Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114.
Department of Medicine, Division of Hospital Medicine, University of California, San Francisco, CA 94117.
Proc Natl Acad Sci U S A. 2023 Feb 14;120(7):e2209414120. doi: 10.1073/pnas.2209414120. Epub 2023 Feb 7.
While social characteristics are well-known predictors of mortality, prediction models rely almost exclusively on demographics, medical comorbidities, and function. Lacking an efficient way to summarize the prognostic impact of social factor, many studies exclude social factors altogether. Our objective was to develop and validate a summary measure of social risk and determine its ability to risk-stratify beyond traditional risk models. We examined participants in the Health and Retirement Study, a longitudinal, survey of US older adults. We developed the model from a comprehensive inventory of 183 social characteristics using least absolute shrinkage and selection operator, a penalized regression approach. Then, we assessed the predictive capacity of the model and its ability to improve on traditional prediction models. We studied 8,250 adults aged ≥65 y. Within 4 y of the baseline interview, 22% had died. Drawn from 183 possible predictors, the Social Frailty Index included age, gender, and eight social predictors: neighborhood cleanliness, perceived control over financial situation, meeting with children less than yearly, not working for pay, active with children, volunteering, feeling isolated, and being treated with less courtesy or respect. In the validation cohort, predicted and observed mortality were strongly correlated. Additionally, the Social Frailty Index meaningfully risk-stratified participants beyond the Charlson score (medical comorbidity index) and the Lee Index (comorbidity and function model). The Social Frailty Index includes age, gender, and eight social characteristics and accurately risk-stratifies older adults. The model improves upon commonly used risk prediction tools and has application in clinical, population health, and research settings.
虽然社会特征是众所周知的死亡率预测因素，但预测模型几乎完全依赖于人口统计学、医学合并症和功能。由于缺乏有效方法来总结社会因素的预后影响，许多研究完全排除了社会因素。我们的目标是开发和验证一种社会风险综合衡量指标，并确定其在传统风险模型之外进行风险分层的能力。我们研究了健康与退休研究（Health and Retirement Study）的参与者，这是一项针对美国老年人的纵向调查。我们使用最小绝对收缩和选择算子（一种惩罚回归方法）从一个包含 183 种社会特征的综合清单中开发了该模型。然后，我们评估了该模型的预测能力及其改善传统预测模型的能力。我们研究了 8250 名年龄≥65 岁的成年人。在基线访谈后的 4 年内，22%的人死亡。社会脆弱指数（Social Frailty Index）从 183 个可能的预测因素中提取，包括年龄、性别和 8 个社会预测因素：邻里清洁度、对财务状况的控制感、每年与孩子见面少于一次、不有偿工作、积极与孩子互动、志愿服务、感到孤立和受到不礼貌或不尊重的待遇。在验证队列中，预测和观察到的死亡率之间存在很强的相关性。此外，社会脆弱指数在查尔森评分（医学合并症指数）和李指数（合并症和功能模型）之外，对参与者进行了有意义的风险分层。社会脆弱指数包含年龄、性别和 8 个社会特征，能够准确地对老年人进行风险分层。该模型改进了常用的风险预测工具，并在临床、人群健康和研究环境中有应用。
