Surveillance Systems

Swaziland adopted the Integrated Disease Surveillance and Response (IDSR) strategy in 2010 to strengthen Public Health Surveillance (PHS) that fulfills International Health Regulations (2005) and the Global Health Security Agenda (GHSA). This strategy allows the Ministry of Health (MoH), Epidemiology and Disease Control Unit (EDCU) to monitor, prevent and control priority diseases in the country. We used a health systems strengthening approach to pilot an intervention model for IDSR implementation at five hospitals in Swaziland over a pilot phase of three months.

Objective:

To strengthen public health surveillance and monitor implementation of Integrated Disease Surveillance and Response in the Kingdom of Swaziland.

Wearable devices are a low cost, minimally invasive way to monitor health. Sensor data provides real-time physiological indictors of an individual’s health status without the requirement of health care professionals or facilities. Information gleamed from wearable sensors can be used to better understand physiological stressors and prodromal symptoms. In addition, this data can be used to monitor individuals that are in high risk of health-related problems. However, raw data from wearable sensors can be overwhelming to process and laborious to monitor for an individual and, even more so, for a group of individuals. Often specific combination of ranges of sensor readings are indicative of changes to health status and need to be evaluated together or used to calculate specific signal parameters. In addition, the environment surrounding the individual needs to be considered when interpreting the data. To address these issues, PNNL has developed an application that collects, analyzes, and integrates wearable sensor data with geographic landscape and weather information to provide a real-time early alert and situational awareness tool for monitoring the health of groups and individuals.

Objective:

The Wearable Sensor Application developed by Pacific Northwest National Laboratory (PNNL) provides an early warning system for stressors to individual and group health using physiologic and environmental indicators. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and landscape data, and calculates the corresponding physiological strain index. The information is presented to the analyst in a group and individual view with real-time alerting of abnormal health parameters. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agency's Biosurveillance Ecosystem (BSVE).

In 2016, twelve states received Center for Disease Control and Prevention (CDC) Enhanced State Opioid Overdose Surveillance grants. The purpose of the grant is to explore enhanced data sources to track nonfatal opioid overdoses. One data source is ambulance runs. Wisconsin collects ambulance run information within the Wisconsin Ambulance Runs Data System (WARDS). Around 84% of all Wisconsin administrative services report into this electronic system. This is a timely, robust data system that has not been used previously to examine drug overdoses and presents an analytical challenge as it contains many free text fields.

Objective:

1. Develop an understanding of the benefits and challenges of analyzing free text fields on a population level.

2. Observe how a complex surveillance definition can be created from free text fields.

3. Observe how an ambulance data system can be used to describe the opioid epidemic.

Over the past decade Swaziland has experienced recurring drought episodes. In 2016 the country experienced challenges regarding water supplies in both urban and rural areas due to the drought impact. A rapid health and Nutrition Assessment was conducted in 2016 revealed an increase in number of cases of acute watery diarrhea of all age groups. While there is a high demand for epidemiological data in the country a passive system through Health Management Information System (HMIS) and Immediate Disease Notification System (IDNS) has been used to monitor acute watery diarrhea and a set of priority notifiable diseases in the country.

Objective:

To evaluate the difference in sensitivity between passive and active diarrheal and malnutrition disease surveillance system post-drought period in Swaziland

NBIC integrates, analyzes, and distributes key information about health and disease events to help ensure the nation’s responses are well-informed, save lives, and minimize economic impact. To meet its mission objectives, NBIC utilizes a variety of data sets, including open source information, to provide comprehensive coverage of biological events occurring across the globe. NBIC Biofeeds is a digital tool designed to improve the efficiency of analyzing large volumes of open source reporting and increase the number of relevant insights gleaned from this dataset. Moreover, the tool provides a mechanism to disseminate tailored, electronic message notifications in near-real time so that NBIC can share specific information of interest to its interagency partners in a timely manner. NBIC is deploying the tool for operational use by the Center and eventual use by federal partners with biosurveillance mission objectives. Core functionality for data collection, curation, and dissemination useful to other federal agencies was implemented, and NBIC is incorporating custom taxonomies for capturing metadata specific to the unique missions of NBIC partners.

Objective:

The National Biosurveillance Integration Center (NBIC) is deploying a scalable, flexible open source data collection, analysis, and dissemination tool to support biosurveillance operations by the U.S. Department of Homeland Security (DHS) and its federal interagency partners.

