Making Your Blood Boil: Challenges and Opportunities for Using Emergency Department Data to Understand the Impact of Structural Inequities on Health.

Utilizing Syndromic Surveillance for Hurricane Irma-Related CO Poisonings in Florida

ObjeciveIdentify surveillance priorities for emergency department (ED) and Oregon Poison Center (OPC) data ahead of the 2017 Great American Solar Eclipse gatherings in Oregon and create a suite of queries for use in the Health Intelligence Section of the Oregon Public Health Division (OPHD) Incident Management Team (IMT).IntroductionOregon’s statewide syndromic surveillance system (Oregon ESSENCE) has been operational since 2012. Non-federal emergency department data (and several of their associated urgent care centers) are the primary source for the system, although other data sources have been added, including de-identified call data from OPC in 2016 (1).OPHD epidemiologists have experience monitoring mass gatherings (2) and have a strong relationship with OPC, collaborating on a regular basis for routine and heightened public health surveillance. Nevertheless, surveillance for the Great American Solar Eclipse (August 2017) presented a challenge due to the 107 reported simultaneous statewide eclipse-watching events planned for the day of the eclipse (some with estimated attendance of greater than 30,000 people and most in rural or frontier regions of the state).Scientific literature is limited on mass gathering surveillance in the developed world (3), particularly in rural settings (4), so OPC and OPHD worked together to develop a list of health conditions of interest, including some that would warrant both an ED visit and a call to OPC (e.g., snake bites). Monitoring visits in both data sources in would allow for assessment of total burden on the healthcare system, especially in the case of snake bites where only specific bites require administration of anti-venom.MethodsAhead of the planned mass gatherings, OPHD Health Intelligence and OPC compiled a list of expected risks from the literature (4,5) and input from members of the IMT including the Public Information Officer, who monitored media for stories about health. Priority health conditions presented a clear risk to public health (e.g., limited supply of snake anti-venom warranted surveillance of snake bites) or were the subject of substantial media coverage. Query development focused on risks that had specific, well-defined health effects and that would be captured by syndromic ED and OPC data.During an enhanced surveillance period (8/18-8/24), OPHD Health Intelligence reviewed and interpreted trends in common queries with OPC and disseminated a daily statewide surveillance report.ResultsOPHD and OPC created four new queries for both ED and OPC data streams: snake bites, psychedelic mushrooms, 2nd and 3rd degree body burns and eye-related calls and visits. ED queries used chief complaint, discharge diagnosis, or triage note. OPC queries used generic code, therapy and clinical effect.From 8/18-8/22, OPHD Health Intelligence distributed daily surveillance reports to the OPHD IMT and external partners. An increased in eye-related injuries was identified on the day after the eclipse, prompting OPHD Health Intelligence to consult with OPC. ED surveillance data indicated that the increase in eye-related visits was likely a seasonal trend. OPC staff reviewed the charts of patient calls captured by the query and concluded the calls were not related to retinal issues from looking at the sun. No other trends were noted in the joint OPHD/OPC queries.ConclusionsOPHD Health Intelligence piloted four new queries for surveillance during this mass gathering event and exercised the process for disseminating trend information from OPC and ED data. The eclipse event was fairly quiet and very few trends of note were captured by surveillance. Prior to this event, OPC data had not been a part of the Health Intelligence surveillance plan. However, assessing trends in OPC data provides an opportunity to better understand trends seen in ED data (e.g., whether or not a surge in ED visits for snake bites is accompanied by a surge in OPC calls for anti-venom is meaningful). By building a process to review disparate data in tandem, OPHD and OPC strengthened regional surveillance for this event. Applicable queries will continue to be used for planned event surveillance and several additional queries are currently under development.

