Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030

Panupong Tantirat, Repeepong Suphanchaimat, Thanit Rattanathumsakul and Thinakorn Noree
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Panupong Tantirat: Division of Epidemiology, Disease Control, Ministry of Public Health, Nonthaburi 11000, Thailand
Repeepong Suphanchaimat: Division of Epidemiology, Disease Control, Ministry of Public Health, Nonthaburi 11000, Thailand
Thanit Rattanathumsakul: Division of Epidemiology, Disease Control, Ministry of Public Health, Nonthaburi 11000, Thailand
Thinakorn Noree: International Health Policy Program (IHPP), Ministry of Public Health, Nonthaburi 11000, Thailand

IJERPH, 2020, vol. 17, issue 22, 1-12

Abstract: The objective of this study is to predict the volume of the elderly in different health status categories in Thailand in the next ten years (2020–2030). Multistate modelling was performed. We defined four states of elderly patients (aged ≥ 60 years) according to four different levels of Activities of Daily Living (ADL): social group; home group; bedridden group; and dead group. The volume of newcomers was projected by trend extrapolation methods with exponential growth. The transition probabilities from one state to another was obtained by literature review and model optimization. The mortality rate was obtained by literature review. Sensitivity analysis was conducted. By 2030, the number of social, home, and bedridden groups was 15,593,054, 321,511, and 152,749, respectively. The model prediction error was 1.75%. Sensitivity analysis with the change of transition probabilities by 20% caused the number of bedridden patients to vary from between 150,249 and 155,596. In conclusion, the number of bedridden elders will reach 153,000 in the next decade (3 times larger than the status quo). Policy makers may consider using this finding as an input for future resource planning and allocation. Further studies should be conducted to identify the parameters that better reflect the transition of people from one health state to another.

Keywords: bedridden; long-term care; multi-state modelling; elderly; Thailand (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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