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

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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
    International Health Policy Program (IHPP), 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)

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.

Suggested Citation

  • Panupong Tantirat & Repeepong Suphanchaimat & Thanit Rattanathumsakul & Thinakorn Noree, 2020. "Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030," IJERPH, MDPI, vol. 17(22), pages 1-12, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8703-:d:449755
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    1. Joan Costa-Font & Raphael Wittenberg & Concepció Patxot & Adelina Comas-Herrera & Cristiano Gori & Alessandra di Maio & Linda Pickard & Alessandro Pozzi & Heinz Rothgang, 2008. "Projecting Long-Term Care Expenditure in Four European Union Member States: The Influence of Demographic Scenarios," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 86(2), pages 303-321, April.
    2. Lagergren, Marten, 2005. "Whither care of older persons in Sweden?--a prospective analysis based upon simulation model calculations, 2000-2030," Health Policy, Elsevier, vol. 74(3), pages 325-334, November.
    3. Rickayzen, B.D. & Walsh, D.E.P., 2002. "A Multi-State Model of Disability for the United Kingdom: Implications for Future Need for Long-Term Care for the Elderly," British Actuarial Journal, Cambridge University Press, vol. 8(2), pages 341-393, June.
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    1. Jinpitcha Mamom & Hanvedes Daovisan, 2022. "Listening to Caregivers’ Voices: The Informal Family Caregiver Burden of Caring for Chronically Ill Bedridden Elderly Patients," IJERPH, MDPI, vol. 19(1), pages 1-15, January.
    2. Sakchai Pattra & Cung Nawl Thawng & Sanhawat Chaiwong, 2023. "“Four Joints of Power” Innovation of Community Involvement in Medical Waste Management of Bed-Bound Patients in Thailand," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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