Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
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, Volume 6, Article 100453, Year 2023
Notification
URL copied to clipboard!
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. © 2023 The Authors
Authors & Co-Authors
Osei-Yeboah, James
Netherlands, Amsterdam
Amsterdam Public Health
Ghana, Kumasi
Kwame Nkrumah University of Science & Technology
Kengne, Andre-Pascal Pascal
South Africa, Tygerberg
South African Medical Research Council
Owusu-Dabo, Ellis
Ghana, Kumasi
Kwame Nkrumah University of Science & Technology
Schulze, Matthias Bernd
Germany, Nuthetal
German Institute of Human Nutrition
Germany, Oberschleissheim
Deutsches Zentrum Für Diabetesforschung
Germany, Potsdam
Universität Potsdam
Meeks, Karlijn Anna Catharina
Netherlands, Amsterdam
Amsterdam Public Health
United States, Bethesda
National Institutes of Health Nih
Klipstein-Grobusch, Kerstin
Netherlands, Utrecht
Universiteit Utrecht
South Africa, Johannesburg
University of the Witwatersrand Faculty of Health Sciences
Smeeth, Liam L.
United Kingdom, London
London School of Hygiene & Tropical Medicine
Bahendeka, Silver Karaireho
Uganda, Kampala
Uganda Martyrs University
Beune, Erik J.A.J.
Netherlands, Amsterdam
Amsterdam Public Health
Moll van Charante, Eric Peter
Netherlands, Amsterdam
Amsterdam Public Health
Agyemang, Charles O.
Netherlands, Amsterdam
Amsterdam Public Health
Statistics
Authors: 11
Affiliations: 11
Identifiers
Doi:
10.1016/j.puhip.2023.100453
ISSN:
26665352
Research Areas
Noncommunicable Diseases
Study Design
Cross Sectional Study
Study Approach
Quantitative