scholarly journals Evaluation of an arboviral syndrome query used in Maricopa County, Arizona

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kaitlyn Sykes ◽  
Rasneet S. Kumar ◽  
Melissa Kretschmer ◽  
Jessica R. White
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Maricopa County ◽  
Diagnosis Codes ◽  
Arboviral Diseases ◽  
Surveillance Protocol ◽  
The Difference ◽  
Arboviral Disease

ObjectiveTo evaluate Arizona’s arboviral syndromic surveillance protocol in Maricopa County.IntroductionTimely identification of arboviral disease is key to prevent transmission in the community, but traditional surveillance may take up to 14 days between specimen collection and health department notification. Arizona state and county health agencies began monitoring National Syndromic Surveillance Program BioSense 2.0 data for patients infected with West Nile virus (WNV), St. Louis encephalitis virus (SLEV), chikungunya, or dengue virus in August 2015. Zika virus was added in April 2016. Our novel methods were presented at the International Society for Disease Surveillance 2015 Annual Conference. [1] Twice per week, we queried patient records from 15 Maricopa County BioSense-enrolled emergency department and inpatient hospitals for chief complaint keywords and discharge diagnosis codes. Our “Case Investigation Decision Tree” helped us determine whether records had a high or low degree of evidence for arboviral disease and necessitated further investigation. This study evaluated how Arizona’s protocol for conducting syndromic surveillance compared to traditional arboviral surveillance in terms of accuracy and timeliness in Maricopa County from August 2015 through December 2016.MethodsWe followed guidelines from the Centers for Disease Control and Prevention (CDC) to evaluate two major attributes of the protocol: accuracy [measured as positive predictive value (PPV) and sensitivity] and timeliness. [2] Arizona’s Medical Electronic Disease Surveillance Intelligence System (MEDSIS) was considered the “gold standard” system and BioSense was the test system. PPV was calculated as the proportion of records identified by BioSense that were reported to MEDSIS, regardless of final case classification. Sensitivity was the proportion of confirmed or probable cases in MEDSIS identified by BioSense. Though not all MEDSIS cases were seen at BioSense-reporting facilities, the sensitivity demonstrates how each query contributed to arboviral surveillance overall. We assessed timeliness in two ways: (1) the difference between the date when keywords or diagnosis codes were first identified by BioSense and the date the same patient was first reported to MEDSIS; and (2) the difference between the date the BioSense record was first reviewed by the Maricopa County Department of Public Health (MCDPH) syndromic surveillance team and the date the same patient was first investigated through MEDSIS by the MCDPH disease investigators. We assessed whether timeliness was affected by the method in which a record was identified in BioSense (i.e., chief complaint keyword or discharge diagnosis code).ResultsThe arboviral syndromic surveillance queries identified 62 records during the evaluation period (Table). For each arboviral query, the proportion of BioSense records that were also reported through MEDSIS ranged from 25.0% to 32.4%, except chikungunya, which had a PPV of 0%. BioSense records that had a high degree of evidence for arboviral disease tended to have a higher PPV compared to those with low evidence. BioSense records that were not already reported to MEDSIS met neither clinical nor exposure criteria for the arboviral diseases and were not deemed a public health risk. The sensitivities of the WNV and Zika queries to detect confirmed or probable cases in MEDSIS were 8.2% and 5.6%, respectively, while SLEV, chikungunya, and dengue queries were 0%. On average, patients were reported to MEDSIS 7 days prior to BioSense identifying keywords or diagnosis codes. In addition, MEDSIS cases were investigated by MCDPH disease investigators 10 days prior to MCDPH syndromic surveillance team review of BioSense records, on average. The average time between MEDSIS report date and BioSense identification date was shorter for BioSense records identified by chief complaint keywords than by diagnosis codes (4 and 8 days after MEDSIS, respectively).ConclusionsArizona’s arboviral syndromic surveillance protocol provided MCDPH with situational awareness, but BioSense data were not available more quickly than traditional mandated reporting. Through this process, we reviewed patient records that mentioned arboviral diseases and confirmed that these reportable conditions were captured in our traditional surveillance system. The decision tree was effective at prioritizing records for further investigation. Timeliness may be improved by updating the queries to include more chief complaint keywords and reviewing BioSense more than twice per week. MCDPH plans to evaluate Arizona’s updated arboviral syndromic surveillance protocol, which was adapted for BioSense Platform’s Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE).References1. White, J. R., Imholte, S., & Collier, K. (2016). Using Syndromic Surveillance to Enhance Arboviral Surveillance in Arizona. Online J Public Health Inform, 8(1), e81.2. German, R. R., et al. (2001). Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR Recomm Rep, 50(RR-13), 1-35.

