Table 3.
Comparison of the Saudi IMPACT T2DM Forecast Model against other modelling studies.
| Shaw et al. [6] | Wild et al. [7] | King et al. [8] | Amos et al. [9] | Saudi IMPACT Diabetes Forecast Model | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Estimated DM prevalence in Saudi Arabia (%) | 2010 | Total: 13.6 | 2000 | Total: 6.2 | 1995 | Total: 8.7 | 1995 | Total: 10.0 | 1995 | Total: 11.1 |
| 2030 | Total: 17.0 | 2030 | Total: 8.1 | 2000 | Total: 9.1 | 2000 | Total: 12.0 | 2000 | Total: 17.2 | |
| 2025 | Total: 10.1 | 2010 | Total: 13.8 | 2010 | Total: 28.1 | |||||
| 2022 | Total: 44.1 | |||||||||
| Age of study population (years) | 20–79 | 20+ | 20+ | 20+ | 25+ | |||||
| Main data sources for DM prevalence in Saudi Arabia | Al-Nuaim et al. [31] El-Hazmi et al. [32] Al-Nozha et al. [26] |
El-Hazmi et al. [32] | Asfour et al. [27](Study from Oman) | El-Hazmi et al. [33] Asfour et al. [27](study from Oman) |
Warsy and El-Hazmi [17] (for starting year prevalence) WHO STEPS [12] (for validation) |
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| Estimation methodology | Logistic regression modelling | DISMOD 2 | Age-specific diabetes prevalence estimates were applied to UN population estimates and projections | Country-specific diabetes prevalence data were applied to the corresponding national age distribution | Markov modelling | |||||
| Covariates used for estimating DM prevalence | • Demographic changes • Urbanisation |
• Demographic changes • Urbanisation |
• Trends in population size and age structure • Urbanisation |
• Level of economic development (GNP per capita) • Urbanisation |
• Trends in population structure • Trends in obesity prevalence • Trends in smoking prevalence • Estimated incidence of T2DM • Estimated case-fatality rate • Evidence-based estimates of RRs for transition probabilities |
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