Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study
Publication date
2023-12
Authors
Osei-Yeboah, James
Kengne, Andre-Pascal
Owusu-Dabo, Ellis
Schulze, Matthias B.
Meeks, Karlijn A.C.
Klipstein-Grobusch, Kerstin
Smeeth, Liam
Bahendeka, Silver
Beune, Erik
Moll van Charante, Eric P.
Editors
Advisors
Supervisors
Document Type
Article
Metadata
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License
cc_by
Abstract
Background Non-invasive diabetes risk models are a cost-effective tool in large-scale population screening to identify those who need confirmation tests, especially in resource-limited settings. Aims This study aimed to evaluate the ability of six non-invasive risk models (Cambridge, FINDRISC, Kuwaiti, Omani, Rotterdam, and SUNSET model) to identify screen-detected diabetes (defined by HbA1c) among Ghanaian migrants and non-migrants. Study design A multicentered cross-sectional study. Methods This analysis included 4843 Ghanaian migrants and non-migrants from the Research on Obesity and Diabetes among African Migrants (RODAM) Study. Model performance was assessed using the area under the receiver operating characteristic curves (AUC), Hosmer-Lemeshow statistics, and calibration plots. Results All six models had acceptable discrimination (0.70 ≤ AUC
Keywords
Diabetes risk prediction, External validation, Migrant population, Sub-Saharan Africa population
Citation
Osei-Yeboah, J, Kengne, A-P, Owusu-Dabo, E, Schulze, M B, Meeks, K A C, Klipstein-Grobusch, K, Smeeth, L, Bahendeka, S, Beune, E, Moll van Charante, E P & Agyemang, C 2023, 'Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study', Public health in practice, vol. 6, 100453. https://doi.org/10.1016/j.puhip.2023.100453