Detection of Outbreaks

This section describes studies that address Hypotheses 2 and 3, which we reproduce here for convenient reference:

Hypothesis 2: When aggregated with the chief complaints of other patients in a region, chief complaints can discriminate between whether there is an outbreak of type Y or not.

Hypothesis 3: When aggregated with the chief complaints of other patients in a region, algorithmic monitoring of chief complaints can detect an outbreak of type Y earlier than current best practice (or some other reference method).

As discussed in Chapter 20, studies of outbreak detection are more difficult to conduct than studies of syndrome (case) detection. These studies require chief complaint data collected from multiple healthcare facilities in a region affected by an outbreak. Research groups often expend significant time and effort building biosurveillance systems to collect the chief complaint data needed to conduct this type of research. Because outbreaks are rare events, many of the studies we will discuss are of common diseases such as influenza or of seasonal outbreaks (winter) that may be caused by multiple organisms.

Adequate sample size (of outbreaks) is difficult to achieve in studies of outbreak detection. Only one of the studies computed a confidence interval on its measurements of sensitivity or time of outbreak detection. The scientific importance of adequate sample size cannot be overstated. There are two possible approaches to increasing sample size: (1) conduct research in biosurveillance systems that span sufficiently large geographic regions that they are expected to encounter sufficient numbers of outbreaks, or (2) meta-analysis. To emphasize the importance of adequate sample size, we divide this section into studies of multiple outbreaks (labeled N>1), prospective studies, and studies of single outbreaks (N=1).

N>1 Studies. Ivanov and colleagues used the detection-algorithm method and correlation analysis to study detection of six seasonal outbreaks in children from CoCo classifications of patients into respiratory and gastrointestinal based on chief complaints (Ivanov et al., 2003). They studied the daily visits to ED of a pediatric hospital during annual winter outbreaks due to diseases such as rotavirus, gastroenteritis and influenza for the four-year period 1998-2001.

The researchers identified outbreaks for study using the following procedure: They created two ICD-9 code sets, corresponding to infectious diseases of children that are respiratory or gastrointestinal, and used them to create two reference time series from ICD9-coded hospital discharge diagnoses. Figure 23.3 (top) from the publication is a plot of the daily respiratory time series and the reference time series of respiratory disease created from hospital discharge diagnoses of children under age five. Figure 23.3 (bottom) is a similar comparison of gastrointestinal time series and the reference time series of infectious gastrointestinal conditions.

The detection algorithm (exponentially weighted moving average) identified three respiratory and three gastrointestinal outbreaks in the hospital discharge data (the reference standard

6 Even were the specificity to be 100%, the diagnostic precision of the syndrome categories studied is low. As a result, the syndromes used in research have many causes other than acute infectious diseases, which are the diseases of interest in biosurveillance. When monitoring aggregate daily counts, even a syndrome with 100% specificity will show baseline levels of counts in the absence of an outbreak due to the presence of these chronic and sporadic conditions.

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