New SVM tool could be a game-changer for measuring social vulnerability in healthcare

Research just released in the August, 2023 edition of the journal HEALTH SERVICES RESEARCH demonstrates a new tool called Social Vulnerability Metric (SVM). The research shows that this new tool, utilizing zip-code level data and Multidimensional Item Response Theory (MIRT), appears to offer meaningful improvement over existing SDoH composite measures.

The shocking headline today is that life expectancy in the US has declined for the third year in a row, driven by “deaths of despair” from drug overdoses, suicides, and alcoholism among less educated middle-aged Americans. At the same time, the gaps in life expectancy between the most and least advantaged communities have widened dramatically.

A systematic review of the literature describing “the construction of geographic indices of socioeconomic disadvantage” shows that for many years, the US is behind European nations to accurately demonstrate high need populations. These alarming trends highlight the urgent need for policy makers and health systems to accurately identify and assist the most socially vulnerable populations being left behind. But the current methods may be missing the mark.

In the research, the SVM tool provided a more precise measurement of social vulnerability—across geographic areas and health metrics. And the new tool excludes race or ethnicity in both its model creation and scoring.

According to the study:

“Race and ethnicity are not a part of the SVM score. However, this is also a strength of the SVM when assessing SDoH. Given the high level of collinearity between health-related social needs and race/ethnicity, the SVM was developed to be a metric that could focus specifically on SDoH independently of race/ethnicity.”
  •  The widely used Social Vulnerability Index (SVI) from the CDC explains only 12% of differences in death rates across US counties. But the researchers demonstrated the Social Vulnerability Metric (SVM) boosting its metric correlation to 46% – nearly a four-fold increase! 
More specifics on the all-cause age-adjusted mortality results are here.


Social Vulnerability Metric (SVM) and the Center for Disease Control and Prevention Social Vulnerability Index correlations with all-cause age-adjusted mortality. r, correlation coefficient; SVI, Social Vulnerability Index; SVM, Social Vulnerability Metric. Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research data all-cause age-adjusted mortality.


Summary of how the SVM was developed and validated:

•  The SVM was derived from 94 social determinants of health (SDoH) variables in the AHRQ SDoH Database spanning 5 key domains: demographic, education, economic, infrastructure, and healthcare.

•  It was constructed using a multivariate item response theory (MIRT) model called the bifactor model, which allows integrating multiple factors relevant to SDoH into a single overall score. And unlike the SVI and its measures —such as Area Deprivation Index (ADI)—it does not give equal weight to all variables

•  The model was calibrated using data on over 33,000 zip code tabulation areas (ZCTAs) in the US.  

•  The final calibrated model included 24 SDoH variables with factor loadings indicating their correlation with overall social vulnerability.

•  The SVM score represents the primary dimension integrating all the SDoH factors and subdomains. Higher scores indicate greater social vulnerability.

•  The SVM was validated by testing its association with county-level mortality data and zip code-level COVID-19 and asthma outcomes in multiple states.


The authors share that by using Zip Code Tabulation Area (ZCTA), the SVM provides an estimate of social determinants of health that can be determined as a single score. It allows for more precise measurement of disadvantage/vulnerability.

More accurate tools to identify high-risk groups will serve to strengthen health equity and drive those most vulnerable to community resources. SDoH measurament is extremely important for understanding health disparities, developing effective interventions, and improving research.

The SVM shows promise to provide a major leap forward, but also reveals how far we still have to go. Nearly half of differences in health outcomes remain unexplained, suggesting there are critical social factors we still don’t measure well.

As policy makers and health systems work urgently to tackle worsening health inequities, they need the best possible data. We cannot afford to leave people behind simply because we failed to see them. 

ABOUT:   Steve Ambrose helps healthcare and health IT clients by improving target market awareness through senior-level content creation and strategy. His written, video, and audio content assets have been recommended by C-suite leaders and help companies in their sales, marketing, and product efforts.

Learn more how he helps clients here.

Reach him at: steveambroseUCLA[at]

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