Medical Surveillance for Biological Terrorism Agents
ABSTRACT
The first recognition of a bioterrorist attack may be sick patients. Recognition and subsequent notification of an attack at the earliest possible moment will assist in rapidly instituting protective measures. New methods of medical surveillance can assist with rapid detection. Syndromic surveillance is the use of newly created or pre-existing databases to track the health of communities. The data can include medical and non-medical information, but typically contains non-specific indicators of health status. These systems are currently being tested and fielded in multiple locations, and nationwide systems are being developed in the United States. Retrospective and prospective analyses have demonstrated the ability of the data to detect disease outbreaks, but the systems have yet to be proven to lead to more effective interventions.
Key Words: syndromic surveillance, evaluation, bioterrorism.
INTRODUCTION
Surveillance is the usual way disease outbreaks are detected. Traditional surveillance systems rely on both formal and informal networks. Most formal methods rely on either active case finding or laboratory confirmation. Active surveillance is usually done during a period of crisis and cannot be sustained for long periods of time. Laboratory confirmation results in a significant time delay and misses any patients not tested. Therefore, disease surveillance often relies on medical practitioners to alert public health departments of any unusual diseases or clusters of disease.
If medical surveillance is the first to pick up a biological attack, then the situation is already critical. Because of this, law enforcement efforts and environmental sensors are being deployed to either thwart or detect an attack at its release. However, if an attack occurs and is not detectable through currently available means, or is released away from a detector, then the first warning will probably be ill patients. Rapid implementation of a post- attack prophylaxis program can significantly reduce morbidity and mortality and costs (Kaufmann et al. 1997). The earlier the release is detected, the better the response. Even for agents with no effective treatment, early recognition and institution of measures to stop disease spread can often prevent further cases. For those with effective treatments, the sooner they are provided, the more morbidity and mortality will decrease.
For detection of unusually severe cases of illness, the astute clinician is an important link in the public health chain. In the anthrax attack in 2001, a clinician notified the Florida state health department of a case of probable anthrax. This notification started an intensive hunt for the cause of the disease and for other cases. With this patient, the detection of Gram-positive rods on a cerebrospinal fluid Gram stain allowed early recognition. However, another case of anthrax occurred earlier and was hospitalized, but not recognized as anthrax or reported (Jernigan et al. 2001). Without assistance from the laboratory, it can be very difficult to recognize the early symptoms of bioterrorist diseases. In addition, with milder diseases that occur in large numbers but are seen by a variety of practitioners, the individual clinician may not recognize the increased total number of cases. They may also not know how to, or that they should, contact the health department.
There is a need for better awareness of the health of communities- a way to quickly detect shifts in potentially infectious diseases, whether of natural origin or not. This is the premise that underlies the recent work in health indicator surveillance systems, commonly known as syndromic surveillance. What follows is a description of the components of these systems and their successes and limitations.
SURVEILLANCE DEFINITIONS
The U.S. Centers for Disease Control and Prevention (CDC) defines public health surveillance as the ongoing, systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality (CDC 2001). Surveillance data can be used for a number of purposes including monitoring trends of disease over time, determining where disease is occurring and how it spreads, determining priorities for research and interventions based on the burden of disease in a population, planning for needed resources, and evaluating the effectiveness of control measures. Although not historically a primary purpose of surveillance, one of the most important current uses in a bioterrorist attack is for outbreak detection.
Syndromic surveillance has been defined as the ongoing, systematic collection, analysis, and interpretation of data that precede diagnosis and can warn of a potential disease outbreak earlier than public health authorities would usually be notified (Sosin 2003a). The data used in syndromic surveillance systems are usually nonspecific potential signs and symptoms of an illness spectrum that disease may be higher than expected in a community, and can be from new or existing data streams. For bioterrorism, the emphasis is on timeliness with automated analysis and visualization tools to provide information that enables a public health investigation to occur sooner (Sosin 2003b).
DATA FOR SURVEILLANCE
Traditional surveillance data are collected to track disease incidence and prevalence in a community. For infectious diseases, these data typically consist of positive laboratory tests for the disease or, in cases of extreme urgency such as SARS, may include the number of cases that meet a clinical case definition, but have not yet been confirmed. These traditional data can be labeled “clinical diagnostic” and are usually quite accurate but require an intensive effort of reporting and case finding, or are delayed by the time it takes to run a laboratory test.
