Introduction: Approximately 80% of modifiable factors of population health outcomes are attributed to Social determinants of health (SDOH). Among individuals with diabetes, SDOH factors are crucial in shaping both physical and psychosocial outcomes. This study aims to examine the significance of SDOH factors on diabetes outcomes among adults participating in a quality improvement (QI) program.

Methods: Adults with elevated HbA1c levels (>8.5%) were enrolled in the QI program. Participants were categorized as responders (HbA1c reduction ≥ 0.5%) or non-responders (HbA1c reduction < 0.5%) at follow-up. A total of 41 predictive features were identified from LexisNexis socioeconomic health attributes. ANOVA and Fisher's Exact tests were employed to compare responders versus non-responders. Three machine learning techniques yielded the most efficient features for inclusion in the predictive model.

Results: The study included 475 individuals from the diabetes program; (mean) age 55.5 years, pre-intervention HbA1c (9.0%), post-intervention HbA1c (8.3%), and change in HbA1c (-0.72%). Significant differences between groups were observed in terms of crime and burglary indices and address stability (p < 0.05). Final selected features included household characteristics (age and income) and neighborhood-level attributes (income, home values, and crime). Logistic regression demonstrated improved accuracy (0.55), while Support Vector Regression recorded the highest Mean Squared Error (MSE) of 0.32.

Discussion: A clinically significant decrease in HbA1c is closely associated with patients’ SDOH factors, collectively accounting for over 30% of intervention outcomes. Future interventions must include social support navigators to identify and address patients’ neighborhood stressors and socioeconomic barriers that may impact intervention outcomes.