories to test this hypothesis. 

Objective

Our objective was to determine if the detection performance of current surveillance algorithms to detect call clusters is improved by stratifying by exposure category.