Confronta i metodi

	Social Vulnerability Index	Multilevel Neighborhood Effects
Campo	Social Epidemiology	Social Epidemiology
Famiglia	Process / pipeline	Regression model
Anno di origine	2011	2000
Ideatore	Barry Flanagan et al. (CDC/ATSDR); Susan Cutter, Bryan Boruff & W. Lynn Shirley (SoVI)	Ana V. Diez Roux; Juan Merlo and colleagues
Tipo	Composite percentile-rank index of community social vulnerability	Hierarchical regression model for contextual effects on individual health
Fonte seminale	Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A Social Vulnerability Index for Disaster Management. Journal of Homeland Security and Emergency Management, 8(1), Article 3.	Diez Roux, A. V. (2000). Multilevel Analysis in Public Health Research. Annual Review of Public Health, 21, 171-192.
Alias	SVI, CDC SVI, CDC/ATSDR Social Vulnerability Index, Community Vulnerability Index	Contextual Effects Models, Hierarchical Neighborhood Health Models, Multilevel Analysis of Place and Health, Variance Partition / MOR Analysis
Correlati	4	3
Sintesi	The Social Vulnerability Index (SVI) measures how vulnerable a community is to the harmful effects of disasters and public-health emergencies, based on the social and economic characteristics of the people who live there. The CDC/ATSDR version, introduced by Flanagan and colleagues in 2011, percentile-ranks census variables, groups them into themes (socioeconomic status, household composition and disability, racial and ethnic minority status and language, and housing type and transportation), and aggregates them into an overall ranking for each census tract or county. It builds on the broader social-vulnerability concept developed by Cutter, Boruff, and Shirley, whose 2003 Social Vulnerability Index to environmental hazards (SoVI) used factor analysis to show that susceptibility to disaster losses is socially patterned. The SVI is widely used to plan disaster response, allocate resources, and target public-health interventions toward the communities least able to cope.	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.