Syndromic Surveillance

Clinical data captured in electronic health records (EHR) for patient health care could be used for chronic disease surveillance, helping to inform and prioritize interventions at a state or community level. While there has been significant progress in the collection of clinical information such as immunizations for public health purposes, greater attention could be paid to the collection of data on chronic illness. Obesity is a chronic disease that affects over a third of the US adult population1 , making it an important public health concern. Both HL7 v.2.5.12 and Clinical Document Architecture (CDA) messages3 can be used to facilitate the collection of HW EHR data. These standards include anthropometric and demographic information along with the option to transmit behavioral, continuity of care, community resource identification and care plan information. We worked with vendors participating in the Integrating the Healthcare Enterprise initiative (IHE) in developing, testing and showcasing scenarios to facilitate system development, increase the visibility of HW standards and demonstrate potential usages of obesity-related information.

Objective

To demonstrate the feasibility of using healthy weight (HW) IT standards in public health surveillance through the collection and visualization of patient height, weight and behavioral data.

In 2011, injury by firearms accounted for 32,351 deaths (10.4 deaths per 100,000 population) in the United States. This rate was higher than any infectious or parasitic disease (the highest being 2.5 for both viral hepatitis and HIV disease). Furthermore, death by gunshots accounted for over half of all suicides and over two-thirds of all homicides in the US. Despite the disproportionate media coverage of mass shootings and assault weapon violence, the vast majority of these deaths are attributable to non-mass shootings and to handguns. Though a contentious issue in the United States, understanding this cause of death is vital to confronting the issue locally and nationally. Traditionally, death certificates, crime data, cross-sectional studies, and retrospective studies have most commonly been utilized in this endeavor; however, the collection of real-time emergency department (ED) visit information presents a unique opportunity to track gunrelated injuries to supplement our current understanding of this issue. The Houston Department of Health and Human Services (HDHHS) has been receiving this information for over a decade from EDs in the greater-Houston area, and the department is currently connected to 32 of the largest EDs in the area. The current study aims to enhance the understanding of gunshot-related injuries in the Houston area and present a model for utilizing RODS information for this purpose.

Objective

To introduce a model to track gunshot-related injuries, describe gun-related injuries in Houston, and investigate the association between gun-related injuries and social determinants of health using syndromic surveillance data.

The majority of farmed animals are sent to slaughterhouses, making them a focal point for potential collection of health data. However, these data are not always available to health officials, and remain under-used for cattle health monitoring. Meat inspection data are mainly non-diagnostic (condemned portion and reasons for condemnation) and cover a large population. These characteristics make them a good candidate for syndromic surveillance. Whole carcass condemnation rate is linked to acute infections which reduces the dilution bias due to the variable period of time between cattle infection and the detection of lesions at the slaughterhouse.

Objective

The objective of the work was to assess the performance of several algorithms for outbreak detection based on weekly proportions of whole carcass condemnation

SCRAs are accessible and affordable, sold online, in gas stations, and in “head” shops for $5-30 per package.[1] While marijuana is a schedule 1 narcotic, unavailable for any use, SCRAs navigate the legal landscape with marketing as non-consumable and frequent modifications to the active ingredients that outpace lawmakers’ updates. When consumed, SCRAs bind the same receptor as the active ingredient in marijuana with 10-1000 times the affinity. Physical reactions to marijuana use include breathing problems, increased heart rate, hallucinations, paranoia, lower blood pressure, and dizziness. [2] Health departments have reported varying clinical presentations in response to SCRAs, including extreme agitation and tachycardia. Ongoing reports of SCRA reactions and rising marijuana legalization emphasize the imperative to leverage syndromic surveillance to monitor trends, detect emerging outbreaks, and observe changes in clinical presentations or user demographics.

Objective

Use syndromic surveillance to identify and monitor adverse health events resulting from synthetic cannabinoid receptor agonists (SCRAs) or marijuana. Characterize the current trend of SCRAs and marijuana use among emergency department (ED) and urgent care center (UCC) visits in Virginia to determine whether findings align with utilization trends identified by other states from poison control center calls and ED visits.

Arizona is facing multiple public health threats from arboviral diseases. State and local public health departments are monitoring two mosquito-borne outbreaks within its borders and two in adjacent territories. To prevent transmission, viremic patients must be identified in a timely manner and encouraged to avoid additional mosquito exposure and vector control actions must be implemented. Using traditional surveillance, however, health departments may not be alerted until the laboratory confirms and reports a positive result, which may take up to 14 days after specimen collection. The Arizona Department of Health Services (ADHS) partnered with local public health jurisdictions to enhance traditional arboviral surveillance by incorporating syndromic surveillance.

