SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Objective

To prospectively examine arthritis-related productivity losses, work changes, and leaving employment, the relationships among these work transitions, and the factors associated with them.

Methods

Participants with inflammatory arthritis or osteoarthritis were interviewed at 4 time points, 18 months apart, using a structured questionnaire. At baseline (T1), all participants (n = 490; 381 women, 109 men) were employed. At T2, T3, and T4, the sample decreased to 413, 372, and 349 participants, respectively. Respondents were recruited using community advertising and from rheumatology and rehabilitation clinics. Work transitions considered were productivity losses (absenteeism, job disruptions), work changes (reduced hours, changing jobs), and leaving employment. Also measured were demographic, illness, work context, and psychological variables. Generalized estimation equations modeled predictors of work transitions over time.

Results

Although 63.1% of respondents remained employed throughout the study, work transitions were common (reported by 76.5% of participants). Productivity losses, especially job disruptions such as being unable to take on extra work, were the most frequently reported. Work transitions were related to subsequently making other work transitions, including leaving employment. Age, sex, education, activity limitations, control, depression, and arthritis-work spillover were also associated with work transitions.

Conclusion

This study sheds light on a process of diverse employment changes that may occur in the lives of many individuals with arthritis. It emphasizes the interrelationships among work transitions, as well as other factors in predicting work transitions, and it provides insight into work changes that may signal impending difficulties with remaining employed.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Considerable evidence links arthritis to loss of employment and identifies clinical, personal, and environmental factors associated with changes in work status (1–13), but until recently, few studies have focused on the employment experiences of people with arthritis. This is changing, however, with increased emphasis on the diverse experiences of individuals with arthritis at work (14–21) and research being done to measure work place disability and productivity and the indirect costs of arthritis (22–34).

The shift in research to include a better understanding of working with arthritis has highlighted underemployment and the work transitions that individuals make in an effort to remain employed. Underemployment captures a wide range of adverse employment situations that result in reduced job satisfaction and earnings (35). These include unemployment, involuntary part-time work, and a mismatch between training and job requirements (e.g., being overqualified). Work transitions, a less evaluative term, describes labor force changes that may signal problems resulting in an individual leaving the workforce or may reflect positive changes enabling individuals to remain employed (22, 36–40). To our knowledge, there are few studies of work transitions in arthritis. The objective of our study was to prospectively examine arthritis-related work transitions, the relationships among different types of work transitions, and the factors associated with them.

Although there is little arthritis research examining work transitions, the benefits of mapping work trajectories for evaluating health services, productivity, and costs have been noted (22). Moreover, by examining whether occasional productivity losses and work changes are related to one another and to subsequently leaving employment, we are in keeping with research that conceptualizes participation in employment as multifaceted rather than a simple dichotomy of working versus not working (41).

In this study, we examined 3 categories of arthritis-related work transitions: productivity losses, namely absenteeism and job disruptions (e.g., not being able to take on extra work); work changes (reduced work hours, changing jobs); and leaving the labor force (Figure 1). Productivity losses are likely to be reported more frequently than work changes or leaving employment. Moreover, relationships among work transitions are expected to be complex. For example, if absenteeism occurs on an occasional basis, it may not create job disruptions or result in forgoing employment. However, if job disruptions and absenteeism become chronic, individuals may be more likely to make work changes or leave employment. Arthritis-related work changes such as reducing hours or changing jobs are also expected strategies for avoiding employment loss. However, it is unclear whether these strategies enable individuals to remain employed or whether they are associated with subsequently leaving employment (8, 21).

thumbnail image

Figure 1. Categories of arthritis-related work transitions.

