Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations - The RODAM study

James Osei-Yeboah; Andre-Pascal Kengne; Ellis Owusu-Dabo; Matthias B Schulze; Karlijn A C Meeks; Kerstin Klipstein-Grobusch; Liam Smeeth; Silver Bahendeka; Erik Beune; Eric P Moll van Charante; Charles Agyemang

doi:10.1016/j.puhip.2023.100453

Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations - The RODAM study

Public Health Pract (Oxf). 2023 Nov 23:6:100453. doi: 10.1016/j.puhip.2023.100453. eCollection 2023 Dec.

Authors

James Osei-Yeboah^{1

2}, Andre-Pascal Kengne³, Ellis Owusu-Dabo², Matthias B Schulze^{4

5

6}, Karlijn A C Meeks^{1

7}, Kerstin Klipstein-Grobusch^{8

9}, Liam Smeeth¹⁰, Silver Bahendeka¹¹, Erik Beune¹, Eric P Moll van Charante^{1

12}, Charles Agyemang¹

Affiliations

¹ Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands.
² Department of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
³ Non-communicable Disease Research Unit, South African Medical Research Council, Cape Town, South Africa.
⁴ Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
⁵ German Center for Diabetes Research (DZD), Germany.
⁶ Institute of Nutritional Science, University of Potsdam, Germany.
⁷ Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
⁸ Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
⁹ Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
¹⁰ Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
¹¹ MKPGMS-Uganda Martyrs University, Kampala, Uganda.
¹² Department of General Practice, Amsterdam UMC, University of Amsterdam, Amsterdam Public health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands.

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 <0.80) for screen-detected diabetes in the overall/combined population. Model performance did not significantly differ except for the Cambridge model, which outperformed Rotterdam and Omani models. Calibration was poor, with a consistent trend toward risk overestimation for screen-detected diabetes, but this was substantially attenuated by recalibration through adjustment of the original model intercept.

Conclusion: Though acceptable discrimination was observed, the original models were poorly calibrated among populations of African ancestry. Recalibration of these models among populations of African ancestry is needed before use.

Keywords: Diabetes risk prediction; External validation; Migrant population; Sub-Saharan Africa population.