Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Hout, Ardo van den; Ogurtsova, Ekaterina; Gampe, Jutta; Matthews, Fiona E.

doi:10.4054/demres.2014.30.42

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…In addition, the survival curve of the probability for staying in each state was estimated (Hinchliffe et al 2013). Life expectancy, healthy life expectancy, and life expectancy with disability from the baseline survey and the proportion of healthy life expectancy in total life expectancy were estimated by calculating the area under the curve (van den Hout et al 2014).…”

Section: Statistical Analysesmentioning

confidence: 99%

Dental Status and Compression of Life Expectancy with Disability

et al. 2017

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Abstract:Background: This study examined whether number of teeth contributes to the compression of morbidity, measured as a shortening of the life expectancy with disability, an extension of healthy life expectancy, and overall life expectancy. Methods: A prospective cohort study was conducted. A self-reported baseline survey was conducted to 126,438 community-dwelling older people aged ≥65 years in Japan in 2010, and 85,161 (67.4%) responded. The onset of functional disability and all-cause mortality were followed-up for 1,374 days (follow-up rate = 96.1%). A sex-stratified illness-death model was applied to estimate the adjusted hazard ratios (aHRs) for three health transitions (healthy to dead, healthy to disabled, and disabled to dead). Absolute differences in life expectancy, healthy life expectancy, and life expectancy with disability according to the number of teeth were also estimated. Age, denture use, socioeconomic status, health status, and health behavior were adjusted. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Methods: A prospective cohort study was conducted. A self-reported baseline survey 5 was conducted to 126,438 community-dwelling older people aged ≥65 years in Japan in 6 2010, and 85,161 (67.4%) responded. The onset of functional disability and all-cause 7 mortality were followed-up for 1,374 days (follow-up rate = 96.1%). A sex-stratified 8 illness-death model was applied to estimate the adjusted hazard ratios (aHRs) for three 9 health transitions (healthy to dead, healthy to disabled, and disabled to dead). Absolute 10 differences in life expectancy, healthy life expectancy, and life expectancy with 11 disability according to the number of teeth were also estimated. Age, denture use, 12 socioeconomic status, health status, and health behavior were adjusted. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 Conclusions:The presence of remaining teeth was associated with significant 6 compression of morbidity: older Japanese adults' life expectancy with disability was 7 compressed by 35-55 days within the follow-up for 1,374 days.

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Section: Statistical Analysesmentioning

confidence: 99%

Dental Status and Compression of Life Expectancy with Disability

et al. 2017

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“…For instance, Langeheine and van de Pol (1990) have pointed out two commonly occurring reasons why the Markov model might be inappropriate: measurement error and population heterogeneity. Both issues have been addressed in the literature (e.g., Heckman and Singer, 1982;Gill, 1992;Liu et al, 1997;van den Hout et al, 2014). While these works show that potential issues can be tackled, the methods presented in this paper do not resolve them.…”

Section: Resultsmentioning

confidence: 86%

Expanding the Markov chain tool box: distributions of occupation times and waiting times

Dudel¹

2017

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“…For instance, Langeheine and van de Pol (1990) have pointed out two commonly occurring reasons why the Markov model might be inappropriate: measurement error and population heterogeneity. Both issues have been addressed in the literature (e.g., Gill 1992; Heckman and Singer 1982; Liu et al 1997; van den Hout et al 2014). While these works show that potential issues can be tackled, the methods presented in this article do not resolve them.…”

Section: Resultsmentioning

confidence: 99%

Expanding the Markov Chain Toolbox: Distributions of Occupation Times and Waiting Times

Dudel

2018

Sociological Methods & Research

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Markov models are important tools for quantitative social research. In this article, new methods for discrete Markov chains are presented. These methods allow us to calculate the distribution of the occupation time in a subset of the state space, the distribution of the waiting time to first entry into a subset of the state space, and the distribution of the waiting time to final exit from a subset of the state space. To demonstrate the usefulness of these methods, we apply them to working life tables for Spanish males to assess how the recent financial crisis affected the length of working life. The results show that the duration of working life decreased considerably, a pattern that can largely be explained by later entry to and earlier exit from the labor market. The findings also indicate that inequality in the length of working life increased.

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Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Cited by 9 publications

References 22 publications

Dental Status and Compression of Life Expectancy with Disability

Dental Status and Compression of Life Expectancy with Disability

Expanding the Markov chain tool box: distributions of occupation times and waiting times

Expanding the Markov Chain Toolbox: Distributions of Occupation Times and Waiting Times

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