Linganisha mbinu

	Area Deprivation Index	Multilevel Neighborhood Effects
Nyanja	Social Epidemiology	Social Epidemiology
Familia	Process / pipeline	Regression model
Mwaka wa asili	2003	2000
Mwanzilishi	Gopal K. Singh; Amy J. H. Kind & William R. Buckingham (Neighborhood Atlas)	Ana V. Diez Roux; Juan Merlo and colleagues
Aina	Composite area-level socioeconomic deprivation index	Hierarchical regression model for contextual effects on individual health
Chanzo asilia	Singh, G. K. (2003). Area Deprivation and Widening Inequalities in US Mortality, 1969-1998. American Journal of Public Health, 93(7), 1137-1143.	Diez Roux, A. V. (2000). Multilevel Analysis in Public Health Research. Annual Review of Public Health, 21, 171-192.
Majina mbadala	ADI, Neighborhood Deprivation Index, Singh Area Deprivation Index, Neighborhood Atlas ADI	Contextual Effects Models, Hierarchical Neighborhood Health Models, Multilevel Analysis of Place and Health, Variance Partition / MOR Analysis
Zinazohusiana	4	3
Muhtasari	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.	Multilevel models of neighborhood effects estimate how the places people live shape their health, over and above who those people are. Individuals are nested within neighborhoods, so their outcomes are not independent: residents of the same area share an environment and tend to be more alike than residents drawn at random. Ana Diez Roux's foundational synthesis showed that ordinary single-level regression ignores this clustering and conflates contextual effects (features of the place) with compositional effects (the mix of people in it), whereas a hierarchical model with neighborhood random effects separates the two. Juan Merlo and colleagues turned the method into an epidemiological toolkit by reframing the random-effect variance as substantively interpretable measures of variation, such as the variance partition coefficient and the median odds ratio, so that a study can report not only whether a neighborhood characteristic matters on average but how much of the health difference between people is attributable to where they live.