scholarly journals Real-time monitoring of a mass K2-related overdose outbreak – Connecticut, 2018

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristen Soto ◽  
Erin Grogan ◽  
Alexander Senetcky ◽  
Susan Logan
Keyword(s):  
Emergency Department ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Emergency Department Utilization ◽  
Syndromic Surveillance System ◽  
Ed Visits ◽  
State And Local

ObjectiveTo describe the use of syndromic surveillance data for real-time situational awareness of emergency department utilization during a localized mass overdose event related to the substance K2.IntroductionOn August 15, 2018, the Connecticut Department of Public Health (DPH) became aware of a cluster of suspected overdoses in an urban park related to the synthetic cannabinoid K2. Abuse of K2 has been associated with serious adverse effects and overdose clusters have been reported in multiple states. This investigation aimed to characterize the use of syndromic surveillance data to monitor a cluster of suspected overdoses in real time.MethodsThe EpiCenter syndromic surveillance system collects data on all emergency department (ED) visits at Connecticut hospitals. ED visits associated with the event were identified using ad hoc keyword analyses. The number of visits by facility location for the state, county, and city were communicated to state and local partners in real time. Gender, age, and repeated ED visits were assessed. After the event, surveillance findings were summarized for partnersResultsDuring the period of August 15–16, 2018 the number of ED visits with a mention of K2 in the chief complaint increased from three to 30 in the impacted county, compared to a peak of 5 visits during the period of March–July, 2018. An additional 25 ED visits were identified using other related keywords (e.g., weed). After the event, 72 ED visits were identified with K2 and location keywords in the chief complaint or triage notes. These 72 visits comprised 53 unique patients, with 12 patients returning to the ED 2–5 times over the two day period. Of 53 patients, 77% were male and the median age was 40 years (interquartile range 35–51 years). Surveillance findings were shared with partners in real time for situational awareness, and in a summary report on August 21.ConclusionsData from the EpiCenter system were consistent with reports from other data sources regarding this cluster of suspected drug overdoses. Next steps related to this event involve: monitoring data for reference to areas of concentrated substance use, enabling automated alerts to detect clusters of interest, and developing a plan to improve coordinate real-time communication with stakeholderswithin DPH and with external partners during events.

scholarly journals Use of Emergency Department Data to Monitor and Respond to an Increase in Opioid Overdoses in New Hampshire, 2011-2015

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 73S-79S ◽  
Author(s):  
Elizabeth R. Daly ◽  
Kenneth Dufault ◽  
David J. Swenson ◽  
Paul Lakevicius ◽  
Erin Metcalf ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
New Hampshire ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
The United States ◽  
Chief Complaint ◽  
Public Health Response ◽  
Syndromic Surveillance System ◽  
Icd Codes

Objectives: Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem. Methods: We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire’s syndromic surveillance ED data system by querying for (1) chief complaint text related to the words “fentanyl,” “heroin,” “opiate,” and “opioid” and (2) opioid-related International Classification of Diseases ( ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes. Results: Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being “pain” (n = 3335, 18%), “overdose” (n = 1555, 8%), “suicidal” (n = 816, 4%), “drug” (n = 803, 4%), and “detox” (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015. Conclusions: Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire’s ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.

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 Validation of Syndromic ILI Data for Use in CDC’s ILINet Surveillance, Pennsylvania

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sameh W Boktor ◽  
Kristen Waller ◽  
Lenee Blanton ◽  
Krista Kniss
Keyword(s):  
Sore Throat ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Correlation Coefficients ◽  
Chief Complaint ◽  
The State ◽  
Surveillance Data ◽  
Syndromic Surveillance System ◽  
Ed Visits ◽  
Number Of Visits