scholarly journals Evaluation of a Novel Syndromic Surveillance Query for Heat-Related Illness Using Hospital Data From Maricopa County, Arizona, 2015

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 31S-39S ◽  
Author(s):  
Jessica R. White ◽  
Vjollca Berisha ◽  
Kathryn Lane ◽  
Henri Ménager ◽  
Aaron Gettel ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Hospital Data ◽  
Maricopa County ◽  
Predictive Values ◽  
Diagnosis Codes ◽  
Illness Prevention ◽  
Council Of State

Objectives: We evaluated a novel syndromic surveillance query, developed by the Council of State and Territorial Epidemiologists (CSTE) Heat Syndrome Workgroup, for identifying heat-related illness cases in near real time, using emergency department and inpatient hospital data from Maricopa County, Arizona, in 2015. Methods: The Maricopa County Department of Public Health applied 2 queries for heat-related illness to area hospital data transmitted to the National Syndromic Surveillance Program BioSense Platform: the BioSense “heat, excessive” query and the novel CSTE query. We reviewed the line lists generated by each query and used the diagnosis code and chief complaint text fields to find probable cases of heat-related illness. For each query, we calculated positive predictive values (PPVs) for heat-related illness. Results: The CSTE query identified 674 records, of which 591 were categorized as probable heat-related illness, demonstrating a PPV of 88% for heat-related illness. The BioSense query identified 791 patient records, of which 589 were probable heat-related illness, demonstrating a PPV of 74% for heat-related illness. The PPV was substantially higher for the CSTE novel and BioSense queries during the heat season (May 1 to September 30; 92% and 85%, respectively) than during the cooler seasons (55% and 29%, respectively). Conclusion: A novel query for heat-related illness that combined diagnosis codes, chief complaint text terms, and exclusion criteria had a high PPV for heat-related illness, particularly during the heat season. Public health departments can use this query to meet local needs; however, use of this novel query to substantially improve public health heat-related illness prevention remains to be seen.

scholarly journals Tracking drug-related overdoses at the local level: Using Syndromic Surveillance in the CO-NCR

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Emery Shekiro ◽  
Lily Sussman ◽  
Talia Brown
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Drug Overdose ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Chief Complaint ◽  
Discharge Diagnosis ◽  
Diagnosis Codes ◽  
Drug Overdoses

Objective: In order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the Colorado North Central Region (CO-NCR). Secondarily, we used free text fields to understand the use of unspecified diagnosis fields.Introduction: The United States is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. In Colorado the rate of fatal drug overdose increased 68% from 2002-2014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. The CDC’s National Syndromic Surveillance Program (NSSP) provides near-real time monitoring of emergency department (ED) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. Colorado North Central Region (CO-NCR) receives data for 4 local public health agencies from 25 hospitals across Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties.Access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. However, use of these data also comes with many challenges. For example, we learned from key informant interviews with ED staff in Boulder and Denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses.Methods: Boulder County Public Health (BCPH) and Denver Public Health (DPH) developed a query in Early Notification of Community Based Epidemics (ESSENCE) using ICD-10-CM codes to identify cases of drug-related overdose [T36-T51] from October 2016 to September 2017. The Case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. Cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. The first diagnosis code in the file was considered the primary diagnosis. Chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the CO-NCR. We also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county.Results: The query identified 2,366 drug-related overdoses in the CO-NCR. The prevalence of drug overdoses differed across age groups. The detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). Females comprised 56.1% of probable general drug overdoses. The majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (T50), narcotics (T40) (12.6%), or non-opioid analgesics (T39) (7.8%). For some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields.Conclusions: Syndromic surveillance offers the opportunity to capture drug-related overdose data in near-real time. We found variation in drug-related overdose by demographic groups. Unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. Leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. Chart reviews and access to more fields from the ED electronic health record could improve local drug surveillance.

