Social determinants of healthy aging: An investigation using the all of us cohort - Wei-Han Chen; Yao-An Lee; Huilin Tang; Chenyu Li; Ying Lu; Yu Huang; Rui Yin; Melissa J. Armstrong; Yang Yang; Gregor Štiglic; Jiang Bian; Jingchuan Guo (PLoS ONE, 2026)
Introduction The increasing aging population raises significant concerns about the ability of individuals to age healthily, avoiding chronic diseases and maintaining cognitive and physical functions. However, the pathways through which SDOH factors are associated with healthy aging remain unclear. Methods This retrospective cohort study uses the registered tier data from the All of Us Research Program (AoURP) registered tier dataset v7. Eligible study participants are those aged 50 and older who have responded to any of the SDOH survey questions with available EHR data. Three different algorithms were trained (logistic regression [LR], multi-layer perceptron [MLP], and extreme gradient boosting [XGBoost]). The outcome is healthy aging, which is measured by a composite score of the status for 1) comorbidities, 2) cognitive conditions, and 3) mobility function. We evaluate the model performance by area under the receiver operating characteristic curve (AUROC) and assess the fairness of best-performed model through predictive parity. Feature importance is analyzed using SHapley Additive exPlanations (SHAP) values. Results Our study included 99,935 participants aged 50 and above, and the mean (SD) age was 74 (9.3), with 55,294 (55.3%) females, 67,457 (67.5%) Whites, 11,109 (11.1%) Hispanic ethnicity, and 44,109 (44.1%) are classified as healthy aging. Most of the individuals lived in their own house (64%), were married (51%), obtained college or advanced degrees (74%), and had Medicare (56.2%). The best predictive model was XGBoost with random oversampler, with a performance of AUROC [95% CI]: 0.793 [0.788–0.796], F1 score: 0.697 [0.692–0.701], recall: 0.739 [0.732–0.748], precision: 0.659 [0.655–0.663], and accuracy: 0.716 [0.712–0.720], and the XGBoost model achieved predictive parity by similar positive and negative predictive values across race and sex groups (0.86–1.06). In feature importance analysis, health insurance type is ranked as the most predictive feature, followed by employment status, substance use, and health insurance coverage (yes/no). Conclusion In this cohort study, XGBoost model accurately predicted individuals achieving healthy aging, outperforming LR and MLP. Our findings underscore the significant role of health insurance in contributing to healthy aging.