IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v32y2015i3.html
   My bibliography  Save this article

A multistate model to project elderly disability in case of limited data

Author

Listed:
  • Nicole L. Van Der Gaag

    (Nederlands Interdisciplinair Demografisch Instituut (NIDI))

  • Joop de Beer

    (Nederlands Interdisciplinair Demografisch Instituut (NIDI))

  • Govert Ewout Bijwaard

    (Nederlands Interdisciplinair Demografisch Instituut (NIDI))

  • Luc Bonneux

    (Groenhuysen)

Abstract

Background: Prevalence of disability depends on when a person becomes disabled (disability incidence) and when he or she dies (mortality). Multistate projection models can take into account both underlying processes of disability prevalence. The application of these models, however, is often hampered by high data requirements. Objective: This paper describes a generic estimation procedure for calculating disability incidence rates and mortality rates by disability status from data on disability prevalence and overall mortality. The procedure allows for the addition of risk factors. Methods: We estimate disability incidence rates from disability prevalence and mortality rates by disability status using prevalence data on disability from SHARE and mortality data from Eurostat and the Rotterdam Study of Health (ERGO). We use these rates to project future trends of ADL-disability prevalence among the elderly in the Netherlands for the period 2008-2040 using the multistate projection model LIPRO. Results: This paper shows that even in the case of limited data, multistate projection models can be applied to project trends in disability prevalence. In a scenario that assumes constant disability incidence rates, disability prevalence among the elderly will increase even though the mortality rates of disabled persons exceed those of non-disabled people. In a scenario that assumes declining incidence rates at the same pace as declining mortality rates, disability prevalence will be significantly lower. This latter scenario results in an almost similar decline in disability prevalence as the scenario assuming a strong reduction of age-specific obesity among the elderly. One conclusion, therefore, could be that the prevalence of obesity should be seriously reduced to reach a strong reduction of disability incidence. Conclusions: The strength of this method to calculate disability incidence-rates based on disability prevalence-rates is that the relationship between changes in mortality and changes in disability is taken into account, and that the effects of risk factors can be estimated. The improved transparency of the projections, the generic nature of the model and the applicability to all (European) countries with disability prevalence data make it a useful instrument for making plausible projections of future patterns of disability prevalence based on disability incidence.

