Compare methods

	Multilevel Neighborhood Effects	Small-Area Health Estimation
Field	Social Epidemiology	Social Epidemiology
Family	Regression model	Regression model
Year of origin	2000	1979
Originator	Ana V. Diez Roux; Juan Merlo and colleagues	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
Type	Hierarchical regression model for contextual effects on individual health	Model-based estimator for reliable indicators in data-sparse areas
Seminal source	Diez Roux, A. V. (2000). Multilevel Analysis in Public Health Research. Annual Review of Public Health, 21, 171-192.	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	Contextual Effects Models, Hierarchical Neighborhood Health Models, Multilevel Analysis of Place and Health, Variance Partition / MOR Analysis	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
Related	3	3
Summary	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.	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.