Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death.

Yongkang Zhang,Jessica S Ancker,Marianne Sharko,Yiyuan Wu,Meghan Reading Turchioe,Evan Sholle,Yiye Zhang,Sajjad Abedian,Juan F. Orueta

doi:10.1371/journal.pone.0235064

Abstract

ObjectivesEarly 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.MethodsWe 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.ResultsThe 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.ConclusionsPatients 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.

Highlights

Hospital readmissions are both common and costly [1]
Patients from certain subgroups may be more likely to be affected by social risk factors
Incorporating social determinants of health (SDH) into predictive models may be helpful to identify these patients and reduce health disparities associated with vulnerable social conditions

Summary

Introduction

Hospital readmissions are both common and costly [1]. For example, one in five patients enrolled in Medicare—a US public health insurance plan for people 65 or older or people with disability—is readmitted within 30 days after discharge, at a cost of over $26 billion per year [2]. Some readmissions are unavoidable (e.g., regularly scheduled admissions for chemotherapy), a considerable proportion of readmissions are unnecessary and potentially preventable [3]. These readmissions are generally considered to indicate underlying issues with quality of care and can potentially be averted through appropriate interventions [1]. To improve the value of healthcare, federal, state, and commercial payers have included hospital readmission as one of the core quality measures in pay-for-performance programs. Under the Centers for Medicare and Medicaid Services (CMS) Hospital Readmission Reduction Program, hospitals face payment cuts if they have excess risk-standardized 30-day readmission rates relative to other hospitals [4]

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