Bio-terrorism is covert by its very nature. It is unannounced and it is hidden – it has to be. Moreover, by its nature, it can stay hidden – for at least a while – adding to its destructive power. Every minute of every hour that a biological-warfare attack continues without being detected means that much more time to infect that many more victims.
An explosion happens. It does its damage and is finished, and its energy is expended. Chemical or radiological dispersion device (dirty bomb) attacks kill, wound, or destroy within the given volume of contaminant, and therefore have a finite destructive power.
Biological agents are unique, though. Given the right conditions, they can and will – through the natural process of reproduction – increase in the volume of space and number of persons affected. In addition, if the disease caused by a specific bio-agent is person-to-person transmissible, every victim is a potential ally in spreading the effect of the weapon.
The effects are cumulative. The more time passes the sicker the patients will be when they reach care providers. In addition, in this time, more people will be infected, and the circle of damage caused by the attack will continue to widen. Conversely, the sooner any victim starts treatment the more likely it will be that he or she will survive.
Fundamentally, a bio-weapon causes an artificial outbreak of disease. Historically, the accepted method of detecting any outbreak of a disease is to make it reportable – by doctors, hospitals, or laboratories. On the federal level the process is embodied in the National Notifiable Disease Surveillance System (NNDSS), which depends on doctors, hospitals, and laboratories to fill out disease case reports and pass the information forward to public health officials. These well educated professionals, experts in diagnosing diseases, are license holders and often are regulated by the same public health officials who are tasked with the identification of outbreaks.
One Case Does Not an Outbreak Make
In practice, the reporting model tracks instances of a known, reportable disease – the plague, for example. There are some instances of plague every year, so a few cases do not necessarily signify an unusual or unnatural outbreak. However, if three times the annual national average number of cases occur in a city over the course of just a few weeks, it is reasonable to suspect that something unusual is going on.
The diseases caused by traditional bio-weapons (read article) (anthrax, botulism, brucellosis, cholera, plague, tularemia, and western equine encephalitis, and eastern equine encephalitis) are rare enough that a single case can be considered significant depending on its geographic location. A study by the Centers for Disease Control and Prevention (CDC) explains the situation this way: “Even with such low incidence, we identified patterns in disease incidence that better prepare us to identify potential bioterrorism events. In this analysis, certain diseases appear to be endemic in certain geographic areas.” (read article)
A case of smallpox, which has been eradicated from the natural world, will always be considered an act of terrorism or war unless otherwise explainable. Similarly, the incidence of naturally occurring anthrax is so low that any and all cases – particularly outside of sheep/goat-raising areas or the wool industry – should raise the index of suspicion.
Improvements in data technology have made paper reporting of diseases nearly obsolete; on-line reporting is now the norm. Reporting professionals can easily add their data to the greater public-health picture without leaving their offices. This advance is more than just a convenience, though. Electronic reporting improves the amount of reports actually filed. Electronic reporting also increases the number of reports actually filed. Further, the information is immediately available to public health analysts.
Symptoms, Syndromes, and Surveillance
According to the CDC, the term “syndromic surveillance” applies to surveillance “using health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response.” In other words, it refers to the collection of data on clusters of symptoms that do not depend on the final diagnosis.
The New York City Department of Health and Mental Hygiene (DOHMH) has established a number of symptom clusters that hospital emergency rooms (ERs) in New York City report daily. There are eight syndromes they track: Common cold, Sepsis, Respiratory, Diarrhea, Fever, Rash, Asthma, and Vomiting. As is evident, the syndromes are defined more by their symptoms than by the actual causes of the symptoms.
During the early phase of an outbreak, the symptoms reported are identified only as unusual activity. An increase in the number of patients complaining of the symptoms of a common cold is identified in the data by the DOHMH as evidence that something unusual may be going on. The information does not, though, identify precisely what is going on – that still has to be determined. The next step would be for a DOHMH investigator to determine the cause; it might be, for example, that there has been an outbreak of anthrax.
The initial onset of symptoms of inhalation anthrax is nonspecific, though, and similar in certain respects to the symptoms of a common cold. For that reason, the first indication of a real anthrax attack might be a rise in treatment for and complaints of nonspecific symptoms at hospital ERs and in the offices of private providers.
Later symptoms of inhalation anthrax become more specific and can be diagnosed clinically. Public health officials can use reports of patients with the specific diagnoses to identify actual cases. At this point, remedial and/or preventive actions can be taken based on the specifics of the disease. The public health officials can issue critical information to care providers, for example, or to the general public. Parallel to the case investigations, the law-enforcement community can – if appropriate – start criminal investigations.
A primary goal of syndromic surveillance, therefore, is to recognize that something unusual is going on – and, for that reason, to start field investigations early. Today, because of the increased risk of terrorist attacks, this sequence of events might well be, literally, a matter of life and death.
Size, Seasons, and Suspicions
Among the several factors affecting syndromic surveillance and its usefulness are the following:
- Size: the event has to reach a statistically significant threshold level. If there are 55 cases of a particular disease in an average year, an outbreak of six cases in one month may not be enough to sound the alarm.
- Population Mobility/Commuting: A population that moves from one jurisdiction to another means two things to the syndromic surveillance system: Those infected spread a contagious disease across a larger geographic area; and the population of infected victims is now part of a larger overall population pool. In today’s commuter age, an attack that originates in New York City could quickly spread to a four-state region just by being carried by daily commuters across state lines (or it could become worldwide if the attack were to take place in any international airport).
