{"title":"Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients.","authors":"Jaysón Davidson, Rohit Vashisht, Kendra Radtke, Ayan Patel, Suneil K Koliwad, Atul J Butte","doi":"10.1101/2025.03.26.25324631","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2 diabetes (T2D).</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>Socioeconomic factors influence second-line T2D treatment choices. Addressing these disparities is essential to ensuring equitable access to advanced T2D therapies.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974982/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv : the preprint server for health sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2025.03.26.25324631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.