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The social vulnerability metric (SVM) as a new tool for public health
Authors:Loren Saulsberry PhD  Ankur Bhargava MD  MPH  Sharon Zeng BA  Jason B Gibbons PhD  Cody Brannan MS  Diane S Lauderdale PhD  Robert D Gibbons PhD
Institution:1. Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA;2. Department of Pediatrics, The University of Chicago, Chicago, Illinois, USA;3. Pritzker School of Medicine, The University of Chicago, Chicago, Illinois, USA;4. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA;5. Center for Health Statistics, The University of Chicago, Chicago, Illinois, USA;6. Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA

Center for Health Statistics, The University of Chicago, Chicago, Illinois, USA

Department of Medicine, The University of Chicago, Chicago, Illinois, USA

Abstract:

Objective

To derive and validate a new ecological measure of the social determinants of health (SDoH), calculable at the zip code or county level.

Data Sources and Study Setting

The most recent releases of secondary, publicly available data were collected from national U.S. health agencies as well as state and city public health departments.

Study Design

The Social Vulnerability Metric (SVM) was constructed from U.S. zip-code level measures (2018) from survey data using multidimensional Item Response Theory and validated using outcomes including all-cause mortality (2016), COVID-19 vaccination (2021), and emergency department visits for asthma (2018). The SVM was also compared with the existing Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) to determine convergent validity and differential predictive validity.

Data Collection/Extraction Methods

The data were collected directly from published files available to the public online from national U.S. health agencies as well as state and city public health departments.

Principal Findings

The correlation between SVM scores and national age-adjusted county all-cause mortality was r = 0.68. This correlation demonstrated the SVM's robust validity and outperformed the SVI with an almost four-fold increase in explained variance (46% vs. 12%). The SVM was also highly correlated (r ≥ 0.60) to zip-code level health outcomes for the state of California and city of Chicago.

Conclusions

The SVM offers a measurement tool improving upon the performance of existing SDoH composite measures and has broad applicability to public health that may help in directing future policies and interventions. The SVM provides a single measure of SDoH that better quantifies associations with health outcomes.
Keywords:biostatistical methods  determinants of health/population health/socioeconomic causes of health  health care disparities  health equity  health policy  social determinants of health
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