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. 2024 Mar 20;19(3):e0297155. doi: 10.1371/journal.pone.0297155

Musculoskeletal pain affects the age of retirement and the risk of work cessation among older people

Nils Georg Niederstrasser 1,*, Elaine Wainwright 2, Martin J Stevens 2
Editor: Amin Nakhostin-Ansari3
PMCID: PMC10954148  PMID: 38507357

Abstract

Objectives

Many people with chronic pain cannot work, while working despite chronic pain is linked to absenteeism and presenteeism and a host of other deleterious effects. This disproportionately affects older adults, who are closer to retirement, while the exact relationship between pain and work cessation as well as retirement among older adults is not known. We explore longitudinally the relationship between chronic pain and the risk of ceasing work and entering retirement.

Methods

Data from 1156 individuals 50 years or older living in England taking part in the English Longitudinal Study of Ageing were used in this study. Cox proportional hazards regression analyses were used to examine the nature of the relationship between musculoskeletal pain and work cessation as well as retirement longitudinally over the course of fourteen years.

Results

Suffering from frequent musculoskeletal pain was associated with an increased risk of ceasing work and retiring at an earlier age, as did work dissatisfaction, higher perceived social status, female gender, and not receiving the recognition they felt they deserved in their job. Severity of depressive symptoms, psychosocial job demands, decision authority, and social support did not influence the age at which participants reported work cessation or retirement.

Conclusions

Frequent musculoskeletal pain may increase the risk of earlier work exit and earlier retirement. Further research should establish the mechanisms and decision making involved in leaving the workforce in people with frequent musculoskeletal pain.

Introduction

Chronic pain is defined as pain that may fluctuate but lasts for three months or more [1]. Chronic pain, in particular musculoskeletal (MSK) pain such as low back pain, is a leading cause of disability worldwide [2]. Work which is physically and psychologically safe is good for our health and wellbeing [3,4]. This is still the case for people who live with chronic pain, including chronic MSK pain [3,5,6]. Yet up to 42% of people with chronic pain cannot work [7] and people who live with chronic pain are significantly more likely to leave work than people without pain [8]. People who are not in employment because of ill health or disability are more likely to have chronic pain than those who are employed [9]. Chronic pain impacts on those who live with it who remain at work, as it is linked to absenteeism and presenteeism [10,11]. It is also associated with a range of deleterious impacts for the worker including reduced working capacity [12], reduced income [13], and experiencing stigma for being seen as an unproductive worker [10,14].

There is an added important dimension when we consider older workers with pain. Since the prevalence of people living with chronic MSK pain conditions increases as we age, pain-related disability is a problem which is growing concomitant with aging populations in many countries including the UK [15]. Older workers may be defined as those aged 50 or over [16] as this represents the age at which labour force participation starts to reduce [17] although there is variation in age cuts off e.g., Leijten et al [18] used people aged 45–64. Older workers who have recently moved into economic inactivity due to the Covid pandemic were more likely to find themselves in poverty [19]. This combined with the longstanding agenda to extend working lives [20] has thrown into sharp relief the need to continue working for many people, including those in pain.

There are many cross-sectional studies that examine associations between living with chronic pain and employment status and outcomes [9]. However, there are very few longitudinal analyses of older workers with pain. An exception is by Leijten et al. [18]. They assessed the influence of seven chronic health problems, one of which was musculoskeletal pain, on work ability and productivity at work, via longitudinal analyses with data from older workers aged 45–64. They found that workers with musculoskeletal and mental health issues had lower productivity at work at one year follow up than workers without those health problems. Such analyses can be important for understanding the trajectories people living with pain experiences in their working lives, in terms of impact of pain on work, work retention, and retirement decisions. Longer time periods may be helpful to understand with more granularity the relationship between chronic pain and its interference with work as well as the risk of work cessation and retirement decisions, all key aspects of issues with which the older citizen with chronic pain must currently contend.

Since 2011 there are no general provisions for a mandatory retirement age in the UK, meaning that most people are at least notionally able to choose when to retire [21]. This is coupled with changes to the age at which people can receive state pension benefits which has been increasing incrementally since 2010 towards a current target of 68. However, despite the notional choice it is clear that people in less favourable work scenarios may choose to leave work earlier and/or be pushed out of their work. The decision to retire is multi-factorial, which may include health considerations and work factors [22,23].

Therefore, we explored longitudinally the relationship between chronic pain and the risk of work cessation; between chronic pain and how old people are when they retire.

Precise research questions were: does living with musculoskeletal pain affect the age of retirement and the risk of work cessation, while controlling for the influence of job satisfaction, depressive symptoms, self-perceived social status, sex, wealth, and working conditions.

