IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v14y2006i9.html
   My bibliography  Save this article

A model for geographical variation in health and total life expectancy

Author

Listed:
  • Peter Congdon

    (Queen Mary University of London)

Abstract

This paper develops a joint approach to life and health expectancy based on 2001 UK Census data for limiting long term illness and general health status, and on registered death occurrences in 2001. The model takes account of the interdependence of different outcomes (e.g. ill health and mortality) as well as spatial correlation in their patterns. A particular focus is on the proportionality assumption or ‘multiplicative model’ whereby separate age and area effects multiply to produce age-area mortality rates. Alternative non-proportional models are developed and shown to be more parsimonious as well as more appropriate to actual area-age interdependence. The application involves mortality and health status in the 33 London Boroughs.

Suggested Citation

  • Peter Congdon, 2006. "A model for geographical variation in health and total life expectancy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 14(9), pages 157-178.
    • Handle: RePEc:dem:demres:v:14:y:2006:i:9
    DOI: 10.4054/DemRes.2006.14.9
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol14/9/14-9.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/DemRes.2006.14.9?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. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    2. Ying C. MacNab & C. B. Dean, 2001. "Autoregressive Spatial Smoothing and Temporal Spline Smoothing for Mapping Rates," Biometrics, The International Biometric Society, vol. 57(3), pages 949-956, September.
    3. Geweke, John F & Meese, Richard, 1981. "Estimating Regression Models of Finite but Unknown Order," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 55-70, February.
    4. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    5. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    6. Ronald Lee, 2000. "The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 80-91.
    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. 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.
    2. Taylor, Joanna & Twigg, Liz & Moon, Graham, 2014. "The convergent validity of three surveys as alternative sources of health information to the 2011 UK census," Social Science & Medicine, Elsevier, vol. 116(C), pages 187-192.

    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. Lanza Queiroz, Bernardo & Lobo Alves Ferreira, Matheus, 2021. "The evolution of labor force participation and the expected length of retirement in Brazil," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    2. Mason, Carl N. & Miller, Timothy, 2018. "International projections of age specific healthcare consumption: 2015–2060," The Journal of the Economics of Ageing, Elsevier, vol. 12(C), pages 202-217.
    3. Ahbab Mohammad Fazle Rabbi & Stefano Mazzuco, 2021. "Mortality Forecasting with the Lee–Carter Method: Adjusting for Smoothing and Lifespan Disparity," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 97-120, March.
    4. Koissi, Marie-Claire & Shapiro, Arnold F., 2006. "Fuzzy formulation of the Lee-Carter model for mortality forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 39(3), pages 287-309, December.
    5. Koissi, Marie-Claire & Shapiro, Arnold F. & Hognas, Goran, 2006. "Evaluating and extending the Lee-Carter model for mortality forecasting: Bootstrap confidence interval," Insurance: Mathematics and Economics, Elsevier, vol. 38(1), pages 1-20, February.
    6. Jackie Li & Atsuyuki Kogure, 2021. "Bayesian Mixture Modelling for Mortality Projection," Risks, MDPI, vol. 9(4), pages 1-12, April.
    7. Lee, Yung-Tsung & Wang, Chou-Wen & Huang, Hong-Chih, 2012. "On the valuation of reverse mortgages with regular tenure payments," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 430-441.
    8. Jackie Li, 2014. "An application of MCMC simulation in mortality projection for populations with limited data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(1), pages 1-48.
    9. Craig Anderson & Louise M. Ryan, 2017. "A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia," IJERPH, MDPI, vol. 14(2), pages 1, February.
    10. Sarkka, Aila & Renshaw, Eric, 2006. "The analysis of marked point patterns evolving through space and time," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1698-1718, December.
    11. Olivieri, Annamaria & Pitacco, Ermanno, 2008. "Assessing the cost of capital for longevity risk," Insurance: Mathematics and Economics, Elsevier, vol. 42(3), pages 1013-1021, June.
    12. Doukhan, P. & Pommeret, D. & Rynkiewicz, J. & Salhi, Y., 2017. "A class of random field memory models for mortality forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 97-110.
    13. Simon Schnürch & Torsten Kleinow & Ralf Korn, 2021. "Clustering-Based Extensions of the Common Age Effect Multi-Population Mortality Model," Risks, MDPI, vol. 9(3), pages 1-32, March.
    14. Post Thomas, 2012. "Individual Welfare Gains from Deferred Life-Annuities under Stochastic Mortality," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 6(2), pages 1-26, June.
    15. Post, Thomas & Hanewald, Katja, 2013. "Longevity risk, subjective survival expectations, and individual saving behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 200-220.
    16. Laurent Callot & Niels Haldrup & Malene Kallestrup-Lamb, 2016. "Deterministic and stochastic trends in the Lee–Carter mortality model," Applied Economics Letters, Taylor & Francis Journals, vol. 23(7), pages 486-493, May.
    17. Monica Alexander & Kivan Polimis & Emilio Zagheni, 2022. "Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(1), pages 1-28, February.
    18. Areti Boulieri & Silvia Liverani & Kees Hoogh & Marta Blangiardo, 2017. "A space–time multivariate Bayesian model to analyse road traffic accidents by severity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 119-139, January.
    19. Carlo Maccheroni & Samuel Nocito, 2017. "Backtesting the Lee–Carter and the Cairns–Blake–Dowd Stochastic Mortality Models on Italian Death Rates," Risks, MDPI, vol. 5(3), pages 1-23, July.
    20. Luis E. Nieto-Barajas, 2022. "Bayesian nonparametric dynamic hazard rates in evolutionary life tables," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 319-334, April.

    More about this item

    Keywords

    healthy life expectancy; life tables; proportionality assumption; spatial effects; disease burden;
    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:14:y:2006:i:9. 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.