The Impact of Systematic Trend and Uncertainty on Mortality and Disability in a Multi-State Latent Factor Model for Transition Rates

33 Pages Posted: 13 Feb 2016

See all articles by Zixi Li

Zixi Li

UNSW Australia Business School, School of Risk & Actuarial Studies

Adam Wenqiang Shao

University of New South Wales - ARC Centre of Excellence in Population Ageing Research (CEPAR); Milliman

Michael Sherris

University of New South Wales - ARC Centre of Excellence in Population Ageing Research and School of Risk and Actuarial Studies; UNSW Australia Business School

Date Written: February 8, 2016

Abstract

Multiple state functional disability models do not generally include systematic trend and uncertainty. We develop and estimate a multi-state latent factor intensity model with transition and recovery rates depending on a stochastic frailty factor to capture trend and uncertainty. We estimate the model parameters using U.S. Health and Retirement Study (HRS) data between 1998 and 2012 with Monte Carlo maximum likelihood estimation method. The model shows significant reductions in disability and mortality rates during this period and allows us to quantify uncertainty in transition rates arising from the stochastic frailty factor. Recovery rates are very sensitive to the stochastic frailty. There is an increase in expected future lifetimes as well as an increase in future healthy life expectancy. The proportion of lifetime spent in disability on average remains stable with no strong support in the data for either morbidity compression or expansion. The model has widespread application in costing of government funded aged care and pricing and risk management of LTC insurance products.

Keywords: long term care, systematic trend and uncertainty, disability, multi-state transitions, latent factor

JEL Classification: C15, C23, G22, H51, I11

Suggested Citation

Li, Zixi and Shao, Adam Wenqiang and Sherris, Michael, The Impact of Systematic Trend and Uncertainty on Mortality and Disability in a Multi-State Latent Factor Model for Transition Rates (February 8, 2016). UNSW Business School Research Paper No. 2016ACTL04. Available at SSRN: https://ssrn.com/abstract=2731492 or http://dx.doi.org/10.2139/ssrn.2731492

Zixi Li

UNSW Australia Business School, School of Risk & Actuarial Studies

Room 653, Level 6, East Wing,
UNSW Business School (E12), Kensington Campus
UNSW, NSW 2052
Australia

Adam Wenqiang Shao

University of New South Wales - ARC Centre of Excellence in Population Ageing Research (CEPAR) ( email )

CEPAR, Level 3
East Wing, NICTA Building, UNSW
Sydney, New South Wales NSW 2052
Australia

Milliman ( email )

One Pennsylvania Plaza 38th Floor
New York, NY 10119
United States

Michael Sherris (Contact Author)

University of New South Wales - ARC Centre of Excellence in Population Ageing Research and School of Risk and Actuarial Studies ( email )

UNSW Business School
Risk and Actuarial Studies
Sydney, NSW 2052
Australia
+61 2 9385 2333 (Phone)
+61 2 9385 1883 (Fax)

HOME PAGE: http://www.asb.unsw.edu.au/schools/Pages/MichaelSherris.aspx

UNSW Australia Business School ( email )

Sydney, NSW 2052
Australia

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