Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016

Lall, Ramona; Abdelnabi, Jasmine; Ngai, Stephanie; Parton, Hilary; Saunders, Kelly; Sell, Jessica; Wahnich, Amanda; Weiss, Don; Mathes, Robert

doi:10.1177/0033354917711183

Cited by 35 publications

(45 citation statements)

References 16 publications

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“…The DOHMH syndromic surveillance system collects daily ED data from all 53 NYC hospitals to identify clusters of illness and monitor near real-time trends in ED visits. 3 Enhanced syndromic surveillance can be targeted toward a specific event or disaster to provide information for a prompt public health response. Following Hurricane Maria landfall in Puerto Rico, an algorithm for related patient visits was developed.…”

Section: Methodsmentioning

confidence: 99%

“…Both chief complaint and discharge diagnosis can vary in their degree of detail, and the latter may not be filled for every visit. 3 Identification of visits was dependent on the mention of Puerto Rico or Hurricane Maria in a patient's chief complaint or on a Puerto Rico Zip Code on record with the patient's address. Visits without any of these identifiers would have been missed and thus the visits described here may not be representative of those displaced by Hurricane Maria who sought care in NYC EDs.…”

Section: Figurementioning

confidence: 99%

“…In NYC, syndromic surveillance has been useful for routine surveillance on a variety of health outcomes, as well as for informing situational awareness during public health emergencies. 3 However, this analysis represents the first time that NYC has used enhanced syndromic surveillance to identify and characterize ED visits among an impacted group.…”

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confidence: 99%

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

Wong

Parton

2019

Disaster med. public health prep.

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Objective: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.

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Section: Methodsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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

Wong

Parton

2019

Disaster med. public health prep.

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“…Nevertheless, although SyS data can be a valuable tool for public and animal health practices, few systems are currently operational in animal health (7). Despite the substantial number of methodological developments involving SyS, there are few fully operational SyS systems and few field feedback experiences about SyS systems especially in animal health (8)(9)(10)(11). This contradiction reveals the difficulties in (i) convincing public or private entities to fund the evaluation of these systems in the field, (ii) finding the business model for operational deployment, and (iii) designing these systems in such a way that they are not limited to large health services that have sufficient expertise and staff to review alarms.…”

Section: Introductionmentioning

confidence: 99%

Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool

Sala

Vinard

Pandolfi³

et al. 2020

Front. Vet. Sci.

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Between May 2018 and 2019, a syndromic bovine mortality surveillance system (OMAR) was tested in 10 volunteer French départements (French intermediate-level administrative unit) to assess its performance in real conditions, as well as the human and financial resources needed to ensure normal functioning. The system is based on the automated weekly analysis of the number of cattle deaths reported by renderers in the Fallen Stock Data Interchange Database established in January 2011. In our system, every Thursday, the number of deaths is grouped by ISO week and small surveillance areas and then analyzed using traditional time-series analysis steps (cleaning, prediction, signal detection). For each of the five detection algorithms implemented (i.e., the exponentially weighted moving average chart, cumulative sum chart, Shewhart chart, Holt-Winters, and historical limits algorithms), seven detection limits are applied, giving a signal score from 1 (low excess mortality) to 7 (high excess mortality). The severity of excess mortality (alarm) is then classified into four categories, from very low to very high, by combining the signal scores, the relative excess mortality, and the persistence of the signal(s) over the previous 4 weeks. Detailed and interactive weekly reports and a short online questionnaire help pilot départements and the OMAR central coordination cell assess the performance of the system. During the 1-year test, the system showed highly variable sensitivity among départements. This variability was partly due not only to the demographic distribution of cattle (very few signals in low-density areas) but also to the renderer's delay in reporting to the Fallen Stock Data Interchange Database (on average, only 40% of the number of real deaths had been transmitted within week, with huge variations among départements). As a result, in the pilot départements, very few alarms required on-farm investigation and excess mortality often involved a small number of farms already known to have health or welfare problems. Despite its perfectibility, the system nevertheless proved useful in the daily work of animal health professionals for collective and individual surveillance. The test is still ongoing for a second year in nine départements to evaluate the effectiveness of the improvements agreed upon at the final meeting.

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“…It is vital to determine basic ways and factors that induce contagion; to do that, experts who perform sanitary-epidemiologic investigaions simultaneously detect infected people and those who were exposed to risks of contagion, question and examine them, process data and analyze obtained results [23][24][25][26][27][28][29]. Such examinations are based on questioning that, as a rule, is a primary tool for collecting complaints and symptoms of a disease that allow preliminary diagnosing and assuming how an infectious agent can spread and be caught; they also allow collecting data for epidemiologic case histories or data on consumed food products [30][31][32][33][34].…”

mentioning

confidence: 99%

Information technologies for data collection and processing when establishing determinants of epidemic processes

Bogomolov¹,

Chikova²,

Zueva³

2019

Health Risk Analysis

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Providing biological safety of population is determined by a current situation with the state sanitary-epidemiologic system and its being ready to detect, localize and eliminate infective episodes. As threats of bioterrorism attacks against people are growing and infectious diseases are becoming more widely spread, it calls for greater efficiency of sanitary-epidemiologic examinations due to optimized collection and processing of data that are necessary for decision making related to revealing basic determinants of an epidemic process, as well as causes and conditions for infection occurrence and spread. The paper dwells on a technology for automated data processing that helps to efficiently reveal all determinants of an epidemic process, and causes and conditions for infection occurrence and spread. The technology also allows automated checking of all proposed hypotheses basing on generalization of results obtained via independent research. Proposed solutions are verified within a sanitary-epidemiologic examination that focused on dysentery episode in an organized team with its members staying on a closed territory and having their meals provided for them at a canteen. The authors compared an already existing system for collection and processing of statistical data (applied to reveal basic determinants of an epidemic process) and a proposed system for data collection and processing. Obtained results supplement and develop existing theoretical and practical achievements as regards IT implementation into sanitary-epidemiologic examinations; they have considerable practical significance especially bearing in mind a future transition to electronic circulation of documents within public healthcare and medical provision. When applied, a proposed approach allows considerable reduction in time spent on generalization of data obtained via sanitary-epidemiologic examinations as well as a significant increase in validity of accomplished statistical calculations necessary to reveal factors that cause infectious agent transmission.

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Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016

Abstract: Sharing syndromic surveillance use cases can foster new ideas and build capacity for public health preparedness and response.

Cited by 35 publications

References 16 publications

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

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

Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool

Information technologies for data collection and processing when establishing determinants of epidemic processes

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