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Projection models for health expenses

Published online by Cambridge University Press:  18 December 2017

Marcus Christiansen*
Affiliation:
Institute for Mathematics, University of Oldenburg, Carl von Ossietzky Straβe 9-11, D-26111, Oldenburg, Germany
Michel Denuit
Affiliation:
Institute of Statistics, Biostatistics and Actuarial Science, Université Catholique de Louvain (UCL), Voie du Roman Pays 20/L1.04.01, B-1348, Louvain-la-Neuve, Belgium
Nathalie Lucas
Affiliation:
Institute of Statistics, Biostatistics and Actuarial Science, Université Catholique de Louvain (UCL), Voie du Roman Pays 20/L1.04.01, B-1348, Louvain-la-Neuve, Belgium
Jan-Philipp Schmidt
Affiliation:
Institute for Insurance Studies (ivwKöln), TH Köln – University of Applied Sciences, Gustav-Heinemann-Ufer 54, D-50968 Köln, Cologne, Germany
*
*Correspondence to: Marcus Christiansen, Institute for Mathematics, University of Oldenburg, Oldenburg, Germany. E-mail: marcus.christiansen@uni-oldenburg.de

Abstract

This note proposes a practical way for modelling and projecting health insurance expenditures over short time horizons, based on observed historical data. The present study is motivated by a similar age structure generally observed for health insurance claim frequencies and yearly aggregate losses on the one hand and mortality on the other hand. As an application, the approach is illustrated for German historical inpatient costs provided by the Federal Financial Supervisory Authority. In particular, similarities and differences to mortality modelling are addressed.

Type
Papers
Copyright
© Institute and Faculty of Actuaries 2017 

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References

Bell, W.R. (1997). Comparing and assessing time series methods for forecasting age-specific fertility and mortality rates. Journal of Official Statistics, 13, 279303.Google Scholar
Brouhns, N., Denuit, M. & Van Keilegom, I. (2005). Bootstrapping the Poisson log-bilinear model for mortality projection. Scandinavian Actuarial Journal, 2005, 212224.Google Scholar
Brouhns, N., Denuit, M. & Vermunt, J.K. (2002a). A Poisson log-bilinear approach to the construction of projected life tables. Insurance: Mathematics and Economics, 31, 373393.Google Scholar
Brouhns, N., Denuit, M. & Vermunt, J.K. (2002b). Measuring the longevity risk in mortality projections. Bulletin of the Swiss Association of Actuaries, 2002, 105130.Google Scholar
Christiansen, M.C., Denuit, M.M. & Lazar, D. (2012). The Solvency II square-root formula for systematic biometric risk. Insurance: Mathematics and Economics, 50, 257265.Google Scholar
Cossette, H., Delwarde, A., Denuit, M., Guillot, F. & Marceau, E. (2007). Pension plan valuation and dynamic mortality tables. North Americal Actuarial Journal, 11, 134.CrossRefGoogle Scholar
Delwarde, A., Denuit, M. & Partrat, C.H. (2007). Negative Binomial version of the Lee-Carter model for mortality forecasting. Applied Stochastic Models in Business and Industry, 23, 385401.Google Scholar
Denuit, M., Dhaene, J., Hanbali, H., Lucas, N. & Trufin, J. (2017). Updating mechanism for lifelong insurance contracts subject to medical inflation. European Actuarial Journal, 7, 133163.Google Scholar
Dhaene, J., Godecharle, E., Antonio, K., Denuit, M. & Hanbali, H. (2017). Lifelong health insurance covers with surrender value: updating mechanisms in the presence of medical inflation. ASTIN Bulletin, 47, 803836.Google Scholar
Dowd, K., Blake, D. & Cairns, A.J.G. (2010). Facing up to uncertain life expectancy: the longevity fan charts. Demography, 47, 6778.Google Scholar
Koissi, M.C., Shapiro, A.F. & Hognas, G. (2006). Evaluating and extending the Lee-Carter model for mortality forecasting: bootstrap confidence intervals. Insurance: Mathematics and Economics, 38, 120.Google Scholar
Lee, R.D. (2000). The Lee-Carter method of forecasting mortality, with various extensions and applications. North American Actuarial Journal, 4, 8093.Google Scholar
Lee, R.D. & Carter, L. (1992). Modeling and forecasting the time series of US mortality. Journal of the American Statistical Association, 87, 659671.Google Scholar
Levantesi, S. & Menzietti, M. (2012). Managing longevity and disability risks in life annuities with long term care. Insurance: Mathematics and Economics, 50, 391401.Google Scholar
Milbrodt, H. & Röhrs, V. (2016). Aktuarielle Methoden der deutschen Privaten Krankenversicherung (Vol. 34). Verlag Versicherungswirtsch.Google Scholar
Renshaw, A.E. & Haberman, S. (2008). On simulation-based approaches to risk measurement in mortality with specific reference to Poisson Lee–Carter modelling. Insurance: Mathematics and Economics, 42, 797816.Google Scholar
Turner, H. & Firth, D. (2007). Gnm: a package for generalized nonlinear models. R News, 7, 812.Google Scholar
Vercruysse, W., Dhaene, J., Denuit, M., Pitacco, E. & Antonio, K. (2013). Premium indexing in lifelong health insurance. Far East Journal of Mathematical Sciences, 365384.Google Scholar