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Day figure 23.4 Daily counts of respiratory cases,Washington County,Pennsylvania, June-July 2003.The small increase in early June 2003 corresponds to new hospitals being added to the surveillance system.
for outbreaks). The detection from chief complaints preceded detection from automatic analysis of hospital discharge diagnoses by a mean of 10.3 days (95% confidence interval [CI], 15.15-35.5) for respiratory outbreaks and 29 days (95% CI, 4.23-53.7) for gastrointestinal outbreaks (Table 23.7). The researchers used the date of admission rather than the date of discharge in constructing the reference time series.
The correlation analysis of three respiratory outbreaks showed that on average the chief complaint time series was 7.4 days earlier (95% CI, 8.34-43.3), although the 95% CI included zero. For the three gastrointestinal outbreaks, the chief complaint time series was 17.6 days earlier (95% CI, 3.4-46.7).
Prospective Studies. Prospective studies are field evaluations of a biosurveillance system. In a prospective evaluation, the detection algorithms are operated at a fixed detection threshold for an extended period and the ability of the biosurveillance system to detect known outbreaks or to identify new outbreaks is measured.
Heffernan et al. (2004b) used the detection-algorithm method prospectively to study respiratory and fever syndrome monitoring in New York City (Heffernan et al., 2004b). They studied the New York City Department of Health and Mental Hygiene (DOHMH) syndromic system for the one-year period
November 2001-November 2002. Note they also report the DOHMH one-year experience monitoring diarrhea and vomiting, however, the paper by Balter, which we discuss next, included that year in a three-year analysis, so we do not discuss it here.
In New York City, EDs transmit chief complaints to the DOHMH on a daily basis as email attachments or via FTP. The researchers estimated that the DOHMH system received chief complaint data for approximately 75% of ED visits in New York City. The NLP program was a keyword-based system that assigned each patient to exactly one syndrome from the set: common cold, sepsis/dead on arrival, respiratory, diarrhea, fever, rash,asthma, vomiting, and other (Table 23.4).The NLP program was greedy, which means that the algorithm assigned a patient to the first syndrome from the list of syndromes whose definition was satisfied and did not attempt further assignment.
DOHMH used the detection-algorithm method to identify potential outbreaks from daily counts of respiratory and fever. They used a univariate detection algorithm on data aggregated for the entire city (citywide), and spatial scanning for data aggregated by patient home zip code and by hospital (separate analyses).
The citywide monitoring of respiratory found 22 above-threshold anomalies (called signals), of which the researchers table 23.7 Detection Algorithm Analysis of Timeliness of Detection from Chief Complaints
Syndrome Gold Standard Outbreak Sensitivity Specificity Timeliness (95% CI)
Respiratory Seasonal outbreaks of pediatric respiratory 100% 100% 10 days (-15-35) illness (bronchiolitis, P&I)
Gastrointestinal Seasonal outbreaks of pediatric gastrointestinal 100% 100% 29 days (4-53) illness (rotavirus gastroenteritis)
CI, confidence interval; P&I, pneumonia and influenza.
stated that 14 (64%) occurred during periods of peak influenza activity. The first citywide signal occurred in December 2001 and it was followed by additional signals in both respiratory and fever signals on the six successive days. The authors commented that these signals coincided with a sharp increase in positive influenza test results, but did not report a correlation analysis. They also commented that the reports of influenza-like illness (ILI) from the existing sentinel physician ILI system showed increases three weeks after the first signal. Three other respiratory signals occurred during periods of known increases in asthma activity. The remaining five signals occurred during periods of increasing visits for respiratory disease. Thus, there were no signals that could not be attributed to known disease activity.
The citywide monitoring of fever generated 22 signals, of which 21 (95%) occurred during periods of peak influenza activity.
The hospital monitoring of respiratory and fever produced 25 signals. The home zip code monitoring of these two syndromes produced 18 signals. Investigations of these 43 (25+18) signals found no associated increases in disease activity.
Balter and colleagues analyzed the DOHMH three-year experience (November 2001-August 2004) monitoring diarrhea and vomiting using the same biosurveillance system as described in the previous paragraphs (Balter et al., 2005). The authors estimate that by the end of the study period, the monitoring system received data for approximately 90% of ED visits in New York City.
During the three years, the DOHMH system signaled 236 times (98 citywide and 138 hospital or zip code) for diarrhea or vomiting. Of 98 citywide signals, 73 (75%) occurred during what the authors referred to as "seasonal'' outbreaks likely due to norovirus (fall and winter) and rotavirus (spring). One citywide signal after the August 2003 blackout was believed to have represented a true increase in diarrheal illness. Their investigations of the 138 hospital or zip code signals found no increased disease activity.
During the same period, DOHMH investigated 49 GI outbreaks involving ten or more cases; none of which were detected by monitoring of diarrhea or vomiting. In 36 of these outbreaks, few or no patients went to EDs. In two outbreaks, the victims were visitors to New York City who returned to their homes before onset of symptoms. In three outbreaks, victims visited EDs not participating in the monitoring system. In three outbreaks, victims visited EDs over a "days or weeks'' (the algorithms used by DOHMH were sensitive to rapid increases, not gradual increases in daily counts of syndromes). In two outbreaks, the victims presented to the ED as a group and their chief complaints were recorded by reference to the group (e.g., "school incident''). In two outbreaks, a combination of the above causes explained the failure.