Methods

Study design and setting

This longitudinal cohort study draws on data from participants taking part in the English Longitudinal Study of Ageing (ELSA), a national panel study comprising participants aged 50 years and older residing in England. Participants are followed over successive waves (2-year intervals) and, in this study, we draw on data from ELSA waves 2 (2004/2005) through 9 (2018/2019).

Participants

Participants taking part in ELSA were initially taken from the Health Survey for England (HSE) for wave 1. Samples from the HSE stemming from 1998, 1999, and 2001 were used to recruit participants for ELSA, as these were deemed recent and large enough samples. Only those households where there was at least one adult over 50 years of age who had consented to being re-contacted subsequent to taking part in the HSE were approached for inclusion in the ELSA study. The ELSA sample has been designed specifically to represent individuals residing in private households aged 50 or older in England, as such participants were only included if they fulfilled these criteria. Given the respondents’ age, ELSA data need to be refreshed each certain waves with new participants taken from the HSE to maintain its representative nature. This occurred at waves 3, 4, 6, 7, and 9. Additional details relating to sampling are described in Steptoe et al. [24].

Participants were eligible for inclusion in this study if they had complete data for all relevant variables spanning waves 2 to 9 and had not yet retired or were in work at wave 2. The entire data set at baseline (wave 2) comprised 8780 participants out of which 2405 reported being in employment and 2582 reported not being retired and so were eligible for inclusion in the study. After removal of missing values due to incomplete data and loss to follow up the final numbers were 1281 and 1156 eligible participants respectively. Out of the eligible participants, 1156 belonged to both categories, i.e., currently in employment and not yet retired (Fig 1). Full written consent was obtained from participants prior to taking part, while the London Multicentre Research Ethics Committee (MREC/01/2/91) granted ethical approval for the data collection and archiving.

Fig 1. Participant flow diagram.

Fig 1

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Variables

In ELSA, participants either self-report data and/or data are collected through nurse visits. Overall, ELSA aims to collect data about the same topics at each wave, but there have been additions and omissions regarding the topics over successive waves. All visits are conducted face-to-face, whereby the data collection process is facilitated through the use of computer assisted interviewing overseen by a qualified nurse or using pen and paper for self-completion questionnaires. Interviews are conducted individually or in case of multiple eligible individuals in a single household concurrently in a single session. Additional details relating to data collection methods are described in Steptoe et al. [24].

Outcomes

Work cessation

To determine whether participants had been employed in the last month they were asked to indicate whether they were in paid employment last month (yes, no).

Retirement

Participants self-reported whether they were retired. They were asked to select from a list what best describes their situation, i.e., retired, employed, self-employed, unemployed, permanently sick or disabled, looking after home or family, other, and semi-retired. We considered participants to be retired if they had selected “retired” or “semi-retired” from the list as best describing their current situation. All other responses were coded as not retired.

Predictors

Job satisfaction

Participants indicated whether they felt satisfied with their job, selecting from strongly agree, agree, disagree, and strongly disagree. To facilitate analysis and interpretation, the item was dichotomised, coding “strongly agree”, and “agree” as a positive response and strongly disagree and disagree as a negative response.

Depressive symptoms

Participants completed the Centre for Epidemiologic Studies Depression Scale (CES-D), an eight-item measure assessing depressive symptoms [25]. Participants report the presence of absence of somatic complaints and negative affect they experienced in the previous week. The summed total score was divided by 8, resulting in scores ranging from 0 (no symptoms) to 1 (all symptoms).

Self-perceived social status

Participants were asked to rate their self-perceived (subjective) social status on a scale from 0 to 100, subdivided into 20 five-point increments. 0 represents those being “worst off” and 100 represents those being “best off”. Scores range from 0 to 20.

Frequent musculoskeletal pain

Following previous investigations using the same data set [26,27], we used participants’ self-reports (yes/no) to indicate whether they were often troubled by bone, joint, or muscle pain.

Age. Age was self-reported; however, if participants were older than 90 their ages were changed to 99 to maintain anonymity.

Sex. Participants self-reported their sex during nurse interviews.

Wealth quintile

First, participants’ total net wealth was determined. This was the total of their housing wealth, physical wealth (including additional property wealth, wealth related to business and other physical assets), and financial wealth (including savings, stock certificates and bank accounts) minus any financial and mortgage debt. Then, based on the total net wealth, participants were organised into quintiles.

