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A comparison of the grade of membership measure with alternative health indicators in explaining costs for older people

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  • Paul McNamee

Abstract

An important focus in empirical econometric studies of health care concerns estimation of the relationship with health. This paper describes a non‐parametric technique, the grade of membership (GoM) procedure, which is used to construct summary indicators of health. Health status is also assessed using three alternative measures commonly employed in the literature: an index of problems of activities of daily living (ADL), an index of health conditions and overall self‐rated health. Using data from a sample of frail older people, the relationship between costs and different measures of health are estimated using ordinary least squares (OLS) and instrumental variables (IV) methods. The results show that health influences costs more strongly than other variables, an effect which persists after controlling for potential endogeneity. It is shown that health can be usefully classified using GoM techniques. An alternative measure, based on an index of ADL, also performs well. Measures based on overall self‐assessed health and the number of health conditions present were less satisfactory. Although health indicators derived from GoM techniques may be more theoretically attractive, in terms of capturing more fully the multidimensional nature of health, simpler alternatives such as those based on ADL indices may offer practical computational advantages. Copyright © 2003 John Wiley & Sons, Ltd.

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  • 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.
  • Handle: RePEc:wly:hlthec:v:13:y:2004:i:4:p:379-395
    DOI: 10.1002/hec.833
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    1. Ravensbergen, W.M. & Drewes, Y.M. & Hilderink, H.B.M. & Verschuuren, M. & Gussekloo, J. & Vonk, R.A.A., 2019. "Combined impact of future trends on healthcare utilisation of older people: A Delphi study," Health Policy, Elsevier, vol. 123(10), pages 947-954.
    2. 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.
    3. Wouterse, Bram & Huisman, Martijn & Meijboom, Bert R. & Deeg, Dorly J.H. & Polder, Johan J., 2013. "Modeling the relationship between health and health care expenditures using a latent Markov model," Journal of Health Economics, Elsevier, vol. 32(2), pages 423-439.
    4. 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.
    5. Abdul Suleman, 2017. "A fuzzy clustering approach to evaluate individual competencies from REFLEX data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2513-2533, October.
    6. Liu, Li-Fan & Tian, Wei-Hua & Yao, Hui-Ping, 2012. "Utilization of health care services by elderly people with National Health Insurance in Taiwan: The heterogeneous health profile approach," Health Policy, Elsevier, vol. 108(2), pages 246-255.
    7. Li-Fan Liu, 2014. "The Health Heterogeneity of and Health Care Utilization by the Elderly in Taiwan," IJERPH, MDPI, vol. 11(2), pages 1-14, January.

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