Evaluation of Data Sources for Carbon Monoxide Poisoning Surveillance in New York City

CONTEXT: Carbon monoxide (CO) exposure can be life-threatening. Suspected and confirmed cases of CO poisoning warranting health care in New York City (NYC) are reportable to the NYC Poison Control Center (PCC). OBJECTIVES: We evaluated 4 hospital-based sources of CO surveillance data to identify ways to improve data capture and reporting. DESIGN: Suspected and confirmed CO poisoning records from October 2015 through December 2016 were collected from the NYC emergency department (ED) syndromic surveillance system, New York State Statewide Planning and Research Cooperative System (SPARCS) ED billing data, NYC PCC calls made from hospitals, and the Electronic Clinical Laboratory Reporting System (ECLRS). Syndromic and SPARCS records were person- and visit-matched. SPARCS and ECLRS records were also matched to PCC records on combinations of name, demographic characteristics, and visit information. SETTING: Hospitals in NYC. PARTICIPANTS: Individuals who visited NYC hospitals for CO-related health effects. MAIN OUTCOME MEASURES: We assessed the validity of syndromic data, with SPARCS records as the gold standard. We matched SPARCS and ECLRS records to PCC records to analyze reporting rates by case characteristics. RESULTS: The sensitivity of syndromic surveillance was 60% (225 true-positives detected among 372 visit-matched SPARCS cases), and positive predictive value was 46%. Syndromic records often missed CO flags because of a nonspecific or absent International Classification of Diseases code in the diagnosis field. Only 15% of 428 SPARCS records (total includes 56 records not visit-matched to syndromic) and 16% of 199 ECLRS records were reported to PCC, with male sex and younger age associated with higher reporting. CONCLUSIONS: Mandatory reporting makes PCC useful for tracking CO poisoning in NYC, but incomplete reporting and challenges in distinguishing between confirmed and suspected cases limit its utility. Simultaneous tracking of the systems we evaluated can best reveal surveillance patterns.