Syndromic Surveillance

The semantic web is an emerging technology for expressing rich descriptions of a problem domain in the form  of  ontologies.    An  ontology  provides  a  domain  specific  knowledge  base  for  the  communication  and  sharing  of  knowledge  between  various  human  and  computer agents [1].   Many  public  health  organizations  have  adopted  syndromic surveillance systems but criteria for the selection  of  appropriate  data  sources,  syndrome  definitions,   and   applicable   outbreak   detection   methods   have not been established [2].  Application of semantic web technology to the field of syndromic surveil-lance has been seen to be successful in an experimental  environment  through  the  BioSTORM  project  at  the  SMI  labs  at  the  Stanford  University  School  of  Medicine  [3].    The  semantic  web  shows  promise  for  providing  a  universal  problem  description  layer  that  will allow for easier integration between heterogeneous data sources and problem solving techniques. 

Objective

A syndromic surveillance system which uses a semantic web description layer is more extensible than existing systems. This will be shown through the application of appropriate software metrics, as well as a case based review that targets three major system design components.

Early detection of new diseases such as bovine spongiform encephalopathy is the subject of great interest (Gibbens et al., 2008). Understanding whether a disease is infectious or sporadic becomes essential for the application of control measures. Consistent and robust ways to the assessment of temporal trends are required to help in the elucidation of this question. Clustering of cases in space, or time and space, is also relevant in the understanding of the aetiology of a new disease. This paper presents a third approach: knowledge by comparison, either of diseases, surveillance sources or both. We applied this approach to the current debate about the nature of atypical scrapie, a fatal neurological animal disease, by comparing the spatial distribution of this form of scrapie with that of classical scrapie. A similar spatio-temporal distribution of these two diseases would indicate shared environmental disease determinants and help in the generation of hypotheses about the aetiology of atypical scrapie.

For syndromic and related public health surveillance systems to be effective, state and local health departments and the Centers for Disease Control and Prevention (CDC) need access to a variety of types of health data. Since the development and implementation of syndromic surveillance systems in recent years, health departments have gained varied levels of access to personal health information for inclusion in these systems. A variety of federal, state, and local laws enable, restrict, and otherwise infl uence the sharing of health information between health care providers and public health agencies for surveillance, as well as research, purposes. Some health care providers have expressed reluctance or refused to provide identifi able data for syndromic surveillance to health departments (1), citing state privacy laws or the Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule (2). Although the HIPAA Privacy Rule permits health care providers to disclose protected health information without patients’ consent to public health agencies for authorized purposes, it does not supersede state laws that provide greater protection of individual privacy (2,3). The use of individuals’ health information for syndromic surveillance poses challenging questions regarding the interpretation and future development of ethical and legal standards for public health practice and research. While the practice of syndromic surveillance extends the longstanding tradition of public health surveillance as an essential element of public health practice (4), it raises in a new light equally longstanding questions about governments’ authority to collect and use health information (5). As the practice of syndromic surveillance evolves, it is in the national interest to clarify the conditions under which health information can be shared, the ways that privacy and confi dentiality can be protected, and the ways that local, state, and federal public health agencies can legally, ethically, and effectively exercise their respective responsibilities to detect, monitor, and respond to public health threats.

 

For syndromic and related surveillance systems to be effective public health tools, state and local health departments and CDC need access to a variety of types of health data.  However, since the development and implementation of syndromic surveillance systems began in recent years, experience in gaining access to personal health data has been mixed.  Although some have argued that the HIPAA Privacy Rule permits data owners to disclose protected health information to public health authorities, covered entities have cited HIPAA in refusing to provide data to researchers and health departments.  In addition to HIPAA, a variety of federal, state, and local public health laws enable, restrict, and otherwise influence the ability to share data for public health surveillance purposes.  Concerns about protecting proprietary data also influence data sharing for public health purposes.  It is in the national interest to clarify the conditions under which data can be shared, balancing privacy and confidentiality with the ability of public health agencies at all levels of jurisdiction to access information needed to protect the public from disease.  As the practice of syndromic surveillance evolves, it is equally important to assure that data are collected and used ethically as well as legally. The methods and uses of syndromic surveillance pose challenging questions regarding the interpretation and future development of ethical and legal standards for public health practice and research. The discussion will not be confined to the legal and ethical issues surrounding the release of data but will also address these issues as they concern the subsequent transmission, storage, replication, and display of health data by local, state, and federal public health users, including how the information is used for both early event detection and situational awareness functions. 