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.

scholarly journals Impact of the NSSP’s transition to ESSENCE on chief complaint field-based syndromes

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Rasneet S Kumar ◽  
Jessica R White
Keyword(s):  
Time Series ◽  
Syndromic Surveillance ◽  
Data Access ◽  
Case Finding ◽  
Chief Complaint ◽  
Analytical Tool ◽  
Free Text ◽  
Guidance Document ◽  
Maricopa County ◽  
Analytical Tools

Objective: To evaluate the effect and implications of changing the chief complaint field during the National Syndromic Surveillance Program (NSSP) transition from BioSense 2.0 analytical tools to BioSense Platform – ESSENCEIntroduction: In January 2017, the NSSP transitioned their BioSense analytical tools to Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE). The chief complaint field in BioSense 2.0 was a concatenation of the record’s chief complaint, admission reason, triage notes, and diagnostic impression. Following the transition to ESSENCE, the chief complaint field was comprised of the first chief complaint entered or the first admission reason, if the chief complaint was blank. Furthermore, the ESSENCE chief complaint field was electronically parsed (i.e., the original chief complaint text was altered to translate abbreviations and remove punctuation). This abstract highlights key findings from Maricopa County Department of Public Health’s evaluation of the new chief complaint field, its impact on heat-related illness syndromic surveillance, and implications for ongoing surveillance efforts.Methods: For this evaluation, we used the heat-related illness query recommended in Council of State and Territorial Epidemiologists’ (CSTE)2016 Guidance Document for Implementing Heat-Related Illness Syndromic Surveillance. Before the transition, we used BioSense 2.0’s, phpMyAdmin analytical tool to generate a list of patients who visited Maricopa County emergency departments or inpatient hospitals between 5/1/2016 – 9/30/2016 due to heat-related illness. After the transition, we used the CC and DD Category “Heat-related Illness, v1” in ESSENCE, which was based on the CSTE heat-related illness query, to generate a list of patients for the same time period. We compared the line-lists and time-series trends from phpMyAdmin and ESSENCE.Results: The phpMyAdmin analytical tool identified 785 heat-related illness records with the query (Figure). 642 (82%) of these heat-related illness records were also captured by ESSENCE. Reasons for 143 (18%) records not being identified by ESSENCE included: the patient’s admission reason field contained keywords that were not available in the ESSENCE chief complaint field (n=94, 66%); data access changed, which disabled access to patients who resided in zip codes that crossed a county border (30, 21%); discrepancies between ESSENCE parsing and text in the original chief complaint (11, 8%); heat-related illness discharge diagnoses were removed by the facility after the phpMyAdmin line-list for heat-related illness was extracted (7, 5%); and one record was undetermined. Conversely, ESSENCE captured 36 additional heat-related illness records, not previously captured by phpMyAdmin. Reasons included: a query exclusion term was located in the patient’s admission reason but not the ESSENCE chief complaint field (16, 44%); a heat-related illness discharge diagnosis code was added by the facility after the data were extracted by phpMyAdmin (4, 11%); and 16 (44%) were undetermined. Time-series trend evaluation revealed a significant correlation between the two surveillance tools (Pearson coefficient = 0.97, p < 0.01).Conclusions: Though the data trends over time were not significantly affected by changes in the chief complaint field, differences in the field’s composition have important implications for syndromic surveillance practitioners. Free-text queries designed to search the chief complaint field in ESSENCE may not retrieve records previously identified with BioSense 2.0 analytical tools, which may limit individual case-finding capacity. The elimination of admission reason from the chief complaint field in ESSENCE has the greatest effect on case-finding capacity. Furthermore, surveillance reports produced by ESSENCE cannot be directly compared to reports that were previously published with data from BioSense 2.0. These limitations may be addressed if ESSENCE creates a feature that allows users to easily query fields (e.g., admission reason) in addition to the chief compliant field.

scholarly journals Precision public health through clinic-based syndromic surveillance in communities

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Ta-Chien Chan ◽  
Yung-Chu Teng ◽  
Yen-Hua Chu ◽  
Tzu-Yu Lin
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Early Stage ◽  
Age Distribution ◽  
Local Health ◽  
Syndromic Surveillance System ◽  
Taipei City ◽  
Kaohsiung City

ObjectiveSentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. The traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ILI), and hand, foot, and mouth disease (HFMD) to health authorities on a weekly basis. However, this is labor-intensive and time-consuming work. In this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites.IntroductionIn December 2009, Taiwan’s CDC stopped its sentinel physician surveillance system. Currently, infectious disease surveillance systems in Taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (RODS) from emergency rooms, and the outpatient and hospitalization surveillance system from National Health Insurance data. However, the timeliness of data exchange and the number of monitored syndromic groups are limited. The spatial resolution of monitoring units is also too coarse, at the city level. Those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. Based on past epidemic experience, daily and small area surveillance can detect early aberrations. In addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. Traditional disease-based reporting systems cannot capture this kind of signal. Therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. Through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. Local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases.MethodsWe collected data on 23 syndromic groups from participating clinics in Taipei City (in northern Taiwan) and Kaohsiung City (in southern Taiwan). The definitions of 21 of those syndromic groups with ICD-10 diagnoses were adopted from the International Society for Disease Surveillance (https://www.surveillancerepository.org/icd-10-cm-master-mapping-reference-table). The definitions of the other two syndromic groups, including dengue-like illness and enterovirus-like illness, were suggested by infectious disease and emergency medicine specialists.An enhanced sentinel surveillance system named “Sentinel plus” was designed for sentinel clinics and community hospitals. The system was designed with an interactive interface and statistical models for aberration detection. The data will be computed for different combinations of syndromic groups, age groups and gender groups. Every day, each participating clinic will automatically upload the data to the provider of the health information system (HIS) and then the data will be transferred to the research team.This study was approved by the committee of the Institutional Review Board (IRB) at Academia Sinica (AS-IRB02-106262, and AS-IRB02-107139). The databases we used were all stripped of identifying information and thus informed consent of participants was not required.ResultsThis system started to recruit the clinics in May 2018. As of August 2018, there are 89 clinics in Kaohsiung City and 33 clinics and seven community hospitals in Taipei City participating in Sentinel plus. The recruiting process is still ongoing. On average, the monitored volumes of outpatient visits in Kaohsiung City and Taipei City are 5,000 and 14,000 per day.Each clinic is provided one list informing them of the relative importance of syndromic groups, the age distribution of each syndromic group and a time-series chart of outpatient rates at their own clinic. In addition, they can also view the village-level risk map, with different alert colors. In this way, medical practitioners can know what’s going on, not only in their own clinics and communities but also in the surrounding communities.The Department of Health (Figure 1) can know the current increasing and decreasing trends of 23 syndromic groups by red and blue color, respectively. The spatial resolution has four levels including city, township, village and clinic. The map and bar chart represent the difference in outpatient rate between yesterday and the average for the past week. The line chart represents the daily outpatient rates for one selected syndromic group in the past seven days. The age distribution of each syndromic group and age-specific outpatient rates in different syndromic groups can be examined.ConclusionsSentinel plus is still at the early stage of development. The timeliness and the accuracy of the system will be evaluated by comparing with some syndromic groups in emergency rooms and the national notifiable disease surveillance system. The system is designed to assist with surveillance of not only infectious diseases but also some chronic diseases such as asthma. Integrating with external environmental data, Sentinel plus can alert public health workers to implement better intervention for the right population.References1. James W. Buehler AS, Marc Paladini, Paula Soper, Farzad Mostashari: Syndromic Surveillance Practice in the United States: Findings from a Survey of State, Territorial, and Selected Local Health Departments. Advances in Disease Surveillance 2008, 6(3).2. Ding Y, Fei Y, Xu B, Yang J, Yan W, Diwan VK, Sauerborn R, Dong H: Measuring costs of data collection at village clinics by village doctors for a syndromic surveillance system — a cross sectional survey from China. BMC Health Services Research 2015, 15:287.3. Kao JH, Chen CD, Tiger Li ZR, Chan TC, Tung TH, Chu YH, Cheng HY, Liu JW, Shih FY, Shu PY et al.: The Critical Role of Early Dengue Surveillance and Limitations of Clinical Reporting -- Implications for Non-Endemic Countries. PloS one 2016, 11(8):e0160230.4. Chan TC, Hu TH, Hwang JS: Daily forecast of dengue fever incidents for urban villages in a city. International Journal of Health Geographics 2015, 14:9.5. Chan TC, Teng YC, Hwang JS: Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models. BMC Public Health 2015, 15:168.6. Ma HT: Syndromic surveillance system for detecting enterovirus outbreaks evaluation and applications in public health. Taipei, Taiwan: National Taiwan University; 2007. 

scholarly journals Delay between Discharge and Admit Time Delay in ADT-A03 messages via LEEDS

