Demographic differences in willingness to share electronic health records in the All of Us Research Program

Prediction of Readmission Following Sepsis Using Social Determinants of Health

OBJECTIVES

To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables.

DESIGN

Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data.

SETTINGS

Thirty-five hospitals across the United States from 2017 to 2021.

PATIENTS

Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission.

CONCLUSIONS

In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.

Enabling Health Data Sharing with Fine-Grained Privacy

Sharing health data is vital in advancing medical research and transforming knowledge into clinical practice. Meanwhile, protecting the privacy of data contributors is of paramount importance. To that end, several privacy approaches have been proposed to protect individual data contributors in data sharing, including data anonymization and data synthesis techniques. These approaches have shown promising results in providing privacy protection at the dataset level. In this work, we study the privacy challenges in enabling fine-grained privacy in health data sharing. Our work is motivated by recent research findings, in which patients and healthcare providers may have different privacy preferences and policies that need to be addressed. Specifically, we propose a novel and effective privacy solution that enables data curators (e.g., healthcare providers) to protect sensitive data elements while preserving data usefulness. Our solution builds on randomized techniques to provide rigorous privacy protection for sensitive elements and leverages graphical models to mitigate privacy leakage due to dependent elements. To enhance the usefulness of the shared data, our randomized mechanism incorporates domain knowledge to preserve semantic similarity and adopts a block-structured design to minimize utility loss. Evaluations with real-world health data demonstrate the effectiveness of our approach and the usefulness of the shared data for health applications.