Syndromic Surveillance

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), and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011). 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.

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.

The Great American Solar Eclipse of 2017 provided a rare opportunity to view a complete solar eclipse on the American mainland. Much of Oregon was in the path of totality and forecasted to have clear skies. Ahead of the event, OPHD aggregated a list of 107 known gatherings in mostly rural areas across the state, some with estimated attendance of up to 30,000 attendees. Temporary food vendors and a range of sanitation solutions (including open latrines) were planned. International travelers were expected, along with large numbers of visitors traveling by car on the day of the eclipse. The potential for multiple simultaneous mass gatherings across the state prompted OPHD to activate an incident management team (IMT) and to create a Health Intelligence Section to design a mass gathering surveillance strategy. Statewide syndromic surveillance (Oregon ESSENCE) has been used to monitor previous mass gatherings (1) and captures statewide emergency department (ED), urgent care, Oregon Poison Center, and reportable disease data.

Objective:

Develop a public health surveillance plan for the Oregon Public Health Division (OPHD) in anticipation of the expected influx of visitors for the 2017 Great American Solar Eclipse.

EpiCenter, NJ’s statewide syndromic surveillance system, collects ED registration data. The system uses chief complaint data to classify ED visits into syndrome categories and provides alerts to state and local health departments for surveillance anomalies. After the 2014 Ebola outbreak in West Africa, the New Jersey Department of Health (NJDOH) started collecting medical notes including triage notes, which contain more specific ED visit information than chief complaint, from 10 EDs to strengthen HAI syndromic surveillance efforts. In 2017, the NJDOH was aware of one NJ resident whose surgical site was infected following a cosmetic procedure outside of the US. This event triggered an intensive data mining using medical notes collected in EpiCenter. The NJDOH staff searched one week of medical notes data in EpiCenter with a specific keyword to identify additional potential cases of surgical-site infections (SSI) that could be associated with medical tourism.

Objective:

Medical notes provide a rich source of information that can be used as additional supporting information for healthcare-associated infection (HAI) investigations. The medical notes from 10 New Jersey (NJ) emergency departments (ED) were searched to identify cases of surgical-site infections (SSI).

There are currently 123 healthcare facilities sending data to the Washington (WA) State syndromic surveillance program. Of these facilities, 30 are sending to the National Syndromic Surveillance Program'™s (NSSP) production environment. The remainder are undergoing validation or in queue for validation. Given the large number of WA healthcare facilities awaiting validation, staff within the state syndromic surveillance program developed methods in R to reduce the amount of time required to validate data from an individual facility.

Objective:

To share practical, user-friendly data validation methods in R that result in shorter validation time and simpler code.

Syndromic surveillance seeks to systematically leverage health-related data in near "real-time" to understand the health of communities at the local, state, and federal level. The product of this process provides statistical insight on disease trends and healthcare utilization behaviors at the community level which can be used to support essential surveillance functions in governmental public health authorities (PHAs). Syndromic surveillance is particularly useful in supporting public health situational awareness, emergency response management, and outbreak recognition and characterization. Patient encounter data from healthcare settings are a critical inputs for syndromic surveillance; such clinical data provided by hospitals and urgent care centers to PHAs are authorized applicable local and state laws. The capture, transformation, and messaging of these data in a standardized and systematic manner is critical to this entire enterprise. In August 2015, a collaborative effort was initiated between the CDC, ISDS, the Syndromic Surveillance Community, ONC and NIST to update the national electronic messaging standard which enables disparate healthcare systems to capture, structure, and transmit administrative and clinical data for public health surveillance and response. The PHIN Messaging Guide for Syndromic Surveillance -Release 2.0 (2015) provided an HL7 messaging and content reference standard for national, syndromic surveillance electronic health record technology certification as well as a basis for local and state syndromic surveillance messaging implementation guides. This standard was further amended with the release of the PHIN Messaging Guide for Syndromic Surveillance - Release 2.0, Erratum (2015) and the HL7 Version 2.5.1 PHIN Messaging Guide for Syndromic Surveillance- Release 2.0, NIST Clarifications and Validation Guidelines, Version 1.5 (2016). ISDS is now engaged in a process, supported by a CDC Cooperative Agreement, to formally revise the existing guide and generate an HL7 V 2.5.1 Implementation Guide (IG) for Syndromic Surveillance v2.5 for HL7 balloting in 2018. This roundtable will provide a forum to present and discuss the HL7 Balloting process and the outstanding activities in which the Syndromic Surveillance community must participate during the coming months for this activity to be successful.

Objective:

To provide a forum to engage key stakeholders to discuss the process for updating and revising the Implementation Guide (IG) for Syndromic Surveillance (formerly the PHIN Message Guide for Syndromic Surveillance) and underscore the critically of community and stakeholder involvement as the Implementation Guide is vetted through the formal Health Level Seven (Hl7) balloting process in 2018.

In 2011, the CDC released the PHIN Implementation Guide (IG) for Syndromic Surveillance v.1 under the Public Health Information Network. In the intervening years, new technological advancements, EHR capabilities as well as epidemiological and Meaningful Use requirements have led to the periodic update and revision of the IG through informal and semi-structured solicitation and collection of comments from across public health, governmental, academic, and EHR vendor stakeholders. Following the IG v.2.0 release in 2015, CDC initiated a multi-year endeavor to update the IG in a more systematic manner and released further updates via an Erratum and a technical document developed with NIST to clarify validation policies and testing parameters. These documents were consolidated into the Message Guide v.2.1 release and used to inform the development of the NIST Syndromic Surveillance Test Suite (http://hl7v2-ss-r2-testing.nist.gov/ss-r2/#/home), Validation Test Cases, and develop a new rules-based IG built using NIST’s Implementation Guide Authoring and Management Tool (IGAMT). As part of a Cooperative Agreement initiated in 2017, CDC and ISDS built upon prior activities and renew efforts in engaging the Syndromic Surveillance Community of Practice for comment on the IG with the goal of having the final product to become an "HL7 V 2.5.1 Implementation Guide for Syndromic Surveillance Standard for Trial Use" following a formal HL7 balloting process in 2018.

Objective:

To describe the process to update the Implementation Guide (IG) for Syndromic Surveillance via community and stakeholder engagement and highlight significant modifications as the IG is vetted through the formal HL7 balloting process.

Globally, there have been various studies assessing trends in Google search terms in the context of public health surveillance1. However, there has been a predominant focus on individual health outcomes such as influenza, with limited evidence on the added value and practical impact on public health action for a range of diseases and conditions routinely monitored by existing surveillance programmes. A proposed advantage is improved timeliness relative to established surveillance systems. However, these studies did not compare performance against other syndromic data sources, which are often monitored daily and already offer early warning over traditional surveillance methods. Google search data could also potentially contribute to assessing the wider population health impact of public health events by supporting estimation of the proportion of the population who are symptomatic but may not present to healthcare services.

Objective:

To carry out an observational study to explore what added value Google search data can provide to existing routine syndromic surveillance systems in England for a range of conditions of public health importance and summarise lessons learned for other countries.