Based on the six criteria we propose, we have attempted to construct a time-series syndromic surveillance system capable of detecting a bioterrorism or other public health event against the background of normal ILI clinic visits. Patient use patterns and seasonality have a considerable effect on the distribution of the dataset, an effect that must be considered when designing the autoregressive model.
Because the HPMG network offers same-day scheduling for its members, many patients do not seek care on the weekend, when only urgent care facilities are open. This delay results in an increased caseload on Monday, a situation that is further exacerbated on a 3-day weekend. The distribution of data is also affected by limited clinic access associated with holidays. The HPMG clinic network operates at a reduced capacity on New Year's Day, Memorial Day, Independence Day, Labor Day, Thanksgiving Day, Christmas Eve Day, and Christmas Day. These holidays often occur on different days of the week from year to year, and therefore generate lower-than-expected counts in the dataset. Additionally, ILI events occur with greater frequency in the winter, which generates a seasonal effect associated with the HPMG ICD-9 data.
shows general agreement between the distribution of ILI in the HPMG clinic network and influenza and pneumonia deaths in the greater metropolitan area during the same period. In the Minneapolis-St. Paul metropolitan area, a lag of 1 to 2 weeks occurs between time of initial signs and symptoms for ILI in HPMG clinics and an increase in influenza and pneumonia related deaths. This lag is less than that noted in other studies (27
Influenza season in Minnesota is variable; onset ranges from early October through mid-January. illustrates a large, sustained increase of ILI beginning December 12, 2000. The Minnesota Department of Health Public Health Laboratory confirmed the season's first positive influenza isolate on December 13, 2000. This signal suggests that the rapid detection of ILI in the community is attainable by monitoring ICD-9 counts representative of ILI in a clinic network. When persons >65 years of age were separated into a distinct ILI syndrome category, a statistically significant signal is observed from November 18 to November 20. This increase in the >65-year category precedes the relatively large signal in the general population by approximately 3 weeks, demonstrating the utility of analyzing subsets of the patients as possible sentinel populations.
The ability of the system to detect additional bioterrorism-related cases is apparent in the hypothetical scenarios illustrated in , , and . When background levels of ILI are relatively low, the system quickly detected additional cases associated with the anthrax release. At best, the system detected the outbreak only 2 days after the first case-patients began to visit the clinics. In winter months, when background ILI is higher, the system was slower to detect the outbreak-associated cases. In December 2001, a 5-day delay occurred between the appearance of symptomatic patients to the clinics and the recognition of the outbreak by the system. Twenty-five additional patients were seen at clinics on December 24, 2001, a holiday, and the system calculated a significant CUSUM alarm of 4.48. The ability of this system to detect the outbreak-associated cases at different times of the year, on weekends, and on holidays shows that the autoregressive model adequately controls for variance and autocorrelation in the dataset.
These scenarios demonstrate that the system possesses the ability to detect the cumulative sum of a small amount of additional counts. The practical success of this surveillance system is limited only by the availability and quality of the source data.