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	Social Vulnerability Index	Small-Area Health Estimation
Ämnesområde	Social Epidemiology	Social Epidemiology
Familj	Process / pipeline	Regression model
Ursprungsår	2011	1979
Upphovsperson	Barry Flanagan et al. (CDC/ATSDR); Susan Cutter, Bryan Boruff & W. Lynn Shirley (SoVI)	Robert E. Fay & Roger A. Herriot; J. N. K. Rao & Isabel Molina
Typ	Composite percentile-rank index of community social vulnerability	Model-based estimator for reliable indicators in data-sparse areas
Ursprungskälla	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.	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.
Alias	SVI, CDC SVI, CDC/ATSDR Social Vulnerability Index, Community Vulnerability Index	Small Area Estimation for Health, Fay-Herriot Health Estimation, Model-Based Small-Area Prevalence, Local Health Indicator Estimation
Närliggande	4	3
Sammanfattning	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.	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.