Other clinical data, termed here “clinical nondiagnostic,” comprise information captured by the medical system that does not include a diagnosis-it is usually recorded early in the process before the diagnosis is known, or is from a data source that does not include a diagnosis. These data include chief complaints of patients who visit an emergency room, ambulance runs, laboratory test orders, or medication prescriptions. This category can also include International Classification of Diseases, 9th Revision (ICD- 9) codes recorded at the time of an outpatient visit, as many of these codes record symptoms and not diagnoses (e.g., cough, vomiting). Outpatient ICD-9 codes can also be considered diagnostic when such codes as upper respiratory infection or gastroenteritis are used.
A third category of information is related directly to the illness, but does not result from a medical visit. This group can be termed “pre-clinical” and includes such information as over-the- counter (OTC) pharmacy sales and calls to nurse advice lines and doctors’ offices. These data can be very valuable in early detection of a possible infectious disease outbreak, but are often nonspecific.
A final category in the recognition of an outbreak is “non- medical.” This group uses a proxy that may reflect a change in behavior because of an illness. It includes absenteeism from school or work, public transportation usage, or even television viewing. Although these data sources may provide corroboration of a potential medical event, their use is hampered by lack of specificity.
Table 1 outlines various data sources. The timeline in Figure 1 shows where in the course of disease these potential sources could be elevated.
WHICH DATA SOURCES WORK AND HOW TO CAPTURE THEM
One way to ensure that captured information reflects the diseases of interest is to create data specifically for the surveillance system. Syndromic information, such as “vomiting,”"diarrhea,”"fever,” or “cough” alone or in combination can be collected and analyzed to detect an increase in a particular syndrome. This method has been used in a number of locations, and is often instituted during high-profile events (Suzuki et al 2003; GOSS et al. 2003; Cho et al. 2003; Foldy et al. 2003; Miller et al. 2003b; Jorm et al. 2003; Osaka et al. 2002).
Although these systems have higher specificity than some syndromic surveillance systems that rely on preexisting data sources, participation may not be optimal, as they require additional work on the part of the healthcare provider. For example, in New York City after the 2001 World Trade Center terrorist attacks, the CDC dispatched 80 Epidemic Intelligence Service officers (EISOs) to New York City to run a syndromic surveillance system in 15 emergency rooms. After the EISOs left, the system was unsustainable and was discontinued (CDC 2002; Das et al. 2003).
Table 1. Potential data sources for medical surveillance.
Preexisting data can be a more reliable source than newly created data, and usually less expensive. However, preexisting data may not accurately reflect the specific disease or syndrome of interest. Therefore, it must be determined what data sources are effective proxies of actual disease incidence in a community.
Figure 1. Timcline of disease outbreak with potential data sources.
As expected, analysis of data sources has shown diagnostic da\ta to be more specific, and totally non-medical data unreliable as a sole data source (Lombardo et al. 2003). For this reason, most researchers use early clinical and preclinical sources of information, such as calls to nurse advice lines, emergency room chief complaints, early clinician diagnosis codes and over-the- counter pharmacy sales, allowing them to find early indicators of illness with some degree of specificity. Using multiple data sources at one time can increase the trust one can place in system results. The diagnostic data are still useful for confirmation arid for retrospective data analysis studies. The non-medical data are useful for further corroboration of early medical data, but should not be relied on as a sole source of outbreak information. The results of some studies are listed in Table 2.
Acquiring pertinent information in a useful formal may prove a challenge. Ideally, data thai are already in an electronic format will allow rapid data transfer, analysis, and dissemination. Increasingly, more medical data are available from an electronic medical record, but the most desirable data for early recognition, such as chief complaints, may still be paper-based. Sales data from large retail pharmacy chains are usually available electronically, but other non-medical data may not be, such as absenteeism. Privacy restrictions, both patient and commercial, may also restrict information that is available for use.
Table 2. Results of data analysis studies.
EXAMINATION AND DISSEMINATION OF USEFUL DATA
After obtaining and determining the usefulness of data sources, the data must be analyzed to detect changes in disease incidence. The analysis includes determining what syndrome groups the data source reflects, what elements of the data should be grouped together, and then developing statistical algorithms to alert when normally expected levels are exceeded.
