ISDS Conference

The  ability  to  accurately  predict  influenza  infection  by  symptoms  and  local  epidemiology  prior  to  lab  confirmation  warrants  further  study  and  is  particular  concern as the threat of pandemic flu heightens.  Antiviral drugs are effective when given within 48 hours of  symptom  onset,  but  this  usually  precludes  culture  confirmation. Further,  rapid  tests  can  be  clinically  helpful   but   lack   the   sensitivity   of   viral   culture. Hence,  ILI  symptoms  are  a  potentially  important  covariate  in  the  early  diagnosis  of  flu. However,  gaps  remain  in  several  areas  of  flu  symptom  research,  including  knowledge  of  potential  differences  between  symptoms  of  Influenza  A  and  of  Influenza  B  [1]. Therefore,  an  examination  of  symptoms  generally  associated  with  Influenza  infection  was  begun,  as  well  as  an  examination  of  symptoms  specifically  associated with Flu A and Flu B. An additional focus in  this  study  was  to  evaluate  the  performance  of  the  current  ILI  case  definition  used  by  the  DoD  flu  program.  This definition is useful to identify individuals who  are  likely  to  be  infected  with  influenza,  as  the  ability  to  capture  and  characterize  novel  strains  of  influenza is an important component to this program. Better yields of influenza mean less time and money spent processing negative specimens.

Objective

This study describes clinical symptoms reported in conjunction with influenza, non-influenza respiratory viruses, and negative viral cultures from the Department of Defense (DoD) Global Influenza Surveillance Program; influenza-like illness (ILI) case questionnaires were linked to corresponding laboratory specimen results for the 2005-06 influenza season for analysis.

Electronic  Health  Record  (EHR)  data  offers  the  researcher a potentially rich source of data for tracking disease  syndromes. Procedures  performed  on  the  patient, medications prescribed (not necessarily filled by  the  patient),  and  reason  for  visit  are  just  some  characteristics of the patient encounter that are available  through  an  EHR  that  can  be  used  to  define  surveillance  syndromes.    Since  procedures  have  not  been used frequently in defining syndromes, encounter  level  procedures  data,  extracted  from  the  EHR  of  a   large   local   primary   care   practice   with   about   200,000 patient encounters per year was used to identify  procedures  associated  with  an  established  respiratory syndrome.

Objective

To investigate the utility of different sources of patient encounter information, particularly in the primary care setting, that can be used to characterize surveillance syndromes, such as respiratory or flu.

The spatial scan statistic is the usual measure of strength of a cluster [1]. Another important measure is its geometric regularity [2]. A genetic multiobjective algorithm was developed elsewhere to identify irregularly shaped clusters [3]. A search is executed aiming to maximize two objectives, namely the scan statistic and the regularity of shape (using the compactness concept). The solution presented is a Pareto-set, consisting of all the clusters found which are not simultaneously worse in both objectives. A significance evaluation is conducted in parallel for all clusters in the Pareto-set through Monte Carlo simulation, then finding the most likely cluster. \

Objective

Situations where a disease cluster does not have a regular shape are fairly common. Moreover, maps with multiple clustering, when there is not a clearly dominating primary cluster, also occur frequently. We would like to develop a method to analyze more thoroughly the several levels of clustering that arise naturally in a disease map divided into m regions.

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.

HealthMap (www.healthmap.org) is a freely accessible, automated real-time system that monitors, organizes, integrates, filters, and maps online news about emerging diseases. The system performs geographic parsing (“geo-parsing”) of disease outbreaks by assigning incoming alerts to low resolution geographic descriptions, such as  country, with the help of a purposely crafted gazetteer. However, the system is limited by the size of the gazetteer, precluding high resolution assignment of place. In this study, we use the prior knowledge encoded in the gazetteer to expand the capabilities of the geo-parsing system.

 

Objective

Discovering geographic references in text is a task that human readers perform using both their lexical and contextual knowledge. Automating this task for real-time surveillance of informal sources on epidemic intelligence therefore requires efforts beyond dictionary-based pattern matching. Here, we describe an automated approach to learning the particular context in which outbreak locations appear and by this means extending prior knowledge encoded in a gazetteer.

Currently, Indiana monitors emergency department patient chief complaint data from 73 geographically dispersed hospitals. These data are analyzed using the Electronic Surveillance System for the Early Notification of Community-based Epidemics application. 

While researchers continue to improve syndromic detection methods, there is significant interest among public health practitioners regarding how to most effectively use the currently available tools. The Public Health Emergency Surveillance System (PHESS) staff have developed and refined a daily syndromic alert analysis and response process based on experiences gained since November 2004.

Objective

This paper describes how the Indiana State Department of Health PHESS staff responded to a syndromic surveillance alert related to a bioterrorism preparedness event.