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jose Serrano
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Surveillance Systems ◽  
Hospital Emergency ◽  
Timely Manner ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology ◽  
The Difference ◽  
Disease Specific

ObjectiveTo explore the difference between the reported date of admissionand discharge date using discharge messages (A03), from hospitalemergency departments participating in the Louisiana Early EventDetection System (LEEDS.IntroductionThe Infectious Disease Epidemiology Section (IDEpi) within theOffice of Public Health (LaOPH) conducts syndromic surveillanceof emergency departments by means of the Louisiana Early EventDetection System (LEEDS). LEEDS accepts ADT (admit-discharge-transfer) messages from participating hospitals, predominately A04(registration) and A03 (discharge), to obtain symptom or syndromeinformation on patients reporting to hospital emergency departments.Capturing the data using discharge messages (A03) only could resultin a delay in receipt of data by LaOPH, considering the variability inthe length of stay of a patient in the ED.MethodsEmergency department data from participating hospitals isimported daily to LEEDS and processed for syndrome classification.IDEpi syndromic surveillance messages received for the period ofCDC week 1632 and 1636 (8/8/16-9/8/16) using MS Access andExcel to calculate the difference (in days) between the reported admitdate and discharge date in A03 messages.Results88.1% of the A03 messages submitted in the 4 week analysisperiod exhibited no delay (delay=0 days) between the admit date andthe reported discharge date, compared to only 10.7% showing a delayof one day (delay = 1 day) and 1.06% showing a delay of 2 days ormore (delay≥2 days). Less than 0.2% of the messages had missinginformation regarding discharge date (Table 1).ConclusionsSyndromic surveillance systems operate under a constant need forimprovement and enhancement. The quality of the data, independentof the quality of the system, should always strive to be of the highestpedigree in order to inform disease-specific programs and detectpublic health aberrations. In order to identify these potential concerns,it is imperative that the data be submitted to public health agenciesin a timely manner. Based on this analysis, the lapse in time betweenadmit and discharge results in little to no patient syndromic data delayfor those hospital ED’s that exclusively send A03 messages. Thisstatement is supported by the finding that close to 99% of messagesdemonstrated a delay between admit date and discharge date of oneday or less.Table 1. Delay between reported Admit and Discharge date in A03 messagessubmitted to LEEDS

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.

scholarly journals Using Syndromic Surveillance Alert Protocols for Epidemiologic Response in Georgia

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Rene Borroto ◽  
Bill Williamson ◽  
Patrick Pitcher ◽  
Lance Ballester ◽  
Wendy Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Health Care Providers ◽  
Syndromic Surveillance ◽  
Emergency Department Patient ◽  
Chief Complaint ◽  
Care Providers ◽  
Public Health Response ◽  
Presumptive Diagnosis ◽  
Notifiable Diseases

ObjectiveDescribe how the Georgia Department of Public Health (DPH) usessyndromic surveillance to initiate review by District Epidemiologists(DEs) to events that may warrant a public health response (1).IntroductionDPH uses its State Electronic Notifiable Disease SurveillanceSystem (SendSS) Syndromic Surveillance (SS) Module to collect,analyze and display results of emergency department patient chiefcomplaint data from hospitals throughout Georgia.MethodsDPH prepares a daily SS report, based upon the analysis ofdaily visits to 112 Emergency Department (EDs). The visits areclassified in 33 syndromes. Queries of chief complaint and dischargediagnosis are done using the internal query capability of SendSS-SSand programming in SAS/BASE. Charting of the absolute countsor percentage of ED visits by syndromes is done using the internalcharting capability of SendSS-SS. A daily SS report includes thefollowing sections:Statewide Emergency Department Visitsby Priority Syndromes(Bioterrorism, BloodyRespiratory,FeverRespiratory, FeverChest, FeverFluAdmit, FeverFluDeaths,VeryIll, andPoxRashFever, Botulism, Poison, BloodyDiarrhea,BloodyVomit, FeverGI, ILI, FeverFlu, RashFever, Diarrhea,Vomit).Statewide Flag Analysis: Is intended to detect statewideflags, by using theChartscapability in SendSS SS.Possible caseswith presumptive diagnosis of potentially notifiable diseases: Isintended to provide early-warning to the DEs of possible cases thatare reportable to public health immediately or within 7 days usingqueries in the Chief Complaint and Preliminary Diagnosis fields ofSendSS-SS.Possible clusters of illness: Since any cluster of illnessmust be reported immediately to DPH, this analysis is aimed atquerying and identifying possible clusters of patients with similarsymptoms (2).Possible travel-related illness: Is intended to identifypatients with symptoms and recent travel history.Other events ofinterest: Exposures to ill patients in institutional settings (e.g. chiefcomplaint indicates that other children in the daycare have similarsymptoms).Trend Analysis: Weekly analysis of seasonality andtrends of 14 syndromes. Finally, specific events are notified to andreviewed by the 18 DEs, who follow up by contacting the InfectionPreventionists of the hospitals to identify the patients using medicalrecords or other hospital-specific identification numbers and followup on the laboratory test results.ResultsSince 05/15/2016, 12 travel-related illnesses, 29 vaccine-preventable diseases, 14 clusters, and 3 chemical exposures havebeen notified to DEs. For instance, a cluster of chickenpox in childrenwas identified after the DE contacted the Infection Preventionist ofa hospital, who provided the DE with the laboratory results and thephysician notes about the symptoms of the patients. These actionshave resulted in earlier awareness of single cases or cluster of illness,prompt reporting of notifiable diseases, and successful interactionbetween DEs and health care providers. In addition, SS continues totrack the onset, peak, and decline of seasonal illnesses.ConclusionsThe implementation of SS in the State of Georgia is helping withthe timely detection and early responses to disease events and couldprove useful in reducing the disease burden caused by a bioterroristattack.

