To compare locally-developed influenza-like syndrome definitions (derived from emergency department (ED) chief complaints) when applied to data from two ISDS DiSTRIBuTE Project participants: Boston and New York City (NYC) [1].
To compare the completeness of emergency department (ED) visit and hospital admissions data collected electronically for syndromic surveillance and data collected manually for a field surveillance exercise.
To examine data from 911/Emergency Medical Services (EMS) and determine whether these data provide a useful addition to syndromic surveillance (SS) when used with emergency department (ED) chief complaint (CC) data.
This paper describes the issues associated with the creation of a statewide emergency department syndromic surveillance system, part of the South Carolina Aberration Alerting Network (SCAAN), in a predominately rural state.
This paper develops a new Bayesian method for cluster detection, the ìBayesian spatial scan statistic,î and compares this method to the standard (frequen-tist) scan statistic approach on the task of prospective disease surveillance.
This paper describes a Bayesian algorithm for diagnosing the CDC Category A diseases, namely, anthrax, smallpox, tularemia, botulism and hemorrhagic fever, using emergency department chief complaints. The algorithm was evaluated on real data and on semi-synthetic data, and this paper summarizes the results of that evaluation.
Discusses the current state of syndromic surveillance using inpatient and ambulatory clinical data in the United States and the potential utility of the data. The Meaningful Use Stages 2 and 3 regulations incentivize the use of these data sources. Existing systems effectively perform a range of activities from influenza-like illness surveillance to heart disease risk factor surveillance. With further development, ambulatory and inpatient data could become an integral part of syndromic surveillance practice.
Objective
To document the current evidence base for the use of electronic health record (EHR) data for syndromic surveillance using emer- gency department, urgent care clinic, hospital inpatient, and ambula- tory clinical care data.
Health care processes consume increasing volumes of digital data. However, creating and leveraging high quality integrated health data is challenging because large-scale health data derives from systems where data is captured from varying workflows, yielding varying data quality, potentially limiting its utility for various uses, including population health. To ensure accurate results, it’s important to assess the data quality for the particular use. Examples of sub-optimal health data quality abound: accuracy varies for medication and diagnostic data in hospital discharge and claims data; electronic laboratory data used to identify notifiable public-health cases shows varying levels of completeness across data sources; data timeliness has been found to vary across different data sources. Given that there is clear increasing focus on large health data sources; there are known data quality issues that hinder the utility of such data; and there is a paucity of medical literature describing approaches for evaluating these issues across integrated health data sources, we hypothesize that novel methods for ongoing monitoring of data quality in rapidly growing large health data sets, including surveillance data, will improve the accuracy and overall utility of these data.
Objective
We describe how entropy, a key information measure, can be used to monitor the characteristics of chief complaints in an operational surveillance system.
Public health disease surveillance is defined as the ongoing systematic collection, analysis and interpretation of health data for use in the planning, implementation and evaluation of public health, with the overarching goal of providing information to government and the public to improve public health actions and guidance. Since the 1950s, the goals and objectives of disease surveillance have remained consistent. However, the systems and processes have changed dramatically due to advances in information and communication technology, and the availability of electronic health data. At the intersection of public health, national security and health information technology emerged the practice of syndromic surveillance.
Objective
Review of the origins and evolution of the field of syndromic surveillance. Compare the goals and objectives of public health surveillance and syndromic surveillance in particular. Assess the science and practice of syndromic surveillance in the context of public health and national security priorities. Evaluate syndromic surveillance in practice, using case studies from the perspective of a local public health department.
Epidemiological models that simulate the spread of Foot-and-Mouth Disease within a herd are the foundation of decision support tools used by governments to help advise and inform strategy to combat outbreaks. Contact transmission data used to parameterize these models, contrary to assumption, contain a significant amount of variability and uncertainty. The implications of this finding suggest that the resultant model output might not accurately simulate the spread of an outbreak. If this is true, the potential impact due to uncertainty inherent to the decision support tools used by governments might be significant.
Objective
The objective of this project is to understand how parametric un- certainty within intra-herd Foot-and-Mouth disease epidemiological models affects the outbreak simulations and what implications this has on surveillance and control strategy and policy.