Objective: Discuss use of syndromic surveillance as a source for the state’s ILI/Influenza surveillanceDiscuss reliability of syndromic data and methods to address problems caused by data outliers and inconsistencies.Introduction: ILINet is a CDC program that has been used for years for influenza-like illness (ILI) surveillance, using a network of outpatient providers who volunteer to track and report weekly the number of visits due to ILI and the total number of visits to their practice. Pennsylvania has a network of 95 providers and urgent care clinics that submit data to ILINet. However, ongoing challenges in recruiting and retaining providers, and inconsistent weekly reporting are barriers to receiving accurate, representative, and timely ILI surveillance data year-round. Syndromic surveillance data have been used to enhance outpatient ILI surveillance in a number of jurisdictions, including Pennsylvania. At present, 156 hospitals, or 90% of all Pennsylvania hospitals with emergency departments (EDs), send chief complaint and other information on their ED visits to the Department of Health’s (PADOH) syndromic surveillance system. PADOH evaluated the consistency and reliability of ILI syndromic data as compared to ILINet data, to confirm that syndromic data were suitable for use in ILINet.Methods: Pennsylvania ILINet data from the past 6 influenza seasons (2011-2012 to 2016-2017, or 314 weeks of data) were downloaded from the CDC’s ILINet website. The statewide weekly percent of visits due to ILI in ILINet was used as the standard for comparisons. For syndromic surveillance, PADOH uses the Epicenter platform hosted by Health Monitoring Systems (HMS); visit-level data are also stored in SAS datasets at PADOH, and HMS forwards a subset of data to the National Syndromic Surveillance System Program. Using syndromic data from the same time period, the proportion of weeks with no syndromic data available was calculated for each facility. A state-developed ILI algorithm (very similar to the 2016 algorithm developed by the ISDS Syndrome Definitions Workgroup) was applied to ED visit chief complaint data to identify visits likely to be due to ILI. The algorithm flags the ER visit as ILI if chief complaint has any combinations of words for flu or fever plus either cough and sore throat or fever and both cough or sore throat . The percent of ED visits due to ILI per the syndromic algorithm (ILIsyn) was calculated for each week by hospital and state-wide. Facility ILIsyn trends were compared to the State level percent ILI data from ILINet by visually examining plots and by calculating Pearson correlation coefficients. Facilities that had >=15 weeks where ILIsyn differed from percent ILI in ILINet by more than 5% were considered to be poorly correlated.Results: A total of 156 hospitals were evaluated in the study. Twenty of the hospitals were excluded because they did not have syndromic data for at least 50% of the weeks in the study period, and an additional 20 were excluded because they had not agreed to have data forwarded to CDC. Of the remaining 116 facilities, individual facility correlation coefficients between ILIsyn and ILINet trends ranged from 0.03 to 0.82 (examples are in Figure 1). Twenty-four hospitals (20.7%) were determined to be poorly correlated. When data from the remaining 92 hospitals were combined, the state ILINet and state-wide ILIsyn trends were strongly correlated statistically and graphically (r=0.82, p <0.0001, Figure 2). Syndromic data from these 92 facilities were deemed acceptable for inclusion in ILINet. Conclusions: Syndromic surveillance data are a valuable source for ILI surveillance. However, evaluation at the hospital-specific level revealed that useful information is not obtained from all facilities. This project demonstrated that validation of data at the facility level is crucial to obtaining reliable and meaningful information. More work is needed to understand which factors distinguish well-correlated from poorly-correlated facilities, and how to improve the quality of information obtained from poorly-correlated facilities.

scholarly journals Using Syndromic Surveillance to Classify and Capture Non-Fatal Occupational Injuries and Illnesses

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Marija Borjan ◽  
Margaret Lumia
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Occupational Injuries ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Free Text ◽  
Syndromic Surveillance System ◽  
Work Related ◽  
Data Source

ObjectivesTo evaluate the use of a real-time surveillance tool to track a variety of occupationally-related emergency room visits through the state based syndromic surveillance system, EpiCenter.IntroductionThis study uses data from the New Jersey syndromic surveillance system (EpiCenter) as a data source to enhance surveillance of current non-fatal occupational injuries, illnesses, and poisonings. EpiCenter was originally developed for early detection and monitoring of the health of communities using chief complaints from people seeking acute care in hospital emergency rooms to identify health trends. Currently, syndromic surveillance has not been widely applied to identify occupational injuries and illnesses. Incorporating syndromic surveillance data from EpiCenter, along with hospital discharge data, will enhance the classification and capture of work-related non-fatal injuries with possible improved efforts at prevention.MethodsEpiCenter Emergency Department data from January to December 2014 was evaluated, using work-related keywords and ICD-9 codes, to determine its ability to capture non-fatal work-related injuries. A collection of keywords and phrases specific to work-related injuries was developed by manually assessing the free text chief complaint data field’s. Sensitivity, specificity, and positive predictive value (PPV), along with descriptive statistics was used to evaluate and summarize the occupational injuries identified in EpiCenter.ResultsOverall, 11,919 (0.3%) possible work-related injuries were identified via EpiCenter. Of these visits 956 (8%) indicated Workman’s Compensation as payer. Events that resulted in the greatest number of ED visits were falls, slips, trips (1,679, 14%). Nature of injury included cuts, lacerations (1,041, 9%), burns (255, 2%), and sprains, strains, tears (185, 2). The part of the body most affected were the back (1,414, 12%). This work-related classifier achieved a sensitivity of 5.4%, a specificity of 99.8%, and a PPV of 2.8%.ConclusionsEvaluating the ability and performance of a new and existing surveillance data source to capture work-related injuries can lead to enhancements in current data collection methods. This evaluation successfully demonstrated that the chief complaint reporting system can yield real-time knowledge of incidents and local conditions for use in identifying opportunities for prevention of work-related injuries. 

