Using Sydromic Surveillance to Track E-cigarette Related Emergency Department Visits

Jill Baber, Tracy Miller

Abstract


ObjectiveTo explore the use of emergency department syndromicsurveillance data to identify adverse health events related to electroniccigarettes in order in enhance existing surveillance.IntroductionThe North Dakota Department of Health (NDDoH) investigatedthe feasibility of using syndromic surveillance (SyS) data to identifyhealth care visits due to electronic cigarette (e-cigarette) use.E-cigarettes have been associated with injuries and fatalities in allage groups, including young children attracted to the colorful liquidnicotine carriage packaging [1]. Previously, poison control data wasthe only resource available to the NDDoH for e-cigarette adverseoutcomes surveillance.MethodsData for all visits from June 28, 2014 to June 28, 2015 weredownloaded using the BioSense 2.0 SyS analytic tool. Excel was usedto identify visits containing key words related to e-cigarettes in line-level data. We initially searched for visits using variations of the term“e-cigarette.” After meeting with NDDoH subject matter experts, weexpanded our search to include other related terms: nicotine, clouding,vaping and variations of “electronic nicotine delivery system(ENDS)”. No diagnosis codes were used as none refer specifically toe-cigarettes. Visits were identified solely through searching free textchief complaint and triage notes fields. Not all facilities participatingin the NDDoH SyS program during this time period submitted freetext data.ResultsOut of 650,069 unique visits, four e-cigarette-related visits wereidentified in rich-text data fields searching for “E-cig” and “E cig.”An additional visit was identified using the search term “nicotine,”although this search primarily identified visits including referencesto nicotine patches. Of the five visits identified, two were poisoningsresulting from small children sucking on liquid nicotine cartridges,one referred to eye irritation as a result of accidentally using liquidnicotine as eye drops, and two referred to cardiac issues (chestpain, heart palpitations) after e-cigarette use. Searches includingterms “clouding” and “vaping,” street terms related to e-cigarettes,did not result in the identification of any additional visits related toe-cigarettes; nor did searches related to ENDS. Poison control datafrom the same time period yielded two calls related to e-cigaretteadverse events.ConclusionsIt is possible to identify emergency department visits associatedwith e-cigarette use utilizing SyS data. More visits were identifiedusing SyS data than poison control data, although neither sourceidentified many occurrences of adverse outcomes related toe-cigarettes. E-cig, e cig and nicotine were the most useful searchterms, although a search for “nicotine” must exclude the word “patch”to avoid false identifications. The NDDoH receives free-text data fora majority of the visits in our system, but not all facilities submitfree-text fields, and the number that did varied over the study period.Because no drop-down chief complaints or diagnosis codes relatedto e-cigarettes exist, data from facilities that did not provide free textdata were not helpful in identifying e-cigarette-related visits. Thisinvestigation emphasizes the need for free text fields when using SySto investigate emerging issues.

Full Text:

PDF


DOI: https://doi.org/10.5210/ojphi.v9i1.7734



Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org