Compare methods

	Indirect Age Standardization	Small-Area Health Estimation
Field	Social Epidemiology	Social Epidemiology
Family	Process / pipeline	Regression model
Year of origin	2001	1979
Originator	Classical demography / vital statistics (formalized in Preston, Heuveline & Guillot)	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
Type	Rate-standardization pipeline for comparing populations under unstable stratum rates	Model-based estimator for reliable indicators in data-sparse areas
Seminal source	Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell Publishers. ISBN: 9781557864512	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.
Aliases	Indirect Standardization, Standardized Mortality Ratio (SMR), Indirectly Standardized Rate, SMR Method	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
Related	3	3
Summary	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.	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.