Syndromic Surveillance

To describe an R package that was designed to provide ready implementation of veterinary syndromic surveillance systems, from classified data to the generation of alerts and an html interface.

Introduction

Introduction

The field of veterinary syndromic surveillance (VSS) is developing fast, with countries exploring a great variety of data sources. After implementing two VSS systems we have demonstrated that the steps from classified data to full system implementation can be streamlined, and published a guideline for implementation. All the steps described have been made available in an R package (https:// github.com/nandadorea/vetsyn). We aim to demonstrate the utility and potential of this streamlined approach.

Veterinary syndromic surveillance (VSS) is a fast growing field, but development has been limited by the limited use of standards in recording animal health events and thus their categorization into syndromes. The adoption of syndromic classification standards would allow comparability of outputs from systems using a variety of animal health data sources (clinical data, laboratory tests, slaughterhouse records, rendering plants data, etc), in addition to improving the ability to compare outputs among countries. The project “Standardising Syndromic Classification in Animal Health Data” (SSynCAHD) aims to standardize the classification of animal health records into syndromes.

Objective

To develop an ontology for the classification of animal health data into syndromes with application to syndromic surveillance.

 

Syndromic surveillance generally refers to the monitoring of disease related events, sets of clinical features (i.e. syndromes), or other indicators in a population. Originally conceived as a tool for the early detection of potential bioterrorism outbreaks, syndromic surveillance is also used by health departments as a tool for monitoring seasonal illness, evaluating health interventions, and other health surveillance activities. Over the past decade, the Tennessee Department of Health (TDH) has utilized syndromic surveillance at the jurisdictional level. These standalone, jurisdictional systems utilized chief complaint data from local emergency departments (EDs) and the Early Aberration Reporting System (EARS) developed by CDC. Some jurisdictions integrated other local data for analysis in EARS including 911 call center data, over the counter drug sales, and other non-traditional data sources. The analyses conducted on the data varied from jurisdiction to jurisdiction. CDC dismantled the EARS program in 2011, prompting the need for a complete syndromic surveillance overhaul. TDH decided to implement a centralized, statewide system that would maintain all the capabilities that jurisdictions currently had while allowing for statewide data analysis and aggregation. During this implementation process, TDH has been balancing the short term goal of supporting and maintaining the existing jurisdictional systems while moving forward with acquiring a statewide syndromic surveillance solution and establishing the infrastructure to support it.

Objective

To share lessons learned in Tennessee during its transition from a jurisdictional syndromic surveillance system to a state-wide, centralized system.

 

The Syndromic Surveillance Program (SSP) of the Georgia Department of Public Health collects chief complaint data from hospitals to characterize health trends in near real time. These data were critical for situational awareness during the 2009 H1N1 pandemic. In 2012, SSP and the Effingham County Schools began a project to collect syndromic surveillance data from school clinics. The hypothesis was that these data may be used to inform interventions during a pandemic, guide school health programs, elucidate health priorities in school-age populations, and quantify nursing staff needs in schools. Analysis of data from the first two pilot years has provided a novel look at the disparate burden of disease among students across schools in the county.

Objective

This project was designed to demonstrate the feasibility of schoolbased nurse clinic visit syndromic surveillance. Additional objectives include using clinic visit data to identify opportunities for health interventions at participating schools and to characterize the type and number of student visits to the school nurses. An electronic module was developed in the State Electronic Notifiable Disease Surveillance System (SendSS) to facilitate data entry by participating school nurses and data management by the Georgia Department of Public Health.

 

The MSSS, described elsewhere, has been in use since 2003 and records ED chief complaint data. As of September 2014, there were 88/136 hospital EDs enrolled in MSSS, capturing 83% of the annual hospital ED visits in Michigan.

On April 1, 2014 the Healthy Michigan Plan (HMP) was launched. HMP provides healthcare benefits to low-income adult residents who do not qualify for Medicaid or Medicare. The plan incorporates both federally and state mandated Essential Health Benefits, which includes emergency services.

As insurance coverage expands, more people will have the ability to utilize the services of primary care and other providers. In particular, this will affect previously uninsured, low-income populations who are disproportionately affected by chronic disease.

