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Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA)
Background: Demographic ageing is a population health success story but poses unprecedented policy challenges in the 21st century. Policymakers must prepare health systems, economies and societies for these challenges. Policy choices can be usefully informed by models that evaluate outcomes and trad...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554695/ https://www.ncbi.nlm.nih.gov/pubmed/36262382 http://dx.doi.org/10.12688/hrbopenres.13525.2 |
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author | May, Peter Normand, Charles Matthews, Soraya Kenny, Rose Anne Romero-Ortuno, Roman Tysinger, Bryan |
author_facet | May, Peter Normand, Charles Matthews, Soraya Kenny, Rose Anne Romero-Ortuno, Roman Tysinger, Bryan |
author_sort | May, Peter |
collection | PubMed |
description | Background: Demographic ageing is a population health success story but poses unprecedented policy challenges in the 21st century. Policymakers must prepare health systems, economies and societies for these challenges. Policy choices can be usefully informed by models that evaluate outcomes and trade-offs in advance under different scenarios. Methods: We developed a dynamic demographic-economic microsimulation model for the population aged 50 and over in Ireland: the Irish Future Older Adults Model (IFOAM). Our principal dataset was The Irish Longitudinal Study on Ageing (TILDA). We employed first-order Markovian competing risks models to estimate transition probabilities of TILDA participants to different outcomes: diagnosis of serious diseases, functional limitations, risk-modifying behaviours, health care use and mortality. We combined transition probabilities with the characteristics of the stock population to estimate biennial changes in outcome state. Results: IFOAM projections estimated large annual increases in total deaths, in the number of people living and dying with serious illness and functional impairment, and in demand for hospital care between 2018 and 2040. The most important driver of these increases is the rising absolute number of older people in Ireland as the population ages. The increasing proportion of older old and oldest old citizens is projected to increase the average prevalence of chronic conditions and functional limitations. We deemed internal validity to be good but lacked external benchmarks for validation and corroboration of most outcomes. Conclusion: We have developed and validated a microsimulation model that projects health and related outcomes among older people in Ireland. Future research should address identified policy questions. The model enhances the capacity of researchers and policymakers to quantitatively forecast health and economic dynamics among older people in Ireland, to evaluate ex ante policy responses to these dynamics, and to collaborate internationally on global challenges associated with demographic ageing. |
format | Online Article Text |
id | pubmed-9554695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-95546952022-10-18 Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA) May, Peter Normand, Charles Matthews, Soraya Kenny, Rose Anne Romero-Ortuno, Roman Tysinger, Bryan HRB Open Res Method Article Background: Demographic ageing is a population health success story but poses unprecedented policy challenges in the 21st century. Policymakers must prepare health systems, economies and societies for these challenges. Policy choices can be usefully informed by models that evaluate outcomes and trade-offs in advance under different scenarios. Methods: We developed a dynamic demographic-economic microsimulation model for the population aged 50 and over in Ireland: the Irish Future Older Adults Model (IFOAM). Our principal dataset was The Irish Longitudinal Study on Ageing (TILDA). We employed first-order Markovian competing risks models to estimate transition probabilities of TILDA participants to different outcomes: diagnosis of serious diseases, functional limitations, risk-modifying behaviours, health care use and mortality. We combined transition probabilities with the characteristics of the stock population to estimate biennial changes in outcome state. Results: IFOAM projections estimated large annual increases in total deaths, in the number of people living and dying with serious illness and functional impairment, and in demand for hospital care between 2018 and 2040. The most important driver of these increases is the rising absolute number of older people in Ireland as the population ages. The increasing proportion of older old and oldest old citizens is projected to increase the average prevalence of chronic conditions and functional limitations. We deemed internal validity to be good but lacked external benchmarks for validation and corroboration of most outcomes. Conclusion: We have developed and validated a microsimulation model that projects health and related outcomes among older people in Ireland. Future research should address identified policy questions. The model enhances the capacity of researchers and policymakers to quantitatively forecast health and economic dynamics among older people in Ireland, to evaluate ex ante policy responses to these dynamics, and to collaborate internationally on global challenges associated with demographic ageing. F1000 Research Limited 2022-06-10 /pmc/articles/PMC9554695/ /pubmed/36262382 http://dx.doi.org/10.12688/hrbopenres.13525.2 Text en Copyright: © 2022 May P et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Method Article May, Peter Normand, Charles Matthews, Soraya Kenny, Rose Anne Romero-Ortuno, Roman Tysinger, Bryan Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA) |
title | Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA) |
title_full | Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA) |
title_fullStr | Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA) |
title_full_unstemmed | Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA) |
title_short | Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA) |
title_sort | projecting future health and service use among older people in ireland: an overview of a dynamic microsimulation model in the irish longitudinal study on ageing (tilda) |
topic | Method Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554695/ https://www.ncbi.nlm.nih.gov/pubmed/36262382 http://dx.doi.org/10.12688/hrbopenres.13525.2 |
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