| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Public Health in Practice, Vol 6, Iss , Pp 100453- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2666-5352 |
| DOI: |
10.1016/j.puhip.2023.100453 |
| Popis: |
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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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