A paper by Saulsberry et al. (2023) argues that the Social Vulnerability Metric (SVM) is an enchancment over earlier social determinants of well being (SDOH) measures such because the Social Vulnerability Index (SVI). SVI makes use of census tract stage knowledge to assemble total group rankings primarily based on variables included in 4 themes:
- “Socioeconomic Standing” together with percentages under poverty, unemployed, earnings, no highschool diploma;
- “Family Composition & Incapacity” together with proportions aged 65 or older, aged 17 or youthful, civilian with a incapacity, single-parent households;
- “Minority Standing & Language” together with proportions minority and speaks English “lower than effectively,”
- “Housing Kind & Transportation” together with proportions multi-unit buildings, cellular houses, crowding, no automobile, and group quarters
I’ve summarized how SVI might be used as a part of a distributional value effectiveness evaluation (DCEA) strategy right here.
Alternatively, the Social Vulnerability Metric (SVM) proposed by Saulsberry et al. (2023) was constructed utilizing multidimensional Merchandise Response Idea (MIRT), utilizing knowledge from the Company for Healthcare Analysis and High quality (AHRQ) SDoH Database. Key variations from SVI.
- Significance somewhat than equal weighting. SVM was setting up utilizing the MIRT strategy (particularly a full-information merchandise bifactor mannequin). As in comparison with a normal latent variable regression, MIRT permits for a number of latent variable constructs. The the bifactor mannequin particularly “imposes restrictions on the standard merchandise issue evaluation by requiring that every merchandise hundreds on a major dimension (e.g., SDoH) and just one subdomain (e.g., bodily infrastructure).” The coefficients from the MIRT are used to weight variables in SVM. This strategy differs from CDC’s SVI and measures such because the Space Deprivation Index (ADI), which give equal weight to all variables,
- Unit of geography. SVM was primarily based on ZIP code stage observations whereas SVI historically makes use of census tract knowledge. Nonetheless, AHRQ stated that the their SDoH knowledge base might be up to date to report on the
county, zip code, and census tract block teams sooner or later (and SVM might be calculated for every of those).
The variables included in SVM span 5 domains.
- Demographic (e.g., age and race/ethnicity),
- Training,
- Financial context (e.g., unemployment price),
- Bodily infrastructure (e.g., housing and transportation),
- Well being care (e.g., medical health insurance protection).
Be aware that race/ethnicity just isn’t included in SVM, partially as a result of this manner SVM can be utilized to match SDoH throughout race and ethnic teams.
Outcomes
When evaluating SVM vs. SVI, SVM did a greater job of predicting all-cause, age-adjusted mortality (r=0.68 vs. r=0.34). SVM was additionally negatively correlated with receipt of a number of COVID-19 vaccinations (r = −0.68) and completion of full vaccination (r = −0.70) and positively correlated with age-adjusted ED visits for bronchial asthma for people aged 0-18 (r = 0.62) and aged 18 years and older (r = 0.60).
Whereas SVM does seem superior to SVI, it’s considerably extra difficult to create provided that weights are usually not equal, and the coefficients in reality span a number of latent variables.
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