Some data sources can only reflect certain types of diseases. For example, there are some specific drugs for gastrointestinal and respiratory diseases, but not for fevers of unknown origin. Similarly, some disease syndromes do not have specific laboratory tests that are ordered, or the test can be ordered for a variety of reasons (e.g., complete blood count). Non-medical data sources cannot provide any specific syndrome information at all. For more specific data, such as chief complaints or ICD-9 codes, syndrome groupings can be created (Graham et al. 2003). Some commonly used syndrome groups are: respiratory (upper and lower), gastrointestinal (upper and lower), neurological, botulism-like, rash, fever of unknown origin, hemorrhagic fever-like illness, and unexplained death. A list of potential syndromes for bioterrorism surveillance with their definitions can be found at
Once syndrome groups have been selected, the data must be subdivided to best reflect these groups. Analysis on military outpatient visits and emergency visits in New Jersey by the Walter Reed Army Institute of Research (WRAIR), CDC, the Emergency Medical Associates of New Jersey Research Foundation, and the New York City Department of Health and Mental Hygiene, has resulted in a suggested list of ICD-9 codes for various syndrome groups. The list can be found at
The next step is to present data in a useful way. Raw data alone can be adequate when presented in a graphical form. An example of data from the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) that uses outpatient military ICD-9 codes is shown in Figure 2. This example shows an outbreak of gastroenteritis that is readily apparent without any advanced analytic techniques.
Subtle changes in disease incidence can be harder to detect, and it may also be too time consuming to review raw data from multiple locations. For this reason, temporal analysis programs have been developed. These include regression, exponentially weighted moving average, cumulative summation and temporal filter methods (Lombardo et al. 2003; Wong et al. 2003; Reis et al. 2003; Hutwagner et al. 2003). In Figure 3, an example from ESSENCE shows an aberration detected by autoregression that may not have been noticed because it occurred on a Friday when counts are usually lower. Weekday and holiday effects can be factored into a statistical program. Automated alerts can also assist those who do not have time to check data on a regular basis.
Figure 2. ESSENCE gastroenteritis syndrome group at a military installation.
Figure 3. ESSENCE respiratory alert at a military installation.
If geographic location data are available, such as zip code, census tract, or street address of the patient, hospital, pharmacy, or school, it may be possible to analyze data spatially. Besides simple geographic mapping, statistical clustering techniques for syndromic data can be used (Burkom 2003; Kleinman et al. 2004). Using geographic locators, it is possible to determine specific regions that have higher than expected counts of patients with a designated syndrome-even if the total count for the entire region is not higher than expected.
Finally, the information must be disseminated. Some systems have used a password-protected Web-based system to provide the information (Lombardo et al. 2003; Brillman et al. 2003). With Web access, data can be updated frequently and are immediately available, and can be accessed from any location and shared immediately. Other options include an e-mail broadcast, which is less costly to maintain, to provide a daily or more frequent update. However the information is provided, there must be a way for the user to contact the originator for questions or to request further information.
STATUS OF SYNDROMIC SURVEILLANCE
A number of syndromic surveillance systems have been developed and tested in recent years. Lessons learned from these systems for disease outbreak and bioterrorism detection can help determine syndromic surveillance system usefulness.
The systems have been used to detect, track, and categorize disease outbreaks. The ability to collect and analyze data and to retrospectively correlate with known outbreaks has been demonstrated. With both simulated and natural data, it has been shown that currently used statistical algorithms can detect shifts in disease counts very early in the epidemic curve, but few systems have been tested prospectively to see if they actually assist with the early recognition of disease outbreaks in a manner that leads to more effective interventions (Sosin 2003b). It has been argued that syndromic surveillance systems may not detect outbreaks faster than traditional methods, and even if they do, the advanced warning may not assist with disease mitigation (Buehler et al. 2003).
Because a bioterrorist attack can present in numerous ways (related to the agent, population, and environment), it is impossible to determine how a particular system will perform. It is important to outline the expected capabilities of a syndromic surveillance system, including what types of outbreaks it should detect. There are limitations to any surveillance system, and most syndromic surveillance systems should augment existing traditional systems by extending the ability to detect outbreaks early, and to provide useful information to assist in disease outbreak investigations. Some limitations as they relate to bioterrorism are:
1. Syndromic surveillance will not detect small outbreaks or individual cases of serious disease. Syndromic surveillance systems are best at detecting large, diffuse outbreaks that have long prodromal periods. Small outbreaks, such as seen in the anthrax attacks in 2001, will probably not be detected (unless the syndrome is quite rare or specialized intensive care unit surveillance is in place). In addition, clinicians will almost definitely recognize a very large outbreak with a steep epidemic curve before all the data are available in a syndromic surveillance system.
