Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, United States of America.
Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States of America.
PLoS One. 2020 Jun 25;15(6):e0235064. doi: 10.1371/journal.pone.0235064. eCollection 2020.
Early hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission or death and target interventions. However, existing models usually do not incorporate social determinants of health (SDH) information, although this information is of great importance to address health disparities related to social risk factors. The objective of this study is to examine the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission.
We extracted electronic health record data for 19,941 hospital admissions between January 2015 and November 2017 at an academic medical center in New York City. We applied the Simplified HOSPITAL score model to predict potentially avoidable 30-day readmission or death and examined if incorporating individual- and community-level SDH could improve the prediction using cross-validation. We calculated the C-statistic for discrimination, Brier score for accuracy, and Hosmer-Lemeshow test for calibration for each model using logistic regression. Analysis was conducted for all patients and three subgroups that may be disproportionately affected by social risk factors, namely Medicaid patients, patients who are 65 or older, and obese patients.
The Simplified HOSPITAL score model achieved similar performance in our sample compared to previous studies. Adding SDH did not improve the prediction among all patients. However, adding individual- and community-level SDH at the US census tract level significantly improved the prediction for all three subgroups. Specifically, C-statistics improved from 0.70 to 0.73 for Medicaid patients, from 0.66 to 0.68 for patients 65 or older, and from 0.70 to 0.73 for obese patients.
Patients from certain subgroups may be more likely to be affected by social risk factors. Incorporating SDH into predictive models may be helpful to identify these patients and reduce health disparities associated with vulnerable social conditions.
医院早期再入院或死亡是按绩效付费计划中的关键医疗质量指标。预测模型可以识别再入院或死亡风险较高的患者，并针对这些患者进行干预。然而，现有的模型通常不包含健康的社会决定因素（social determinants of health，SDH）信息，尽管这些信息对于解决与社会风险因素相关的健康差异非常重要。本研究的目的是检验健康的社会决定因素对预测模型在预测可避免的 30 天再入院方面的影响。
我们从纽约市一所学术医疗中心 2015 年 1 月至 2017 年 11 月期间的 19941 例住院记录中提取了电子健康记录数据。我们应用简化 HOSPITAL 评分模型来预测可避免的 30 天再入院或死亡，并通过交叉验证检验纳入个体和社区层面的健康的社会决定因素是否可以提高预测效果。我们使用逻辑回归计算了每个模型的判别 C 统计量、准确性的 Brier 评分和校准的 Hosmer-Lemeshow 检验。分析包括所有患者和三个可能受到社会风险因素不成比例影响的亚组，即医疗补助计划患者、65 岁或以上的患者和肥胖患者。
简化 HOSPITAL 评分模型在我们的样本中与之前的研究相比表现相似。添加健康的社会决定因素并不能提高所有患者的预测效果。然而，在使用美国人口普查区层面的个体和社区层面健康的社会决定因素时，所有三个亚组的预测效果都得到了显著提高。具体来说，医疗补助计划患者的 C 统计量从 0.70 提高到 0.73，65 岁或以上患者的 C 统计量从 0.66 提高到 0.68，肥胖患者的 C 统计量从 0.70 提高到 0.73。
某些亚组的患者可能更容易受到社会风险因素的影响。将健康的社会决定因素纳入预测模型可能有助于识别这些患者，并减少与脆弱社会状况相关的健康差异。
