Abstract
Aims
Methods
Results
Conclusions
Keywords
1. Introduction
2. Methods
2.1 Literature review
2.2 Data source review and data extraction
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2.3 Data source characteristics and selection
2.4 Estimation of diabetes prevalence
2.5 Extrapolation of diabetes estimates for countries without data or with only low-quality in-country data
2.6 Estimation of global and regional diabetes prevalence for 2021
2.7 Estimation of future diabetes prevalence
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. 2013. URL https://www.R-project.org/.
2.8 Estimation of uncertainty intervals
2.8.1 Simulation to estimate uncertainty in raw data
2.8.2 Uncertainty due to study selection
2.9 Estimation of health expenditure
3. Results
3.1 Study sources

3.2 Estimates of numbers of people with diabetes and diabetes prevalence
2021 | 2045 | |||||
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World Bank income classification | Number of people with diabetes (millions) | Diabetes prevalence (%) | Comparative diabetes prevalence (%) | Number of people with diabetes (millions) | Diabetes prevalence (%) | Comparative diabetes prevalence (%) |
World | 536.6 (424.2–612.3) | 10.5 (8.3–12.0) | 9.8 (7.7–11.2) | 783.2 (605.2–898.6) | 12.2 (9.5–14.0) | 11.2 (8.6–12.9) |
High-income countries | 103.9 (87.6–116.9) | 11.1 (9.3–12.5) | 8.4 (7.1–9.6) | 117.7 (98.4–132.4) | 12.4 (10.4–14.0) | 10.3 (8.8–11.7) |
Middle-income countries | 414.0 (321.6–470.8) | 10.8 (8.4–12.3) | 10.5 (8.0–11.9) | 623.3 (473.0–709.3) | 13.1 (9.9–14.9) | 12.0 (9.0–13.6) |
Low-income countries | 18.7 (15.0–24.6) | 5.5 (4.4–7.2) | 6.7 (5.5–9.0) | 42.2 (33.8–56.9) | 6.1 (4.9–8.3) | 7.0 (5.8–9.7) |


2021 | 2045 | ||||||
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Rank | IDF Region | Number of people with diabetes (millions) | Diabetes prevalence (%) | Comparative diabetes prevalence (%) | Number of people with diabetes (millions) | Diabetes prevalence (%) | Comparative diabetes prevalence (%) |
World | 536.6 (424.2–612.3) | 10.5 (8.3–12.0) | 9.8 (7.7–11.2) | 783.2 (605.2–898.6) | 12.2 (9.5–14.0) | 11.2 (8.6–12.9) | |
1 | MENA | 72.7 (38.2–81.8) | 16.2 (8.5–18.3) | 18.1 (9.5–20.3) | 135.7 (71.2–153.5) | 19.3 (10.1–21.9) | 20.4 (10.7–23.1) |
2 | NAC | 50.5 (44.5–57.1) | 14.0 (12.3–15.8) | 11.9 (10.0–13.6) | 62.8 (55.5–71.4) | 15.2 (13.4–17.3) | 14.2 (12.1–16.2) |
3 | SEA | 90.2 (76.8–99.9) | 8.7 (7.4–9.7) | 10.0 (8.6–11.1) | 151.5 (130.9–167.1) | 11.3 (9.8–12.5) | 11.3 (9.7–12.4) |
4 | WP | 205.6 (174.7–233.2) | 11.9 (10.1–13.5) | 9.9 (8.4–11.3) | 260.2 (220.0–297.3) | 14.4 (12.1–16.4) | 11.5 (9.8–13.1) |
5 | SACA | 32.5 (27.5–40.4) | 9.5 (8.1–11.9) | 8.2 (6.9–10.2) | 48.9 (41.1–61.3) | 11.9 (10.0–14.9) | 9.8 (8.3–12.3) |
6 | EUR | 61.4 (47.5–69.9) | 9.2 (7.1–10.4) | 7.0 (5.5–8.1) | 69.2 (51.8–78.3) | 10.4 (7.7–11.7) | 8.7 (6.6–9.9) |
7 | AFR | 23.6 (15.0–29.8) | 4.5 (2.8–5.7) | 5.3 (3.6–6.7) | 54.9 (34.8–69.7) | 5.2 (3.3–6.6) | 5.6 (3.8–7.2) |
2021 | 2045 | ||||
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Rank | Country | Comparative diabetes prevalence (%) | Rank | Country | Comparative diabetes prevalence (%) |
1 | Pakistan | 30.8 | 1 | Pakistan | 33.6 |
2 | French Polynesia | 25.2 | 2 | Kuwait | 29.8 |
3 | Kuwait | 24.9 | 3 | French Polynesia | 28.2 |
4 | New Caledonia | 23.4 | 4 | Mauritius | 26.6 |
5 | Northern Mariana Islands | 23.4 | 5 | New Caledonia | 26.2 |
6 | Nauru | 23.4 | 6 | Northern Mariana Islands | 26.2 |
7 | Marshall Islands | 23.0 | 7 | Nauru | 26.2 |
8 | Mauritius | 22.6 | 8 | Marshall Islands | 26.0 |
9 | Kiribati | 22.1 | 9 | Kiribati | 24.1 |
10 | Egypt | 20.9 | 10 | Egypt | 23.4 |
2021 | 2045 | ||||
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Rank | Country | Number of people with diabetes (millions) | Rank | Country | Number of people with diabetes (millions) |
1 | China | 140.9 | 1 | China | 174.4 |
2 | India | 74.2 | 2 | India | 124.9 |
3 | Pakistan | 33.0 | 3 | Pakistan | 62.2 |
4 | USA | 32.2 | 4 | USA | 36.3 |
5 | Indonesia | 19.5 | 5 | Indonesia | 28.6 |
6 | Brazil | 15.7 | 6 | Brazil | 23.2 |
7 | Mexico | 14.1 | 7 | Bangladesh | 22.3 |
8 | Bangladesh | 13.1 | 8 | Mexico | 21.2 |
9 | Japan | 11.0 | 9 | Egypt | 20.0 |
10 | Egypt | 10.9 | 10 | Turkey | 13.4 |
3.3 Health expenditure


