The cumulative effect of living with disability on mental health in working-age adults: an analysis using marginal structural models

  • Amalia KarahaliosEmail author
  • Frank Pega
  • Zoe Aitken
  • Allison Milner
  • Julie A. Simpson
  • Anne M. Kavanagh
Original Paper



Previous studies have shown that acquiring a disability is associated with a reduction in mental health, but they have not considered the cumulative impact of having a disability on mental health. We used acquisition of a non-psychological disability to estimate the association of each additional year lived with disability on mental health (measured using the Mental Component Summary score of the Short Form Health Survey).


We used the first 13 waves of data (years 2001–2013) from the Household, Income and Labour Dynamics in Australia Survey. The sample included 4113 working-age (18–65 years) adults who were disability-free at waves 1 and 2. We fitted marginal structural models with inverse probability weights to estimate the association of each additional year of living with disability on mental health, employing multiple imputation to handle the missing data.


Of the 4113 participants, 7.7 percent acquired a disability. On average, each additional year lived with disability was associated with a decrease in the mean Mental Component Summary score (β = − 0.42; 95% CI − 0.71, − 0.14).


This study provides evidence that each additional year lived with non-psychological disability is associated with a decline in mental health among working-age Australians.


Epidemiology Epidemiologic methods Disability Mental health 



This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the author and should not be attributed to either DSS or the Melbourne Institute. The data used in this paper were extracted from HILDA using the Add-On package PanelWhiz for Stata (Hahn MH, Haisken-DeNew JP. PanelWhiz and the Australian longitudinal data infrastructure in economics. Aust Econ Rev 2013; 46(3): 397-86).

Author contributions

FP is a technical officer for the World Health Organization, but was a research fellow at the University of Melbourne and a postdoctoral fellow at the University of Otago at the time of writing.


This study was funded by the University of Melbourne through an ARC Discovery Project Grant DP170101434 and an NHMRC Centre of Research Excellence Grant APP1116385. FP was funded by the University of Melbourne and a Health Sciences Career Development Postdoctoral Fellowship from the University of Otago. JAS is funded by an Australian National Health and Medical Research Council (NHMRC) Senior Research Fellowship 1104975.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

127_2019_1688_MOESM1_ESM.pdf (3.1 mb)
Supplementary material 1 (PDF 3191 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneAustralia
  2. 2.Centre for Health Equity, Melbourne School of Population and Global HealthThe University of MelbourneMelbourneAustralia
  3. 3.Burden of Disease Epidemiology, Equity and Cost-Effectiveness Programme (BODE3), Department of Public HealthUniversity of OtagoWellingtonNew Zealand
  4. 4.Population Health Strategic Research Centre, School of Health & Social DevelopmentDeakin UniversityBurwoodAustralia

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