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Phan TP, Alkema L, Tai ES, et al. Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ Open Diabetes Res Care. 2014;2(1):e000012doi: 10.1136/bmjdrc-2013-000012.
Phan, T. P., Alkema, L., Tai, E. S., Tan, K. H., Yang, Q., Lim, W. Y., Teo, Y. Y., Cheng, C. Y., Wang, X., Wong, T. Y., Chia, K. S., & Cook, A. R. (2014). Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ open diabetes research & care, 2(1), e000012. https://doi.org/10.1136/bmjdrc-2013-000012
Phan, Thao P, et al. "Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore." BMJ open diabetes research & care vol. 2,1 (2014): e000012. doi: https://doi.org/10.1136/bmjdrc-2013-000012
Phan TP, Alkema L, Tai ES, Tan KH, Yang Q, Lim WY, Teo YY, Cheng CY, Wang X, Wong TY, Chia KS, Cook AR. Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ Open Diabetes Res Care. 2014 Jun 11;2(1):e000012. doi: 10.1136/bmjdrc-2013-000012. eCollection 2014. PMID: 25452860; PMCID: PMC4212579.
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BMJ Open Diabetes Res Care. 2014 Jun 11;2(1):e000012. doi: 10.1136/bmjdrc-2013-000012. eCollection 2014.

Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore.

BMJ open diabetes research & care

Thao P Phan, Leontine Alkema, E Shyong Tai, Kristin H X Tan, Qian Yang, Wei-Yen Lim, Yik Ying Teo, Ching-Yu Cheng, Xu Wang, Tien Yin Wong, Kee Seng Chia, Alex R Cook

Affiliations

  1. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore.
  2. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore ; Department of Statistics and Applied Probability , Faculty of Science, National University of Singapore , Singapore.
  3. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore ; Division of Endocrinology , National University Hospital and National University Health System , Singapore ; Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore.
  4. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore ; Ministry of Health , Singapore.
  5. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore ; Department of Statistics and Applied Probability , Faculty of Science, National University of Singapore , Singapore ; Life Sciences Institute, National University of Singapore , Singapore.
  6. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore ; Department of Ophthalmology , Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore ; Singapore Eye Research Institute , Singapore ; Center for Quantitative Medicine, Duke-NUS Graduate Medical School , Singapore.
  7. Department of Ophthalmology , Yong Loo Lin School of Medicine, National University of Singapore and National University Health System , Singapore ; Singapore Eye Research Institute , Singapore.
  8. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore ; Department of Statistics and Applied Probability , Faculty of Science, National University of Singapore , Singapore ; Program in Health Services and Systems Research, Duke-NUS Graduate Medical School , Singapore ; Yale-NUS College , Singapore.

PMID: 25452860 PMCID: PMC4212579 DOI: 10.1136/bmjdrc-2013-000012

Abstract

OBJECTIVE: Singapore is a microcosm of Asia as a whole, and its rapidly ageing, increasingly sedentary population heralds the chronic health problems other Asian countries are starting to face and will likely face in the decades ahead. Forecasting the changing burden of chronic diseases such as type 2 diabetes in Singapore is vital to plan the resources needed and motivate preventive efforts.

METHODS: This paper describes an individual-level simulation model that uses evidence synthesis from multiple data streams-national statistics, national health surveys, and four cohort studies, and known risk factors-aging, obesity, ethnicity, and genetics-to forecast the prevalence of type 2 diabetes in Singapore. This comprises submodels for mortality, fertility, migration, body mass index trajectories, genetics, and workforce participation, parameterized using Markov chain Monte Carlo methods, and permits forecasts by ethnicity and employment status.

RESULTS: We forecast that the obesity prevalence will quadruple from 4.3% in 1990 to 15.9% in 2050, while the prevalence of type 2 diabetes (diagnosed and undiagnosed) among Singapore adults aged 18-69 will double from 7.3% in 1990 to 15% in 2050, that ethnic Indians and Malays will bear a disproportionate burden compared with the Chinese majority, and that the number of patients with diabetes in the workforce will grow markedly.

CONCLUSIONS: If the recent rise in obesity prevalence continues, the lifetime risk of type 2 diabetes in Singapore will be one in two by 2050 with concomitant implications for greater healthcare expenditure, productivity losses, and the targeting of health promotion programmes.

Keywords: Adult Diabetes; Demographics; Simulation; Statistical Methods

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