Evidence map›Paper›PMID 40104116›Full record

ArticleFrontiers in public health2025

Validating the Social Vulnerability Index for alternative geographies in the United States to explore trends in social determinants of health over time and geographic location.

Carmen D Ng, Pluto Zhang, Stacey Kowal

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Carmen D NgGenentech, Inc., South San Francisco, CA, United States.
Pluto ZhangGenentech, Inc., South San Francisco, CA, United States.
Stacey KowalGenentech, Inc., South San Francisco, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To create county-, 5-digit ZIP code (ZIP-5)-, and 3-digit ZIP code (ZIP-3)-level datasets of the Social Vulnerability Index (SVI) and its components for 2016-2022 to validate the methodology beyond county level, explore trends in SVI over time and space, and demonstrate its usage in an enrichment exercise with health plan claims. Materials and methods: The SVI consolidates 16 structural, economic, and demographic variables from the American Community Survey (ACS) into 4 themes: socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation. ACS estimates of the 16 variables for 2016-2022 were extracted for counties and ZIP code tabulation areas and for ZIP code geographies, crosswalked to ZIP-5, and aggregated to ZIP-3. Areas received a percentile ranking (range, 0-1) for SVI and each variable and composite theme, with higher values indicating greater social vulnerability. Results: SVI estimates were produced for up to 3,143 counties, 32,243 ZIP-5s, and 886 ZIP-3s. SDoH trends across the US were largely consistent from 2016 to 2022 despite slight local changes over time. SVI varied across regions, with generally higher vulnerability in the South and lower vulnerability in the North and Northeast. When linked with health plan claims data, higher SVI (i.e., higher vulnerability) was associated with greater comorbidity burden. Conclusion: SVI can be estimated at the ZIP-3 and ZIP-5 levels to provide area-level context, allowing for more routine integration of socioeconomic and health equity-related concepts into health claims and other datasets.

Indexed as

Residence CharacteristicsSocial Determinants of HealthSocial VulnerabilityFemaleGeographyHumansMaleSocioeconomic FactorsUnited Statesgeographyhealth equityreal-world datasocial determinants of healthSocial Vulnerability Index

Identifiers

PMID40104116
PMCPMC11915720

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.