Importance of the Working Environment for Early Retirement: Prospective Cohort Study with Register Follow-Up

Thorsen, Sannie Vester; Rugulies, Reiner; Larsen, Mona; Thomassen, Kristina; Andersen, Lars L.

doi:10.3390/ijerph18189817

Cited by 12 publications

(15 citation statements)

References 33 publications

(59 reference statements)

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“…The prevalence of disability retirement is associated with the high demands in terms of physical work and with the deficient psychosocial conditions at work 18 - 19 . There is diverse evidence about high demands and low control at work as predictors for disability retirement 20 .…”

Section: Discussionmentioning

confidence: 99%

Quality of life and factors associated among public university employees retired due to disabilities

Moreira

Martins

Robazzi

et al. 2023

Rev. Latino-Am. Enfermagem

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Objective: to analyze quality of life and factors associated among public university employees retired due to disabilities. Method: a cross-sectional study conducted with a sample of public university employees retired due to disabilities. A characterization questionnaire and the World Health Organization Quality of Life - Disabilities instrument were applied via telephone or online contacts from November 2019 to September 2020. The associated factors were verified through multiple linear regression. Results: of the 80 retirees due to disability, 15% were professors and 85% had a technical-administrative career. As for the factors associated with Quality of Life, continuous medication use (βadj: -0.25; p=0.02) and problems in the nervous system (βadj: -0.21; p<0.05) were associated with the Overall domain; continuous medication use (βadj: -0.23; p=0.04), to the Physical domain; smoking (βadj: -0.21; p<0.05) and mental and behavioral disorders (βadj: -0.21; p<0.01), to the Psychological domain; smoking (βadj: -0.46; p<0.01) and respiratory (βadj: -0.21; p=0.03) and circulatory (βadj: -0.21; p=0.03) problems, to the Social domain; smoking (βadj: -0.33; p<0.01) and problems in the nervous system (βadj: -0.22; p=0.04), to the Environmental domain; mental and behavioral disorders, to the Disabilities module (βadj: -0.29; p<0.01) and to the Discrimination domain (βadj: -0.21; p<0.05); and smoking (βadj: -0.32; p<0.01) and problems in the nervous system (βadj: -0.20; p<0.05), to the Inclusion domain. The Autonomy domain did not present any association. Conclusion: the retirees under study presented impaired Quality of Life.

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Section: Discussionmentioning

confidence: 99%

Quality of life and factors associated among public university employees retired due to disabilities

Moreira

Martins

Robazzi

et al. 2023

Rev. Latino-Am. Enfermagem

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“…Other aspects (e.g., health, financial security, family pull) play a smaller, albeit significant role, even though some individual studies have noted large effects in particular contexts. Sundstrup et al [68], for example, point in a recent publication to the importance of poor health and physical work demands in explaining early retirement in Denmark. This relates to Scharn et al's systematic review [2], which underlines the disparate findings of studies across countries, which may be related to the institutional regime at stake.…”

Section: Retirementmentioning

confidence: 99%

Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE)

et al. 2022

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Do machine learning algorithms perform better than statistical survival analysis when predicting retirement decisions? This exploratory article addresses the question by constructing a pseudo-panel with retirement data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis consists of two methodological steps prompted by the nature of the data. First, a discrete Cox survival model of transitions to retirement with time-dependent covariates is compared to a Cox model without time-dependent covariates and a survival random forest. Second, the best performing model (Cox with time-dependent covariates) is compared to random forests adapted to time-dependent covariates by means of simulations. The results from the analysis do not clearly favor a single method; whereas machine learning algorithms have a stronger predictive power, the variables they use in their predictions do not necessarily display causal relationships with the outcome variable. Therefore, the two methods should be seen as complements rather than substitutes. In addition, simulations shed a new light on the role of some variables—such as education and health—in retirement decisions. This amounts to both substantive and methodological contributions to the literature on the modeling of retirement.

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“…if it would pay off better economically) as one of the most prevalent expected reasons to prolong working life [23]. Access to early retirement benefits through a voluntary early retirement program has also been shown to predict early retirement among older Danish workers [24]. Early retirement benefits have, therefore, been considered a factor that pulls people out of the labour market, but may also be considered a way out of the labour market for those experiencing strong push factors, e.g.…”

Section: Introductionmentioning

confidence: 99%

Expected reasons for leaving the labour market and loss of paid employment among older workers: prospective cohort study

Meng

Skovlund

Albertsen³

et al. 2023

BMC Public Health

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Background Surveying expected reasons for retirement may be a useful strategy to maintain labor market affiliation. The aim was to investigate the prospective association between self-reported expected reasons for leaving the labour market and subsequent loss of paid employment before the state pension age among older workers. Methods The prospective risk of loss of paid employment before the official state pension age was estimated from expected reasons for leaving the labour market among 10,320 currently employed older workers (50–63 years) from the SeniorWorkingLife study. In 2018, participants replied to 15 randomly ordered questions about expected reasons for leaving the labour market and were in 2020 followed in a national register containing information on labour market participation. Results Loss of paid employment before state pension age was predicted by expected reasons related to ‘Health, work demands and occupational well-being’: ‘Poor physical health’ (RR 1.47, 95% CI 1.45–1.49), ‘Poor mental health’ (RR 1.36, 95% CI 1.32–1.40), ‘Not being capable of doing the job’ (RR 1.20, 95% CI 1.18–1.22), and ‘Not thriving at the workplace’ (RR 1.14, 95% CI 1.11–1.17). Expected reasons related to the possibility of receiving voluntary early retirement benefits also increased this risk. Expected reasons related to ‘Leisure’ (‘Wish for more self-determination’; ‘Wish for more time for hobbies’), ‘Economy’ (‘Economic considerations’; ‘Possibility of receiving pension’), and ‘Norms’ (‘Retirement norms’; ‘To make space for younger employees’) decreased the risk of loss of paid employment before state pension age. Age-stratified analyses revealed that expected reasons related to the domain of ‘Health, work demands and occupational well-being’ predicted risk of loss of paid employment to a greater extent among workers aged 50–55 compared to those aged 56–63. Conclusions Expected reasons for leaving the labour market predicted actual labour market participation among older workers in Denmark. Expected reasons related to poor physical and mental health, and not being capable of doing the job seem to be stronger PUSH-factors among workers aged 50–55 compared to those aged 56–63. Preventing early labour market detachment should take the worker’s expected reasons for leaving into account.

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Importance of the Working Environment for Early Retirement: Prospective Cohort Study with Register Follow-Up

Cited by 12 publications

References 33 publications

Quality of life and factors associated among public university employees retired due to disabilities

Quality of life and factors associated among public university employees retired due to disabilities

Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE)

Expected reasons for leaving the labour market and loss of paid employment among older workers: prospective cohort study

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