Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study
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, Kuw...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Public Health in Practice |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266653522300099X |
| _version_ | 1827591064512364544 |
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| author | 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 |
| author_facet | 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 |
| author_sort | James Osei-Yeboah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T01:26:32Z |
| format | Article |
| id | doaj.art-98edbb0dc943497babdab71b19a1b60e |
| institution | Directory Open Access Journal |
| issn | 2666-5352 |
| language | English |
| last_indexed | 2024-03-09T01:26:32Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Public Health in Practice |
| spelling | doaj.art-98edbb0dc943497babdab71b19a1b60e2023-12-10T06:18:31ZengElsevierPublic Health in Practice2666-53522023-12-016100453Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM studyJames Osei-Yeboah0Andre-Pascal Kengne1Ellis Owusu-Dabo2Matthias B. Schulze3Karlijn A.C. Meeks4Kerstin Klipstein-Grobusch5Liam Smeeth6Silver Bahendeka7Erik Beune8Eric P. Moll van Charante9Charles Agyemang10Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands; Department of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Corresponding author. Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands.Non-communicable Disease Research Unit, South African Medical Research Council, Cape Town, South AfricaDepartment of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam‐Rehbruecke, Nuthetal, Germany; German Center for Diabetes Research (DZD), Germany; Institute of Nutritional Science, University of Potsdam, GermanyDepartment of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands; Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USAJulius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South AfricaDepartment of Non‐Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UKMKPGMS-Uganda Martyrs University, Kampala, UgandaDepartment of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the NetherlandsDepartment of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the Netherlands; Department of General Practice, Amsterdam UMC, University of Amsterdam, Amsterdam Public health Research Institute, Meibergdreef 9, Amsterdam, the NetherlandsDepartment of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, Amsterdam, the NetherlandsBackground: 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.http://www.sciencedirect.com/science/article/pii/S266653522300099XDiabetes risk predictionExternal validationSub-Saharan Africa populationMigrant population |
| spellingShingle | 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 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 Diabetes risk prediction External validation Sub-Saharan Africa population Migrant population |
| title | Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study |
| title_full | Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study |
| title_fullStr | Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study |
| title_full_unstemmed | Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study |
| title_short | Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study |
| title_sort | validation of prevalent diabetes risk scores based on non invasively measured predictors in ghanaian migrant and non migrant populations the rodam study |
| topic | Diabetes risk prediction External validation Sub-Saharan Africa population Migrant population |
| url | http://www.sciencedirect.com/science/article/pii/S266653522300099X |
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