A Population Dynamic Model to Assess the Diabetes Screening and Reporting Programs and Project the Burden of Undiagnosed Diabetes in Thailand
by
Wiriya Mahikul 1, Lisa J White 2,3, Kittiyod Poovorawan 4, Ngamphol Soonthornworasiri 1, Pataporn Sukontamarn 5, Phetsavanh Chanthavilay 2,6, Wirichada Pan-ngum 1,2,*
and Graham F Medley 7
Wiriya Mahikul 1, Lisa J White 2,3, Kittiyod Poovorawan 4, Ngamphol Soonthornworasiri 1, Pataporn Sukontamarn 5, Phetsavanh Chanthavilay 2,6, Wirichada Pan-ngum 1,2,* and Graham F Medley 7
1
Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
2
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
3
Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
4
Department of Clinical Tropical Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
5
College of Population Studies, Chulalongkorn University, Bangkok 10330, Thailand
6
Institute of Research and Education Development, UHS, Vientiane 7444, Laos
7
Centre for Mathematical Modelling of Infectious Disease & Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(12), 2207; https://doi.org/10.3390/ijerph16122207
Received: 4 May 2019 / Revised: 29 May 2019 / Accepted: 30 May 2019 / Published: 21 June 2019
(This article belongs to the Special Issue Global Burden of Disease: Diversity of Socioeconomic Consequences Worldwide)
Diabetes mellitus (DM) is rising worldwide, exacerbated by aging populations. We estimated and predicted the diabetes burden and mortality due to undiagnosed diabetes together with screening program efficacy and reporting completeness in Thailand, in the context of demographic changes. An age and sex structured dynamic model including demographic and diagnostic processes was constructed. The model was validated using a Bayesian Markov Chain Monte Carlo (MCMC) approach. The prevalence of DM was predicted to increase from 6.5% (95% credible interval: 6.3–6.7%) in 2015 to 10.69% (10.4–11.0%) in 2035, with the largest increase (72%) among 60 years or older. Out of the total DM cases in 2015, the percentage of undiagnosed DM cases was 18.2% (17.4–18.9%), with males higher than females (p-value < 0.01). The highest group with undiagnosed DM was those aged less than 39 years old, 74.2% (73.7–74.7%). The mortality of undiagnosed DM was ten-fold greater than the mortality of those with diagnosed DM. The estimated coverage of diabetes positive screening programs was ten-fold greater for elderly compared to young. The positive screening rate among females was estimated to be significantly higher than those in males. Of the diagnoses, 87.4% (87.0–87.8%) were reported. Targeting screening programs and good reporting systems will be essential to reduce the burden of disease.
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Keywords:
population dynamic model; diabetes; undiagnosed diabetes; aging population; screening; reporting; mortality; Bayesian MCMC
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MDPI and ACS Style
Mahikul, W.; J White, L.; Poovorawan, K.; Soonthornworasiri, N.; Sukontamarn, P.; Chanthavilay, P.; Pan-ngum, W.; F Medley, G. A Population Dynamic Model to Assess the Diabetes Screening and Reporting Programs and Project the Burden of Undiagnosed Diabetes in Thailand. Int. J. Environ. Res. Public Health 2019, 16, 2207.
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