The use of SatScan software to map spatiotemporal trends and detect disease clusters: a systematic review

Low and Middle-Income Countries (LMICs) often experience a disproportionate burden in health issues. One public health, epidemiology, and spatial statistics software tool has emerged as a stalwart for detecting disease clusters, mapping spatiotemporal trends, and analyzing health-related data-SatScan.

This systematic review aims to provide a comprehensive overview of the extent of the use of spatiotemporal analysis, namely the use of SatScan for understanding health inequalities within LMICs within space and time parameters, shedding light on its potential to inform evidence-based public health interventions and policies. A systematic search was conducted in six electronic databases: PubMed, ScienceDirect, Web of Science, Cochrane, Scopus, and Embase. It included all human health-related articles, looking into data from LMICs. A descriptive analysis and quality assessment of the articles was performed.

Out of 5215 articles from different databases, 719 are included. Over 516 articles include themes on communicable diseases and over 50% of the articles come from China, Ethiopia, and Brazil. The Poisson-based model is the most commonly used model type, and more than 85% use secondary data sources, with the Demographic Health Surveys datasets being the most used.

This systematic review allows us to understand which areas have been studied and which LMICs have developed research. This helps us detect health issues that have been neglected and the countries which require additional resources to increase their research capacities in this domain.