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Published in: European Journal of Epidemiology 8/2016

Open Access 01-08-2016 | MORTALITY

Disability and all-cause mortality in the older population: evidence from the English Longitudinal Study of Ageing

Authors: Benedetta Pongiglione, Bianca L. De Stavola, Hannah Kuper, George B. Ploubidis

Published in: European Journal of Epidemiology | Issue 8/2016

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Abstract

Despite the vast body of literature studying disability and mortality, evidence to support their association is scarce. This work investigates the role of disability in explaining all‐cause mortality among individuals aged 50+ who participated in the English Longitudinal Study of Aging. The aim is to explain the gender paradox in health and mortality by analysing whether the association of disability with mortality differs between women and men. Disability was conceived following the International Classification of Functioning, Disability and Health (ICF), proposed by the WHO, that conceptualizes disability as a combination of three components: impairment, activity limitation and participation restriction. Latent variable models were used to identify domain-specific factors and general disability. The association of the latter with mortality up to 10 years after enrolment was estimated using discrete-time survival analysis. Our work confirms the validity of the ICF framework and finds that disability is strongly associated with mortality, with a time-varying effect among men, and a smaller constant effect for women. Adjusting for demographic, socioeconomic and behavioural factors attenuated the association for both sexes, but overall the effects remained high and significant. These findings confirm the existence of gender paradox by showing that, when affected by disability, women survive longer than men, although if men survive the first years they appear to become more resilient to disability. Sensitivity analyses suggested that the gender paradox cannot be solely explained by gender-specific health conditions: there must be other mechanisms acting within the pathway between disability and mortality that need to be explored.
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Footnotes
1
One-step analysis was performed as a robustness check. It consists of estimating the measurement model using the disability items at baseline and jointly performing a discrete time survival analysis for the 10-year period, without storing factor scores (first step) and then introducing them in the survival model (second step). Both analyses returned very similar results, therefore, for practical reasons only the results from the two-step analysis are reported here (results from the one step analysis available from corresponding author).
 
2
Second-order model had a good fit (CFI = 0.945, TLI = 0.942, RMSEA = 0.042), but presented some problems: activity measured disability very poorly and its factor loading had an extreme value and was not significant (28.2 and 95 % CI [−120.3, 176.8]; p value = 0.71). At wave 2, the value was even more extreme and the model did not converge.
 
3
Maximum likelihood estimator would have been too cumbersome given the large number of dimensions to be integrated.
 
4
Hazard probability is the term used in Muthen's and Masyn’s paper. The authors defined the sample-estimated hazard probability for time period j as the number of events that are observed to occur in time period j divided by the total number of subjects at risk in time period j (p. 33). In the context of our analysis, we will also be using the term mortality risk instead of hazard probability.
 
5
In a general latent variable framework, the likelihood for a latent class model with binary indicators gives the probability of the event indicator being equal to one; in Mplus it is a (negative) “threshold” which defines the cut-point in the latent variable distribution for the switch from ‘category’ 0–1, and it is estimated for each time interval (i.e. her we estimate ten thresholds).
 
6
The notation is the one used in Muthen and Masyn [17].
 
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Metadata
Title
Disability and all-cause mortality in the older population: evidence from the English Longitudinal Study of Ageing
Authors
Benedetta Pongiglione
Bianca L. De Stavola
Hannah Kuper
George B. Ploubidis
Publication date
01-08-2016
Publisher
Springer Netherlands
Published in
European Journal of Epidemiology / Issue 8/2016
Print ISSN: 0393-2990
Electronic ISSN: 1573-7284
DOI
https://doi.org/10.1007/s10654-016-0160-8

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