scholarly journals Detecting Suicide-Related Emergency Department Visits Among Adults Using the District of Columbia Syndromic Surveillance System

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 88S-94S ◽  
Author(s):  
S. Janet Kuramoto-Crawford ◽  
Erica L. Spies ◽  
John Davies-Cole
Keyword(s):  
Emergency Department ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
District Of Columbia ◽  
Chief Complaint ◽  
Emergency Department Visits ◽  
Added Value ◽  
Discharge Diagnosis ◽  
Syndromic Surveillance System ◽  
Ed Visits

Objectives: Limited studies have examined the usefulness of syndromic surveillance to monitor emergency department (ED) visits involving suicidal ideation or attempt. The objectives of this study were to (1) examine whether syndromic surveillance of chief complaint data can detect suicide-related ED visits among adults and (2) assess the added value of using hospital ED data on discharge diagnoses to detect suicide-related visits. Methods: The study data came from the District of Columbia electronic syndromic surveillance system, which provides daily information on ED visits at 8 hospitals in Washington, DC. We detected suicide-related visits by searching for terms in the chief complaints and discharge diagnoses of 248 939 ED visits for which data were available for October 1, 2015, to September 30, 2016. We examined whether detection of suicide-related visits according to chief complaint data, discharge diagnosis data, or both varied by patient sex, age, or hospital. Results: The syndromic surveillance system detected 1540 suicide-related ED visits, 950 (62%) of which were detected through chief complaint data and 590 (38%) from discharge diagnosis data. The source of detection for suicide-related ED visits did not vary by patient sex or age. However, whether the suicide-related terms were mentioned in the chief complaint or discharge diagnosis differed across hospitals. Conclusions: ED syndromic surveillance systems based on chief complaint data alone would underestimate the number of suicide-related ED visits. Incorporating the discharge diagnosis into the case definition could help improve detection.

scholarly journals Using Syndromic Data for Opioid Overdose Surveillance in Utah

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Wei Hou ◽  
Elizabeth Brutsch ◽  
Angela C Dunn ◽  
Cindy L Burnett ◽  
Melissa P Dimond ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Emergency Department Visit ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Urgent Care ◽  
Mortality Data ◽  
Opioid Overdose

