scholarly journals Using Emergency Department Data to Conduct Dog and Animal Bite Surveillance in New York City, 2003–2006

2012 ◽  
Vol 127 (2) ◽  
pp. 195-201 ◽  
Author(s):  
Brooke Bregman ◽  
Sally Slavinski
Keyword(s):  
Emergency Department ◽  
New York ◽  
New York City ◽  
York City ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
The United States ◽  
Chief Complaint ◽  
Free Text ◽  
Animal Bites

Objectives. Most animal bites in the United States are due to dogs, with approximately 4.7 million reports per year. Surveillance for dog and other animal bites requires a substantial investment of time and resources, and underreporting is common. We described the use and findings of electronic hospital emergency department (ED) chief complaint data to characterize patients and summarize trends in people treated for dog and other animal bites in New York City (NYC) EDs between 2003 and 2006. Methods. Retrospective data were obtained from the syndromic surveillance system at the NYC Department of Health and Mental Hygiene. We used a statistical program to identify chief complaint free-text fields as one of four categories of animal bites. We evaluated descriptive statistics and univariate associations on the available demographic data. The findings were also compared with data collected through the existing passive reporting animal bite surveillance system. Results. During the study period, more than 6,000 animal bite patient visits were recorded per year. The proportion of visits for animal bites did not appear to change over time. Dog bites accounted for more than 70% and cat bites accounted for 13% of animal bite patient visits. Demographic characteristics of patients were similar to those identified in NYC's passive surveillance system. Conclusions. Our findings suggest that the use of ED data offers a simple, less resource-intensive, and sustainable way of conducting animal bite surveillance and a novel use of syndromic surveillance data. However, it cannot replace traditional surveillance used to manage individual patients for potential rabies exposures.

Monitoring Emergency Department Visits From Puerto Rico in the Aftermath of Hurricane Maria Using Syndromic Surveillance – New York City, 2017

2019 ◽  
Vol 14 (1) ◽  
pp. 44-48
Author(s):  
Priscilla W. Wong ◽  
Hilary B. Parton
Keyword(s):  
Emergency Department ◽  
New York ◽  
New York City ◽  
Puerto Rico ◽  
York City ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Syndromic Surveillance System ◽  
Ed Visits

ABSTRACTObjective:Syndromic surveillance has been useful for routine surveillance on a variety of health outcomes and for informing situational awareness during public health emergencies. Following the landfall of Hurricane Maria in 2017, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) implemented an enhanced syndromic surveillance system to characterize related emergency department (ED) visits.Methods:ED visits with any mention of specific key words (“Puerto,” “Rico,” “hurricane,” “Maria”) in the ED chief complaint or Puerto Rico patient home Zip Code were identified from the DOHMH syndromic surveillance system in the 8-week window leading up to and following landfall. Visit volume comparisons pre- and post-Hurricane Maria were performed using Fisher’s exact test.Results:Analyses identified an overall increase in NYC ED utilization relating to Puerto Rico following Hurricane Maria landfall. In particular, there was a small but significant increase in visits involving a medication refill or essential medical equipment. Visits for other outcomes, such as mental illness, also increased, but the differences were not statistically significant.Conclusions:Gaining this situational awareness of medical service use was informative following Hurricane Maria, and, following any natural disaster, the same surveillance methods could be easily established to aid an effective emergency response.

scholarly journals Building a Better Syndromic Surveillance System: the New York City Experience

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Robert Mathes ◽  
Jessica Sell ◽  
Anthony W. Tam ◽  
Alison Levin-Rector ◽  
Ramona Lall
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Alternative Methods ◽  
Detection Methods ◽  
Syndromic Surveillance System ◽  
Aberration Detection ◽  
Ed Visits

The New York City (NYC) syndromic surveillance system has been monitoring syndromes from city emergency department (ED) visits since 2001. We conducted an evaluation of statistical aberration detection methods currently in use in our system as well as alternative methods, applying six temporal and four spatio-temporal aberration detection methods to two years of ED visits in NYC spiked with synthetic outbreaks. We found performance varied between the methods in regard to sensitivity, specificity, and timeliness, and implementation of these methods will depend on needs, frequency of signals, and technical skill.