Working conditions

Based on the methodology laid out in Carr et al. [28] we calculated indexes of physical job demands, psychosocial job demands, decision authority, social support, and recognition. Physical job demands were derived by asking participants to rate their level of agreement with the statement “my job is physically demanding” (‘strongly disagree’, ‘disagree’, ‘agree’ or ‘strongly agree’) and the level of physical exertion in their current job, from sedentary (sitting) to heavy manual, resulting in sum scores ranging from 2–8 with higher scores indicating higher demand. The same approach was used for psychosocial demands. The extent of agreement with the items (‘‘considering the things I have to do at work, I have to work very fast”) and time pressure (‘‘I am under constant time pressure due to a heavy workload”) were summed (range 2–8). Decision authority was operationalised by summing the rate of agreement with the items on job control (‘‘I feel I have control over what happens in most situations”) and job autonomy (‘‘I have very little freedom to decide how I do my work”; reversed) resulting in scores ranging from 2–8. Social support was measured through a binary item (‘‘I receive adequate support in difficult situations”), as was low recognition (‘‘I receive the recognition I deserve for my work”). For these items, responses of ‘agree’ or ‘strongly agree’ were coded as 0 and ‘disagree’ or ‘strongly disagree’ were coded as 1.

Marital status

Respondents indicated whether they were married, cohabiting, or neither. “Married” and “cohabiting” were coded into one category, whereas “neither” was used as the reference category.

Statistical methods

R version 4.2.2 was used to run the statistical analyses. We performed Cox proportional hazards regressions to explore the relationship between the predictors and retirement as well as work cessation. Participants already retired or out of work at baseline (wave 2) were excluded from the respective analyses.

A single data set was derived, combining data from ELSA waves 2 through 9, whereby data from wave 2 (baseline) were used for the predictors. Participants’ age was used to denote survival time until retiring or reporting work cessation. Data analysis in R was operationalised through the “survival” package and the results visualised using the “survminer” package [29]. Prior to running the multivariate model, individual univariate proportional hazard regressions for each potential predictor were run. Only those predictors significant in the univariate analysis were added to the multivariate model. Finally, additional sensitivity analyses were carried out to rule out reverse causation. This involved repeating the multivariate analyses while excluding participants who had retired/were out of work at baseline (wave 2) and those who retired or reported being out of work in wave 3.

Results

Sample characteristics

Table 1 presents an overview of the continuous variables employed in both analyses. The counts for the categorical variables can be found in Figs 2 and 3. Variations in sample size are due to differences in the number of complete responses.

Table 1. Sample overview for measures at baseline.

Variables Retirement Analysis (n = 1156) Employment Analysis (n = 1281)
Age—mean (SD; range) 57.8 (3.9); 52–75 58.3 (4.5); 52–99
Self-described social status—mean (SD), range 0–20 12.4 (2.9) 12.5 (3.0)
Psychosocial Job Demands—mean (SD), range 2–8 4.9 (1.5) 5.0 (1.5)
Decision Authority—mean (SD), range 0–8 4.1 (1.2) 4.1 (1.2)
Depressive Symptoms—mean (SD), range 0–1 0.1 (0.2) 0.1 (0.2)
Recognition Yes = 842 Yes = 954
No = 314 No = 327
Social Support Yes = 878 Yes = 983
No = 278 No = 298
Sex Male = 587 (50.8%) Male = 637 (49.7%)
Female = 569 (49.2%) Female = 644 (50.3%)
Musculoskeletal Pain (at baseline) Yes = 288 Yes = 323
No = 868 No = 958
Work Satisfaction Satisfied = 1045 Satisfied = 1167
Dissatisfied = 111 Dissatisfied = 114
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Fig 2. Hazard ratios for retirement.

Fig 2

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***values are rounded. Please note that for self-perceived social status the hazard ratio indicates the change in risk per five-point increment on a scale from 0–100.

Fig 3. Hazard ratios for Wok Cessation.

Fig 3

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***values are rounded. Please note that for self-perceived social status the hazard ratio indicates the change in risk per five-point increment on a scale from 0–100.

In unadjusted models, wealth, marital status, and physical job demands did not influence the time to retirement or reporting being out of work and were therefore not included in subsequent analyses. Next, we used the remaining significant univariate predictors to run two multivariate Cox proportional hazard regression analyses to examine how they jointly impacted on time to retirement and reporting work cessation in the previous month.

Retirement

In total of 1156 not yet retired participants were included in the analysis, of which 1073 retired over the course of 14 years (Fig 2). Work dissatisfaction was associated with earlier retirement compared to reporting satisfaction with work (HR = 1.29, CI = 1.03–1.62). Those reporting musculoskeletal pain complaints tended to retire earlier compared to pain free participants (HR = 1.30, CI = 1.12–1.49). Female participants had a 1.27-increased risk (CI = 1.13–1.44) of retiring earlier compared to male participants. Higher self-perceived social status was associated with earlier retirement age (HR = 1.01, CI = 1.00–1.01). Participants who feel they receive the recognition they deserve at work tended to retire at a later age compared to those who disagreed with this statement (HR = 0.78, CI = 0.66–0.91). Older age at baseline was also associated with retiring later (HR = 0.89, CI = 0.88–0.91), but this is likely owed to the statistical approach of using years, and by extension age, to indicate survival time, i.e., age at retirement. Those having a higher age at baseline tended to retire at a later age, because they were already older at that point and everyone was followed for the same duration, i.e., 14 years, and so this effect merely suggests that older people who were still working retired at an older age. Severity of depressive symptoms, psychosocial job demands, decision authority, and social support did not influence how soon participants retired in the adjusted model.

