Division of Health Care, RAND Corporation, Santa Monica, California, USA.
Division of Health Care, RAND Corporation, Pittsburgh, Pennsylvania, USA.
Health Serv Res. 2022 Jun;57(3):458-471. doi: 10.1111/1475-6773.13884. Epub 2021 Oct 20.
To investigate (a) the magnitude of the independent associations of neighborhood-level and person-level social risk factors (SRFs) with quality, (b) whether neighborhood-level SRF associations may be proxies for person-level SRF associations, and (c) how the association of person-level SRFs and quality varies by neighborhood-level SRFs.
2015-2016 Medicare Advantage HEDIS data, Medicare beneficiary administrative data, and 2016 American Community Survey (ACS).
Mixed effects linear regression models (1) estimated overall inequities by neighborhood-level and person-level SRFs, (2) compared neighborhood-level associations to person-level associations, and (3) tested the interactions of person-level SRFs with corresponding neighborhood-level SRFs.
DATA COLLECTION/EXTRACTION METHODS: Beneficiary-level SES and disability administrative data and five-year ACS neighborhood-level SRF information were each linked to HEDIS data.
For all or nearly all HEDIS measures, quality was worse in neighborhoods lower in SES and in neighborhoods with higher proportions of residents with a disability. Quality by neighborhood racial and ethnic composition was mixed. Accounting for corresponding person-level SRFs reduced neighborhood SRF associations by 25% for disability, 43% for SES, and 74%-102% for racial and ethnic groups. Person-level SRF coefficients were not consistently reduced in models that added neighborhood-level SRFs. In 19 of 35 instances, there were significant (p < 0.05) interactions between neighborhood-level and corresponding person-level SRFs. Significant interactions were always positive for disability, SES, Black, and Hispanic, indicating more negative neighborhood effects for people with SRFs that did not match their neighborhood and more positive neighborhood effects for people with SRFs that matched their neighborhood.
Relying solely on neighborhood-level SRF models that omit similar person-level SRFs overattributes inequities to neighborhood characteristics. Neighborhood-level characteristics account for much less variation in these measures' scores than similar person-level SRFs. Inequity-reduction programs may be most effective when targeting neighborhoods with a high proportion of people with a given SRF.
（a）调查邻里层面和个人层面社会风险因素（SRF）与质量的独立关联程度，（b）邻里层面 SRF 关联是否可以代表个人层面 SRF 关联，以及（c）个人层面 SRF 和质量的关联如何因邻里层面 SRF 而异。
2015-2016 年医疗保险优势 HEDIS 数据、医疗保险受益人的管理数据和 2016 年美国社区调查（ACS）。
混合效应线性回归模型（1）通过邻里层面和个人层面 SRF 来估计整体不公平程度，（2）比较邻里层面的关联与个人层面的关联，以及（3）测试个人层面 SRF 与相应邻里层面 SRF 的相互作用。
数据收集/提取方法：受益人的 SES 和残疾管理数据以及五年 ACS 邻里层面 SRF 信息都与 HEDIS 数据相关联。
对于所有或几乎所有 HEDIS 指标，SES 较低的邻里地区和残疾居民比例较高的邻里地区的质量较差。邻里地区的种族和民族构成的质量参差不齐。在考虑到相应的个人层面 SRF 后，残疾的邻里 SRF 关联减少了 25%，SES 减少了 43%，种族和民族群体减少了 74%-102%。在添加邻里层面 SRF 的模型中，个人层面 SRF 系数并没有一致减少。在 35 个实例中的 19 个中，邻里层面和相应的个人层面 SRF 之间存在显著（p<0.05）的相互作用。对于残疾、SES、黑人以及西班牙裔，这些相互作用总是为正，这表明对于与邻里特征不匹配的个人而言，邻里的影响更为负面，而对于与邻里特征匹配的个人而言，邻里的影响更为积极。
仅仅依靠忽略类似个人层面 SRF 的邻里层面 SRF 模型，会将不平等归因于邻里特征。邻里层面特征比类似的个人层面 SRF 对这些指标得分的变化解释要少得多。当针对具有特定 SRF 的人比例较高的邻里时，减少不平等的计划可能最为有效。