Suggested Citation

  • Nicole L. Van Der Gaag & Joop de Beer & Govert Ewout Bijwaard & Luc Bonneux, 2015. "A multistate model to project elderly disability in case of limited data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 32(3), pages 75-106.
    • Handle: RePEc:dem:demres:v:32:y:2015:i:3
    DOI: 10.4054/DemRes.2015.32.3
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol32/3/32-3.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/DemRes.2015.32.3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bartosz Przywara, 2010. "Projecting future health care expenditure at European level: drivers, methodology and main results," European Economy - Economic Papers 2008 - 2015 417, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    2. Roberto Impicciatore & Francesco Billari, 2011. "MAPLES: A general method for the estimation of age profiles from standard demographic surveys (with an application to fertility)," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(29), pages 719-748.
    3. Ehsan Khoman & James Mitchell & Martin Weale, 2008. "Incidence‐based estimates of life expectancy of the healthy for the UK: coherence between transition probabilities and aggregate life‐tables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 203-222, January.
    4. Liming Cai & James Lubitz, 2007. "Was there compression of disability for older Americans from 1992 to 2003?," Demography, Springer;Population Association of America (PAA), vol. 44(3), pages 479-495, August.
    5. James W. Vaupel, 2010. "Biodemography of human ageing," Nature, Nature, vol. 464(7288), pages 536-542, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. William Lim & Gaurav Khemka & David Pitt & Bridget Browne, 2019. "A method for calculating the implied no-recovery three-state transition matrix using observable population mortality incidence and disability prevalence rates among the elderly," Journal of Population Research, Springer, vol. 36(3), pages 245-282, September.
    2. Hal Caswell & Silke van Daalen, 2021. "Healthy longevity from incidence-based models: More kinds of health than stars in the sky," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(13), pages 397-452.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. William Lim & Gaurav Khemka & David Pitt & Bridget Browne, 2019. "A method for calculating the implied no-recovery three-state transition matrix using observable population mortality incidence and disability prevalence rates among the elderly," Journal of Population Research, Springer, vol. 36(3), pages 245-282, September.
    2. Hyungmin Cha & Mark D. Hayward & Chi-Tsun Chiu & Mateo Farina, 2024. "The importance of education for understanding variability of dementia onset in the United States," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 50(26), pages 733-762.
    3. Siegfried Geyer & Sveja Eberhard & Bernhard Magnus W Schmidt & Jelena Epping & Juliane Tetzlaff, 2018. "Morbidity compression in myocardial infarction 2006 to 2015 in terms of changing rates and age at occurrence: A longitudinal study using claims data from Germany," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-19, August.
    4. Edouard Debonneuil & Anne Eyraud-Loisel & Frédéric Planchet, 2017. "Conditions Of Interest Of A Longevity Megafund For Pension Funds," Working Papers hal-01571937, HAL.
    5. D. Dragone & H. Strulik, 2017. "Human Health and Aging over an Infinite Time Horizon," Working Papers wp1104, Dipartimento Scienze Economiche, Universita' di Bologna.
    6. Melinda Heinz & Nicholas Cone & Grace Da Rosa & Alex J. Bishop & Tanya Finchum, 2017. "Examining Supportive Evidence for Psychosocial Theories of Aging within the Oral History Narratives of Centenarians," Societies, MDPI, vol. 7(2), pages 1-21, April.
    7. Duncan Gillespie & Meredith Trotter & Shripad Tuljapurkar, 2014. "Divergence in Age Patterns of Mortality Change Drives International Divergence in Lifespan Inequality," Demography, Springer;Population Association of America (PAA), vol. 51(3), pages 1003-1017, June.
    8. Gregorio Gimenez & Ana Isabel Gil-Lacruz & Marta Gil-Lacruz, 2021. "Is Happiness Linked to Subjective Life Expectancy? A Study of Chilean Senior Citizens," Mathematics, MDPI, vol. 9(17), pages 1-12, August.
    9. Kashnitsky, Ilya, 2017. "A cohort is not representative of humanity," OSF Preprints 524kg, Center for Open Science.
    10. Edouard Debonneuil & Anne Eyraud-Loisel & Frédéric Planchet, 2018. "Can Pension Funds Partially Manage Longevity Risk by Investing in a Longevity Megafund?," Risks, MDPI, vol. 6(3), pages 1-27, July.
    11. Böhm, Sebastian & Grossmann, Volker & Strulik, Holger, 2021. "R&D-driven medical progress, health care costs, and the future of human longevity," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    12. Felipe Vásquez & Gibran Vita & Daniel B. Müller, 2018. "Food Security for an Aging and Heavier Population," Sustainability, MDPI, vol. 10(10), pages 1-19, October.
    13. McCarthy, David G. & Wang, Po-Lin, 2021. "Pooling mortality risk in Eurozone state pension liabilities: An application of a Bayesian coherent multi-population cohort-based mortality model," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 459-485.
    14. Iñigo Calvo-Sotomayor & Ekhi Atutxa & Ricardo Aguado, 2020. "Who Is Afraid of Population Aging? Myths, Challenges and an Open Question from the Civil Economy Perspective," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    15. Yevgeniy Goryakin & Sophie P Thiébaut & Sébastien Cortaredona & M Aliénor Lerouge & Michele Cecchini & Andrea B Feigl & Bruno Ventelou, 2020. "Assessing the future medical cost burden for the European health systems under alternative exposure-to-risks scenarios," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-14, September.
    16. Tao Bu & Rui Ma & Yueheng Wang & Xinyuan Wang & Daisheng Tang & Liyuan Deng, 2024. "Measurement on Health Capital of Workforce: Evidence from China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 174(2), pages 569-587, September.
    17. Ting Li & James Anderson, 2013. "Shaping human mortality patterns through intrinsic and extrinsic vitality processes," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(12), pages 341-372.
    18. Vladimir Canudas-Romo & Collin Payne & Tianyu Shen, 2021. "The role of reductions in old-age mortality in old-age population growth," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(44), pages 1073-1084.
    19. Kulinskaya, Elena & Gitsels, Lisanne Andra & Bakbergenuly, Ilyas & Wright, Nigel R., 2021. "Dynamic hazards modelling for predictive longevity risk assessment," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 222-231.
    20. Liming Cai & Eileen Crimmins & Yasuhiko Saito & Mark D. Hayward & James Lubitz & Aaron Hagedorn, 2010. "Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(6), pages 129-158.

    More about this item

    Keywords

    projections; long-term care; obesity; multistate models; disability;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dem:demres:v:32:y:2015:i:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.