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

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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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 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

Original language	English
Article number	100453
Journal	Public health in practice
Volume	6
DOIs	https://doi.org/10.1016/j.puhip.2023.100453
Publication status	Published - Dec 2023

Keywords

Diabetes risk prediction
External validation
Migrant population
Sub-Saharan Africa population

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10.1016/j.puhip.2023.100453Licence: CC BY

1-s2.0-S266653522300099X-mainFinal published version, 461 KBLicence: CC BY

https://www.sciencedirect.com/science/article/pii/S266653522300099X

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Osei-Yeboah, J., Kengne, A.-P., Owusu-Dabo, E., Schulze, M. B., Meeks, K. A. C., Klipstein-Grobusch, K., Smeeth, L., Bahendeka, S., Beune, E., Moll van Charante, E. P., & Agyemang, C. (2023). 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, 6, Article 100453. https://doi.org/10.1016/j.puhip.2023.100453

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

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 ",

keywords = "Diabetes risk prediction, External validation, Migrant population, Sub-Saharan Africa population",

author = "James Osei-Yeboah and Andre-Pascal Kengne and Ellis Owusu-Dabo and Schulze, \{Matthias B.\} and Meeks, \{Karlijn A.C.\} and Kerstin Klipstein-Grobusch and Liam Smeeth and Silver Bahendeka and Erik Beune and \{Moll van Charante\}, \{Eric P.\} and Charles Agyemang",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = dec,

doi = "10.1016/j.puhip.2023.100453",

language = "English",

volume = "6",

journal = "Public health in practice",

issn = "2666-5352",

publisher = "Elsevier",

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Osei-Yeboah, J, Kengne, A-P, Owusu-Dabo, E, Schulze, MB, Meeks, KAC, Klipstein-Grobusch, K, Smeeth, L, Bahendeka, S, Beune, E, Moll van Charante, EP & Agyemang, C 2023, '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, vol. 6, 100453. https://doi.org/10.1016/j.puhip.2023.100453

TY - JOUR

T1 - Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations – The RODAM study

AU - Osei-Yeboah, James

AU - Kengne, Andre-Pascal

AU - Owusu-Dabo, Ellis

AU - Schulze, Matthias B.

AU - Meeks, Karlijn A.C.

AU - Klipstein-Grobusch, Kerstin

AU - Smeeth, Liam

AU - Bahendeka, Silver

AU - Beune, Erik

AU - Moll van Charante, Eric P.

AU - Agyemang, Charles

PY - 2023/12

Y1 - 2023/12

N2 - 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

AB - 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

KW - Diabetes risk prediction

KW - External validation

KW - Migrant population

KW - Sub-Saharan Africa population

UR - https://www.scopus.com/pages/publications/85178208511

U2 - 10.1016/j.puhip.2023.100453

DO - 10.1016/j.puhip.2023.100453

M3 - Article

C2 - 38034345

SN - 2666-5352

VL - 6

JO - Public health in practice

JF - Public health in practice

M1 - 100453

ER -

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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Keywords

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