ObjectiveEpidemiologists will understand the differences between syndromic and discharge emergency department data sources, the strengths and limitations of each data source, and how each of these different emergency department data sources can be best applied to inform a public health response to the opioid overdose epidemic.IntroductionTimely and accurate measurement of overdose morbidity using emergency department (ED) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the United States. However, from jurisdiction to jurisdiction, differing sources and types of ED data vary in their quality and comprehensiveness. Many jurisdictions collect timely emergency department data through syndromic surveillance (SyS) systems, while others may have access to more complete, but slower emergency department discharge datasets. State and local epidemiologists must make decisions regarding which datasets to use and how to best operationalize, interpret, and present overdose morbidity using ED data. These choices may affect the number, timeliness, and accuracy of the cases identified.MethodsCDC partnered with 45 states and the District of Columbia to combat the worsening opioid overdose epidemic through three cooperative agreements: Prevention for States (PFS), Data Driven Prevention Initiative (DDPI), and Enhanced State Opioid Overdose Surveillance (ESOOS). To support funded jurisdictions in monitoring non-fatal opioid overdoses, CDC developed two different sets of indicator guidance for measuring non-fatal opioid overdoses using ED data, with each focusing on different ED data sources (SyS and discharge). We report on the following attributes for each type of ED data source1,2: 1) timeliness; 2) data quality (e.g., percent completeness by field); 3) validity; and 4) representativeness (e.g., percent of facilities included).ResultsWhen comparing timeliness across data sources, SyS data has clear advantages, with many jurisdictions receiving data within 24 hours of an event. For discharge data, timeliness is more variable with some jurisdictions receiving data within weeks while others wait over 1.5 years before receiving a complete discharge dataset. Data quality and completeness tends to be stronger in discharge datasets as facilities are required to submit complete discharge records with valid ICD-10-CM codes in order to be reimbursed by payers. By contrast, for SyS data systems, participating facilities may not consistently submit data for all possible fields, including diagnosis. Validity is dependent on the data source as well as the case definition or syndrome definition used; with this in mind, SyS data overdose indicators are designed to have high sensitivity, with less attention to specificity. Discharge data overdose indicators are designed to have a high positive predictive value, while sensitivity and specificity are both important considerations. Discharge datasets often include records for 100% of ED visits from all nonfederal, acute care-affiliated facilities in a state included. By contrast, representativeness of facilities in SyS data systems varies widely across states with some states having less than 50% of facilities reporting.ConclusionsCDC funded partners share overdose morbidity data with CDC using either ED SyS data, ED discharge data, or both. CDC indicator guidance for ED discharge data is designed for states to track changes in health outcomes over time for descriptive, performance monitoring, and evaluation purposes and to create rates that are more comparable across injury category, time, and place. Considering these objectives, CDC placed a higher priority on data quality, validity (i.e., positive predictive value), and representativeness, all of which are stronger attributes of discharge data. CDC’s indicator guidance for ED SyS data is designed for states to rapidly identify changes in nonfatal overdoses and to identify areas within a particular state that are experiencing rapid change in the frequency or types of overdose events. When considering these needs, CDC prioritized timeliness and validity in terms of sensitivity, both of which are stronger attributes of SyS data. SyS and discharge ED data each lend themselves to different informational applications and interpretations based on the strengths and limitations of each dataset. An effective, informed public health response to the opioid overdose epidemic requires continued investment in public health surveillance infrastructure, careful consideration of the needs of the data user, and transparency regarding the unique strengths and limitations of each dataset.

BackgroundPeople who inject drugs (PWID) have been identified as frequent users of emergency department (ED) and hospital inpatient services. The specific challenges of record linkage in cohorts with numerous administrative health records occurring in close proximity are not well understood. Here, we present a method for patient-specific record linkage of ED and hospital admission data for a cohort of PWID.MethodsData from 688 PWID were linked to two state-wide administrative health databases identifying all ED visits and hospital admissions for the cohort between January 2008 and June 2013. We linked patient-specific ED and hospital admissions data, using administrative date-time timestamps and pre-specified linkage criteria, to identify hospital admissions stemming from ED presentations for a given individual. The ability of standalone databases to identify linked ED visits or hospital admissions was examined.ResultsThere were 3459 ED visits and 1877 hospital admissions identified during the study period. Thirty-four percent of ED visits were linked to hospital admissions. Most links had hospital admission timestamps in-between or identical to their ED visit timestamps (n = 1035, 87%). Allowing 24-h between ED visits and hospital admissions captured more linked records, but increased manual inspection requirements. In linked records (n = 1190), the ED ‘departure status’ variable correctly reflected subsequent hospital admission in only 68% of cases. The hospital ‘admission type’ variable was non-specific in identifying if a preceding ED visit had occurred.ConclusionsLinking ED visits with subsequent hospital admissions in PWID requires access to date and time variables for accurate temporal sorting, especially for same-day presentations. Selecting time-windows to capture linked records requires discretion. Researchers risk under-ascertainment of hospital admissions if using ED data alone.

