ObjectiveTo describe the strategy and process used by the Florida Department of Health (FDOH) Bureau of Epidemiology to onboard emergency medical services (EMS) data into FDOH’s syndromic surveillance system, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL).IntroductionSyndromic surveillance has become an integral component of public health surveillance efforts within the state of Florida. The near real-time nature of these data are critical during events such as the Zika virus outbreak in Florida in 2016 and in the aftermath of Hurricane Irma in 2017. Additionally, syndromic surveillance data are utilized to support daily reportable disease detection and other surveillance efforts. Although syndromic systems typically utilize emergency department (ED) visit data, ESSENCE-FL also includes data from non-traditional sources: urgent care center visit data, mortality data, reportable disease data, and Florida Poison Information Center Network (FPICN) data. Inclusion of these data sources within the same system enables the broad accessibility of the data to more than 400 users statewide, and allows for rapid visualization of multiple data sources in order to address public health needs. Currently, the ESSENCE-FL team is actively working to incorporate EMS data into ESSENCE-FL to further increase public health surveillance capacity and data visualization.MethodsThe ESSENCE-FL team worked collaboratively with various public health program stakeholders to bring EMS data, aggregated by the FDOH Bureau of Emergency Medical Oversight Emergency Medical Services Tracking and Reporting System (EMSTARS) team, into ESSENCE-FL. The ESSENCE-FL team met with the EMSTARS team to discuss use cases, demonstrate both systems, and to obtain project buy-in and support. Initial project meetings included review of ESSENCE-FL system support, user types (roles and access), as well as data security and compliance. An overall project timeline was established, and deliverables were added into system support contracts. Multiple stakeholders, across disciplines representing each key use case, reviewed the Florida version of the National Emergency Medical Services Information System (NEMSIS) version 3.4 data dictionary to identify program-specific data element needs. An element scoring spreadsheet was returned to the ESSENCE-FL team. These scores were aggregated and discordant scores were reviewed by the ESSENCE-FL team. A one-month extract of EMS data was reviewed to assess variable completeness and relevance. Monthly team meetings facilitated the final decisions on the data elements by leveraging lessons learned through onboarding other data sources, findings from the analysis of the one-month extract, stakeholder comments, and advice from other states known to be leveraging EMS data for public health surveillance.ResultsThrough a collaborative and broad approach with partners, the ESSENCE-FL team attained stakeholder buy-in and identified 81 data elements to be included in the EMS feed to ESSENCE-FL. The final list of data elements was determined to best support health surveillance of this population prior to presenting to the ED. The inclusion of the EMS data in ESSENCE-FL will increase the epidemiologic characterization and analysis of the opioid epidemic in Florida. Additional key use cases identified during this project included enhanced injury surveillance, enhanced occupational health surveillance, and characterization of potential differences between EMS and ED visits.ConclusionsThis comprehensive approach can be used by other jurisdictions considering adding EMS data to their syndromic surveillance systems. When considering onboarding a new data source into a surveillance system, it is important to work closely with stakeholders from disciplines representing each of the key use cases to broaden buy-in and support for the project. Through employing this comprehensive approach, syndromic surveillance systems can be better developed to include data that are widely utilizable to many different stakeholders in the public health community.

ObjectiveAssess the validity of Florida (FL) Enhanced State Opioid Overdose Surveillance (ESOOS) non-fatal syndromic case definitions.IntroductionIn 2017, FL Department of Health (DOH) became one of thirty-two states plus Washington, D.C funded by the Center for Disease Control and Prevention (CDC) under the ESOOS program. One of the objectives of this funding was to increase the timeliness of reporting on non-fatal opioid overdoses through syndromic surveillance utilizing either the emergency department (ED) or Emergency Medical Services (EMS) data systems. Syndromic case validation is an essential requirement under ESOOS for non-fatal opioid-involved overdose (OIOD). FL’s ESOOS program conducted OIOD validation and quality monitoring of EMS case definitions, using data from FL’s Emergency Medical Services Tracking and Reporting System (EMSTARS). We examined measurement validity with OIOD cases identified from FL’s statewide hospital billing database, FL Agency for Health Care Administration (AHCA).MethodsFrom FL-EMSTARS, we extracted EMS data where the type of service requested was a 911 response, the patient was treated then transported by EMS to a hospital facility in Florida and was 11 years of age or older. Additionally, all incident-patient encounters excluded those who were dead at the scene. We included all responses with dispatch dates between January 1, 2016, and December 31, 2016. From FL-AHCA, we extracted ED and inpatient discharge information with admission dates and patient age covering the same ranges as our EMS encounters. We classified FL-EMSTARS cases based on