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Health and mortality of the elderly: the grade of membership method, classification and determination

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  • France Portrait
  • Maarten Lindeboom
  • Dorly Deeg

Abstract

With the aging of society, issues concerning the reform of the Dutch health care system are ranked high on the political agenda. Sensible reforms of the health care system for the elderly require a thorough understanding of the health status of the old and of its dynamics preceding death. The health status of the elderly is intrinsically a multidimensional and dynamic concept and a rich set of indicators is needed to capture this concept in its full extent. This feature of health requires techniques to reduce dimensionality as, in general, it is difficult to simultaneously handle all indicators in any economic analysis. In the first part of this paper we focus on methods that comprise these multidimensional measures into a limited number of indices. The Grade of Membership (GoM) approach introduced by Manton and Woodbury (Methods of Information in Medicine 1982; 21) is specifically designed to characterize the complex concept of health. The method simultaneously identifies all dimensions of the concept of interest and the degrees to which an individual belongs to each of these types (i.e. grades of membership). We apply the method to a set of 21 indicators from a rich database of the Longitudinal Aging Study Amsterdam (LASA). The individual degrees of involvement in the different health dimensions obtained from this method are used in subsequent analyses of health and mortality. Copyright © 1999 John Wiley & Sons, Ltd.

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  • France Portrait & Maarten Lindeboom & Dorly Deeg, 1999. "Health and mortality of the elderly: the grade of membership method, classification and determination," Health Economics, John Wiley & Sons, Ltd., vol. 8(5), pages 441-458, August.
    • Handle: RePEc:wly:hlthec:v:8:y:1999:i:5:p:441-458
    DOI: 10.1002/(SICI)1099-1050(199908)8:5<441::AID-HEC452>3.0.CO;2-O
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    References listed on IDEAS

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    1. Martelin, Tuija, 1994. "Mortality by indicators of socioeconomic status among the finnish elderly," Social Science & Medicine, Elsevier, vol. 38(9), pages 1257-1278, May.
    2. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
    3. Kaplan, G.A. & Seeman, T.E. & Cohen, R.D. & Knudsen, L.P. & Guralnik, J., 1987. "Mortality among the elderly in the Alameda County study: Behavioral and demographic risk factors," American Journal of Public Health, American Public Health Association, vol. 77(3), pages 307-312.
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    Cited by:

    1. Fabrice Etilé & Carine Milcent, 2006. "Income‐related reporting heterogeneity in self‐assessed health: evidence from France," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 965-981, September.
    2. Alessandra Andreotti & Nadia Minicuci & Paul Kowal & Somnath Chatterji, 2009. "Multidimensional Profiles of Health Status: An Application of the Grade of Membership Model to the World Health Survey," PLOS ONE, Public Library of Science, vol. 4(2), pages 1-14, February.
    3. France Portrait & Maarten Lindeboom & Dorly Deeg, 2001. "Life expectancies in specific health states: Results from a joint model of health status and mortality of older persons," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 525-536, November.
    4. Deb, Partha & Gangaram, Anjelica & Khajavi, Hoda Nouri, 2021. "The impact of the State Innovation Models Initiative on population health," Economics & Human Biology, Elsevier, vol. 42(C).
    5. Partha Deb & Anjelica Gangaram & Hoda Khajavi, 2019. "Can Diffuse Delivery System Reforms Improve Population Health? A Study of the State Innovation Models Initiative," NBER Working Papers 26360, National Bureau of Economic Research, Inc.
    6. Bartolucci, Francesco & Giorgio E., Montanari & Pandolfi, Silvia, 2012. "Item selection by an extended Latent Class model: An application to nursing homes evaluation," MPRA Paper 38757, University Library of Munich, Germany.
    7. Fabrice Etilé & Carine Milcent, 2006. "Income-related reporting heterogeneity in self-assessed health: evidence from France," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 965-981.
    8. Paul McNamee, 2004. "A comparison of the grade of membership measure with alternative health indicators in explaining costs for older people," Health Economics, John Wiley & Sons, Ltd., vol. 13(4), pages 379-395, April.
    9. France Portrait & Maarten Lindeboom & Dorly Deeg, 2000. "The use of long‐term care services by the Dutch elderly," Health Economics, John Wiley & Sons, Ltd., vol. 9(6), pages 513-531, September.

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    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

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