Objective: To monitor opioid-related overdose in real-time using emergency department visit data and to develop an opioid overdose surveillance report for Utah Department of Health (UDOH) and its public health partners.Introduction: The current surveillance system for opioid-related overdoses at UDOH has been limited to mortality data provided by the Office of the Medical Examiner (OME). Timeliness is a major concern with OME data due to the considerable lag in its availability, often up to six months or more. To enhance opioid overdose surveillance, UDOH has implemented additional surveillance using timely syndromic data to monitor fatal and nonfatal opioid-related overdoses in Utah.Methods: As one of the agencies participating in the National Syndromic Surveillance Program (NSSP), UDOH submits de-identified data on emergency department visit from Utah’s hospitals and urgent care facilities in close to real-time to the NSSP platform. Emergency department visit data are available for analysis using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) system provided by NSSP. ESSENCE provides UDOH with patient-level syndromic data for analysis and early detection of abnormal patterns in emergency visits. A total of 38 out of 48 acute care hospitals and multiple urgent care facilities are enrolled in the system in Utah. More than 90% of these hospitals report chief complaint data, and discharge data are available from about 15% of the facilities. Data were analyzed by querying key terms in the chief complaint field including: any entry of: ‘overdose’, drug and brand names for opioids, street names, ‘naloxone’, and miss-spellings. Exclusion terms included any mention of: ‘denies’, ‘quit’, ‘refill’, ‘withdraw’, ‘dependence’, etc. Data containing any ICD entry of: T40.0-T40.4, T40.60, and T40.69 were included in the analysis.Results: Between September 1, 2016 and August 31, 2017, Utah Department of Health identified 4,063 opioid-related overdose emergency department (ED) visits through the ESSENCE system using both chief complaint and discharge diagnosis queries. Of these visits, 3,865 (95%) were identified using chief complaints alone and 198 (5%) visits were added by searching the discharge diagnosis field. Opioid-related visits comprised approximately 0.3% of the total ED visits (1,267,244) reported during this time (Graph 1). More than half of the opioid-related emergency visits were reported from just five facilities. Rate of opioid-related visits ranging from 0 to 292 visits per 100,000 population per year (median: 108 visits per 100,000 population per year), with an overall rate for the state of 129 visits per100, 000 population per year. The highest rate of opioid-related visits occurred among patients aged 18 to 24 (219 visits per 100,000 population per year), and 59% of all opioid-related patients in Utah were female.Conclusions: The results presented are estimates of opioid-related overdoses reported using close to real-time data. These results would not include visits with incomplete or incorrectly coded chief complaints or discharge codes, or cases of opioid overdose who do not present to an emergency department or urgent care facility. The results from using syndromic data are consistent with existing surveillance findings using mortality data in Utah. This suggests that syndromic surveillance data are useful for rapidly capturing opioid events, which may allow for a timelier public health response. UDOH is currently evaluating syndromic surveillance data versus hospital discharge data for opioid-related emergency department visits, which may further optimize queries in ESSENCE, in order to provide improved opioid surveillance data to local public health partners. This analysis demonstrates that using syndromic surveillance data provides a more time-efficient alternative, enabling more rapid public health interventions, which improved opportunities to reduce opioid-related morbidity and mortality in Utah.

scholarly journals Monitoring Out-of-State Patients during a 2017 Hurricane Response using ESSENCE

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Caleb Wiedeman ◽  
Julie Shaffner ◽  
Kelly Squires ◽  
Jeffrey Leegon ◽  
Rendi Murphree ◽  
...  
Keyword(s):  
Emergency Department ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Chief Complaint ◽  
Free Text ◽  
Chief Complaints ◽  
Major Hurricane ◽  
Ed Visits ◽  
Zip Code ◽  
Hurricane Irma

