Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study
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Date
2023
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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 Gha naian
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.
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This article is published by Elsevier and is also available at www.sciencedirect.com/journal/public-health-in-practice
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Public Health in Practice 6 (2023) 100453