combinations, like that of Rhode Island,1 using providers primary impression (PPI), providers secondary impression (PSI) and response to the administration of naloxone. FL-AHCA cases were defined by the following T and F codes from the International Classification of Diseases 10: T40.0-T40.4, T40.60, T40.69, F11.12, F11.120, F11.121, F11.122, F11.129, F11.22, F11.220, F11.221, F11.222, F11.229, F11.92, F11.920, F11.921, F11.922, F11.929. For all “T” codes, the 6th character was either a “1” or “4,” because ESOOS is focused on unintentional and undetermined drug overdoses, ergo we excluded ED visits that are related to intentional self-harm (i.e., “2”) or assault (i.e., “3”). Lastly, for all “T” codes, the 7th character we included was the initial ED encounter (i.e., “A”) because the purpose of the system is to capture increases or decreases in acute overdoses. To improve our match rate, account for typographical errors, and account for the discriminatory power some values may contain, we employed probabilistic linkage using Link Plus software developed by the CDC Cancer Division. Blocking occurred among social security number (SSN), event date, patient age in years, and date of birth (DOB). Next, we matched both datasets on ten variables: event date, age, sex, DOB, ethnicity, facility code, hospital zip code, race, SSN, and patient’s residence zip code. Further pruning was performed to ensure all matches were within a 24-hour time interval. Data management and statistical analyses were performed using SAS® statistical software, version 9.4 (SAS Institute Inc., Cary, NC, USA). We assessed EMS measurement validity by sensitivity, specificity, and positive predictive value (PPV). Next, risk factors were identified by stepwise multivariable logistic regression to improve the accuracy of the FL-ESOOS definition. Significant risk factors from the parsimonious multivariable model were used to simulate unique combinations to estimate the maximum sensitivity and PPV for OIOD.ResultsPrior to merging, FL-EMSTARS contained 1,308,825 unique incident-patient records, where FL-AHCA contained 8,862,566 unique incident-patient records. Of these, we conservatively linked 892,593 (68.2%) of the FL-EMSTARS dataset with FL-AHCA. Our probabilistic linkage represents an 18.2% linkage improvement over previous FL-DOH deterministic strategies (J Jiang, unpublished CSTE presentation, 2018). Among the matched pairs we estimated 8,526 OIOD, 0.96% prevalence, using the FL-AHCA case definition. Whereas the FL-ESOOS syndromic case definition estimated 6,188 OIOD, 0.69% prevalence. The FL-ESOOS OIOD syndromic case definition demonstrated 31.64% sensitivity, 99.61% specificity, and 43.60% PPV. Among false negatives, the response to administrated naloxone among OIOD was 39.37% “not known,” 37.95% “unchanged,” and 0.28% “worse.” We altered the FL-ESOOS EMSTARS case definition for OIOD to include those who were administered naloxone regardless of their response to the medication. We observed 12.37% sensitivity increase to 44.01%, 0.56% specificity decrease to 99.05%, and 12.78% PPV decrease to 30.82%.Are final multivariable model is as follows: lnOdds(Opioid Overdose)= 12.66 – 0.5459(Med Albuterol) – 0.9568(Med Aspirin) – 0.5765(Med Midazolam Hydrochloride) – 0.8690(Med Morphine Sulfate) + 1.4103 (Med Naloxone) – 0.7694(Med Nitroglycerine) + 0.3622(Med Oxygen) – 0.3702(Med Phenergan) – 0.8820(Med Epinephrine 1:10000) – 0.7397(Med Fentanyl) – 0.6376(Med Sodium Bicarbonate) – 0.2725(Med Normal Saline) + 0.3935(Med Other-Not Listed) + 0.6300(PPI General Malaise) + 0.8476(PPI Other, Non-Traumatic Pain) + 0.8725(PPI Airway Obstruction) + 0.4808(PPI Allergic reaction) + 1.4948(PPI Altered level of consciousness) + 1.5481(PPI Behavioral/psychiatric disorder) + 1.3843(PPI Cardiac arrest) + 2.3913(PPI Poisoning/drug ingestion) + 2.2418(PPI Intentional Drug Use; Related Problems) + 0.2783(PPI Respiratory distress) + 2.0305(PPI Respiratory arrest) + 0.4292(PPI Stroke/CVA) + 0.5402(PPI Syncope/fainting) + 0.5219(PSI Other, Non-Traumatic Pain) + 0.9355(PSI Allergic reaction) + 0.3521(PSI Altered level of consciousness) + 0.9036(PSI Poisoning/drug ingestion) + 0.9661(PSI Intentional Drug Use; Related Problems) + 0.3766(PSI Respiratory Distress) + 1.1802(PSI Respiratory Arrest).We plotted the multivariable sensitivity and PPV by probaiblity cutoff value to determine which would produce the best discrimination (see Figure 1). By incorporating a probability cutoff value ≥ 0.22, we can inprove both sensitivity and PPV. Specifically, we can achieve 45.48% sensitivity, 99.32% specificity, and 45.48% PPV.ConclusionsThe sensitivity of the FL-ESOOS surveillance system is not generally high but could still be useful if subsequent validation shows sensitivity stability. Regarding maximizing FL-ESOOS sensitivity and PPV, we deomonstrated that our mulitvariable model with an appropriate probability cutoff value performes better than the current case definition. This study contributes to the limited literature on Florida non-fatal opioid overdoses with a specific emphasis on validating EMS records. New unique indicator combinations are possible to increase sensitivity and PPV but should be thoroughly investigated to balance the tradeoffs to optimize the system’s ability to detect non-fatal overdoses and to discriminate true cases.References1. Rhode Island Department of Health. Rhode Island Enhanced State Opioid Overdose Surveillance (ESOOS) Case Definition For Emergency Medical Services (EMS).; 2017.2. Jiang J, Mai A, Card K, Sturms J, McCoy S. EMS Naloxone Administration for Implication of Opioid Overdose. Presentation presented at the: 2018; CSTE Annual Conference.