Internal validation of the diabetes outcomes model shows significant impact on health disparities, indicating social determinants influence diabetes progression.
OBJECTIVE: The objective of this study was to develop and internally validate a mathematical model of the relationships between patient clinical and social risk factors and outcomes using data from a multiethnic population with type 2 diabetes. RESEARCH DESIGN AND METHODS: We constructed an incidence cohort of all adults (18 years or older) with newly diagnosed type 2 diabetes in the Kaiser Permanente Northern California (KPNC) healthcare system between 2005 and 2016 (N=129,000), following patients for at least 1 year, but up to 12 years. Using this cohort, we modeled 17 distinct diabetes-related outcomes related to micro and macrovascular disease, as well as atrial fibrillation, depression, and dementia, relevant biomarkers, and mortality. RESULTS: Data were randomly split into 50%, 25%, and 25% samples to perform model estimation, calibration, and validation, respectively. Empirical and simulated data were similar for the events and biomarkers, but some factors required calibration, but after calibration, they closely aligned empirical estimates. CONCLUSIONS: DOMUS is a major step forward in understanding diabetes progression and the role of social determinants of health. This model can be used by scientists, policymakers, and health system managers to better understand how choices can impact population health and health disparities, including the broad diversity of U.S. races and ethnicities. Moreover, this model can be used to realize longer-term comparative effectiveness in cost-effectiveness analyses for diabetes management in the future.
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Winn et al. (2025) studied this question.
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