방법 비교

	Spatial Scan Statistic	Small-Area Health Estimation
분야	Spatial Epidemiology	Social Epidemiology
계열	Process / pipeline	Regression model
기원 연도	1997	1979
창시자	Martin Kulldorff (with Neville Nagarwalla)	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
유형	Likelihood-ratio scanning procedure for detecting and testing geographic disease clusters	Model-based estimator for reliable indicators in data-sparse areas
원전	Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481-1496.	Fay, R. E., & Herriot, R. A. (1979). Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data. Journal of the American Statistical Association, 74(366), 269-277.
별칭	Kulldorff Scan Statistic, SaTScan Cluster Detection, Circular Scan Statistic, Spatial Likelihood-Ratio Scan	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
관련	4	3
요약	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.	Small-area estimation produces reliable health indicators for places where the survey sample is too thin to support a trustworthy direct estimate. A national health survey may interview only a handful of people in a given county or census tract, so a county-level prevalence computed straight from the data swings wildly from area to area. The model-based solution, pioneered by Robert Fay and Roger Herriot in 1979 for estimating income in small places, is to borrow strength: combine each area's noisy direct estimate with a regression prediction built from auxiliary variables that are known for every area, weighting the two by their relative reliability. Rao and Molina's comprehensive treatment codified this area-level mixed model and its variants as the foundation of small area estimation. Applied to public health, the approach underpins local prevalence maps for chronic disease and health behaviors, such as the CDC PLACES project, that decision-makers use to target resources at neighborhood and county scale.