Objective

To develop a protocol for enhancing traditional arboviral surveillance with syndromic surveillance and to evaluate the protocol for accuracy, effectiveness, and timeliness

Syndromic surveillance has historically been used to track infectious disease, but in recent years, many jurisdictions have utilized the systems to conduct all hazards surveillance and provide situational awareness with respect to previously identified issues. Flakka is a synthetic drug (class: cathinones) that recently has been featured in the media. Flakka is a stimulant that causes delusions, aggression, erratic behavior, a racing heart and sometimes death. Two specific counties (one in Florida and one in Kentucky) have been at the center of this emerging epidemic. In August 2015, Florida Department of Health (FDOH) partner agencies requested flakka-related health data in an effort to better understand the epidemiology and context of this problem. ESSENCE-FL is a large syndromic surveillance system, with four main data sources, that captures 87% of all emergency department (ED) visits statewide.

Objective

To characterize flakka usage in Florida using multiple data sources within the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE-FL)

NPDS is a near real-time surveillance system and national database operated by the American Association of Poison Control Centers. NPDS receives records of all calls made to the 55 regional US poison centers (PCs). The Centers for Disease Control and Prevention (CDC) use NPDS to 1) provide public health surveillance for chemical, radiological and biological exposures and illnesses, 2) identify early markers of chemical, radiological, and biological incidents, and 3) find potential cases and enhance situational awareness during a known incident. Anomalies are reviewed daily by a distributed team of PC medical and clinical toxicologists for potential incidents of public health significance (IPHS). Information on anomalies elevated to IPHS is promptly relayed to state epidemiologists or other designated officials for situational awareness and public health response.

Current NPDS surveillance algorithms utilize the Historical Limits Method, which identifies a data anomaly when call volumes exceed a statistical threshold derived from multiple years of historical data. Alternative analysis tools such as those employed by ESSENCE and other computerized data surveillance systems have been sought to enhance NPDS signal analysis capability. Technical improvements have been implemented in 2013 to expand NPDS surveillance capabilities but have not been thoroughly tested. Moreover, other data aberration detection algorithms, such as temporal scan statistics, have not yet been tested on real-time poison center data.

Objective

To compare the effectiveness of current surveillance algorithms used in the National Poison Data System (NPDS) to identify incidents of potential public health significance with 1) new algorithms using expanded NPDS surveillance capabilities and 2) methods beyond the NPDS’ generalized historical limits model.

Healthcare seeking behavior is important to understand when interpreting public health surveillance data, planning for healthcare utilization, or attempting to estimate or model consequences of an adverse event, such as widespread water contamination. Although there is evidence that factors such as perceived susceptibility and benefits affect healthcare seeking behavior, it is difficult to develop accurate assumptions due to a lack of published research on this topic. Current conceptual behavior models, such as the health belief model, are not easily translated into quantifiable terms.

Objective

This paper describes analyses of health seeking behaviors from two surveillance datastreams: Poison Control Center (PCC) calls and Emergency Department (ED) visit records. These analyses were conducted in order to quantify behaviors following the development of symptoms after water contamination exposure and to understand the motivation, decision-making and timing behind healthcare seeking behaviors.

Seasonal rises in respiratory illnesses are a major burden on primary care services. Public Health England (PHE), in collaboration with NHS 111, coordinate a national surveillance system based upon the daily calls received at the NHS 111 telehealth service. Daily calls are categorized according to the clinical ‘pathway’ used by the call handler to assess the presenting complaints of the caller e.g. cold/flu, diarrhoea, rash.

Objective

We compared weekly laboratory reports for a number of seasonal respiratory pathogens with telehealth calls (NHS 111) to assess the burden of seasonal pathogens on this syndromic surveillance system and investigate any potential for providing additional early warning of seasonal outbreaks.

Several countries prospectively monitor influenza-attributable mortality using a variation of the Serfling seasonal time series model that uses sinusoidal terms for seasonality. Typically, a seasonal model from previous years is used to forecast current expected mortality. Using laboratory surveillance time series data in the model may enhance interpretation of the surveillance information.

Objective

To demonstrate use of routine laboratory-confirmed influenza surveillance data to forecast predicted influenza-attributable deaths during the current influenza season. We also assessed whether including information on influenza type produced better surveillance forecasts.