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December 2022 (Volume 100)
Quarterly Article
Kamaria Kaalund
Andrea Thoumi
Nrupen A. Bhavsar
Amy Labrador
Rushina Cholera
Mar 11, 2024
March 2024
Back to The Milbank Quarterly
Policy Points:
Context: There has been unprecedented uptake of disadvantage indices such as the Centers for Disease Control and Prevention Social Vulnerability Index (SVI) to identify place-based patterns of social risk and guide equitable health policy during the COVID-19 pandemic. However, limited evidence around data elements, interoperability, and implementation leaves unanswered questions regarding the utility of indices to prioritize health equity.
Methods: We identified disadvantage indices that were (a) used three or more times from 2018 to 2021, (b) designed using national-level data, and (c) available at the census-tract or block-group level. We used a network visualization to compare social determinants of health (SDOH) domains across indices. We then used geospatial analyses to compare disadvantage profiles across indices and geographic areas.
Findings: We identified 14 indices. All incorporated data from public sources, with half using only American Community Survey data (n 7) and the other half combining multiple sources (n 7). Indices differed in geographic granularity, with county level (n 5) and census-tract level (n 5) being the most common. Most states used the SVI during the pandemic. The SVI, the Area Deprivation Index (ADI), the COVID-19 Community Vulnerability Index (CCVI), and the Child Opportunity Index (COI) met criteria for further analysis. Selected indices shared five indicators (income, poverty, English proficiency, no high school diploma, unemployment) but varied in other metrics and construction method. While mapping of social risk scores in Durham County, North Carolina; Cook County, Illinois; and Orleans Parish, Louisiana, showed differing patterns within the same locations depending on choice of disadvantage index, risk scores across indices showed moderate to high correlation (rs 0.7-1). However, spatial autocorrelation analyses revealed clustering, with discrepant distributions of social risk scores between different indices.
Conclusions: Existing disadvantage indices use varied metrics to represent place-based social risk. Within the same geographic area, different indices can provide differences in social risk values and interpretations, potentially leading to varied public health or policy responses.
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