Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks (Preprint)

Abstract

UNSTRUCTURED Staff at public health departments have few training materials to learn how to design and finetune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease (BCD) at the New York City Department of Health and Mental Hygiene has conducted daily analyses of reportable communicable diseases using SaTScan.™ SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period or geographic location or size. BCD’s systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network locations file setup to account for natural boundaries, probability model (e.g., space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters vs. ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (e.g., persons experiencing homelessness who are unsheltered), and by accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to finetune the system when the detected clusters are too large to be of interest, or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (e.g., patient line lists, temporal graphs, and interactive maps), which became newly available with the July 2022 release of SaTScan v10.1. We explain how to extend the system to detect drop-offs in laboratory reporting, a type of data quality issue. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations, as well as to develop intuition for interpreting results and finetuning the system. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.