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 degree information to assemble general group rankings based mostly on variables included in 4 themes:
- “Socioeconomic Status” together with percentages beneath poverty, unemployed, earnings, no highschool diploma;
- “Household Composition & Disability” together with proportions aged 65 or older, aged 17 or youthful, civilian with a incapacity, single-parent households;
- “Minority Status & Language” together with proportions minority and speaks English “less than well,”
- “Housing Type & Transportation” together with proportions multi-unit constructions, cellular properties, crowding, no car, and group quarters
I’ve summarized how SVI might be used as a part of a distributional value effectiveness evaluation (DCEA) method right here.
On the opposite hand, the Social Vulnerability Metric (SVM) proposed by Saulsberry et al. (2023) was constructed utilizing multidimensional Item Response Theory (MIRT), utilizing information from the Agency for Healthcare Research and Quality (AHRQ) SDoH Database. Key variations from SVI.
- Importance quite than equal weighting. SVM was establishing utilizing the MIRT method (particularly a full-information merchandise bifactor mannequin). As in comparison with a regular latent variable regression, MIRT permits for a number of latent variable constructs. The the bifactor mannequin particularly “imposes restrictions on the traditional item factor analysis by requiring that each item loads on a primary dimension (e.g., SDoH) and only one subdomain (e.g., physical infrastructure).” The coefficients from the MIRT are used to weight variables in SVM. This method differs from CDC’s SVI and measures such because the Area Deprivation Index (ADI), which give equal weight to all variables,
- Unit of geography. SVM was based mostly on ZIP code degree observations whereas SVI historically makes use of census tract information. However, AHRQ stated that the their SDoH information 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),
- Education,
- Economic context (e.g., unemployment price),
- Physical infrastructure (e.g., housing and transportation),
- Health care (e.g., medical health insurance protection).
Note that race/ethnicity shouldn’t be included in SVM, partially as a result of this manner SVM can be utilized to match SDoH throughout race and ethnic teams.
Results
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).
While SVM does seem superior to SVI, it’s considerably extra difficult to create provided that weights usually are not equal, and the coefficients in reality span a number of latent variables.
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