Optimization of Linkage between North Carolina EMS and ED Data: EMS Naloxone Cases

In Maine, rabies postexposure prophylaxis (PEP) administration is reportable to public health. We sought to determine the objectives of the Maine Center for Disease Control and Prevention's (Maine CDC's) PEP administration surveillance system and whether the method of conducting surveillance through a manual health care provider (hereinafter, provider) reporting system meets these objectives. We also compared provider-reported PEP administrations with administrations identified in emergency department (ED) data. During September 2022, we interviewed 8 Maine CDC epidemiologists to determine system objectives. We obtained and compared PEP administration data from provider reporting system and ED data and summarized each dataset by year, exposing animal, and facility. We assessed the ability of each source to address surveillance system objectives by comparing data elements with each objective. Maine CDC epidemiologists described the following objectives of the surveillance system: (1) track potential human exposures to rabid or potentially rabid animals, (2) document PEP administration trends, and (3) ensure PEP is correctly administered. They determined the third objective is not being achieved by the current system. During January 2018-June 2022, we identified 538 provider-reported PEP administrations and 1191 PEP administrations through ED data. ED data were more timely than provider reports and identified more PEP administrations, but 28% of ED records did not contain information on the exposing animal. Maine CDC can use ED data to document PEP administration trends in near-real time. ED data obtained from syndromic surveillance might be used in tandem with or in place of Maine CDC's traditional PEP surveillance system. We are building more complex queries that more fully capture PEP administrations to have a thorough understanding of PEP administered in Maine.

Statement of purpose The NC Division of Public Health, in collaboration with UNC Chapel Hill, is improving injury surveillance data as part of the NC Surveillance Quality Improvement (SQI) Project. The project has focused on improving emergency department (ED) data in the statewide public health surveillance system NC DETECT. Unlike statewide mortality and hospital discharge data, NC DETECT ED data are available in near real time with over 75% of ED visits assigned at least one billing code within two weeks of the visit. One task/goal of the NC SQI project was the development of 12 poisoning and drug overdose surveillance case definitions. Methods/Approach The case definitions drew from existing definitions developed by state and national organisations; content experts in injury epidemiology, surveillance methods, and public health informatics; and end user feedback. Nine of the definitions incorporate diagnosis and/or E-codes (poisoning, unintentional poisoning, acute alcohol poisoning, drug overdose, unintentional drug overdose, opioid overdose, prescription analgesic opioid overdose, methadone overdose, and heroin overdose). Two definitions use a combination of diagnosis codes, E-codes, and keyword searches (drug overdose and heroin-related ED visits). One definition consists of only a keyword search (Narcan/naloxone). Results The new case definitions were added to the NC DETECT web portal in summer 2014. Authorised users can access both current and historical ED data. Authorised users from local health departments can access line-listing data for their county and compare aggregate data to other counties and the state. These case definitions may be revised based on user feedback. In addition, custom-reports can be developed to address specific poisoning topics (e.g. fentanyl overdoses). Conclusions NC DETECT ED data are vital to NC for public health surveillance. The development of 12 poisoning and drug overdose case definitions has streamlined poisoning surveillance activities. Significance and contribution to the field Given the variation among poisoning case definitions available nationwide, the NC SQI project has developed definitions for use in NC, tested the efficacy of these definitions using ED data, and revised these definitions based on expert and user feedback. It is hoped that these definitions may inform surveillance activities in other states.

Introduction: Data collected from telephone helplines that provide health advice can improve the timeliness and accuracy of disease surveillance, contributing to an appropriate and rapid public health response. We show how these data can forewarn health professionals of increased rates of influenza-like illness (ILI) in the community and discuss implications for COVID-19 syndromic surveillance. Methods: The healthdirect helpline (HH) captures demographic details and characteristics of symptoms from users in 6 Australian states and territories. We compare ILI activity in the HH with ILI activity in emergency department (ED), laboratory, Flutracking and general practice (GP) data using cross-correlation functions. Results: Helpline data correlated strongly with ED data (range in yearly correlations from 0.82-0.99), GP data (0.66 – 0.95) and Flutracking data (0.62-0.89), but yearly correlations with laboratory data varied (0.49-0.95). The highest correlation with laboratory and GP data occurred when HH activity was 1-2 weeks in advance of these data, while correlations with ED and Flutracking data were strongest with no time lag. Discussion: Our analysis demonstrates that the number of ILI-related calls to the HH is a reliable indicator of ILI incidence in Australia. An increase in calls is likely to occur simultaneously with an increase in visits to EDs and prior to an increase in positive laboratory influenza tests and visits to GPs. A surveillance system including these data would assist health practitioners to receive timely and accurate estimates of the level of ILI in the community to better respond to and prepare for seasonal and epidemic influenza.

