Friedman Hannah, Li Mingfei, Harvey Kimberly L, Griesemer Ida, Mohr David, Linsky Amy M, Gurewich Deborah
Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA, USA.
CHOIR, VA Bedford Healthcare System, Bedford, MA, USA.
J Gen Intern Med. 2025 Feb;40(2):385-392. doi: 10.1007/s11606-024-08862-z. Epub 2024 Oct 7.
Many social need screening to advance population health and reduce health disparities, but barriers to screening remain. Improved knowledge of patient populations at risk for social needs based on administrative data could facilitate more targeted practices, and by extension, feasible social need screening and referral efforts.
To illustrate the use of cluster analysis to identify patient population segments at risk for social needs.
We used clustering analysis to identify population segments among Veterans (N=2010) who participated in a survey assessing nine social needs (food, housing, utility, financial, employment, social disconnection, legal, transportation, and neighborhood safety). Clusters were based on eight variables (age, race, gender, comorbidity, region, no-show rate, rurality, and VA priority group). We used weighted logistic regression to assess association of clusters with the risk of experiencing social needs.
National random sample of Veterans with and at risk for cardiovascular disease who responded to a mail survey (N=2010).
Self-reported social needs defined as the risk of endorsing (1) each individual social need, (2) one or more needs, and (3) a higher total count of needs.
From the clustering analysis process with sensitivity analysis, we identified a consistent population segment of Veterans. From regression modeling, we found that this cluster, with lower average age and higher proportions of women and racial minorities, was at higher risk of experiencing ≥ 1 unmet need (OR 1.74, CI 1.17-2.56). This cluster was also at a higher risk for several individual needs, especially utility needs (OR 3.78, CI 2.11-6.78).
The identification of characteristics associated with increased unmet social needs may provide opportunities for targeted screenings. As this cluster was also younger and had fewer comorbidities, they may be less likely to be identified as experiencing need through interactions with healthcare providers.
许多社会需求筛查对于促进人群健康和减少健康差距至关重要，但筛查障碍依然存在。基于行政数据更好地了解存在社会需求风险的患者群体，有助于采取更具针对性的措施，进而推动可行的社会需求筛查和转诊工作。
说明如何运用聚类分析来识别存在社会需求风险的患者群体。
我们运用聚类分析，在参与一项评估九项社会需求（食品、住房、水电费、财务、就业、社交隔离、法律、交通和社区安全）调查的退伍军人（N = 2010）中识别群体。聚类基于八个变量（年龄、种族、性别、合并症、地区、爽约率、农村地区状况和退伍军人事务部优先群体）。我们使用加权逻辑回归来评估聚类与经历社会需求风险之间的关联。
对邮件调查做出回应的患有心血管疾病及有心血管疾病风险的退伍军人全国随机样本（N = 2010）。
自我报告的社会需求被定义为认可（1）每项个体社会需求、（2）一项或多项需求以及（3）更高需求总数的风险。
通过带有敏感性分析的聚类分析过程，我们识别出了一个一致的退伍军人群体。从回归模型中，我们发现这个平均年龄较低、女性和少数族裔比例较高的群体，经历≥1项未满足需求的风险更高（比值比1.74，置信区间1.17 - 2.56）。该群体在几项个体需求方面风险也更高，尤其是水电费需求（比值比3.78，置信区间2.11 - 6.78）。
识别与未满足的社会需求增加相关的特征，可能为有针对性的筛查提供机会。由于这个群体年龄也较小且合并症较少，他们通过与医疗服务提供者的互动被识别为有需求的可能性可能较小。