scholarly journals Using ESSENCE to Detect Bomb-Making Activity: What’s Appropriate?

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrew Torgerson
Keyword(s):  
Public Health ◽  
Law Enforcement ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Chemical Mechanism ◽  
Mass Gathering ◽  
Surveillance Period ◽  
Saint Louis ◽  
Ed Visits

ObjectiveTo describe a novel application of ESSENCE by the Saint Louis County Department of Public Health (DPH) in preparation for a mass gathering and to encourage discussion about the appropriateness of sharing syndromic surveillance data with law enforcement partners.IntroductionIn preparation for mass gathering events, DPH conducts enhanced syndromic surveillance activities to detect potential cases of anthrax, tularemia, plague, and other potentially bioterrorism-related communicable diseases. While preparing for Saint Louis to host a Presidential Debate on October 9, 2016, DPH was asked by a partner organization whether we could also detect emergency department (ED) visits for injuries (e.g., burns to the hands or forearms) that could possibly indicate bomb-making activities.MethodsUsing the Electronic Surveillance System for the Notification of Community-Based Epidemics (ESSENCE), version 1.9, DPH developed a simple query to detect visits to EDs in Saint Louis City or Saint Louis County with chief complaints including the word “burn” and either “hand” or “arm.” A DPH epidemiologist reviewed the results of the query daily for two weeks before and after the debate (i.e., from September 25, 2016 to October 23, 2016). If any single ED visit was thought to be “suspicious” – if, for example, the chief complaint mentioned an explosive or chemical mechanism of injury – then DPH would contact the ED for details and relay the resulting information to the county’s Emergency Operations Center.ResultsDuring the 29 day surveillance period, ESSENCE detected 27 ED visits related to arm or hand burns. The ESSENCE query returned a median of 1 ED visit per day (IQR 0 to 2 visits). Of these, one was deemed to merit further investigation – two days before the debate, a patient presented to an ED in Saint Louis County complaining of a burned hand. The patient’s chief complaint data also mentioned “explosion of unspecified explosive materials.” Upon investigation, DPH learned that the patient had been injured by a homemade sparkler bomb. Subsequently, law enforcement determined that the sparkler bomb had been made without any malicious intent.ConclusionsDPH succeeded in using ESSENCE to detect injuries related to bomb-making. However, this application of ESSENCE differs in at least two ways from more traditional uses of syndromic surveillance. First, conventional syndromic surveillance is designed to detect trends in ED visits resulting from an outbreak already in progress or a bioterrorist attack already carried out. In this case, syndromic surveillance was used to detect a single event that could be a prelude to an attack. The potential to prevent widespread injury or illness is a strength of this approach. Second, conventional syndromic surveillance identifies potential outbreak cases or, in the case of a bioterrorist attack, potential victims. In this case, syndromic surveillance was used to identify a potential perpetrator of an attack. While public health and law enforcement agencies would ideally coordinate their investigative efforts in the wake of an attack, this practice has led to conversations within DPH about the appropriateness of routinely sharing public health surveillance data with law enforcement. 