We question if access to these services will affect the utilization of emergency services as more people will have a medical home to manage and prevent diseases that may otherwise become an emergent issue. Furthermore, this increased access to health care services will expand care options for urgent but not emergent issues beyond EDs. Conversely, as more people acquire health care benefits the demand for primary care services may exceed the level of access to these services which may lead to an increase of ED utilization for primary care.

Objective

The purpose of this work is to use the Michigan Syndromic Surveillance System (MSSS) to assess emergency department (ED) utilization before and after the April 2014 implementation of the Healthy Michigan Plan, an expanded Medicaid program.

The May arrival of two cases of Middle East Respiratory Syndrome (MERS) in the US offered CDC’s BioSense SyS Program an opportunity to give CDC’s Emergency Operations Center (EOC) and state-and-local jurisdictions an enhanced national picture of MERS surveillance. BioSense jurisdictions can directly query raw data stored in what is known as “the locker.” However, CDC cannot access these data and critical functions, like creating ad-hoc syndrome definitions within the application are currently not possible. These were obstacles to providing the EOC with MERS information. BioSense staff developed a plan to 1) rapidly generate query definitions regardless of the locally preferred SyS tool and, 2) generate aggregate reports to support the national MERS response.

Objective

Demonstrate that information from disparate syndromic surveillance (SyS) systems can be acquired and combined to contribute to national-level situational awareness of emergent threats.

Production animal health syndromic surveillance (PAHSyS) data are varied: there may be standardized ratios, proportions, counts of adverse events, categorical data and even qualitative ‘intelligence’ that may need to be aggregated up a hierarchy. PAHSyS provides some unique challenges for event detection. Livestock populations are made up of many subpopulations which are constantly moving around between farms and markets to slaughter. Pathogen expression often varies across production types and rearing-intensity levels. The complexity of animal production systems necessitates monitoring many time series; and makes the investigation of statistical signals imperative and at the same time difficult and resource intensive. Having multivariate surveillance methods that can work across multiple data streams to increase both sensitivity and specificity are much needed.

Objective

The question of how to aggregate animal health information derived from multiple data streams that vary in their specificity, scale, and behaviour is not trivial. Our view is that outbreak detection in a multivariate context should be viewed as a probabilistic prediction problem.

The number of US adults who use the internet to access health information has increased from about 95 million in 2005 to 220 million in 2014. The public health impact of this trend is unknown; in theory, patients may be able to better help the doctor arrive at the correct diagnosis, but self-diagnosed patients may also inappropriately self-treat or delay going to the doctor. The current study examines trends in self-diagnoses in NYC EDs, identifies the demographic characteristics of self-diagnosed patients, and compares hospital admission rates of self-diagnosed patients with those who do not self-diagnose.

Objective

To monitor self-reported diagnosis from New York City (NYC) emergency department (ED) chief complaints (CC).

To date, avian influenza virus (AIV) is an unpredictable pathogen affecting both animals, birds and people. The regular emergence of new strains and variants with different properties and pathogenicities requires additional monitoring and careful research of those viruses. It is known that wild birds— especially waterfowl and shorebirds— are the main and primary reservoir of AIV in nature which makes epizootological monitoring of populations of these birds necessary.

Objective

To carry out monitoring studies of circulation of the AIV subtypes H5 and H7 in wild waterfowl and shorebirds around the Azov-Black Sea in Ukraine

Syndromic surveillance can supplement diagnosis-based surveillance in resource-limited settings with limited laboratory infrastructure. Syndromic surveillance allows for early outbreak detection relative to traditional systems and enables community health monitoring during outbreaks. Monitoring and disease diagnosis can be strengthened using pre-diagnostic data and statistical algorithms to detect morbidity trends.

Alerta (2002-11) and Vigila (2011-present) are sequentially implemented electronic disease surveillance systems created by the Peruvian Navy to improve the detection, prevention, and control of disease outbreaks. The phone-, internet-, and radio-based reporting system now covers over 97.5% of the Navy population, encompassing 169 reporting establishments that treat active and retired service members, dependents, and civilian employees. Acute diarrheal disease, respiratory infections, and pneumonias are reported weekly, whereas specific notifiable diseases such as malaria, dengue, and tuberculosis are reported immediately after case detection.

Objective

To use data from the Peruvian Navy’s electronic syndromic surveillance systems to estimate the baseline incidence of acute diarrheal disease (ADD) and detect outbreaks among individuals accessing military medical facilities from 2009-13.