International journal of medical sciences · 2026
Machine Learning with Social Determinants of Health for Cross-Sectional Diabetes Classification: Development and Temporal Validation.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Kung Woon-Man, Yeh Hsien Chang, Huang Abel Po-Hao, Islam Md Mohaimenul, Wu Chieh-Chen, Wang Yao-Chin
Аннотация
AIMS: Our study aimed to determine whether incorporating social determinants of health (SDOH) improves cross-sectional classification of prevalent diagnosed diabetes and to quantify the extent to which model performance is attributable to variables that may occur downstream of diabetes diagnosis.
METHODS: We analyzed data from the 2023 Behavioral Risk Factor Surveillance System (BRFSS) as the development cohort and the 2024 BRFSS as an independent temporal validation cohort. Analyses were restricted to adults aged 35 years or older residing in states that administered the SDOH module. The outcome was self-reported diagnosed diabetes. Before model development, 30% of the 2023 sample was reserved as a test set. Imputation, encoding, feature processing, and hyperparameter tuning were performed within nested cross-validation. Histogram-based gradient boosting and penalized logistic regression were evaluated across six pre-specified nested predictor specifications. Final models were applied unchanged to the 2024 cohort without refitting, re-tuning, or threshold recalibration. Performance estimates incorporated BRFSS survey weights.
RESULTS: The development cohort comprised 179,752 adults (weighted prevalence 16.97%) and the validation cohort 161,092 (18.07%). Discrimination ranged from 0.75 to 0.81 across algorithms, with gradient boosting and penalized logistic regression indistinguishable (0.811 versus 0.808). The full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.811 (95% CI 0.801 - 0.822) and transported to the validation cohort with a calibration slope of 1.040. Adding nine SDOH items changed discrimination by 0.006 (95% CI 0.004 - 0.008), with maximum net benefit gain under 0.003; discrimination rose from 0.743 to 0.811 only when healthcare utilization and comorbidity measures were added. Several SDOH nonetheless showed robust adjusted associations, including food insecurity (OR 1.27 - 1.39) and supplemental nutrition assistance program receipt (OR 1.34, 95% CI 1.11 - 1.61). At a fixed threshold, sensitivity ranged from 0.62 to 0.89 across age groups.
CONCLUSIONS: SDOH were independently associated with prevalent diagnosed diabetes but provided little incremental value beyond demographic, behavioral, and healthcare access measures, a finding that was consistent across consecutive survey cycles. Most of the observed discrimination was attributable to healthcare utilization and comorbidity variables that may plausibly reflect consequences of an established diagnosis rather than characteristics preceding disease recognition.
METHODS: We analyzed data from the 2023 Behavioral Risk Factor Surveillance System (BRFSS) as the development cohort and the 2024 BRFSS as an independent temporal validation cohort. Analyses were restricted to adults aged 35 years or older residing in states that administered the SDOH module. The outcome was self-reported diagnosed diabetes. Before model development, 30% of the 2023 sample was reserved as a test set. Imputation, encoding, feature processing, and hyperparameter tuning were performed within nested cross-validation. Histogram-based gradient boosting and penalized logistic regression were evaluated across six pre-specified nested predictor specifications. Final models were applied unchanged to the 2024 cohort without refitting, re-tuning, or threshold recalibration. Performance estimates incorporated BRFSS survey weights.
RESULTS: The development cohort comprised 179,752 adults (weighted prevalence 16.97%) and the validation cohort 161,092 (18.07%). Discrimination ranged from 0.75 to 0.81 across algorithms, with gradient boosting and penalized logistic regression indistinguishable (0.811 versus 0.808). The full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.811 (95% CI 0.801 - 0.822) and transported to the validation cohort with a calibration slope of 1.040. Adding nine SDOH items changed discrimination by 0.006 (95% CI 0.004 - 0.008), with maximum net benefit gain under 0.003; discrimination rose from 0.743 to 0.811 only when healthcare utilization and comorbidity measures were added. Several SDOH nonetheless showed robust adjusted associations, including food insecurity (OR 1.27 - 1.39) and supplemental nutrition assistance program receipt (OR 1.34, 95% CI 1.11 - 1.61). At a fixed threshold, sensitivity ranged from 0.62 to 0.89 across age groups.
CONCLUSIONS: SDOH were independently associated with prevalent diagnosed diabetes but provided little incremental value beyond demographic, behavioral, and healthcare access measures, a finding that was consistent across consecutive survey cycles. Most of the observed discrimination was attributable to healthcare utilization and comorbidity variables that may plausibly reflect consequences of an established diagnosis rather than characteristics preceding disease recognition.