방법 비교

	Disease Mapping	Indirect Age Standardization
분야	Spatial Epidemiology	Social Epidemiology
계열	Process / pipeline	Process / pipeline
기원 연도	1987	2001
창시자	David Clayton & Jack Kaldor (empirical Bayes); Andrew Lawson (Bayesian hierarchical synthesis)	Classical demography / vital statistics (formalized in Preston, Heuveline & Guillot)
유형	Pipeline for estimating and smoothing small-area disease relative risk from counts	Rate-standardization pipeline for comparing populations under unstable stratum rates
원전	Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43(3), 671-681.	Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell Publishers. ISBN: 9781557864512
별칭	Small-Area Risk Mapping, Relative-Risk Smoothing, Empirical Bayes Disease Mapping, Spatial Risk Estimation	Indirect Standardization, Standardized Mortality Ratio (SMR), Indirectly Standardized Rate, SMR Method
관련	4	3
요약	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.	Indirect age standardization is a demographic technique for comparing the overall event rate (most often mortality) of a study population to a reference, when the population's own age-specific rates are too sparse or unstable to standardize directly. Instead of applying the study population's rates to a standard age structure, it does the reverse: it applies a stable set of standard age-specific rates to the study population's age distribution to compute the number of events that would be expected under the standard schedule. The ratio of observed to expected events is the standardized mortality (or morbidity) ratio, the SMR, and multiplying it by the standard's crude rate yields an indirectly standardized rate. The method is a staple of vital statistics and occupational and small-area epidemiology, and is developed from first principles in Preston, Heuveline and Guillot's demography text.