{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:53:43Z","timestamp":1782921223774,"version":"3.54.5"},"reference-count":29,"publisher":"American Medical Association (AMA)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n \n Race, ethnicity, and social determinants of health (SDOH) contribute to disparities in diabetes outcomes, particularly hemoglobin A\n 1c<\/jats:sub>\n (HbA\n 1c<\/jats:sub>\n ). Although continuous glucose monitoring (CGM) improves glycemic management, it remains unclear whether associations persist between SDOH and CGM metrics, as prior research has focused solely on HbA\n 1c<\/jats:sub>\n .\n <\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To examine the associations of race, ethnicity, and SDOH with glycemic metrics among patients with type 1 or type 2 diabetes who use CGMs.<\/jats:p>\n <\/jats:sec>\n \n Design, Settings, and Participants<\/jats:title>\n This retrospective cross-sectional study evaluated CGM profiles paired with demographic data from individuals receiving outpatient care at a single-center academic health system between October 1, 2017, and February 28, 2025. The cohort included pediatric (aged &amp;lt;18 years) and adult (aged \u226518 years) patients with type 1 or type 2 diabetes who met CGM data sufficiency criteria.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n Race and ethnicity category, individual-level SDOH (eg, insurance type), and neighborhood-level socioeconomic status.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n \n Glycemic outcomes included HbA\n 1c<\/jats:sub>\n , mean glucose, time in range, time above range, glycemic risk index, coefficient of variation, and extended hypoglycemic episodes. Inverse probability weighting and mixed-effects models were used to examine associations between exposures and outcomes.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The study included 1743 participants with 3296 CGM profiles (mean [SD] age, 38.8 [20.8] years; 926 female [53.1%]; 86 identifying as Black [4.9%], 56 as Hispanic [3.2%], 1508 as White [86.5%], and 93 as other [5.3%] race and ethnicity). Black race and public health insurance were associated with higher HbA\n 1c<\/jats:sub>\n (estimate, 0.55%; 95% CI, 0.27%-0.82%) and mean glucose (estimate, 17.88 mg\/dL; 95% CI, 3.69-32.07 mg\/dL). Public insurance was also associated with higher time in range (estimate, \u20133.56%; 95% CI, \u20135.45% to \u20131.66%), time above range (estimate, 3.49%; 95% CI, 1.53%-5.46%), and glycemic risk index (estimate, 4.50; 95% CI, 2.13-6.87). Socioeconomic disadvantage was associated with an increase in coefficient of variation (estimate, 3.26; 95% CI, 0.81-5.70). Associations were most pronounced among children experiencing more socioeconomic disadvantage and Black children across metrics. These associations persisted after adjusting for insulin pump use.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n This cross-sectional study of adult and pediatric CGM users with type 1 or type 2 diabetes found that CGM and insulin pump use alone did not eliminate associations between SDOH and glycemic outcomes. These findings highlight the need for interventions beyond technology, such as diabetes education and behavioral support, to promote equity in diabetes care.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2026.21290","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:38:25Z","timestamp":1782920305000},"page":"e2621290","source":"Crossref","is-referenced-by-count":0,"title":["Social Determinants of Health and Continuous Glucose Monitoring Metrics in Type 1 or Type 2 Diabetes"],"prefix":"10.1001","volume":"9","author":[{"given":"Walter T.","family":"Williamson","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neo","family":"Kok","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joyce M.","family":"Lee","sequence":"additional","affiliation":[{"name":"Susan B. 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