DOI: 10.2337/db23-166-or ISSN: 0012-1797

166-OR: Developing a Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)

  • Endocrinology, Diabetes and Metabolism
  • Internal Medicine

T2D is a public health crisis that must be managed by addressing patients’ unmet social needs. We aimed to develop an electronic health record (EHR)-based individualized polysocial risk score (iPsRS) of T2D, which can be used for identifying patients of high social risk and their unmet social needs. We identified T2D patients using 2015-2020 EHR data from University of Florida Health system and followed a year to record hospitalizations. We (1) used natural language processing to extract person-level social determinants of health (SDoH, e.g., employment, housing and food) from clinical notes; and (2) spatiotemporally linked 117 contextual-level SDoH (e.g., neighborhood walkability). We incorporated both contextual and person-level SDoH and used machine learning(ML) to develop an iPsRS that predicted 1-year hospitalization. We assessed iPsRS’ fairness across racial-ethnic groups (Black or Hispanic vs White) and explored different approaches to mitigate identified bias. Of 10192 T2D patients, mean age was 59±21years, and 16% were hospitalized in a year. Our iPsRS showed a good utility for predicting individuals’ risk of hospitalization (C statistic=0.70). After adjusting for clinical characteristics, iPsRS explained 41% of the increased 1-yr hospitalization risk. The naïve iPsRS was biased on Hispanic vs. White patients, that is, Hispanics (false negative rates [FNR]=0.87) were more likely to be misclassified as having low hospitalization risk compared to their White counterparts (FNR=0.77). We identified that a disparate impact remover approach reduced the bias without compromising much prediction utility, producing a final iPsRS with an optimized prediction (C-statistic=0.69) - fairness (FNR-Hispanic=0.62, FNR-White=0.64) balance. We developed a fair iPsRS that can identify T2D patients at a high risk of hospitalization due to SDoH, supporting the integration of social care with clinical care of T2D.


Y.Huang: None. T.Zhang: None. W.T.Donahoo: None. E.Shenkman: None. J.Guo: Consultant; Pfizer Inc., Research Support; PhRMA Foundation, NIH - National Institutes of Health. J.Bian: None.


National Institute of Diabetes and Digestive and Kidney Diseases (R01DK133465)

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