15 Facilitating Emergency Department Research on Older Adults Through Creation of a Research Data Warehouse

Comparison of emergency medical services and emergency department encounter trends for nonfatal opioid-involved overdoses, nine states, United States, 2020–2022

Background: Good-quality data is required for valid and reliable key performance indicators. Little is known of the facilitators and barriers of capturing the required data for emergency department key performance indicators. This study aimed to explore and understand how current emergency department data collection systems relevant to emergency department key performance indicators are integrated into routine service delivery, and to identify the resources required to capture these data elements. Methods: Following pilot testing, we conducted two focus groups with a multi-disciplinary panel of 14 emergency department stakeholders drawn from urban and rural emergency departments, respectively. Focus groups were analyzed using Attride–Stirling’s framework for thematic network analysis. Results: The global theme “Understanding facilitators and barriers for emergency department data collection systems” emerged from three organizing themes: “understanding current emergency department data collection systems”; “achieving the ideal emergency department data capture system for the implementation of emergency department key performance indicators”; and “emergency department data capture systems for performance monitoring purposes within the wider context”. Conclusion: The pathways to improving emergency department data capture systems for emergency department key performance indicators include upgrading emergency department information systems and investment in hardware technology and data managers. Educating stakeholders outside the emergency department regarding the importance of emergency department key performance indicators as hospital-wide performance indicators underpins the successful implementation of valid and reliable emergency department key performance indicators.

Background: Good-quality data is required for valid and reliable key performance indicators. Little is known of the facilitators and barriers of capturing the required data for emergency department key performance indicators. This study aimed to explore and understand how current emergency department data collection systems relevant to emergency department key performance indicators are integrated into routine service delivery, and to identify the resources required to capture these data elements. Methods: Following pilot testing, we conducted two focus groups with a multi-disciplinary panel of 14 emergency department stakeholders drawn from urban and rural emergency departments, respectively. Focus groups were analyzed using Attride-Stirling's framework for thematic network analysis. Results: The global theme "Understanding facilitators and barriers for emergency department data collection systems" emerged from three organizing themes: "understanding current emergency department data collection systems"; "achieving the ideal emergency department data capture system for the implementation of emergency department key performance indicators"; and "emergency department data capture systems for performance monitoring purposes within the wider context". Conclusion: The pathways to improving emergency department data capture systems for emergency department key performance indicators include upgrading emergency department information systems and investment in hardware technology and data managers. Educating stakeholders outside the emergency department regarding the importance of emergency department key performance indicators as hospital-wide performance indicators underpins the successful implementation of valid and reliable emergency department key performance indicators.

BackgroundA patient’s trajectory through the healthcare system affects resource use and outcomes. Data fields in population-based administrative health databases are potentially valuable resources for constructing care trajectories for entire populations, provided they can capture patient transitions between healthcare services. This study describes patient transitions from the emergency department (ED) to other healthcare settings, and ascertains whether the discharge disposition field recorded in the ED data was a reliable source of patient transition information from the emergency to the acute care settings.MethodsAdministrative health databases from the province of Saskatchewan, Canada (population 1.1 million) were used to identify patients with at least one ED visit to provincial teaching hospitals (n = 5) between April 1, 2006 and March 31, 2012. Discharge disposition from ED was described using frequencies and percentages; and it includes categories such as home, transfer to other facilities, and died. The kappa statistic with 95 % confidence intervals (95 % CIs) was used to measure agreement between the discharge disposition field in the ED data and hospital admission records.ResultsWe identified N = 1,062,861 visits for 371,480 patients to EDs over the six-year study period. Three-quarters of the discharges were to home, 16.1 % were to acute care in the same facility in which the ED was located, and 1.6 % resulted in a patient transfer to a different acute care facility. Agreement between the discharge disposition field in the ED data and hospital admission records was good when the emergency and acute care departments were in the same facility (κ = 0.77, 95 % CI 0.77, 0.77). For transfers to a different acute care facility, agreement was only fair (κ = 0.36, 95 % CI 0.35, 0.36).ConclusionsThe majority of patients who attended EDs did not transition to another healthcare setting. For those who transitioned to acute care, accuracy of the discharge disposition field depended on whether the two services were provided in the same facility. Using the hospital data as reference, we conclude that the discharge disposition field in the ED data is not reliable for measuring transitions from ED to acute care.

Linkage of ambulance service and Accident and Emergency Department data: a study of assault patients in the west midlands region of the UK

National unintentional carbon monoxide poisoning estimates using hospitalization and emergency department data