N=1 Studies. Irvin and colleagues (Irvin et al., 2003) used the detection-algorithm method to retrospectively study the ability of their anthrax syndrome to detect a single influenza outbreak. The paper is not explicit about the anthrax syndrome, but states, "The presence of any of the following symptoms were sufficient to categorize a patient into anthrax: cough, dyspnea, fever, lethargy, pleuritic chest pain, vomiting, generalized abdominal pain, or headache,'' suggesting that the researcher included symptoms with which pulmonary anthrax may present. They studied an atypical monitoring system based on numeric chief-complaint codes from a commercial ED charting system. This charting system, called E/Map (Lynx Medical Systems, Bellevue WA, http://www.lynxmed.com), offers clinicians charting templates for approximately 800 chief complaints. Each template has a numerical code. A clinician's selection of charting template reflects the patient's chief complaint. The detection algorithm used a fixed detection threshold set at two standard deviations from a recent two-month mean. The algorithm signaled when two of the previous three days exceeded the threshold. The reference standard was the Centers for Disease Control and Prevention (CDC) defined peak week of influenza activity. The system signaled one week prior to the CDC peak and signaled one false positive.
Yuan et al. (2004) used the detection-algorithm method to study the timeliness of detection of one influenza outbreak in southeastern Virginia.They manually assigned chief complaints to seven syndromes (fever, respiratory distress, vomiting, diarrhea, rash, disorientation, and sepsis). The detection algorithm was CUSUM, operated at three different moving averages (7-day window, window days 3-9, and 3-day window) and set at a threshold of 3 S.D. They reported that the CUSUM algorithm detected trends in fever and respiratory in one hospital that preceded the local sentinel influenza surveillance system by one week.
A key limitation of N=1 studies is that any correlation found may be spurious. Meta analysis could address this problem if differences among analytic and reporting methods used by studies were reduced so that studies of single outbreaks could be merged analytically. In 2003, the RODS Laboratory developed a case-study series that encourages the use of a standard method of studying single outbreaks that would enable the application of uniform analytic methods across outbreaks (or alerts) occurring in different regions (Rizzo et al., 2005). The objectives of the case report series are to: (1) ensure complete description of outbreak and analytic methods, and (2) collect the raw surveillance data and information about the outbreak in a way that future re-analyses are possible.
Each case study describes the effect of a single outbreak or other public health event, such as low air quality due to forest fires, on surveillance of data available for the event. At present, these case studies are available only to authorized public health users of the NRDM system because of legal agreements with organizations that provide surveillance data (employees of governmental public health organizations can access case studies through the RODS interface by sending e-mail to [email protected]).
Of the 15 case studies developed to date, eight are examples of outbreaks considered "detectable'' from available surveillance data; six were not detectable. These case studies include outbreaks of influenza, salmonella, norovirus, rotavirus, shigella, and hepatitis A. One case study describes a false alarm investigation that resulted from a retailer recording error.
Figure 23.4 is taken from a case study of a large spike in CoCo respiratory cases in a single county outside Pittsburgh that resulted in an alert being sent automatically on Friday July 18, 2003 at 8 PM to an on-call epidemiologist. Normally, daily counts of respiratory cases numbered 10, but on that day they numbered 60 by 8 PM. The epidemiologist logged into the RODS web interface, reviewed the verbatim chief complaints of affected patients and discovered that the cases were related to carbon-monoxide exposure, which a phone call to an ED revealed to be related to a faulty furnace at a day-care center.
The case studies include three studies of the effect of influenza on emergency room visits for the CoCo constitutional and respiratory syndromes. Figure 23.5 illustrates the size of the influenza effect in 2003-2004 in Utah (middle spike) on constitutional, and respiratory, as well as sales of thermometers by pharmacies participating in the National Retail Data Monitor.
These case studies add to the previously described studies the following: Influenza has a strong early effect on free text chief complaints in the constitutional and respiratory categories. Air pollution and small carbon monoxide events may have marked effects on chief complaints in the respiratory category. The results for gastrointestinal outbreaks have been negative for relatively small, protracted outbreaks of Norovirus and Shigella.
Summary of Studies of Outbreak Detection from Chief Complaints. With respect to Hypotheses 2 and 3, the studies we reviewed demonstrate that:
1. Some large outbreaks causing respiratory, constitutional, or gastrointestinal symptoms can be detected from aggregate analysis of chief complaints. Small outbreaks of gastrointestinal illness generally cannot (Hypothesis 2).
2. Research to date is suggestive but not conclusive that influenza can be detected earlier by chief complaint monitoring than current best practice (Hypothesis 3).
3. The false-alarm rates associated with such monitoring can be low. In New York City for city-wide monitoring of respiratory and fever, there were few signals that did not correspond to disease activity. There were more signals that did not correspond to disease activity from monitoring of diarrhea and vomiting. Conversely, all of the signals from spatial monitoring of hospital or zip code were not correlated with known disease activity.
4. The methodological weaknesses in the studies included failure to describe or measure time latencies involved in data collection. Some studies did not report sampling completeness, the method by which chief complaints are parsed, or details of the syndrome categories.
5. In general, the number of published studies is small, perhaps due to the fact that chief complaint monitoring systems are still being constructed. We expect more studies to be published in the near future.
The answers to Hypotheses 2 and 3 for surveillance of chief complaints in isolation may not be as important long term as the question of whether chief complaints contain diagnostic information (Hypothesis 1).The reason is that chief complaints can be used with other surveillance data to detect outbreaks, either through linking at the level of the individual patient or as a second source of evidence. Nevertheless, because of their availability and earliness, and the threat of bioterrorism and large outbreaks, it is important to understand the ability to detect outbreaks solely from this type of data.
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