2. False alarms are possible with syndromic surveillance and may detract public health workers from more important issues. A balance must be struck between too many false alarms and missing potentially serious outbreaks. The system should allow adjustments for optimal sensitivity. There are false alarms in traditional surveillance methods as well-use the information available in syndromic surveillance to determine whether the disease warrants further investigation.
3. The information presented in a syndromic surveillance system may not accurately reflect true health status, because the data are acquired early in the course of illness. Syndromic surveillance systems follow disease trends in a population, they are not used for individual case reporting. Data are also grouped together into syndrome categories to increase sensitivity. However, coding and other errors can occur and should be ruled out before continuing an investigation.
4. Syndromic surveillance systems can be costly, depending on what is needed for data acquisition, analysis, and display. Costs can be greatly decreased if preexisting data are used, as well as analysis and Web-based display systems that have already been developed and tested.
5. Syndromic surveillance, although faster than most traditional methods, may still not be timely enough to truly make a difference. The lag in acquiring data can range from seconds to days, although most analysis and alerting programs are run instantaneously. If the data do lag by a significant amount of time, then it is possible that even if an outbreak is detected first by a syndromic system, it may be too late for effective control measures. The system canstill be used for monitoring and investigation.
6. Even if detected early, morbidity and mortality may still not be affected. This is true for any surveillance system. Sometimes preventive measures are not available, or the outbreak ends before mitigation efforts can commence. The system may still provide valuable information for disease tracking and outbreak investigation.
7. Privacy issues can impact the widespread use of syndromic surveillance. Although an exemption exists for public health purposes for the patient privacy guidelines outlined in the Health Insurance Portability and Accountability Act (HIPAA), patient confidentiality must be protected (Broome et al. 2003). Most syndromic surveillance systems comply with HIPAA and remove patient identifiers. However, this hinders the ability of public health officers to follow up cases of high importance. An ability to link back to patient identity in times of a public health emergency is advisable. Privacy issues can also hamper data sharing between and among different jurisdictions.
Syndromic surveillance still requires careful scrutiny to ensure the system assists in the detection of disease outbreaks, and in responding appropriately to these outbreaks. Other uses of syndromic surveillance, such as for reassurance there is no outbreak, tracking the progression of outbreaks and determining the geographic distribution, should also be taken into account when determining usefulness.
EVALUATION
As with any other surveillance system, a syndromic surveillance system must be initially and periodically evaluated to ensure that it is performing as designed. The CDC has drafted a Framework for Evaluating Syndromic Surveillance Systems for Bioterrorism Preparedness that can be found at
THE FUTURE OF SYNDROMIC SURVEILLANCE
Many government agencies, universities, and private companies have invested in researching aspects of syndromic surveillance and the implementation of systems. In addition to currently operating regional systems, the feasibility of a nationwide system is being evaluated. The National Bioterrorism Syndromic Surveillance Demonstration Project, funded by the CDC, uses nurse triage calls and outpatient encounters from a national database to cover over 20 million people (Platt et al. 2003). The CDC is also developing BioSense, a system to collect nationwide data sources to assist local health departments in acquiring information as well as operating a national system (CDC 2003b). BioNet, a cooperative program between the Department of Homeland Security and the Defense Threat Reduction Agency, has selected San Diego as a pilot city in which to improve detection and event characterization of a biological attack. This effort will include environmental samplers, improved laboratory capabilities, and population health monitoring (DFI International 2004). If effective, the program may expand to other cities.
As new systems are developed, it will be important to continue evaluation and improvement. Any surveillance system for bioterrorism must be dual use and able to detect diseases of natural occurrence, because in most cases it will not be readily apparent if a disease outbreak is natural or manmade. The system must assist public health officers, and not overly burden them with false alarms and unreasonable costs. Finally, the surveillance system must augment other public health practices, and assist in educating clinical colleagues on the importance of maintaining a high index of suspicion and reporting unusual diseases or disease clusters.
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Julie A. Pavlin
Department of Field Studies, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
This article is not subject to U.S. copyright law.
The views expressed are those of the author and should not be construed to represent the positions of the Department of the Army or Department of Defense.
Address correspondence to Dr. Julie A. Pavlin, Chief, Department of Field Studies, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA. E-mail: julie.pavlin@us.army.mil
Copyright CRC Press Jun 2005