Download figure to PowerPoint

We also investigated whether demographic, health, work place, and psychological variables used in previous studies can help develop a more complete understanding of work transitions (10). For example, aging research finds that many older workers desire to gradually reduce work hours prior to retiring (42, 43). Older workers viewed reduced hours as a positive work transition because of the flexibility and the opportunity to remain working. This suggests that age is important in understanding work transitions. Chronic illness and disease factors such as pain and disability are related to greater absenteeism (30, 44–46). These are hypothesized to be especially important to productivity losses, which, in turn, are likely to relate to work changes and leaving employment. To our knowledge, few studies have examined job change, but a study of work place fatigue found that job disruptions, including problems with supervisors and reports of greater physical and emotional strain, preceded employees changing jobs (40). Other research has found that psychological variables such as depression and mastery are related to absenteeism (30, 46). Building on these studies, we expected that perceptions of a lack of job control, depression, and role conflict would be related to increased work transition (18, 30, 40, 46).

SUBJECTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Subjects.

Individuals with inflammatory arthritis or osteoarthritis (OA) residing in southwestern Ontario, Canada were interviewed 4 times (T1–T4), each interview being 18 months apart. To ensure diversity across occupations and that individuals receiving fewer health care services were not systematically excluded, the sample was purposive. Participants were recruited largely from community advertisements, with additional respondents coming from rheumatology and rehabilitation clinics and The Arthritis Society, Ontario Division. Eligibility criteria were a reported physician diagnosis of inflammatory arthritis or OA, arthritis duration of ≥1 year at baseline (T1), paid employment at baseline, no comorbid conditions causing physical disability (e.g., multiple sclerosis), and fluency in English.

Procedure.

An in-depth, structured questionnaire lasting ∼2 hours was administered at participants' homes or a location of their choice. Ethical approval was received and informed written consent was obtained at each interview.

Measures.

Demographics.

Data on age, sex, marital status, living arrangements (live alone: yes/no), and education were collected.

Arthritis type and duration.

Participants reported the type(s) of arthritis diagnosed by their physician and the time since their diagnosis. Arthritis type was coded in 3 categories: inflammatory arthritis (e.g., rheumatoid arthritis [RA], psoriatic arthritis), OA, or both.

Symptom severity.

Pain and fatigue were measured over the preceding month. Pilot and ongoing work place research suggested that the chronicity of severe pain was more relevant to employment than pain severity alone without reference to its frequency (18, 47). Pain was assessed with the question, “How often did you have severe pain from your arthritis?” and fatigue with “How often have you felt fatigue as a result of your arthritis?” Responses were recorded on a 5-point Likert-type scale where 1 = no days and 5 = all days.

Work place activity limitations.

The 11-item Workplace Activity Limitations Scale (WALS) asked about arthritis-related employment activity limitations (17, 47). Patterned after the Health Assessment Questionnaire but specific to the work place, items include getting to, from, and around the work place; sitting/standing for long periods; lifting; reaching; and the schedule and pace of work. Responses were on a 4-point Likert-type scale where 0 = no difficulty and 3 = not able to do. Participants who indicated that an activity was not applicable to their job were assigned a score of 0 (no difficulty) for that activity. Responses were summed with scores ranging from 0 to 33. The reliability of the WALS using Cronbach's alpha was 0.78 at T1 and 0.81 at T2–T4.

Occupation.

Occupation was classified using the Human Resources and Social Development Canada National Occupation Classification Matrix 2001 (48). Occupations were collapsed into 4 groups: business, finance, and administration; health, science, and arts; sales and service; and trades, transportation, and equipment operation.

Work hours and employment status.

Hours worked in an average week were assessed. Categories for employment status were full-time, part-time, leave of absence/short- term disability, long-term disability, unemployed (i.e., looking for work), not employed (i.e., not looking for work), retired, homemaker, and other.

Arthritis-work spillover.

The 6-item Arthritis-Work Spillover scale measured role balance/conflict. Specifically, it examined the extent to which the demands of arthritis interfered with work and work interfered with managing arthritis (18). Responses were on a 5-point Likert-type scale where 1 = strongly disagree and 5 = strongly agree. Scores were summed and ranged from 6 to 30. Examples include, “The demands of my arthritis make it difficult for me to do as good a job at my work as I would like” and “My condition suffers because of the demands of my work.” Cronbach's alpha for the scale was 0.88, 0.86, 0.87, and 0.87 at T1, T2, T3, and T4, respectively.

Depression.

