A model-adjusted space-time scan statistic with an application to syndromic surveillance

Area of Science:

• Epidemiology
• Biostatistics
• Public Health Surveillance

Background:

• The space-time scan statistic is a common tool for detecting disease clusters.
• Existing methods often fail to account for inherent temporal and geographical patterns in illness data.
• This limitation can lead to inaccurate identification of disease outbreaks.

Purpose of the Study:

• To introduce and evaluate a method for adjusting the space-time scan statistic.
• To account for naturally occurring temporal trends and geographical patterns in disease incidence.
• To improve the practicality and accuracy of disease cluster surveillance.

Main Methods:

• Applied the space-time scan statistic to lower respiratory complaints data from a large group practice.
• Compared unadjusted populations (census, group-practice membership) with adjusted populations.
• Adjustments incorporated day of week, month, holidays, and local illness history.

Main Results:

• Incident clusters were identified on 26%, 22%, 4%, and 2% of days for the four respective populations at a 5% false detection rate.
• Unadjusted methods (census, membership) resulted in impractical numbers of detected clusters.
• The proposed adjustment significantly reduced false positives, enhancing surveillance utility.

Conclusions:

• Accounting for natural temporal and geographic trends is essential for accurate space-time scan statistic application.
• Unadjusted approaches are impractical for public health surveillance due to high false detection rates.
• The developed adjustment method enables practical and reliable disease surveillance.