Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-23T21:35:00.269Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimation of Disability Transition Probabilities in Australia I: Preliminary

Published online by Cambridge University Press:  13 December 2013

Evan A. Hariyanto ,
David C.M. Dickson  and
David G.W. Pitt
Evan A. Hariyanto
Affiliation:
AMP, Melbourne, Australia
David C.M. Dickson*
Affiliation:
Faculty of Business and Economics, Centre for Actuarial Studies, The University of Melbourne, Australia
David G.W. Pitt
Affiliation:
Department of Applied Finance and Actuarial Studies, Faculty of Business and Economics, Macquarie University, Australia
*
Correspondence to: David C. M. Dickson, Centre for Actuarial Studies, The University of Melbourne, VIC 3010, Australia. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This is the first of two papers in which we estimate transition probabilities amongst levels of disability as defined in the Australian Survey of Disability, Ageing and Carers. In this paper we describe both the main tools of our estimation and the estimation of the numbers of individuals in different disability categories at annual intervals using survey data that are available at five year intervals. In Paper II we describe our estimation procedure, followed by its implementation, discussion of results and graduation of the estimated transition probabilities.

Keywords

Disabilitymultiple-state modeltransition probabilitylog-linear modeliterative proportional fittinggraduation
Type
Papers
Information
Annals of Actuarial Science , Volume 8 , Issue 1 , March 2014 , pp. 131 - 155
Copyright
Copyright © Institute and Faculty of Actuaries 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References for Papers I and II

Access Economics (2010) The Future of Aged Care in Australia. A public policy discussion paper prepared for National Seniors Australia. http://www.nationalseniors.com.au/icms_docs/Future_of_Aged_Care_ Report.pdf (accessed 17 September 2012).Google Scholar
Albarran, I., Ayuso, M., Guillén, M., Monteverde, M. (2005). A Multiple State Model for Disability Using the Decomposition of Death Probabilities and Cross-Sectional Data. Communications in Statistics – Theory and Methods, 34(9), 2063–2075.Google Scholar
Alegre, A., Pociello, E., Pons, M.A., Sarrasi, F.J., Varea, J. (2004). Modelo Discreto de Transiciones Entre Estados de Dependencia. Anales del Instituto de Actuarios Españoles, 10, 91–114.Google Scholar
Australian Bureau of Statistics (ABS) (1999) Disability, Ageing and Carers: Summary of Findings, Australia, 1998. ABS Cat. No. 4430.0, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2000) Migration, Australia, 1998–99. ABS Cat. No. 3412.0, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2001) Migration, Australia, 1999–2000. ABS Cat. No. 3412.0, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2003) Migration, Australia, 2000–01 and 2001–02. ABS Cat. No. 3412.0, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2004a) Disability, Ageing and Carers: Summary of Findings, Australia, 2003. ABS Cat. No. 4430.0, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2004b) Migration, Australia, 2002–03. ABS Cat. No. 3412.0, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2005) Migration, Australia, 2003–04. ABS Cat. No. 3412.0, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2008) Australian Historical Population Statistics, 2008. ABS Cat. No. 3105.0.65.001, Canberra.Google Scholar
Australian Bureau of Statistics (ABS) (2009) Population by Age and Sex, Australian States and Territories, June 2009. ABS Cat. No. 3201.0, Canberra.Google Scholar
Australian Government Actuary (AGA) (2004) Australian Life Tables 2000–02. Canberra.Google Scholar
Australian Institute of Health and Welfare (AIHW) (2003) Disability Prevalence and Trends. Disability Series AIHW Cat. No. DIS 34. Canberra.Google Scholar
Australian Institute of Health and Welfare (AIHW) (2007) Current and Future Demand for Specialist Disability Services. Disability Series AIHW Cat. No. DIS 50. Canberra.Google Scholar
Australian Institute of Health and Welfare (AIHW) (2008) Disability in Australia: Trends in Prevalence, Education, Employment and Community Living. Bulletin No. 61. Cat. No. AUS 103. Canberra.Google Scholar
Australian Institute of Health and Welfare (AIHW) (2009) Residential Aged Care in Australia 2007–08: A Statistical Overview. Aged Care Statistics Series 28. Cat. No. AGE 58. Canberra.Google Scholar
Barnett, H.A.R. (1985). Criteria of Smoothness. Journal of the Institute of Actuaries, 112, 331–367.CrossRefGoogle Scholar
Benjamin, B., Pollard, J.H. (1993). The Analysis of Mortality and Other Actuarial Statistics, 3rd ed. Institute of Actuaries, London.Google Scholar
Bijak, J., Kupiszewska, D. (2008). Methodolody for the Estimation of Annual Population Stocks by Citizenship Group, Age and Sex in the EU and EFTA Countries. Informatica, 32(2), 133–145.Google Scholar
Birch, M.W. (1963). Maximum Likelihood in Three-Way Contingency Tables. Journal of the Royal Statistical Society Series B, 25(1), 229–233.Google Scholar
Bishop, Y.M., Fienberg, S.E., Holland, P.W. (2007). Discrete Multivariate Analysis: Theory and Applications. Springer Science + Business Media, LLC.Google Scholar
Brelivet, S., Barker, G., Hancock, R., Parker, G., Spiers, N., Jagger, C. (2001). Population Forecasting for Long-Term Care Needs in Old Age: a Programme of Secondary Analysis. University of Leicester, Department of Health Sciences.Google Scholar
Corliss, G., Gagne, R., Koklefsky, B., Lucas, R., Oberman-Smith, S., Purushotham, M., Cavanaugh, L., Crawford, K., Luff, J. (2007). Intercompany Study 1984–2004. Long Term Care Experience Committee, Society of Actuaries.Google Scholar
Davis, B.A., Heathcote, C.R., O'Neill, T.J., Puza, B.D. (2002). The Health Expectancies of Older Australians. Demography Working Paper No. 87. Canberra, Australian National University.Google Scholar
Davis, E., Beer, J., Gligora, C., Thorn, A. (2001) Accounting for Change in Disability and Severe Restriction, 1981–1998: Working Papers in Social and Labour Statistics. ABS Working Paper No. 2001/1, Canberra.Google Scholar
Giles, L.C., Metcalf, P.A., Glonek, G.F.V., Luszcz, M.A., Andrews, G.R. (2004). The Effects of Social Networks on Disability in Older Australians. Journal of Aging and Health, 16(4), 517–538.Google Scholar
Hariyanto, E.A. (2013). Mortality and Disability Modeling. Ph.D. Thesis, The University of Melbourne, Melbourne.Google Scholar
Hariyanto, E.A., Dickson, D.C.M., Pitt, D.G.W. (2014a) Estimation of Disability Transition Probabilities in Australia I: Preliminary. Research Paper Series, Centre for Actuarial Studies, Annals of Actuarial Science.Google Scholar
Hariyanto, E.A., Dickson, D.C.M., Pitt, D.G.W. (2014b) Estimation of Disability Transition Probabilities in Australia II: Implementation. Research Paper Series, Centre for Actuarial Studies, Annals of Actuarial Science.Google Scholar
Joseph, A.W. (1952). The Whittaker-Henderson Method of Graduation. Journal of the Institute of Actuaries, 78, 99–114.Google Scholar
Leung, E. (2004). Projecting the Needs and Costs of Long Term Care in Australia. Australian Actuarial Journal, 10(2), 343–385.Google Scholar
Leung, E. (2006). A Multiple State Model for Pricing and Reserving Private Long Term Care Insurance Contracts in Australia. Australian Actuarial Journal, 12(2), 187–247.Google Scholar
Nuttall, S.R., Blackwood, R.J.L., Bussell, B.M.H., Cliff, J.P., Cornall, M.J., Cowley, A., Gatenby, P.L., Webber, J.M. (1994). Financing Long-Term Care in Great Britain. Journal of the Institute of Actuaries, 121(1), 1–68.Google Scholar
Pritchard, J.D. (2006). Modeling Disability in Long-Term Care Insurance. North American Actuarial Journal, 10(4), 48–75.Google Scholar
Renshaw, A.E. (1991). Actuarial Graduation Practice and Generalised Linear and Non-Linear Models. Journal of the Institute of Actuaries, 118, 295–312.Google Scholar
Rickayzen, B.D., Walsh, D.E.P. (2002). A Multi-State Model of Disability for the United Kingdom: implications for Future Need for Long-Term Care for the Elderly. British Actuarial Journal, 8(2), 341–393.CrossRefGoogle Scholar
Siegel, J.S., Swanson, D.A. (2004). The Methods and Materials of Demography, 2nd edition. Elsevier Academic Press.Google Scholar
Smith, L., Hyndman, R.J., Wood, S.N. (2004). Spline Interpolation for Demographic Variables: The Monotonicity Problem. Journal of Population Research, 21(1), 95–98.Google Scholar
Theil, H. (1958). Economic Forecasts and Policy. North-Holland Publishing Company, Amsterdam.Google Scholar
Treasury (2010) Australia to 2050: Future Challenges. http://archive.treasury.gov.au/igr/igr2010/report/pdf/IGR_2010.pdf (accessed 17 September 2012).Google Scholar
Turner, H., Firth, D. (2011) gnm: Generalized Nonlinear Models. http://CRAN.R-project.org/package=gnm. R package version 1.0–1.Google Scholar
Varadhan, R. (2011) alabama: Constrained nonlinear optimization. http://CRAN.R-project.org/package=alabama. R package version 2011.9-1.Google Scholar
Waidmann, T.A., Liu, K. (2000). Disability Trends Among Elderly Persons and Implications for the Future. Journal of Gerontology: Social Sciences, 55B(5), S298–S307.Google Scholar
Wilmoth, J.R. (2002) Methods Protocol for the Human Mortality Database. http://www.mortality.orgGoogle Scholar