IDEAS home Printed from https://ideas.repec.org/a/spr/eurpop/v29y2013i4d10.1007_s10680-013-9300-y.html
   My bibliography  Save this article

Applying Relational Models to the Graduation of Disability Schedules
[Application de modèles relationnels pour le lissage de schémas d’incapacités]

Author

Listed:
  • Alan Marshall

    (The University of Manchester)

  • Paul Norman

    (The University of Leeds)

  • Ian Plewis

    (The University of Manchester)

Abstract

Age-specific rates of particular disability types are important for planning purposes and are a valuable input to estimates and projections of populations with different disabilities. However, survey estimates of schedules of disability rates display evidence of sampling variability and sub-national disability schedules are often unavailable for reasons of disclosure protection. This paper develops and evaluates a method to smooth sampling variability in national schedules of disability using a technique that has applicability to sub-national estimation of age-specific disability rates. Relational models are used to adjust the limiting long-term illness schedule for England (Census 2001) to represent different disability schedules (Health Survey for England 2000/2001) smoothing sampling fluctuations. For hearing disability a simple Brass relational model involving two parameters provides a good fit. For other disability types a modified version of the Ewbank relational model with three parameters is required. This paper illustrates that relational models can accurately capture the relationship between age-specific rates of limiting long-term illness and various disability types.

Suggested Citation

  • Alan Marshall & Paul Norman & Ian Plewis, 2013. "Applying Relational Models to the Graduation of Disability Schedules [Application de modèles relationnels pour le lissage de schémas d’incapacités]," European Journal of Population, Springer;European Association for Population Studies, vol. 29(4), pages 467-491, November.
  • Handle: RePEc:spr:eurpop:v:29:y:2013:i:4:d:10.1007_s10680-013-9300-y
    DOI: 10.1007/s10680-013-9300-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10680-013-9300-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10680-013-9300-y?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    2. Jan Hoem & Dan Madien & Jørgen Nielsen & Else-Marie Ohlsen & Hans Hansen & Bo Rennermalm, 1981. "Experiments in modelling recent Danish fertility curves," Demography, Springer;Population Association of America (PAA), vol. 18(2), pages 231-244, May.
    3. Peter Congdon, 1993. "Statistical Graduation in Local Demographic Analysis and Projection," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(2), pages 237-270, March.
    4. David Coleman, 2013. "The Twilight of the Census," Population and Development Review, The Population Council, Inc., vol. 38, pages 334-351, February.
    Full references (including those not matched with items on IDEAS)

    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. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    2. Joanne Ellison & Erengul Dodd & Jonathan J. Forster, 2020. "Forecasting of cohort fertility under a hierarchical Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 829-856, June.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    5. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    6. Mikko Myrskylä & Joshua R. Goldstein, 2010. "Probabilistic forecasting using stochastic diffusion models, with applications to cohort processes of marriage and fertility," MPIDR Working Papers WP-2010-013, Max Planck Institute for Demographic Research, Rostock, Germany.
    7. repec:hum:wpaper:sfb649dp2009-015 is not listed on IDEAS
    8. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    9. Chen, Quanrun & Dietzenbacher, Erik & Los, Bart, 2015. "The effects of ageing and urbanization on China's future population and labor force," Research Report 15002-GEM, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    10. Alan J. Auerbach & Ronald Lee, 2009. "Notional Defined Contribution Pension Systems in a Stochastic Context: Design and Stability," NBER Chapters, in: Social Security Policy in a Changing Environment, pages 43-68, National Bureau of Economic Research, Inc.
    11. Osman Gulseven, 2016. "Forecasting Population and Demographic Composition of Kuwait Until 2030," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1429-1435.
    12. Ugofilippo Basellini & Søren Kjærgaard & Carlo Giovanni Camarda, 2020. "An age-at-death distribution approach to forecast cohort mortality," Working Papers axafx5_3agsuwaphvlfk, French Institute for Demographic Studies.
    13. Basellini, Ugofilippo & Kjærgaard, Søren & Camarda, Carlo Giovanni, 2020. "An age-at-death distribution approach to forecast cohort mortality," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 129-143.
    14. Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 777-801, June.
    15. Katja Hanewald & Thomas Post & Helmut Gründl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(3), pages 458-475, July.
    16. Michel Denuit, 2009. "Life Anuities with Stochastic Survival Probabilities: A Review," Methodology and Computing in Applied Probability, Springer, vol. 11(3), pages 463-489, September.
    17. Vianney Costemalle, 2020. "Bayesian Probabilistic Population Projections for France," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 520-521, pages 29-47.
    18. Cristina Rueda-Sabater & Pedro Alvarez-Esteban, 2008. "The analysis of age-specific fertility patterns via logistic models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(9), pages 1053-1070.
    19. Gleditsch Rebecca Folkman & Syse Astri & Thomas Michael J., 2021. "Fertility Projections in a European Context: A Survey of Current Practices among Statistical Agencies," Journal of Official Statistics, Sciendo, vol. 37(3), pages 547-568, September.
    20. Arkadiusz Wiśniowski & Peter Smith & Jakub Bijak & James Raymer & Jonathan Forster, 2015. "Bayesian Population Forecasting: Extending the Lee-Carter Method," Demography, Springer;Population Association of America (PAA), vol. 52(3), pages 1035-1059, June.
    21. Nico Keilman & Dinh Quang Pham, 2000. "Predictive Intervals for Age-Specific Fertility," European Journal of Population, Springer;European Association for Population Studies, vol. 16(1), pages 41-65, March.

    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:spr:eurpop:v:29:y:2013:i:4:d:10.1007_s10680-013-9300-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.