Repeating the analysis, but excluding participants who retired within the two years after baseline (sensitivity analysis), job satisfaction no longer had a significant impact. None of the other associations between the potential determinants and retirement were affected.

Work cessation

A similar approach was employed to examine the contribution of the predictor variables on work cessation. This analysis comprised of 1281 (mean age 58.3, SD = 4.5), participants who were in employment at wave 2 and for whom complete data were available. Over the 14-year period participants were followed, 1196 reported work cessation (Fig 3). Work dissatisfaction was associated with an increased risk of ceasing work at an earlier age (HR = 1.30, CI = 1.04–1.62). Participants suffering from musculoskeletal pain were 1.25 times more likely to cease work sooner (CI = 1.10–1.43). Higher perceived social status was associated with earlier reports of work cessation (HR = 1.01, CI = 1.0–1.01). Female participants were more likely to report work cessation at an earlier age compared to male participants (HR = 1.28, CI = 1.14–1.43). Like the analysis above, age was spuriously associated with being of older age when reporting work cessation (HR = 0.89, CI = 0.88–0.90). Finally, not receiving the recognition they felt they deserved in their job meant that those participants tended to report work cessation at a younger age (HR = 1.27, CI = 1.09–1.48). Severity of depressive symptoms, psychosocial job demands, decision authority, and social support did not influence the age at which participants reported work cessation.

Repeating the analysis, but excluding participants who reported work cessation within the two years following baseline (sensitivity analysis), job satisfaction no longer had a significant impact. None of the other associations between the potential determinants and work cessation were affected.

Discussion

Our analyses have demonstrated that frequent musculoskeletal (MSK) pain was significantly associated with the risk of earlier retirement and of work cessation in a sample of older workers based in England. Frequent musculoskeletal pain remained a significant predictor of earlier retirement and risk of work cessation at earlier ages when controlling for the influence of job satisfaction, depressive symptoms, self-perceived social status, sex, and working conditions.

As well as MSK pain, several predictor variables significantly associated with increased risk of retiring or leaving the workforce earlier. It is perhaps unsurprising that being female associated with leaving the workforce earlier, given that the UK state pension age was lower for females than males during the study period, being equalised in 2018. Mandatory retirement has been abolished in the UK since 2011 which, at least notionally, means workers can choose when to retire [21]. This gives greater scope for work-related factors to play a role in decisions to leave the workforce. As such job satisfaction, a perceived combination of many work-related factors, was a significant predictor in both our analyses. This is in line with a Danish study in general workers where lower work satisfaction has been found to associate with increased risk of retirement [30]. However other studies have demonstrated inconsistent effects of satisfaction in general worker retirement [31], which may partially explain why satisfaction was not a significant predictor in the sensitivity analysis. Finally, recognition or appreciation at work has been associated with retirement in several cohorts of general workers [28,30,32]. Our study reflects these findings in a model that includes chronic MSK pain.

Although we have presented the results of separate analyses of retirement and leaving employment, it should be noted that there is considerable crossover in the analytical samples. For example, a participant with frequent MSK pain who retired early, will also have left employment early. However, the outcomes are conceptually different which justified the dual analysis approach. As with any self-reported predictors there is a chance of recall bias. However, ELSA is a longitudinal data set which minimises such bias as the predictors were measured before the outcomes, which may have occurred many years later. The sample may also be biased in that people with serious MSK pain may have already left the workforce prior to baseline, therefore our results may be an underestimate of the effect of MSK pain on workforce exit. The longitudinal nature of the analysis does not allow for changes in a participant’s working environment or changes in their perception of the same. Similarly, it does not account for changes in the presence of musculoskeletal pain complaints or their intensity. For example, physical job demands (excluded from our analysis at the unadjusted model stage) may not pose a problem to participants when measured at baseline but may become troublesome as the person ages and approaches state retirement age [23]. Further due to the binary nature of the MSK pain question, it was not possible to analyse if different levels of severity of MSK pain have a relationship with the outcomes. Finally, it is important to note that our study was undertaken in an English cohort, and it is not clear if these results will apply to other work and social security contexts.

It is well established that poor health can increase risk of retirement and unemployment [33,34]. However, Fisher et al [35] notes that the relationship is not linear as good health can also encourage earlier retirement, especially amongst those who are in a financial position to do so. Therefore, it is notable that in our study frequent MSK pain associated with both earlier work cessation and retirement.

