Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients is a dataset (2025). On theSindex it has a DataRank of 0, placing it in the top 100% of the data-sharing corpus. Its calibrated FAIR score is 31/100.
Objective This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2 diabetes (T2D). Materials and Methods We conducted an observational study using real-world data from over 9 million patients across five University of California Health centers. The study included patients who initiated a second-line T2D medication after metformin, with hemoglobin A1c (HbA1c) measurements within ±7 days of treatment initiation from 2012 through September 2024. Multinomial regression models assessed the association between socioeconomic status and second-line treatment choices. Additionally, we used the GPT-4 large language model with a zero-shot learning approach to analyze 270 clinical notes from 105 UCSF patients. GPT-4 identified adverse social determinants of health (SDOH) across six domains: transportation, housing, relationships, patients with children, support, and employment. Results Among 15,090 patients (56.7% male, 43.3% female; mean age 59.3 years; mean HbA1c 8.91%), second-line treatments included sulfonylureas (SUs; n = 6,732), DPP4 inhibitors (n = 2,918), GLP-1 receptor agonists (n = 2,736), and SGLT2 inhibitors (n = 2,704). Patients from lower socioeconomic neighborhoods were more likely to receive SUs over other medications: DPP4i (OR = 0.96, [95% CI, 0.95-0.98]), GLP-1RA (OR = 0.94, [95% CI, 0.92-0.96]), SGLT2i (OR = 0.95, [95% CI, 0.93-0.97]). In UCSF clinical notes, we identified adverse SDOH including housing (n=8), transportation (n=1), relationships (n=22), employment (n=12), support (n=1), and patients with children (n=25). Conclusions Socioeconomic factors influence second-line T2D treatment choices. Addressing these disparities is essential to ensuring equitable access to advanced T2D therapies.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →