Promoting Latinx health equity through community-engaged policy and practice reforms in North Carolina.

Introduction: The Latinx Advocacy Team & Interdisciplinary Network for COVID-19 (LATIN-19) is a unique multi-sector coalition formed early in the COVID-19 pandemic to address the multi-level health inequities faced by Latinx communities in North Carolina. Methods: We utilized the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework to conduct a directed content analysis of 58 LATIN-19 meeting minutes from April 2020 through October 2021. Application of the NIMHD Research Framework facilitated a comprehensive assessment of complex and multidimensional barriers and interventions contributing to Latinx health while centering on community voices and perspectives. Results: Community interventions focused on reducing language barriers and increasing community-level access to social supports while policy interventions focused on increasing services to slow the spread of COVID-19. Discussion: Our study adds to the literature by identifying community-based strategies to ensure the power of communities is accounted for in policy reforms that affect Latinx health outcomes across the U.S. Multisector coalitions, such as LATIN-19, can enable the improved understanding of underlying barriers and embed community priorities into policy solutions to address health inequities.

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

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 (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.