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Harnessing Syndromic Surveillance Emergency Department Data to Monitor Health Impacts During the 2015 Special Olympics World Games

Introduction: Mass gatherings that attract a large international presence may cause or amplify point-source outbreaks of emerging infectious disease. The Los Angeles County Department of Public Health customized its syndromic surveillance system to detect increased syndrome-specific utilization of emergency departments (EDs) and other medical encounters coincident to the 2015 Special Olympics World Games. Materials and Methods: We queried live databases containing data on ED visits, California Poison Control System calls, and Los Angeles County coroner-investigated deaths for increases in daily counts from July 19 to August 6, 2015. We chose syndrome categories based on the potential for disease outbreaks common to international travel and dormitory settings, morbidity amplified by high temperatures, and bioterrorism threats inherent to mass gatherings. We performed line-list reviews and trend analyses of total, syndrome-specific, and region-specific daily counts, using cumulative sum-based signals. We also piloted a novel strategy of requesting that ED registrars proactively tag Special Olympics attendees in chief complaint data fields. Results: The syndromic surveillance system showed that the 2015 Special Olympics did not generate large-scale acute morbidities leading to detectable stress on local EDs. We recruited 10 hospitals for proactive patient tagging, from which 16 Special Olympics attendees were detected; these patients reported various symptoms, such as injury, vomiting, and syncope. Practice Implications: As an enhancement to traditional syndromic surveillance, proactive patient tagging can illuminate potential epidemiologic links among patients in challenging syndromic surveillance applications, such as mass gatherings. Syndromic surveillance has the potential to enhance ED patient polling and reporting of exposure, symptom, and other epidemiologic case definition criteria to public health agencies in near-real time.