Ngai Ka Ming, Maroko Andrew, Bilal Saadiyah, Wilder Marcee, Gordon Lauren, Richardson Lynne D
Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
J Clin Transl Sci. 2024 Apr 15;8(1):e77. doi: 10.1017/cts.2024.517. eCollection 2024.
Individuals reside within communities influenced by various social determinants impacting health, which may harmonize or conflict at individual and neighborhood levels. While some experience concordant circumstances, discordance is prevalent, yet poorly understood due to the lack of a universally accepted method for quantifying it. This paper proposes a methodology to address this gap.
We propose a systematic approach to operationalize concordance and discordance between individual and neighborhood social determinants, using household income (HHI) (continuous) and race/ethnicity (categorical) as examples for individual social determinants. We demonstrated our method with a small dataset that combines self-reported individual data with geocoded neighborhood level. We anticipate that the risk profiles created by either self-reported individual data or neighborhood-level data alone will differ from patterns demonstrated by typologies based on concordance and discordance.
In our cohort, it was revealed that 20% of patients experienced discordance between their HHIs and neighborhood characteristics. Additionally, 38% reside in racially/ethnically concordant neighborhoods, 23% in discordant ones, and 39% in neutral ones.
Our study introduces an innovative approach to defining and quantifying the notions of concordance and discordance in individual attributes concerning neighborhood-level social determinants. It equips researchers with a valuable tool to conduct more comprehensive investigations into the intricate interplay between individuals and their environments. Ultimately, this methodology facilitates a more accurate modeling of the true impacts of social determinants on health, contributing to a deeper understanding of this complex relationship.
个体生活在受各种影响健康的社会决定因素所左右的社区中,这些因素在个体和社区层面可能相互协调或冲突。虽然有些人经历的情况是一致的,但不一致的情况很普遍,然而由于缺乏一种普遍接受的量化方法,人们对此了解甚少。本文提出了一种方法来填补这一空白。
我们提出一种系统的方法来衡量个体与社区社会决定因素之间的一致性和不一致性,以家庭收入(连续变量)和种族/民族(分类变量)作为个体社会决定因素的示例。我们用一个小型数据集展示了我们的方法,该数据集将自我报告的个体数据与地理编码的社区层面数据相结合。我们预计,仅由自我报告的个体数据或社区层面数据创建的风险概况将与基于一致性和不一致性的类型学所显示的模式不同。
在我们的队列中,发现20%的患者家庭收入与社区特征之间存在不一致。此外,38%的人居住在种族/民族一致的社区,23%居住在不一致社区,39%居住在中性社区。
我们的研究引入了一种创新方法,用于定义和量化个体属性中与社区层面社会决定因素相关的一致性和不一致性概念。它为研究人员提供了一个有价值的工具,以便对个体与其环境之间的复杂相互作用进行更全面的调查。最终,这种方法有助于更准确地模拟社会决定因素对健康的真正影响,从而更深入地理解这种复杂关系。