More specifically this study complements results from a recent Danish study of older workers with physically demanding work where work limiting pain increased the risk of loss of employment [13]. Our study demonstrated that frequent MSK pain associated with poor work outcomes in a sample of general workers. Our study also utilises a wider definition of pain, which is notionally independent of, but may include, functional interference from pain or work limiting pain which has also been shown to lead to poor work outcomes [13,36]. In a large cohort of Swedish workers sickness absence due to lower back pain was associated with increased disability pensions and early retirement [37]. Our results are largely in line with this, although our measure of MSK pain is again wider as it did not limit the analysis to sickness absence attributed to pain.

Our study does not identify the reasons that people with frequent MSK pain may have left the workforce. De Wind et al [38], identified that poor health can push Dutch workers towards retirement for several reasons, including being unable to work or concern that health may decline in the future. It is therefore possible that participants in our study left work for several reasons including their pain making it impossible to continue, or a perception that work may increase their pain in the future. It is also unclear whether the people with MSK pain were pushed out by employers or have agency in deciding to leave the workforce, for example because of lowered productivity [18] or a declining sense of contributing to the workplace [39]. Therefore, identifying the reasons that people with pain may leave the workforce would be vital to understand what can be done to extend working lives for people with pain.

This study adds a longitudinal analysis of the relationship between frequent MSK pain and employment and retirement in an English sample. Our measure of pain is wider than functionally limiting pain and may suggest that a wider range of pain experiences can also lead to poor work outcomes.

Conclusion

Frequent MSK pain may increase the risk of earlier work cessation and earlier retirement. Further research should establish the mechanisms and decision making involved in leaving the workforce in people with frequent MSK pain.

Data Availability

The ELSA dataset is freely available from the UK Data Service to all bonafide researchers. The dataset can be accessed here: https://discover.ukdataservice.ac.uk/series/?sn=200011.

Funding Statement

The UK Data Archive made available the data. A team of researchers based at University College London, NatCen Social Research, the Institute for Fiscal Studies and the University of Manchester developed the English Longitudinal Study of Ageing.NatCen Social Research collected the data. The National Institute of Aging (R01AG017644) and a consortium of UK government departments coordinated by the Economic and Social Research Council provide funding for ELSA. ELSA is funded by the National Institute on Aging (R01AG017644), and by UK Government Departments coordinated by the National Institute for Health and Care Research (NIHR). The funders had no role in the study design; in the collection, analysis, and interpretation of data; in writing of the report; or in the decision to submit the paper for publication. The developers and funders of ELSA and the Archive do not bear any responsibility for the analyses or interpretations presented here.

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Decision Letter 0

Amin Nakhostin-Ansari

30 Oct 2023

PONE-D-23-31458Pain affects the age of retirement and the risk of work cessation among older peoplePLOS ONE

Dear Dr. Niederstrasser,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

ACADEMIC EDITOR: Thank you very much for submitting your manuscript to PLOS ONE. In addition to the concerns raised by the reviewers, please reorganize the methods section in accordance with the STROBE guidelines. The current format makes it challenging to follow the methods.

Please submit your revised manuscript by Dec 14 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Amin Nakhostin-Ansari

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The data is valuable given the aging of populations around the world.

Therefore, it is advisable to provide detailed information about the subjects of this study. In other words, what kind of work where they engaged in, what was the age distribution of their subjects, and what was the ratio of men to women? It also raises questions about whether they haven't been able to gather information about the location and duration of pain. Therefore, it is desirable to identify what information you can or cannot collect about the subjects of this study. In addition, data that can be presented should be presented in a table.

Interestingly, musculoskeletal pain has been shown to be an independent predictor of early retirement and job interruption. However, information such as pain intensity, location, duration, and presence or absence of disease may be important for planning measures, but can you provide information on these points? Are these data not necessary to achieve the objectives of this study?

The study found that frequent musculoskeletal pain remained a significant predictor of early retirement and the risk of leaving early, even after controlling for the effects of job satisfaction, depressive symptoms, self-perceived social status, gender, wealth, and working conditions. Your argument against this result is lacking.

Are these results considered reasonable? Will the results depend on the cohort in this study, on factors such as English language differentiation, or will they be seen as common results globally? I think you can refer to it from multiple angles.

Reviewer #2: The topic of this manuscript, dealing with consequences of musculoskeletal pain among individuals 50 years and more, is interesting. The data are issued from the English Longitudinal Study of Aging, which includes information about work cessation and retirement, with a rather long follow-up. The subjects are classified (at baseline) as often troubled by bone, joint or muscle pain, yes or no (self-report).

