Bandingkan metode

	Spatial Scan Statistic	Disease Mapping
Bidang	Spatial Epidemiology	Spatial Epidemiology
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1997	1987
Pencetus	Martin Kulldorff (with Neville Nagarwalla)	David Clayton & Jack Kaldor (empirical Bayes); Andrew Lawson (Bayesian hierarchical synthesis)
Tipe	Likelihood-ratio scanning procedure for detecting and testing geographic disease clusters	Pipeline for estimating and smoothing small-area disease relative risk from counts
Sumber perintis	Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481-1496.	Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43(3), 671-681.
Alias	Kulldorff Scan Statistic, SaTScan Cluster Detection, Circular Scan Statistic, Spatial Likelihood-Ratio Scan	Small-Area Risk Mapping, Relative-Risk Smoothing, Empirical Bayes Disease Mapping, Spatial Risk Estimation
Terkait	4	4
Ringkasan	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.	Disease mapping is the set of model-based methods for estimating and displaying the geographic distribution of disease risk across small areas. Its central problem is that raw area-level rates, especially standardized mortality or incidence ratios, are statistically unstable where populations are small: a handful of cases can produce wildly high or low rates that reflect chance rather than true risk. Clayton and Kaldor's 1987 empirical-Bayes paper showed how to stabilize these estimates by shrinking each area's rate toward an overall mean using a Poisson-gamma (or log-normal) hierarchical model, and the approach was developed into the fully Bayesian, spatially smoothed hierarchical framework synthesized in Lawson's textbook. As a pipeline, disease mapping computes expected counts, places the counts in a hierarchical risk model, borrows strength globally and across neighbors to smooth the estimates, and produces a risk map with quantified uncertainty, including probabilities that risk exceeds a threshold.