- The Level of Suspicion Within the Health-Care Community: Among the principal factors determining success or failure in this type of system is the alertness of the health-care community. Providers must understand the need to complete the reporting procedures both fully and accurately. A lack of awareness of both the requirements for and the value of reporting – along with confusion resulting from changing requirements that do not always take into account the threat posed by bio-terrorism – has led in some instances, unfortunately, to incomplete reporting. The index of suspicion within the patient-care community will affect the success of the syndromic surveillance system because the members of that community who are required to report events have to understand both what has to be reported and why the reporting is important.
- The Seasons of the Year: A bio-terrorism attack will take longer, and will be harder to detect, during the seasons when there are natural upswings in disease. Asthma cases increase every fall, for example, because of seasonal increases in natural irritants. The system has to allow for such known causal relationships. Raw daily tallies of a specific syndrome should not be compared to an annual daily average in any case. Instead, they should be measured against statistics that have been controlled and adjusted to remove as many known natural variables as possible.
- Syndromic Surveillance System Design: The components of the syndromic surveillance system will affect the speed and accuracy of any warning that might be issued. Among these components are the source and quality of the data provided. In addition, the speed at which the data can be received and processed will directly affect the usefulness of the results. A data collection and processing system that provides 100 percent accuracy, but returns results in 30 days, would be useless in countering bio-weapons, which almost always have a “working life” – i.e., the time from infection to the end of the disease – of only two weeks or less. During the 2001 anthrax attacks, the time between infection and onset of specific symptoms was 4-6 days.
The thresholds for alarm also will help dictate the usefulness of the system. Like any other alarm system, from radiation meters to smoke detectors, the point at which a bio-terrorism alarm is sounded is very important. If the threshold is set too low the alarms become routine and will be discounted, remaining un-acted upon; if it is set too high the threat is too advanced by the time the alarm sounds.
The human factor always has to be considered. The time when the last false positive was received directly affects the success of the system. The more recently a false positive was received – and the more negative the fallout for those raising the alarm – the more wary those managing the system will be.
Other Data Being Considered
In addition to the hospital ER data on the number of patients complaining of one syndrome or another, several other sources of data have been studied and are being used. Two examples are the number of patients admitted to emergency rooms, and the volume of calls to EMS (emergency medical services) units and agencies. It seems evident that an unusual increase in the number of people coming into the emergency room, and/or in the number of ambulance runs on any given day, may be evidence of a possible outbreak.
Fortunately, EMTs (Emergency Medical Technicians) and Paramedics collect much of the same information about patients and their reasons for calling an ambulance – and it is well within the ability of the qualified Paramedic and/or EMT to describe a patient’s symptoms, which means that these patients can be sorted into the same syndromes as ER patients.
There are, though, certain factors that might affect the validity of this data source. The first is that the jurisdictions of many public health agencies receiving and analyzing this data often are serviced by a large number of EMS agencies – which do not necessarily use the same standardized written reports. Also, they may or may not be required to turn in their reports. Finally, even if there is a centralized report recipient, there may be a built-in time delay and/or no effective way of entering and/or analyzing the data in a timely manner.
In general, it seems to be the administrative bottlenecks that make data received from EMS units less timely – and, therefore, less useful. However, there are a number of electronic ambulance reporting systems now available that either record information into a hand-held device (such as a palm top or tablet computer) or scan handwritten documents to make them available as electronic data as rapidly as they are scanned.
Laboratory case reports also can be useful. Kansas City (Mo.) (read article) has studied the use of data from the main labs used by hospitals and private physicians to track the types of complaints reported by patients. This provides an indirect indicator of the specific syndrome from which a patient might be suffering. For example, if test “A” is the standard of care for patients with an unidentified rash, and is used for little else, then an increase in requests for test A should be indicative of an increase in unknown rashes.
An increase in gross numbers also can tell the disease tracker that something is amiss when the number of requests for lab tests climbs. There is, though, a general problem in relying too much on either the number of EMS calls or the volume of lab test requests, because there are several variables that may affect either or both.
Unfortunately, all three of these sources of data – EMS call volume, ER visit volume, and lab test volume – are indirect methods for extrapolating the same information that is provided by the ER syndrome data. As such, it is more reliable to collect this information directly.
Blood, Dollars, and Confidentiality
Approximately four million people give blood each year. The donors represent all demographic groups and come from all areas of the country. In theory, if a small sample of each unit of blood donated were sent to a lab to be tested the general health of the community could be determined, more or less, by the results. Real-life experience, though, shows this seemingly logical plan to be unworkable.
There are numerous complex legal issues – involving consent and/or confidentiality, for example – governing any medical testing. There also are a large number of laws and regulations governing testing practices and the control of results. In addition, the agencies that collect blood may not be enthusiastic about participation in any data-collection effort. Concern about confidentiality of the test results may keep people away who otherwise might be willing to donate. The need for blood donations grows every year, and the agencies filling that need are not likely to do anything that might reduce the number of potential donors.
There are other factors to be considered. One is that even a blood test that is sensitive enough to identify an infected patient in the first three days of infection would yield only a 26 percent chance of successfully detecting the disease. In addition, the time it takes for that detection would almost certainly be longer than the time it would take for the disease to manifest itself through the outbreak of symptoms (at which