Dataset Information

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

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.

SUBMITTER: Phan TP

PROVIDER: S-EPMC4212579 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

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

Phan Thao P TP Alkema Leontine L Tai E Shyong ES Tan Kristin H X KH Yang Qian Q Lim Wei-Yen WY Teo Yik Ying YY Cheng Ching-Yu CY Wang Xu X Wong Tien Yin TY Chia Kee Seng KS Cook Alex R AR

BMJ open diabetes research & care 20140611 1

<h4>Objective</h4>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.<h4>Methods</h4>This paper describes an individual-level simulation model that uses evidence synth ...[more]

PMID: 25452860

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Project description:ObjectivesThe COVID-19 pandemic (caused by SARS-CoV-2) has introduced significant challenges for accurate prediction of population morbidity and mortality by traditional variable-based methods of estimation. Challenges to modelling include inadequate viral physiology comprehension and fluctuating definitions of positivity between national-to-international data. This paper proposes that accurate forecasting of COVID-19 caseload may be best preformed non-parametrically, by vector autoregression (VAR) of verifiable data regionally.MethodsA non-linear VAR model across 7 major demographically representative New York City (NYC) metropolitan region counties was constructed using verifiable daily COVID-19 caseload data March 12-July 23, 2020. Through association of observed case trends with a series of (county-specific) data-driven dynamic interdependencies (lagged values), a systematically non-assumptive approximation of VAR representation for COVID-19 patterns to-date and prospective upcoming trends was produced.ResultsModified VAR regression of NYC area COVID-19 caseload trends proves highly significant modelling capacity of observed patterns in longitudinal disease incidence (county R2 range: 0.9221-0.9751, all p < 0.001). Predictively, VAR regression of daily caseload results at a county-wide level demonstrates considerable short-term forecasting fidelity (p < 0.001 at one-step ahead) with concurrent capacity for longer-term (tested 11-week period) inferences of consistent, reasonable upcoming patterns from latest (model data update) disease epidemiology.ConclusionsIn contrast to macroscopic variable-assumption projections, regionally-founded VAR modelling may substantially improve projection of short-term community disease burden, reduce potential for biostatistical error, as well as better model epidemiological effects resultant from intervention. Predictive VAR extrapolation of existing public health data at an interdependent regional scale may improve accuracy of current pandemic burden prognoses.

Dataset Information

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

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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