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Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030
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 o...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7700511/ https://www.ncbi.nlm.nih.gov/pubmed/33238588 http://dx.doi.org/10.3390/ijerph17228703 |
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author | Tantirat, Panupong Suphanchaimat, Repeepong Rattanathumsakul, Thanit Noree, Thinakorn |
author_facet | Tantirat, Panupong Suphanchaimat, Repeepong Rattanathumsakul, Thanit Noree, Thinakorn |
author_sort | Tantirat, Panupong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7700511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77005112020-11-30 Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030 Tantirat, Panupong Suphanchaimat, Repeepong Rattanathumsakul, Thanit Noree, Thinakorn Int J Environ Res Public Health Article 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. MDPI 2020-11-23 2020-11 /pmc/articles/PMC7700511/ /pubmed/33238588 http://dx.doi.org/10.3390/ijerph17228703 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tantirat, Panupong Suphanchaimat, Repeepong Rattanathumsakul, Thanit Noree, Thinakorn Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030 |
title | Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030 |
title_full | Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030 |
title_fullStr | Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030 |
title_full_unstemmed | Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030 |
title_short | Projection of the Number of Elderly in Different Health States in Thailand in the Next Ten Years, 2020–2030 |
title_sort | projection of the number of elderly in different health states in thailand in the next ten years, 2020–2030 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7700511/ https://www.ncbi.nlm.nih.gov/pubmed/33238588 http://dx.doi.org/10.3390/ijerph17228703 |
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