ObjectiveTo demonstrate the use of ESSENCE in the BioSense Platform to monitor out-of-State patients seeking emergency healthcare in Tennessee during Hurricanes Harvey and Irma.IntroductionSyndromic surveillance is the monitoring of symptom combinations (i.e., syndromes) or other indicators within a population to inform public health actions. The Tennessee Department of Health (TDH) collects emergency department (ED) data from more than 70 hospitals across Tennessee to support statewide syndromic surveillance activities. Hospitals in Tennessee typically provide data within 48 hours of a patient encounter. While syndromic surveillance often supplements disease- or condition-specific surveillance, it can also provide general situational awareness about emergency department patients during an event or response.During Hurricanes Harvey (continental US landfall on August 25, 2017) and Irma (continental US landfall on September 10, 2017), TDH supported all hazards situational awareness using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) in the BioSense Platform supported by the National Syndromic Surveillance Program (NSSP). The volume of out-of-state patients in Tennessee was monitored to assess the impact on the healthcare system and any geographic- or hospital-specific clustering of out-of-state patients within Tennessee. Results were included in daily State Health Operations Center (SHOC) situation reports and shared with agency response partners such as the Tennessee Emergency Management Agency (TEMA).MethodsData were monitored from August 18, 2017 through September 24, 2017. A simple query was established in ESSENCE using the Patient Location (Full Details) dataset. Data were limited to hospital ED visits reported by Tennessee (Site = “Tennessee”). To monitor ED visits among residents of Texas before, during, and after Major Hurricane Harvey, data were queried for a patient zip code within Texas (State = “Texas”). ED visits among Florida residents were monitored similarly (State = “Florida”) before, during, and after Major Hurricane Irma. Additionally, a free text chief complaint search was implemented for the terms “Harvey”, “Irma, “hurricane”, “evacuee”, “evacuate”, “Florida”, and “Texas”. Chief complaint search results were then filtered to remove encounters with patient zip codes within Tennessee.ResultsFrom August 18, 2017 through September 24, 2017, Tennessee hospital EDs reported 277 patient encounters among Texas residents and 1,041 patient encounters among Florida residents. The number of encounters among patients from Texas remained stable throughout the monitoring period. In contrast, the number of encounters among patients from Florida exceeded the expected value on September 7, peaked September 10 at 116 patient encounters, and returned to expected levels on September 16 (Figure 1). The increase in patients from Florida was evenly distributed across most of Tennessee, with some clustering around a popular tourism area in East Tennessee. No concerning trends in reported syndromes or chief complaints were identified among Texas or Florida patients.The free text chief complaint query first exceeded the expected value on September 9, peaked on September 11 with 5 patient encounters, and returned to expected levels on September 14. From August 18 through September 24, 21 of 30 visits captured by the query were among Florida residents. One Tennessee hospital appeared to be intentionally using the term “Irma” in their chief complaint field to indicate patients from Florida impacted by the hurricane.ConclusionsThe ESSENCE instance in the BioSense platform provided TDH the opportunity to easily locate and monitor out-of-state patients seen in Tennessee hospital EDs. While TDH was unable to validate whether all patients identified as residents of Florida were displaced because of Major Hurricane Irma, the timing of the rise and fall of patient encounters was highly suggestive. Likewise, seeing no substantial increase ED patients with residence in Texas reassured TDH that the effects of Hurricane Harvey were not impacting hospital emergency departments in Tennessee.TDH used information and charts from ESSENCE to support situational awareness in our SHOC and at TEMA. Use of patient zip code to identify out-of-state residents was more sensitive than chief complaint searches by keyword during this event. ESSENCE allowed TDH to see where out-of-state patients appeared to be concentrating in Tennessee and monitor the need for targeting messaging and resources to heavily affected areas. Additionally, close surveillance of chief complaints among out-of-state patients provided assurance that no unusual patterns in illness or injury were occurring.ESSENCE is the only TDH information source capable of rapidly collecting health information on out-of-state patients. ESSENCE allowed TDH to quickly identify a change within the patient population seen at Tennessee emergency departments and monitor the situation until the patient population returned to baseline levels.

scholarly journals Syndromic Surveillance for Situation Awareness: Understanding Syndrome Performance

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kristin Arkin
Keyword(s):  
Emergency Department ◽  
Situation Awareness ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Initial Visit ◽  
Word Count ◽  
Data Submission ◽  
Daily Frequency ◽  
Ed Visits

