Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, United States of America.
Yale/Yale New Haven Health Center for Outcomes Research and Evaluation, New Haven, CT, United States of America.
PLoS One. 2020 Oct 23;15(10):e0240222. doi: 10.1371/journal.pone.0240222. eCollection 2020.
The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain.
To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR)-a quality performance measure in the U.S.
We assessed 71 community variables in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital.
Medicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n = 6,790,723). Patients were linked to community variables using their 5-digit zip code of residence.
We used a random forest algorithm to rank variables for their importance in predicting HWR scores. Variables were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Variables with P-values <0.10 were retained for a final model, after eliminating any that were collinear.
Among 71 community variables, 19 were retained in the 6 domain models and in the final model. Domains which explained the most to least variance in HWR were: physical environment (R2 = 15%); clinical care (R2 = 12%); demographics (R2 = 11%); social and economic environment (R2 = 7%); health behaviors (R2 = 9%); and social capital (R2 = 8%). In the final model, the 19 variables explained more than a quarter of the variance in readmission rates (R2 = 27%).
Readmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital.
患者生活的环境会影响他们的健康结果。然而,社区因素与再入院率的关联程度尚不确定。
估计社区因素对医疗保险和医疗补助服务中心风险标准化医院范围再入院率测量(HWR)的影响——这是美国的一项质量绩效测量。
我们评估了 6 个与健康结果相关领域的 71 个社区变量:临床护理;健康行为;社会经济因素;物理环境;人口统计学;和社会资本。
2014 年 7 月至 2015 年 6 月期间有资格参加 HWR 测量的医疗保险收费服务患者(n = 6,790,723)。患者通过其居住的 5 位数字邮政编码与社区变量相关联。
我们使用随机森林算法对预测 HWR 评分的变量进行重要性排名。按照重要性递减的顺序,将变量输入到 6 个特定于域的多变量回归模型中。保留任何有相关性的变量,对最终模型进行筛选。
在 71 个社区变量中,有 19 个变量保留在 6 个域模型和最终模型中。对 HWR 有最大解释力的域依次为:物理环境(R2 = 15%);临床护理(R2 = 12%);人口统计学(R2 = 11%);社会经济环境(R2 = 7%);健康行为(R2 = 9%);和社会资本(R2 = 8%)。在最终模型中,这 19 个变量解释了超过四分之一的再入院率(R2 = 27%)。
各种临床疾病的再入院率受到与患者居住社区相关的因素的影响。这些发现可以用于有针对性地努力让患者避免住院。