4. Discussion
Sudharsanan N BD. The Demography of Aging in Low- and Middle-Income Countries: Chronological versus Functional Perspectives. In: Majmundar M, Hayward M, editors. Future Directions for the Demography of Aging: Proceedings of a Workshop Washington DC: National Academies of Sciences, Engineering, and Medicine; Division of Behavioral and Social Sciences and Education, National Academies Press 2018.
4.1 Limitations
5. Conclusion
Declaration of Competing Interest
Acknowledgements
References
Heald AH, Stedman M, Davies M, Livingston M, Alshames R, Lunt M, et al. Estimating life years lost to diabetes: outcomes from analysis of National Diabetes Audit and Office of National Statistics data. Cardiovasc Endocrinol Metab. 2020;9:183-5.
- The Lancet Commission on diabetes: using data to transform diabetes care and patient lives.Lancet. 2020; 396: 2019-2082
- Trends in incidence of total or type 2 diabetes: systematic review.BMJ. 2019; 366: l5003
Guariguata L, Whiting D, Weil C, Unwin N. The International Diabetes Federation diabetes atlas methodology for estimating global and national prevalence of diabetes in adults. Diabetes Res Clin Pract. 2011;94:322-32.
- Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement.Lancet. 2016; 388: e19-e23
United Nations. Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019, Online Edition. Rev. 1.
Veritas Health Innovation. Covidence systematic review software. Melbourne, Australia: Veritas Health Innovation; 2020.
- Artefactual inflation of type 2 diabetes prevalence in WHO STEP surveys.Trop Med Int Health. 2019; 24: 477-483
- IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.Diabetes Res Clin Pract. 2018; 138: 271-281
- Decision making with the analytic hierarchy process.Int J Serv Sci. 2008; 1: 83https://doi.org/10.1504/IJSSCI.2008.017590
United Nations. Department of Economic and Social Affairs, Population Division (2018). World Urbanization Prospects: The 2018 Revision, Online Edition.
- Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition.Diabetes Res Clin Pract. 2019; 157: 107843https://doi.org/10.1016/j.diabres.2019.107843
The World Bank. World Bank Country and Lending Groups. 2020.
Central Intelligence Agency. The World Factbook, Ethnic groups. 2015. Washington, DC: Central Intelligence Agency; 2015.
Central Intelligence Agency. The World Factbook: Languages. 2015. Washington, DC: Central Intelligence Agency; 2015.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. 2013. URL https://www.R-project.org/.
- Incorporating uncertainty measurement in the International Diabetes Federation Diabetes Atlas methodology for estimating global and national prevalence of diabetes in adults.Archives of Public Health. 2015; 73: P31
World Health Organisation. Global health expenditure database. Geneva: World Health Organisation; 2018.
Sudharsanan N BD. The Demography of Aging in Low- and Middle-Income Countries: Chronological versus Functional Perspectives. In: Majmundar M, Hayward M, editors. Future Directions for the Demography of Aging: Proceedings of a Workshop Washington DC: National Academies of Sciences, Engineering, and Medicine; Division of Behavioral and Social Sciences and Education, National Academies Press 2018.
- Association between urbanisation and type 2 diabetes: an ecological study.BMJ Glob Health. 2017; 2e000473
- Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings.Lancet Diabetes Endocrinol. 2021; 9: 203-211
- Short-term variability in measures of glycemia and implications for the classification of diabetes.Arch Intern Med. 2007; 167: 1545-1551
- incidence and mortality of type 1 and type 2 diabetes in Denmark 1996–2016.BMJ Open Diabetes Res Care. 2020; 8