scholarly journals In Denial: Symptom Negation in New York City Emergency Department Chief Complaints

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jessica Sell
Keyword(s):  
Emergency Department ◽  
New York ◽  
New York City ◽  
York City ◽  
Fold Increase ◽  
Chief Complaint ◽  
Signal Frequency ◽  
Department Of Health ◽  
Chief Complaints ◽  
Keyword Searches

ObjectiveTo describe the effect of symptom negation in emergencydepartment (ED) chief complaint data received by the New York City(NYC) Department of Health and Mental Hygiene (DOHMH), and todevise a solution to avoid syndrome and symptom misclassificationfor commonly used negations using SAS Perl Regular Expression(PRX) functions.IntroductionIn July 2016, 77% of ED data was transmitted daily via HealthLevel 7 (HL7) messages, compared to only 27% in July 2015(Figure). During this same period, chief complaint (CC) word counthas increased from an average of 3.8 words to 6.0 words, with atwenty-fold increase in the appearance of the word “denies” in thechief complaint (Figure). While HL7 messages provide robust chiefcomplaint data, this may also introduce errors that could lead tosymptom and syndrome misclassification.MethodsUsing SAS 9.4 and Tableau 9.3, we examined data submissionsfrom 14 EDs responsible for 97% of the occurrences of the word‘denies’ in chief complaints in July 2016.To account for variation in chief complaint format among hospitals,we developed three PRX patterns to identify entire phrases in thechief complaint data field that began with conjugations of the word“deny” followed by various combinations of words, punctuation,spaces, and/or characters.Pattern 1: '/DEN(Y|I(ES|ED|NG))(\s|\w|(\/)|(\+)|,|(\\)){1,}((\.)|(\|)|($)|(;)|(\))|(-))/’Pattern 2: '/DEN(Y|I(ES|ED|NG))(\s|\w|(\/)|(\+)|(\\)){1,}((\.)|(\|)|($)|(;)|(\))|(-)|(,))/';Pattern 3: '/DENIES:( |\w|\.|,){1,}/');We separated the ‘denies’ statement from the chief complaint andidentified commonly negated symptoms. We then defined symptomsusing keyword searches of the chief complaint and the ‘denies’statement. We compared symptom classification with and withoutthe consideration of symptom negation.ResultsOf the 14 EDs analyzed, we applied pattern 1 to 8 of the ED’s,pattern 2 to 5 EDs, and patterns 2 and 3 to 1 ED. Approximately98% of denies statements were extracted from chief complaints. Only2% of symptom negation was not captured due to uncommon chiefcomplaint format whose symptom negation didn’t meet one of thepreviously described PRX patterns.The most common words associated with a “denies” statementwere: pain, chest, fever, loc, shortness, breath, vomiting, nausea,travel, headache, recent, trauma, history, abdominal, injury, diarrhea,SOB (shortness of breath), V (vomit), Head, N (nausea), PMH (pastmedical history), suicidal, dizziness, homicidal and D (diarrhea) (seeTable).By not taking negation into consideration in symptom definitions,between 3.5% and 16.5% of symptom visits were misclassified.Symptom misclassification varied greatly by hospital, ranging from0% to 55%.ConclusionsAs hospitals in NYC implement HL7 messaging, symptomnegation is becoming increasingly common in chief complaint data.Current symptom definitions are based on keyword searches that donot take into account symptom negations. This leads to symptommisclassification, and could potentially cause false signals or inflatesyndrome baselines, causing true signals to go undetected. SAS PRXfunctions can be used to flexibly identify symptom negation patternsand exclude them from syndrome definitions. Future studies willquantify the effect symptom negation has had on signal frequency inNYC, and examine symptoms associated with other forms of negationsuch as “Personal Medical History”, “No” and “Negative.”Most Common Symptoms Denied in Emergency Department Chief Complaints

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