The Global Public Health Intelligence Network is a non-traditional all-hazards multilingual surveillance system introduced in 1997 by the Government of Canada in collaboration with the World Health Organization.1 GPHIN software collects news articles, media releases, and incident reports and analyzes them for information about communicable diseases, natural disasters, product recalls, radiological events and other public health crises. Since 2016, the Public Health Agency of Canada (PHAC) and National Research Council Canada (NRC) have collaborated to replace GPHIN with a modular platform that incorporates modern natural language processing techniques to support more ambitious situational awareness goals.

Objective:

To rebuild the software that underpins the Global Public Health Intelligence Network using modern natural language processing techniques to support recent and future improvements in situational awareness capability.

The syndromic surveillance SurSaUD® system developed by Sante© publique France, the French National Public Health Agency collects daily data from 4 data sources: emergency departments (OSCOUR® ED network), emergency general practioners (SOS Medecins network), crude mortality (civil status data) and electronic death certification including causes of death. The system aims to timely identify, follow and assess the health impact of unusual or seasonal events on emergency medical activity and mortality. However some information could be missed by the system especially for non-severe (absence of ED consultation) or, in contrast, highly severe purposes (direct access to intensive care units). The French pre-hospital emergency medical service (SAMU) represents a potential valuable data source to complete the SurSaUD® surveillance system, thanks to reactive pre-hospital data collection and a large geographical coverage on the whole territory. Data are still not completely standardized and computerized but a governmental project to develop a national common IT system involving all French SAMU is in progress and will be experimented in the following years.

Objective:

To evaluate whether SAMU data could be relevant for health surveillance and proposed to be integrated into the French national syndromic surveillance SurSaUD® system.

The Epi Evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. Epi Evident addresses the taxing analytical evaluation of how diseases behave differently across countries. This application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks. Developed at the Pacific Northwest National Laboratory (PNNL), Epi Evident utilizes time-series disease case count data from the Biosurveillance Ecosystem (BSVE) application Epi Archive. This diverse data source is filtered through the flexible Epi Evident workflow for forecast model building designed to integrate any entering combination of country and disease. The application aims to quickly inform analysts of anomalies in disease & location specific behavior and aid in evidence based decision making to help control or prevent disease outbreaks.

Objective:

Epi Evident is a web based application built to empower public health analysts by providing a platform that improves monitoring, comparing, and forecasting case counts and period prevalence of notifiable diseases for any scale jurisdiction at regional, country, or global-level. This proof of concept application development addresses improving visualization, access, situational awareness, and prediction of disease behavior.

The NYC Department of Health and Mental Hygiene (DOHMH) uses ED syndromic surveillance to monitor near real-time trends in pneumonia visits. The original pneumonia algorithm was developed based on ED chief complaints, and more recently was modified following a legionella outbreak in NYC. In 2016, syndromic data was matched to New York State all payer database (SPARCS) for 2010 through 2015. We leveraged this matched dataset to validate ED visits identified by our pneumonia algorithm and suggest improvements. An effective algorithm for tracking trends in pneumonia could provide critical information to inform and facilitate public health decision-making.

Objective:

To validate and improve the syndromic algorithm used to describe pneumonia emergency department (ED) visit trends in New York City (NYC).

Surveillance in nursing homes (Enserink et al., 2011) and day care facilities (Enserink et al., 2012) has been conducted in the Netherlands, but is not commonly practiced in the United States (Buehler et al., 2008). Outbreaks of illnesses within these facilities are required to be reported to the Epidemiology Program, however a small fraction of outbreaks reported come from LTCFs. Without regular communication between LTCFs and the Epidemiology Program, it is likely that many outbreaks are going unreported due to lack of awareness of the reporting requirements by facility staff. To better understand the prevalence of illness in LTCFs and improve communication between LTCFs and DOH-Hillsborough a weekly surveillance survey was created using Epi Info web survey.

Objective:

The Florida Department of Health in Hillsborough County (DOH-Hillsborough) routinely reviews the ESSENCE-FL system to assess syndromic trends in emergency department (ED) and urgent care data (UCC). Collection of this type of symptom data from long term care facilities (LTCFs) and child care centers is of interest in order to better understand how these illness patterns present in vulnerable populations outside of the EDs.