The manuscript should be improved in order to be easier to understand for the reader:

1 - The title could be modified: « musculoskeletal pain » rather than « pain »

2 - The study design must be explained more precisely: data from wave 2 (baseline) were used for the predictors. Retirement and work cessation were recorded from wave 2 to wave 9, fourteen years later. For retirement, 1156 workers at baseline, 1073 retired over the course of 14 years. For work cessation, the corresponding numbers are 1281 and 1197. As if no one had been lost to follow-up? It is said that additional participants were added with each wave of the cohort to maintain the total number of participants. Are there “additional participants” included in the present study?

3 - The potential effect of legal age at retirement is discussed, but it would be useful to have some information about the context of retirement in England earlier in the paper, especially the fact that “workers can choose when to retire”.

4 – Work cessation and retirement: many subjects are in both categories. This is said in the discussion, but it should appear earlier in the paper, with numbers (how many subjects belonging to both categories). Would it be possible to study the category: work cessation (no paid employment last month) but not retired?

5 - Participants’ age was used to denote survival time in the Cox models. This is probably not very usual. It seems that it explains the results observed for age. Does it explain that physical job demands seems not to be associated with time to retirement? it is also surprising that a higher self-perceived social status is associated with an increased risk of ceasing work and retiring at an earlier age.

6 – Marital status is indicated as a variable in “results” but not included in the list of predictors.

7 - For retirement, HR=0.77 for report of musculoskeletal complains. This HR is probably for « no report », one expects an HR larger than 1, associated with report of pain (as for work cessation).

8 – In figures 1 and 2, since some variables are dichotomous, and other ones quantitative, it is not obvious to compare the HRs. It would be useful to have a footnote indicating, for some variables, that the HR corresponds to a change of one unit for the variable. The range of the scores should be clearly presented in the text, and added in table 1: 0 (?) to 8 for depression and psychosocial demands (but the mean is 0.1 for depressive symptoms in table 1), 1 to 100 for social status (leading to a significant HR equal to 1.01!). For decision authority, there is no indication about the range in the text.

Minor comments

Abstract, first line of « result »: « was » is lacking after « pain ». Same in the first line of discussion.

« Frequent musculoskeletal pain » in the list of predictors: delete « they responded ».

Figures 1 and 2: “depression” in figure 1, “depressive symptoms” in figure 2. Probably the same variable.

(second) reference Bevan: some information is lacking (journal…)

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2024 Mar 20;19(3):e0297155. doi: 10.1371/journal.pone.0297155.r002

Author response to Decision Letter 0

9 Nov 2023

Academic Editor: Please reorganize the methods section in accordance with the STROBE guidelines. The current format makes it challenging to follow the methods.

We have rearranged the methods section following the strobe guidelines.

Reviewer #1:

Thank you for your suggestions. We have incorporated them and feel this has greatly improved the manuscript.

The data is valuable given the aging of populations around the world. Therefore, it is advisable to provide detailed information about the subjects of this study. In other words, what kind of work where they engaged in, what was the age distribution of their subjects, and what was the ratio of men to women? It also raises questions about whether they haven't been able to gather information about the location and duration of pain. Therefore, it is desirable to identify what information you can or cannot collect about the subjects of this study. In addition, data that can be presented should be presented in a table.

Thank you for the suggestions. We have added more information pertaining to the sample to Table 1, including the age distribution and ratio of men to women.

Interestingly, musculoskeletal pain has been shown to be an independent predictor of early retirement and job interruption. However, information such as pain intensity, location, duration, and presence or absence of disease may be important for planning measures, but can you provide information on these points? Are these data not necessary to achieve the objectives of this study?

Some information is available pertaining to pain intensity and type of work; however, this is difficult to incorporate, as the data span 14 years and the resolution of the available information is low, as assessments took place every two years. Therefore, the value of providing information pertaining to pain intensity and type of work for each participant may be misleading as there may be multiple changes throughout each interval that are not captured. The finding that mere presence of musculoskeletal pain has such a deleterious impact does, in our opinion, achieve the study’s objectives. We have clarified the discussion section to reflect this issue (see pg. 12).

The study found that frequent musculoskeletal pain remained a significant predictor of early retirement and the risk of leaving early, even after controlling for the effects of job satisfaction, depressive symptoms, self-perceived social status, gender, wealth, and working conditions. Your argument against this result is lacking.

Are these results considered reasonable? Will the results depend on the cohort in this study, on factors such as English language differentiation, or will they be seen as common results globally? I think you can refer to it from multiple angles.

You raise an interesting point. The English Longitudinal Study of Ageing is representative of individuals aged 50 and over residing in England and so generalisability to other nations may be limited. Nevertheless, although the findings likely do apply to other nations with similar demographic profiles and are likely to hold global merit our results do not go this far and so we have acknowledged this limitation in the discussion section (see pg. 13). However, we do consider these results to be reasonable, as they match similar previous investigations. 

Reviewer #2:

We thank reviewer 2 for their comments and suggested revisions. We have taken these on board and revised the manuscript to improve clarity. We feel the paper has been greatly improved following revisions based on suggestions by reviewer 2.

