2014
Abdulkareem J. Al-Quwaidhi; Mark S. Pearce; Eugene Sobngwi; Julia A. Critchley; Martin O’Flaherty
Comparison of type 2 diabetes prevalence estimates in Saudi Arabia from a validated Markov model against the International Diabetes Federation and other modelling studies Article de journal
Dans: Diabetes Research and Clinical Practice, vol. 103, iss. 3, p. 496-503, 2014, ISSN: 01688227.
Résumé | Liens | BibTeX | Étiquettes: Diabetes, Modelling, Prevalence, Saudi Arabia
@article{Al-Quwaidhi2014,
title = {Comparison of type 2 diabetes prevalence estimates in Saudi Arabia from a validated Markov model against the International Diabetes Federation and other modelling studies},
author = {Abdulkareem J. Al-Quwaidhi and Mark S. Pearce and Eugene Sobngwi and Julia A. Critchley and Martin O’Flaherty},
url = {http://dx.doi.org/10.1016/j.diabres.2013.12.036 https://linkinghub.elsevier.com/retrieve/pii/S0168822713004701},
doi = {10.1016/j.diabres.2013.12.036},
issn = {01688227},
year = {2014},
date = {2014-01-01},
journal = {Diabetes Research and Clinical Practice},
volume = {103},
issue = {3},
pages = {496-503},
publisher = {Elsevier Ireland Ltd},
abstract = {Aims: To compare the estimates and projections of type 2 diabetes mellitus (T2DM) prevalence in Saudi Arabia from a validated Markov model against other modelling estimates, such as those produced by the International Diabetes Federation (IDF) Diabetes Atlas and the Global Burden of Disease (GBD) project. Methods: A discrete-state Markov model was developed and validated that integrates data on population, obesity and smoking prevalence trends in adult Saudis aged ≥25 years to estimate the trends in T2DM prevalence (annually from 1992 to 2022). The model was validated by comparing the age- and sex-specific prevalence estimates against a national survey conducted in 2005. Results: Prevalence estimates from this new Markov model were consistent with the 2005 national survey and very similar to the GBD study estimates. Prevalence in men and women in 2000 was estimated by the GBD model respectively at 17.5% and 17.7%, compared to 17.7% and 16.4% in this study. The IDF estimates of the total diabetes prevalence were considerably lower at 16.7% in 2011 and 20.8% in 2030, compared with 29.2% in 2011 and 44.1% in 2022 in this study. Conclusion: In contrast to other modelling studies, both the Saudi IMPACT Diabetes Forecast Model and the GBD model directly incorporated the trends in obesity prevalence and/or body mass index (BMI) to inform T2DM prevalence estimates. It appears that such a direct incorporation of obesity trends in modelling studies results in higher estimates of the future prevalence of T2DM, at least in countries where obesity has been rapidly increasing. © 2013 Elsevier Ireland Ltd.},
keywords = {Diabetes, Modelling, Prevalence, Saudi Arabia},
pubstate = {published},
tppubtype = {article}
}
Aims: To compare the estimates and projections of type 2 diabetes mellitus (T2DM) prevalence in Saudi Arabia from a validated Markov model against other modelling estimates, such as those produced by the International Diabetes Federation (IDF) Diabetes Atlas and the Global Burden of Disease (GBD) project. Methods: A discrete-state Markov model was developed and validated that integrates data on population, obesity and smoking prevalence trends in adult Saudis aged ≥25 years to estimate the trends in T2DM prevalence (annually from 1992 to 2022). The model was validated by comparing the age- and sex-specific prevalence estimates against a national survey conducted in 2005. Results: Prevalence estimates from this new Markov model were consistent with the 2005 national survey and very similar to the GBD study estimates. Prevalence in men and women in 2000 was estimated by the GBD model respectively at 17.5% and 17.7%, compared to 17.7% and 16.4% in this study. The IDF estimates of the total diabetes prevalence were considerably lower at 16.7% in 2011 and 20.8% in 2030, compared with 29.2% in 2011 and 44.1% in 2022 in this study. Conclusion: In contrast to other modelling studies, both the Saudi IMPACT Diabetes Forecast Model and the GBD model directly incorporated the trends in obesity prevalence and/or body mass index (BMI) to inform T2DM prevalence estimates. It appears that such a direct incorporation of obesity trends in modelling studies results in higher estimates of the future prevalence of T2DM, at least in countries where obesity has been rapidly increasing. © 2013 Elsevier Ireland Ltd.