Compară metode

	Area Deprivation Index	Small-Area Health Estimation
Domeniu	Social Epidemiology	Social Epidemiology
Familie	Process / pipeline	Regression model
Anul apariției	2003	1979
Autorul original	Gopal K. Singh; Amy J. H. Kind & William R. Buckingham (Neighborhood Atlas)	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
Tip	Composite area-level socioeconomic deprivation index	Model-based estimator for reliable indicators in data-sparse areas
Sursa seminală	Singh, G. K. (2003). Area Deprivation and Widening Inequalities in US Mortality, 1969-1998. American Journal of Public Health, 93(7), 1137-1143.	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.
Denumiri alternative	ADI, Neighborhood Deprivation Index, Singh Area Deprivation Index, Neighborhood Atlas ADI	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
Înrudite	4	3
Rezumat	The Area Deprivation Index (ADI) summarizes the socioeconomic disadvantage of a small geographic area, such as a census block group, into a single rankable score built from census indicators of income, education, employment, and housing. Gopal Singh constructed the modern US version in 2003, combining seventeen census measures with factor-analytic weights to show that area deprivation gradients in US mortality widened substantially between 1969 and 1998. Amy Kind and William Buckingham later made the index broadly usable through the Neighborhood Atlas, which publishes ADI rankings (national percentiles and state deciles) at the block-group level so researchers and clinicians can attach a neighborhood-disadvantage value to any address. The ADI sits alongside relatives such as the British Townsend and Carstairs indices in a family of composite area-deprivation measures.	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.