Objective

The International Society for Disease Surveillance will convene a group of experts to: (1) share experience with privacy, confidentiality, and other legal and ethical issues in syndromic surveillance; (2) clarify the research, practice, legal, and ethical issues that enable and restrict data sharing; and (3) identify approaches to overcoming barriers in a way that protects privacy and confidentiality while maximizing the usefulness of syndromic and related surveillance systems.

We started an experimental syndromic surveillance using 1)OTC and 2)outpatients visits, in the last year and included 3)ambulance transfer from this year so as to early detect bioterrorism attack (BTA). 

Classical disease monitoring in local public health jurisdictions has been based on a list of “notifiable diseases”, more or less consistent from state-to-state.  While laboratories’ compliance with this requirement is, in general, excellent, clinician reporting is extremely poor [1].  In most circumstances, laboratory reporting is inherently delayed (perhaps by weeks), and most leaders in infectious disease and bioterrorism believe that recognition of abnormal spatiotemporal patterns within hours is essential [2].  Syndromic surveillance systems based on analysis of statistical aberrations in diagnosis code, chief complaint, or analysis of other data streams have been proposed and tested, but have largely failed to meet criteria of timeliness, sensitivity and specificity [3].  In addition, the vast majority of syndromic surveillance systems do not include veterinary surveillance, which may be important given that the vast majority of diseases of human public health importance are zoonotic in origin.  Thus, we have tested the hypothesis put forward by Henderson that “the astute clinician” can serve as the best early-warning indicator [4], with minimal demands on clinician time while simultaneously providing situational awareness to the broad community of health care providers and political decision makers who require such information.

Objective

It is widely agreed that "situational awareness" in disease surveillance is essential for intervening early in an infectious disease (or intoxination) outbreak. We report on 3.5 years of experience of a clinician-based system in a 25,000 square mile area of northwest Texas, a mixed urban, semi-rural and agricultural setting.

The Indiana Public Health Emergency Surveillance System (PHESS) currently receives approximately 5,000 near real-time chief complaint messages from 55 hospital emergency departments daily.  The ISDH partners with the Regenstrief Institute to process, batch, and transmit data every three hours.  The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) tool is utilized to analyze these chief complaint data and visualize generated alerts.1   

 

The ISDH syndromic surveillance team discovered that certain chief complaints of interest were coded into the “other” syndrome and not visible in typical daily alert data.  Staff determined that even a single chief complaint containing keywords related to specific reportable diseases could be of significant public health value and should be made available to investigating epidemiologists.2 

 

In addition, data quality is critical to the success of the program and must be evaluated to ensure optimal system performance.  Metrics related to data flow and completeness were identified to serve as indicators of hospital connectivity or coding problems.  These measures included the percent change in daily admits and the proportion of chief complaints missing the patient address.

Objective

This paper describes the development of targeted query tools and processes designed to maximize the extraction of information from, and improve the quality of, the hospital emergency department chief complaint data stream utilized by the Indiana State Department of Health (ISDH) for syndromic surveillance.

To determine the predictive abilities of systematic emergency room chief complaints for the detection of acute infectious diseases in the Winnipeg population.

58 medical licensure boards require between 12 and 50 hours of Continuing Medical Education (CME) for re-licensure of physicians. 28 states as well as Puerto Rico, the U.S. Virgin Islands, and the Mariana Islands, require continuing nursing education (CNE) for nursing re-licensure, with requirements varying from 5 hours per year to 45 hours every 3 years. Continuing education requirements may include self-directed educational programs, academic education, or research and professional activities. To the best of our knowledge, although there are online public health preparedness programs and journal articles that provide continuing education credits, there is no currently available online course on syndromic surveillance available for CME or CNE.

Objective

The Education and Training Committee of the International Society for Disease Surveillance is developing an introductory online CME curriculum in syndromic surveillance for physicians and other health practitioners. This curriculum would also be available for public health practitioners new to syndromic surveillance. The goal of the curriculum is to provide an introductory knowledge of syndromic surveillance for interested practitioners and stimulate healthcare provider cooperation and involvement with syndromic surveillance.

This paper investigates the use of data-adaptive multivariate statistical process control (MSPC) charts for outbreak detection using real-world syndromic data. The widely used EARS [1] methods and other adaptive implementations assume implicitly that nonsta-tionarity and/or the lack of historic data preclude the conventional Phase I/Phase II approach of SPC. This work examines that assumption formally by evaluating and comparing the false alarm rates and sensitivity of adaptive and non-adaptive MSPC charts applied to simulated outbreaks injected into both desea-sonalized and raw data.