An informatics-based data-led prioritization strategy to facilitate objective and equitable care for an ethnically diverse urban cohort of people with type 1 diabetes: A proof-of-concept study
Panagiotis Pavlou, Khuram Chaudhry, Ollie French, Sarah Keane, Thomas Johnston, Anna Brackenridge, Stephen Thomas, Yuk-Fun Liu, Daghni Rajasingam, Dulmini Kariyawasam, Janaka Karalliedde
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Source: Crossref
Published: Apr 1, 2026
DOI: 10.1177/14604582261436276
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Background Ethnicity and socioeconomic factors contribute to higher morbidity and mortality in people with type 1 diabetes (pwT1D), partly due to reduced access to specialised care and technology. Objectively prioritising high-risk individuals in resource-limited settings is challenging. Data-led prioritisation (DLP) uses health informatics to stratify pwT1D based on new-onset risk factors since their last review. This may help overcome implicit bias, structural racism, and care barriers. However, data on its use in pwT1D are limited. Methods In this proof-of-concept study, DLP was implemented from July to September 2023 in a university hospital serving an ethnically diverse population. Clinical and demographic data were collected from 697 adults with T1D (50.5% female, 23.5% non-White, 37% from poor socioeconomic backgrounds). DLP identified 76 individuals (10.9%) as highest risk. Results Non-White patients were more likely to be in the highest-risk group (30/164, 18.3%) than White patients (35/372, 9.4%), p =0.004. Those from the most deprived socioeconomic backgrounds were also more likely to be high-risk (40/256, 15.7%) vs others (36/433, 8.3%), p =0.008. Conclusion DLP may enable objective risk stratification in pwT1D and could help reduce bias linked to ethnicity and deprivation. Further large-scale research is warranted to demonstrate the use of such systems in diabetes care.
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