ObjectiveIn August 2017, a large influx of visitors was expected to view the total solar eclipse in Idaho. The Idaho Syndromic Surveillance program planned to enhance situation awareness during the event. In preparation, we sought to examine syndrome performance of several newly developed chief complaint and combination chief complaint and diagnosis code syndrome definitions to aid in interpretation of syndromic surveillance data during the event.IntroductionThe August 21, 2017 total solar eclipse in Idaho was anticipated to lead to a large influx of visitors in many communities, prompting a widespread effort to assure Idaho was prepared. To support these efforts, the Idaho Syndromic Surveillance program (ISSp) developed a plan to enhance situation awareness during the event by conducting syndromic surveillance using emergency department (ED) visit data contributed to the National Syndromic Surveillance Program’s BioSense platform by Idaho hospitals. ISSp sought input on anticipated threats from state and local emergency management and public health partners, and selected 8 syndromes for surveillance.Ideally, the first electronic message containing information on an emergency department visit is sent to ISSp within 24 hours of the visit and includes the chief complaint for the visit. Data on other variables, such as diagnosis codes, are updated by subsequent messages for several days after the visit. Chief complaint (CC) text and discharge diagnosis (DD) codes are the primary variables used for syndrome match; delay in reporting these variables adversely affects timely syndrome match of visits. Because our plan included development of new syndrome definitions and querying data within 24 hours of visits, earlier than ISSp had done previously for trend analysis, we sought to better understand syndrome performance.MethodsWe defined messages with completed CC and DD as the last message regarding a visit where term count increased from previous messages regarding that visit, indicating new information was added to the field. We retrospectively assessed the total number of ED visits and calculated the daily frequency of completed CC and DD by days since visit date for visits during June 1–July 31, 2017. Additionally, we calculated facility mean word count in CC fields by averaging the word count of parsed, complete CC fields for visits occurring June 1–July 31, 2017 for each facility.During July 10–24, 2017, we calculated the daily frequency of visits occurring in the previous 90 days for total ED visits and syndrome-matched visits for 8 selected syndromes (heat-related illness; cold exposure; influenza-like-illness; nausea, vomiting, and diarrhea; animal/bug bites and stings; drowning/submersion; alcohol/drug intoxication; and medication replacement). Syndrome-matched visits were defined as visits with CC or DD that match the syndrome definition. We calculated the percent of syndrome-matched visits by syndromes defined with CC or CC and DD combined (CCDD) over time. Syndromes with fewer than 5 matched visits were excluded from analysis.ResultsComplete CCs were received for 99.1% of visits and complete DDs were received for 89.8% of visits. Complete CCs were submitted for 58.2% of visits within 1 day of the visit, 88.9% of visits within 3 days, and 98.9% of visits within 7 days. In contrast, complete DDs were submitted for 24.3% of visits within 1 day, 38.7% of visits within 3 days, and 53.7% of visits within 7 days (Table 1).During the observation period, data submission from facilities representing approximately 33% of visits was interrupted for 5 (36%) of 14 days. Heat-related illness, cold exposure, and drowning/submersion, were excluded from syndrome-match analysis. During the 9 days of uninterrupted data submission, 100% syndrome-matched visits for syndromes defined by CC alone and 69.1% syndrome-matched visits for syndromes defined by CCDD were identified within 6–7 days of initial visit. Facilities with interrupted data submission contributed 75% of CC syndrome-matched visits and 33% of CCDD syndrome-matched visits. The facility mean word count in CC fields from these facilities was >15 compared with 2–4 from other facilities.ConclusionsExamination of syndrome performance prior to a known event quantitated differences in timeliness of CC and DD completeness and syndrome match. CCs and DDs in visit messages were not complete within 24 hours of initial visit. CC completion was nearly 34 percentage points greater than DD completeness 1 day after initial visit and did not converge until ≥15 days after initial visit. Higher percentages of syndrome match within 6–7 days of initial visit were seen by CC alone than CCDD defined syndromes. Facilities using longer CCs contributed disproportionately to syndrome matching using CC, but not CCDD syndrome definitions. Syndromic surveillance system characteristics, including timeliness of CCs and DDs, length of CCs, and characteristics of facilities from which data transmission is interrupted should be considered when building syndrome definitions that will be used for surveillance within 7 days of emergency department visits and when interpreting syndromic surveillance findings.

scholarly journals Using Real-Time Syndromic Surveillance to Analyze the Impact of a Cold Weather Event in New Mexico

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Victoria F. Dirmyer
Keyword(s):  
Real Time ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Cold Weather ◽  
Weather Event ◽  
Diagnosis Codes ◽  
Chief Complaints ◽  
Icd 10 ◽  
Ed Visits ◽  
The Impact

Objective. This report describes the development of a novel syndromic cold weather syndrome for use in monitoring the impact of cold weather events on emergency department attendance. Methods. Syndromic messages from seven hospitals were analyzed for ED visits that occurred over a 12-day period. A cold weather syndrome was defined using terms in the self-reported chief complaint field as well as specific ICD-10-CM codes related to cold weather. A κ statistic was calculated to assess the overall agreement between the chief complaint field and diagnosis fields to further refine the cold weather syndrome definition. Results. Of the 3,873 ED visits that were reported, 487 were related to the cold weather event. Sixty-three percent were identified by a combination of diagnosis codes and chief complaints. Overall agreement between chief complaint and diagnosis codes was moderate (κ=0.50; 95% confidence interval = 0.48–0.52). Conclusion. Due to the near real-time reporting of syndromic surveillance data, analysis results can be acted upon. Results from this analysis will be used in the state’s emergency operations plan (EOP) for cold weather and winter storms. The EOP will provide guidance for mobilization of supplies/personnel, preparation of roadways and pedestrian walkways, and the coordination efforts of multiple state agencies.

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