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To characterize ethnic differences in the severity and clinical management of type 2 diabetes at initial diagnosis. An observational cohort study of 179,886 people with incident type 2 diabetes between 2004 and 2017 in the Clinical Practice Research Datalink was undertaken; 63.4% of the cohort were of white ethnicity, 3.9% south Asian, and 1.6% black. Ethnic differences in clinical profile at diagnosis, consultation rates, and risk factor recording were derived from linear and logistic regression. Cox-proportional hazards regression was used to determine ethnic differences in time to initiation of therapeutic and non-therapeutic management following diagnosis. All analyses adjusted for age, sex, deprivation, and clustering by practice. In the 12 months prior to diagnosis, non-white groups had fewer consultations compared to white groups, but risk factor recording was better than or equivalent to white groups for 9/10 risk factors for south Asian groups and 8/10 risk factors for black groups (p < 0.002). Blood pressure, BMI, cholesterol, eGFR, and CVD risk levels were more favourable in non-white groups, and prevalence of macrovascular disease was significantly lower (p < 0.003). Time to initiation of antidiabetic treatment and first risk assessment was faster in non-white groups relative to white groups, while time to risk factor measurement and diabetes review was slower. We find limited evidence of systematic ethnic inequalities around the time of type 2 diabetes diagnosis. Ethnic disparities in downstream consequences may relate to genetic risk factors, or manifest later in the care pathway, potentially in relation to long-term risk factor control. Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Citation

R Mathur, L Palla, R E Farmer, N Chaturvedi, L Smeeth. Ethnic differences in the severity and clinical management of type 2 diabetes at time of diagnosis: A cohort study in the UK Clinical Practice Research Datalink. Diabetes research and clinical practice. 2020 Feb;160:108006

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PMID: 31923438

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