Duke Margolis Center for Health Policy, Durham NC and Washington, DC.
Duke University Department of Medicine, Durham, NC.
Milbank Q. 2022 Dec;100(4):1028-1075. doi: 10.1111/1468-0009.12588. Epub 2022 Dec 1.
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.
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.
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.
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 0.7-1). However, spatial autocorrelation analyses revealed clustering, with discrepant distributions of social risk scores between different indices.
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.
目的：利用疾病控制与预防中心社会脆弱性指数（SVI）等劣势指数识别基于地点的社会风险模式，并在 COVID-19 大流行期间指导公平的卫生政策，这种做法前所未有。然而，关于数据元素、互操作性和实施的有限证据留下了关于指数在优先考虑公平性方面的效用的未解决问题。
方法：我们确定了以下劣势指数：（a）在 2018 年至 2021 年期间使用过三次或以上；（b）使用国家级数据设计；（c）可在普查地段或街区组层面获得。我们使用网络可视化比较了指数之间的社会决定因素健康（SDOH）领域。然后，我们使用地理空间分析比较了不同指数和地理区域的劣势分布。
发现：我们确定了 14 个指数。所有指数都使用公共来源的数据，其中一半仅使用美国社区调查数据（n = 7），另一半则结合了多种来源（n = 7）。指数在地理粒度上存在差异，最常见的是县（n = 5）和普查地段（n = 5）。在大流行期间，大多数州都使用了 SVI。SVI、区域贫困指数（ADI）、COVID-19 社区脆弱性指数（CCVI）和儿童机会指数（COI）符合进一步分析的标准。选定的指数共享五个指标（收入、贫困、英语水平、没有高中文凭、失业），但在其他指标和构建方法上存在差异。尽管在北卡罗来纳州达勒姆县、伊利诺伊州库克县和路易斯安那州奥尔良教区绘制社会风险得分地图显示了同一地点内的不同模式，但根据劣势指数的选择，风险得分在不同指数之间表现出中等至高度相关性（r 0.7-1）。然而，空间自相关分析显示出聚类，不同指数之间的社会风险得分分布存在差异。
结论：现有的劣势指数使用不同的指标来表示基于地点的社会风险。在同一地理区域内，不同的指数可能会提供不同的社会风险值和解释，这可能导致不同的公共卫生或政策反应。
