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.

BackgroundEthnicity 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.MethodsIn 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.ResultsNon-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.ConclusionDLP 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.