Assessment of Population-Level Disadvantage Indices to Inform Equitable Health Policy

Abstract

Policy Points The rapid uptake of disadvantage indices during the pandemic highlights investment in implementing tools that address health equity to inform policy. Existing indices differ in their design, including data elements, social determinants of health domains, and geographic unit of analysis. These differences can lead to stark discrepancies in place-based social risk scores depending on the index utilized. Disadvantage indices are useful tools for identifying geographic patterns of social risk; however, indiscriminate use of indices can have varied policy implications and unintentionally worsen equity. Implementers should consider which indices are suitable for specific communities, objectives, potential interventions, and outcomes of interest.

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 (r_s 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.

Figure 1

Social determinants of health represented across illustrative disadvantage indices. [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 2

Maps comparing the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), COVID‐19 Community Vulnerability Index (CCVI), and the Child Opportunity Index (COI) in Durham County, NC; Cook County, IL; and Orleans Parish, LA. [Colour figure can be viewed at wileyonlinelibrary.com] Increasing levels of disadvantage are indicated in red (higher scores for ADI, SVI, CCVI and lower scores for COI). Because indices include diverse factors to summarize social risk at differing geographic levels in some cases, different indices can show varied patterns of social vulnerability within the same city. The ADI produces the most granular results as it is measured at the block group level, compared to other indices at the census tract level. The CCVI differs from the SVI in including data about health conditions and health care infrastructure. While the SVI and CCVI show similar results in Durham, the patterns of risk in Cook County and New Orleans Parish differ between the two indices. The COI, which aims to map childhood opportunity, shows substantially different patterns than other indices. For example, in Cook County, the COI identifies much larger regions of disadvantage (indicated by lower child opportunity) than all other indices. Gray areas indicate missing data.

Figure 2

Maps comparing the Area Deprivation Index (ADI), Social Vulnerability Index (SVI), COVID‐19 Community Vulnerability Index (CCVI), and the Child Opportunity Index (COI) in Durham County, NC; Cook County, IL; and Orleans Parish, LA. [Colour figure can be viewed at wileyonlinelibrary.com] Increasing levels of disadvantage are indicated in red (higher scores for ADI, SVI, CCVI and lower scores for COI). Because indices include diverse factors to summarize social risk at differing geographic levels in some cases, different indices can show varied patterns of social vulnerability within the same city. The ADI produces the most granular results as it is measured at the block group level, compared to other indices at the census tract level. The CCVI differs from the SVI in including data about health conditions and health care infrastructure. While the SVI and CCVI show similar results in Durham, the patterns of risk in Cook County and New Orleans Parish differ between the two indices. The COI, which aims to map childhood opportunity, shows substantially different patterns than other indices. For example, in Cook County, the COI identifies much larger regions of disadvantage (indicated by lower child opportunity) than all other indices. Gray areas indicate missing data.

Figure 3

Spatial autocorrelation comparing the ADI, SVI, CCVI, and COI in Durham County, NC; Cook County, IL; and Orleans Parish, LA. [Colour figure can be viewed at wileyonlinelibrary.com] Global Moran's I test was significant (p<0.05) across all indices. Disadvantage and social risk were not randomly distributed. Positive values for local Moran's I statistic indicate areas of clustering, where adjacent neighborhoods demonstrate similar levels of disadvantage.