Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): the example of lower respiratory infection

Abstract

Background: Gaps in disease surveillance capacity, particularly for emerging infections and bioterrorist attack, highlight a need for efficient, real time identification of diseases.

Methods: We studied automated records from 1996 through 1999 of approximately 250,000 health plan members in greater Boston.

Results: We identified 152,435 lower respiratory infection illness visits, comprising 106,670 episodes during 1,143,208 person-years. Three diagnoses, cough (ICD9CM 786.2), pneumonia not otherwise specified (ICD9CM 486) and acute bronchitis (ICD9CM 466.0) accounted for 91% of these visits, with expected age and sex distributions. Variation of weekly occurrences corresponded closely to national pneumonia and influenza mortality data. There was substantial variation in geographic location of the cases.

Conclusion: This information complements existing surveillance programs by assessing the large majority of episodes of illness for which no etiologic agents are identified. Additional advantages include: a) sensitivity, uniformity and efficiency, since detection of events does not depend on clinicians' to actively report diagnoses, b) timeliness, the data are available within a day of the clinical event; and c) ease of integration into automated surveillance systems. These features facilitate early detection of conditions of public health importance, including regularly occurring events like seasonal respiratory illness, as well as unusual occurrences, such as a bioterrorist attack that first manifests as respiratory symptoms. These methods should also be applicable to other infectious and non-infectious conditions. Knowledge of disease patterns in real time may also help clinicians to manage patients, and assist health plan administrators in allocating resources efficiently.

Figure 1

Frequency distribution of intervals between multiple LRI encounters for individual members with more than one encounter over the study period.

Figure 2

Incidence rate (events/person-year) of lower respiratory infection episodes diagnosed in ambulatory care records.

Figure 3

Weekly counts of pneumonia and influenza deaths reported to CDC from U.S. cities, including greater Boston (dashed black line, left hand vertical axis) and lower respiratory infection episodes diagnosed in greater Boston ambulatory settings (solid red line, right hand vertical axis).

Figure 4

Daily counts of lower respiratory infection episodes. The left hand panel shows all years' data. Counts for Monday through Friday are shown in black; counts for Saturdays and Sundays are shown in red. The right hand panel shows the same data for a single month.

Figure 5

Distribution of lower respiratory infection episodes in time and space. Each episode is mapped to the census block group in which the individual resided. Four durations are shown: 4 years (upper left), one month (upper right), one week (lower left), and a single day (lower right). Note that scale of the vertical axis is different for the upper left panel, compared to the other three. The x-axis is longitude, and the y-axis is latitude. The area with no episodes on the right border of each plot is water. Counts are shown rather than rates to correspond to the data shown in Figures 3 and 4. Variations between census tracts are principally due to the distribution of health plan members' residence locations.