Objectives This study investigated the extent that psychosocial job stressors had lasting effects on a scaled measure of mental health. We applied econometric approaches to a longitudinal cohort to: (1) control for unmeasured individual effects; (2) assess the role of prior (lagged) exposures of job stressors on mental health and (3) the persistence of mental health.
Methods We used a panel study with 13 annual waves and applied fixed-effects, first-difference and fixed-effects Arellano-Bond models. The Short Form 36 (SF-36) Mental Health Component Summary score was the outcome variable and the key exposures included: job control, job demands, job insecurity and fairness of pay.
Results Results from the Arellano-Bond models suggest that greater fairness of pay (β-coefficient 0.34, 95% CI 0.23 to 0.45), job control (β-coefficient 0.15, 95% CI 0.10 to 0.20) and job security (β-coefficient 0.37, 95% CI 0.32 to 0.42) were contemporaneously associated with better mental health. Similar results were found for the fixed-effects and first-difference models. The Arellano-Bond model also showed persistent effects of individual mental health, whereby individuals' previous reports of mental health were related to their reporting in subsequent waves. The estimated long-run impact of job demands on mental health increased after accounting for time-related dynamics, while there were more minimal impacts for the other job stressor variables.
Conclusions Our results showed that the majority of the effects of psychosocial job stressors on a scaled measure of mental health are contemporaneous except for job demands where accounting for the lagged dynamics was important.
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Contributors The article was conceived by AM, ZA and DP. AM, DP and ZA conducted the analysis. All the authors contributed to interpretation of results. AM drafted the manuscript with feedback from all the authors. All the authors contributed to the final draft of the manuscript.
Funding The study is funded by a National Health and Medical Research Council (NHMRC) Partnership grant (APP1055333), including contributions from the Victorian Health Promotion Foundation (VicHealth), WorkSafe Victoria, and Victoria Police. Additional support was also provided by a Victorian Health Promotion Foundation Centre (grant number 15732). DP is supported under the Australian Research Council's Discovery Early Career Awards funding scheme (project DE150100309). The Household, Income and Labour Dynamics in Australia (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.
Disclaimer The findings and views reported in this paper, however, are those of the author and should not be attributed to the Australian Research Council, the Australian Government Department of Social Services or the Melbourne Institute of Applied Economic and Social Research.
Competing interests None declared.
Ethics approval The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1075, as revised in 2008.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Data are available on request from the Australian Government Department of Social Services.
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