Compare methods

	Spatial Scan Statistic	Poisson Rate Regression
Field	Spatial Epidemiology	Social Epidemiology
Family	Process / pipeline	Regression model
Year of origin	1997	1983
Originator	Martin Kulldorff (with Neville Nagarwalla)	E. L. Frome (rate formulation); A. C. Cameron & P. K. Trivedi (modern count-data treatment)
Type	Likelihood-ratio scanning procedure for detecting and testing geographic disease clusters	Generalized linear model for event rates and counts with log link and person-time offset
Seminal source	Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481-1496.	Frome, E. L. (1983). The Analysis of Rates Using Poisson Regression Models. Biometrics, 39(3), 665-674.
Aliases	Kulldorff Scan Statistic, SaTScan Cluster Detection, Circular Scan Statistic, Spatial Likelihood-Ratio Scan	Poisson Regression for Rates, Log-Linear Rate Model, Incidence-Rate-Ratio Regression, Poisson Regression with Offset
Related	4	3
Summary	The spatial scan statistic is a likelihood-ratio method for detecting localized clusters of disease without pre-specifying where they are. Introduced by Martin Kulldorff and Neville Nagarwalla (1995) and generalized by Kulldorff (1997), it slides a circular window of varying size and position across the study region, and for each candidate window compares the observed-to-expected case ratio inside the window against outside it using a likelihood ratio under a Poisson or Bernoulli model. The window that maximizes the likelihood ratio is the most likely cluster, and its statistical significance is obtained by Monte Carlo simulation under the null of no clustering, which correctly accounts for the enormous multiplicity of windows examined. Implemented in the widely used SaTScan software, the method has become the standard tool for screening surveillance data for spatial and space-time disease clusters.	Poisson rate regression is the standard generalized linear model for analyzing event rates and counts, such as the number of deaths, hospitalizations, or new cases observed over a span of person-time. It models the logarithm of the expected event rate as a linear function of covariates, using a Poisson likelihood and a log link, and accommodates differing amounts of exposure by including the log of person-time as an offset. Because coefficients enter on the log scale, their exponentials are incidence-rate ratios that quantify multiplicative effects on the rate. The rate formulation was crystallized in Frome's 1983 Biometrics paper, and the model sits within the broader count-data framework developed comprehensively by Cameron and Trivedi, who also detail its central practical concern: overdispersion, where the variance exceeds the Poisson assumption and standard errors must be corrected.