scholarly journals Improving Timeliness of Georgia Emergency Room Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lance Ballester ◽  
Karl Soetebier ◽  
Bill Williamson ◽  
Rene Borroto ◽  
Jessica Grippo ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Room ◽  
Disease Control ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Kernel Density ◽  
Meaningful Use ◽  
Patient Records ◽  
Data Analyses ◽  
Triage Time

ObjectiveTo explore the timeliness of emergency room surveillance data after the advent of federal Meaningful Use initiatives and determine potential areas for improvement.IntroductionTimeliness of emergency room (ER) data is arguably its strongest attribute in terms of its contribution to disease surveillance. Timely data analyses may improve the efficacy of prevention and control measures.There are a number of studies that have looked at timeliness prior to the advent of Meaningful Use, and these studies note that ER data were not fast enough for them to be useful in real time2,3. However, the change in messaging practices in the Meaningful Use era potentially changes this.Other studies have shown that changes in processes and protocol can dramatically improve timeliness1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness.MethodsER data were collected from March 2017 through September 2017 from both the Georgia Department of Public Health’s (GDPH) State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance Module and the Centers for Disease Control and Prevention (CDC) National Syndromic Surveillance Program’s (NSSP) ESSENCE systems. Patients from hospitals missing 10 or more days of data, as well as patients with missing or invalid triage times, and all visits after August 1st were excluded in order to ensure data were representative of a “typical” time period and that a sufficient amount of time was given for visits to arrive from hospitals.The timeliness of individual records was determined in a number of different ways. All timeliness measurements were determined by subtracting the earlier time event from the later time of the event. The overall measure of timeliness is the time between the patient’s triage time and the data being present in the ESSENCE data system. In between, Georgia’s SendSS system receives and processes the data. This is illustrated in Figure 1. Due to the skewed nature of these measures, they were analyzed using medians and Gaussian kernel density plots.ResultsThe study in total included records from 118 Georgia hospitals, 14,203 data files and 1,897,501 patient records. Overall median timeliness of data from Triage Time to being available in SendSS for analyses was 33.62 hours (IQR=28.5), and in ESSENCE was 45.08 hours (IQR=37.05).The distributions of Triage Time of Day, Time Available in SendSS Staging, and Time Available in ESSENCE Analysis can be seen in Figure 2. Additionally, lines were added for when SendSS makes data available for its own analyses and when it sends data to ESSENCE. These latter lines represent places where the SendSS system itself could improve, and potential improved times were noted based on the kernel densities.Peak triage times for Georgia hospitals were between 10 am to 11 pm, shown in black. This represents the ideal timeliness if Hospitals sent their data immediately. However, data was all batched by Georgia hospitals and sent at different times of the day. The distribution of the time patient records arrived at SendSS staging was indicated in blue.During the period of this study, Georgia processed data into its SendSS system at 6:30am and 11:30am every day and sent data to the ESSENCE system at 1pm each day. These times are highlighted on the plot in green, and red respectively. New potential improved times, based on the kernel density of data being available in SendSS staging, are shown in the lighter shades of these colors at 8:30am and 12pm every day, while being sent to ESSENCE at 9am and 12:30pm to ensure time for data to be properly processed. These were determined to be optimal times for reducing lag in the data, however, may not be optimal for daily analysis.The purple line on the plot represents the times that data were available in ESSENCE’s system for analysis. This was notably delayed by a median 4.15 hours after the data was sent to ESSENCE on a typical day.ConclusionsA data driven approach to choosing processing times could improve timeliness of data analyses in the SendSS and ESSENCE systems. By conducting this type of analysis in an ongoing periodic basis, processing lag times can be kept at a minimum.1. Centers for Disease Control. Progress in improving state and local disease surveillance--United States, 2000-2005. MMWR Morbidity and mortality weekly report. 2005;54(33):822-825.2. Jajosky R, Groseclose S. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4(1).3. Travers D, Barnett C, Ising A, Waller A. Timeliness of emergency department diagnoses for syndromic surveillance. AMIA Annual Symposium Proceedings. 2006;Vol. 2006:769.4. Ward M, Brandsema P, van Straten E, Bosman A. Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Eurosurveillance. 2005;10(1):7-8.

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