1. The title could be modified: « musculoskeletal pain » rather than « pain »

Thank you for the suggestion. We have made the amendment.

2. The study design must be explained more precisely: data from wave 2 (baseline) were used for the predictors. Retirement and work cessation were recorded from wave 2 to wave 9, fourteen years later. For retirement, 1156 workers at baseline, 1073 retired over the course of 14 years. For work cessation, the corresponding numbers are 1281 and 1197. As if no one had been lost to follow-up? It is said that additional participants were added with each wave of the cohort to maintain the total number of participants. Are there “additional participants” included in the present study?

There were no additional participants added to the study during the follow up period that were included in the analysis. The mention in the paper was a reference to the general data collection and recruitment methods employed by the ELSA team. Each wave (every two years), due to the age of participants, additional participants are added from that year’s Health Survey for England (HSE) to maintain a representative sample. Our study followed participants present in ELSA at wave 2, which means at wave 2 additional participants from the HSE were added to boost the numbers and maintain a representative sample. We only used data from participants present in wave 2 onwards in our analyses. We have changed the wording in the method section to clarify this point.

The entire data set at baseline data (wave 2) comprised 8780 participants (before removal of incomplete data) out of which 2405 reported being in employment and 2582 reported not being retired and so were eligible for inclusion in the study. After removal of missing values due to incomplete data and loss to follow up the final numbers were 1281 and 1156 eligible participants respectively. We have amended the wording in the method section to clarify this point (see pg. 5-6).

3. The potential effect of legal age at retirement is discussed, but it would be useful to have some information about the context of retirement in England earlier in the paper, especially the fact that “workers can choose when to retire”.

Thank you. We have added a short paragraph into the introduction with a brief overview of retirement in the UK which has been subject to several changes since 2010 (see pg. 4).

4. Work cessation and retirement: many subjects are in both categories. This is said in the discussion, but it should appear earlier in the paper, with numbers (how many subjects belonging to both categories). Would it be possible to study the category: work cessation (no paid employment last month) but not retired?

We have added the number of participants (1156) belonging to both categories to the text (see pg. 6).

This would indeed be a fascinating and worthwhile analysis. We looked into this, and selecting those who were not in paid employment in the last month and had not retired yet led to 91 eligible participants after removing participants with missing data for any of the relevant variables. Nevertheless, the exact analysis cannot be repeated, as these participants, by definition, do not have any data pertaining to job demands and characteristics and so the results would not be comparable to the current analyses. Furthermore, the resultant low sample size invites prudence in interpreting the results from this analysis. We therefore decided that, in the current context, this additional analysis does not bear any additional merit to the paper.

5. Participants’ age was used to denote survival time in the Cox models. This is probably not very usual. It seems that it explains the results observed for age. Does it explain that physical job demands seems not to be associated with time to retirement? it is also surprising that a higher self-perceived social status is associated with an increased risk of ceasing work and retiring at an earlier age.

High physical job demands are associated with occupations involving manual labour. These jobs are further associated with lower pay which often precludes the individual from retiring at the point that the physical aspects of their job become too much to continue working. As a result, changes to less physically demanding jobs often ensue that result in continuous employment, albeit in different sectors.

Higher self-perceived social status is often associated with wealth or perceived wealth. It is therefore reasonable to assume that those with higher self-perceived social status may have higher perceived or actual wealth and so have the option of retiring earlier. In other words, they may be able to afford not to work, as they can live off savings and pensions.

6. Marital status is indicated as a variable in “results” but not included in the list of predictors.

Thank you for pointing out this oversight. This has been amended (see pg. 9).

7. For retirement, HR=0.77 for report of musculoskeletal complains. This HR is probably for « no report », one expects an HR larger than 1, associated with report of pain (as for work cessation).

Thank you for pointing this out. We realise that we were not clear in how this was reported in the manuscript. You are correct that the HR below 1 is for the reverse, i.e., “no report”, and the number in the text should in fact be the inverse, hence, 1.30. We have changed the values to clarify this (see pg. 10).

8. In figures 1 and 2, since some variables are dichotomous, and other ones quantitative, it is not obvious to compare the HRs. It would be useful to have a footnote indicating, for some variables, that the HR corresponds to a change of one unit for the variable. The range of the scores should be clearly presented in the text, and added in table 1: 0 (?) to 8 for depression and psychosocial demands (but the mean is 0.1 for depressive symptoms in table 1), 1 to 100 for social status (leading to a significant HR equal to 1.01!). For decision authority, there is no indication about the range in the text.

