Work

Modeling Disability-Free Life Expectancy With Duration Dependence: A Research Note on the Bias in the Markov Assumption

Year: 2024

Type: article

Abstract: Demographic studies on healthy life expectancy often rely on the Markov assumption, which fails to consider the duration of exposure to risk. To address this limitation, models like the duration-dependent multistate life table (DDMSLT) have been developed. However, these models cannot be directly applied to left-censored survey data, as they require knowledge of the time spent in the initial state, which is rarely known because of survey design. This research note presents a flexible approach for utilizing this type of survey data within the DDMSLT framework to estimate multistate life expectancies. The approach involves partially dropping left-censored observations and truncating the duration length after which duration dependence is assumed to be minimal. Utilizing the U.S. Health and Retirement Study, we apply this approach to compute disability-free/healthy life expectancy (HLE) among older adults in the United States and compare duration-dependent models to the typical multistate model with the Markov assumption. Findings suggest that while duration dependence is present in transition probabilities, its effect on HLE is averaged out. As a result, the bias in this case is minimal, and the Markov assumption provides a plausible and parsimonious estimate of HLE.

Source: Demography

DOI: 10.1215/00703370-11696463

Authors: Tianyu Shen, James O'Donnell

Institution: Australian National University

Language: English

Topic: Insurance, Mortality, Demography, Risk Management

Subfield: Demography

Field: Social Sciences

Domain: Social Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,750