Using only social determinants, we employ an unsupervised clustering methodology that can differentiate high and low expenditure individuals. There are three major implications of this work: (1) clustering algorithms can produce meaningful results; (2) clustering on individuals, not specific variables, can produce predictive clusters; and (3) including comorbidities in cluster formation adds information to better separate the highest expenditure cluster profiles. Using nationally representative data, cluster expenditure distributions are wider for the most expensive clusters and smallest for the least expensive clusters. The clusters using comorbidities show larger separation between the highest two clusters and the remaining clusters than clusters developed excluding comorbidities. Though the profiles designed are representative of U.S. adults, the approach can be applied to any insured population to reveal the impact of the profiles on utilization. Clusters formed using the data without comorbidities can profile new insureds to allow prospective management of certain individuals. The same group profiles can be used in multiple studies with different outcomes, such as inpatient or drug expenditures.

仅使用社会决定因素，我们采用了无监督的聚类方法，可以区分高支出人群和低支出人群。这项工作有三个主要含义：（1）聚类算法可以产生有意义的结果；（2）基于个人而非特定变量的聚类可以产生预测性聚类；（3）在集群形成中包括合并症，增加了信息以更好地区分支出最高的集群概况。使用具有国家代表性的数据，对于最昂贵的集群，集群支出分布更广泛，而对于最便宜的集群，集群支出分布最小。与合并症相比，使用合并症的聚类显示出最高的两个聚类和其余聚类之间的距离比已发展的聚类更大。尽管设计的个人资料代表了美国成年人，该方法可以应用于任何受保人群，以揭示配置文件对利用率的影响。使用没有合并症的数据形成的聚类可以描述新的被保险人，从而可以对某些个人进行前瞻性管理。同一组资料可用于多个研究，但结果不同，例如住院或药物支出。