We thank the reviewer for this suggestion. We forgot to include that the summed score for the number of depressive symptoms was divided by 8, resulting in scores ranging from 0 to 1. Self-perceived social status is operationalised by dividing the range of 0 – 100 into 20 five-point increments. Therefore, the range is 0 – 20 and each single unit increase there, represents a five-point increase on a scale from 0 – 100. We initially presented the mean on a scale from 0 – 100, but realise that this may lead to confusion and have reverted to reporting the mean on a scale from 0 – 20 to better illustrate the impact of a single unit increase on this measure. Ranges have been added where missing in the method section (see pg. 7 – 9) and Table 1.

Footnote to be added to both Figures: Please note that for self-perceived social status the hazard ratio indicates the change in risk per five-point increment on a scale from 0 – 100.

Minor comments

Abstract, first line of « result »: « was » is lacking after « pain ». Same in the first line of discussion.

« Frequent musculoskeletal pain » in the list of predictors: delete « they responded ».

Figures 1 and 2: “depression” in figure 1, “depressive symptoms” in figure 2. Probably the same variable.

(second) reference Bevan: some information is lacking (journal…)

Thank you for pointing out these oversights, which we have corrected.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0297155.s001.docx (136.5KB, docx)

Decision Letter 1

Amin Nakhostin-Ansari

13 Dec 2023

PONE-D-23-31458R1Musculoskeletal pain affects the age of retirement and the risk of work cessation among older peoplePLOS ONE

Dear Dr. Niederstrasser,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Please submit your revised manuscript by Jan 27 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Amin Nakhostin-Ansari

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The comments raised in the previous version have been correctly adressed. This revised manuscript is easy to read and to understand

Reviewer #3: I had the opportunity to review this interesting manuscript on effects of MSK pain on retirement age among older people. The idea is interesting and the manuscript is well-written. There are a few areas where I believe some enhancements could further elevate the quality of the manuscript.

a. According to the title the study aimed to evaluate the effects of MSK pains on work cessation and more focus should be place on MSK pains in the introduction section rather than discussing chronic pain in general.

b. Please briefly describe the sampling and data collection process.

c. Please consider showing the flow of participant in a figure (method section – participant subheading).

d. Please move the variables subheading to the next line.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2024 Mar 20;19(3):e0297155. doi: 10.1371/journal.pone.0297155.r004

Author response to Decision Letter 1

18 Dec 2023

Academic Editor: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

We have reviewed the reference list and made the following changes:

Added Waddell & Burton 2006, Taylor 2017 due to omission from reference list.

Amended De Wind 2014 to De Wind 2013 in the manuscript (the reference list is correct to cite both).

Amended Bevan 2015 to Bevan 2015b, due to oversight.

Added Fimland et al. 2018 in introduction due to reviewer 3’s suggestion to focus more on musculoskeletal pain.

Reviewer #2: The comments raised in the previous version have been correctly addressed. This revised manuscript is easy to read and to understand.

We thank reviewer 2 for the comments and suggestions, which we believe have greatly improved the paper.

Reviewer #3: I had the opportunity to review this interesting manuscript on effects of MSK pain on retirement age among older people. The idea is interesting and the manuscript is well-written. There are a few areas where I believe some enhancements could further elevate the quality of the manuscript.

Thank you for your comments and suggestions. We have revised the manuscript accordingly.

1. According to the title the study aimed to evaluate the effects of MSK pains on work cessation and more focus should be place on MSK pains in the introduction section rather than discussing chronic pain in general.

Thank you for bringing this up. We have made changes to the introduction to highlight the manuscript’s focus on MSK pain, see page 3.

2. Please briefly describe the sampling and data collection process.

We have added additional details pertaining to the sampling and data collection process on pages 5 and 6.

3. Please consider showing the flow of participant in a figure (method section – participant subheading).

Thank you for this suggestion. We have included a figure.

4. Please move the variables subheading to the next line.

We have moved the subheadings to the next line.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0297155.s002.docx (128.7KB, docx)

Decision Letter 2

Amin Nakhostin-Ansari

2 Jan 2024

Musculoskeletal pain affects the age of retirement and the risk of work cessation among older people

PONE-D-23-31458R2

Dear Dr. Niederstrasser,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Amin Nakhostin-Ansari

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

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Reviewer #3: (No Response)

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Reviewer #3: (No Response)

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Reviewer #3: I have read the revised manuscript and I am satisfied with the changes made by the authors. They have improved the clarity, accuracy, and relevance of their paper.

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Reviewer #3: No

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Acceptance letter

Amin Nakhostin-Ansari

24 Feb 2024

PONE-D-23-31458R2

PLOS ONE

Dear Dr. Niederstrasser,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Amin Nakhostin-Ansari

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0297155.s001.docx (136.5KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0297155.s002.docx (128.7KB, docx)

    Data Availability Statement

    The ELSA dataset is freely available from the UK Data Service to all bonafide researchers. The dataset can be accessed here: https://discover.ukdataservice.ac.uk/series/?sn=200011.

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