The 20-item Center for Epidemiologic Studies Depression Scale measured depressive symptoms (49). Respondents reported the frequency of symptoms experienced during the past week (where 0 = rarely or none of the time/less than 1 day and 3 = most or all of the time/5–7 days). Scores are summed and can range from 0 to 60, with scores ≥16 taken as evidence of depression. Reliability for the scale was 0.92 at T1 and T3 and 0.91 at T2 and T4.

Work transitions.

Three categories of work transition were gauged. The productivity losses category measured absenteeism in the previous 6 months related to arthritis (yes/no) and job disruptions, which were assessed with 10 items. Examples include lost work time because of arriving late or leaving early; being unable to attend meetings, take on extra responsibilities, pursue a job promotion, or work the shift/schedule desired; work interruptions of 20 minutes or more; and difficulties with supervisor or coworkers. Respondents indicated whether the job disruptions occurred related to arthritis in the previous 6 months (yes/no) and scores were summed for a total range of 0–10. The work changes category assessed reduced work hours (“Because of your arthritis, have you changed the number of hours you work in an average week since the last interview?” yes/no) and changing jobs (“Since your last interview, have you changed your occupation/job as a result of your arthritis?” yes/no). The left labor force category was assessed by examining employment status. Respondents working full- or part-time were considered employed. Other changes in employment status were categorized as left labor force.

Statistical analysis.

Frequencies, means, and SDs were examined. Participants could experience multiple work transitions (e.g., absenteeism and reduced hours). Except for sex, independent variables could also vary across time. To accommodate individual work changes over time, the data were structured into a person-wave format where each participant had as many records as time points in which he/she participated. Analyses retained all participants with at least 2 time points of data and adjusted final estimations.

To disentangle the interrelationships between work transitions and independent variables, temporal order among variables was addressed using work transitions and independent variables measured in an earlier wave to model subsequent work transitions. A fixed-effect regression model was applied to analyze work transitions (50). The model used generalized estimation equations (GEEs), a method of parameter estimation for repeated observations that is useful when dependent measures are discrete (50, 51). A fixed-effect Poisson model was tested for job disruptions and a binomial distribution was assumed for other work transitions.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

T1 and T4 sample characteristics are presented in Table 1. Seventy-one percent of participants were retained in the sample over 4.5 years: T1 n = 490, T2 n = 413, T3 n = 372, and T4 n = 349. Demographic and occupational characteristics remained similar over time. Approximately three-quarters of the sample were women. At T1, the respondents' age was ≥23 years, with a mean age of 51.1 years. Participants were relatively well educated and 60.6% were married. Approximately two-thirds of respondents worked in business/administration or health/sciences. At T1, 56.7% of participants reported OA and 33.3% reported inflammatory arthritis. Of these, 79% had RA. Fewer respondents reported having both OA and inflammatory arthritis at T1 than at T4 (10% versus 18.3%) (P < 0.001). Not surprisingly given the duration of the study, frequency of severe pain and fatigue were more variable than other characteristics. At T4, participants reported somewhat lower frequency of severe pain and fatigue (P < 0.05).

Table 1. Sample characteristics of all respondents at time 1 and time 4*
VariablesTime 1 (n = 490)Time 4 (n = 349)
NValueNValue
  • *

    Values are the percentage unless otherwise indicated. OA = osteoarthritis; AWS = Arthritis-Work Spillover scale; CES-D = Center for Epidemiologic Studies Depression Scale; WALS = Workplace Activity Limitations Scale.

  • 79% of participants with inflammatory arthritis reported a diagnosis of rheumatoid arthritis.

Demographic    
 Age, mean ± SD years48751.1 ± 9.334656.6 ± 9.0
 Sex    
  Male10922.27822.3
  Female38177.827177.7
 Education    
  Elementary & secondary8517.45917.0
  Some postsecondary11222.97421.3
  Postsecondary19640.214341.2
  Postgraduate9519.57120.5
 Marital status    
  Married/living as married29760.621561.6
  Divorced/separated/widowed11723.98424.1
  Never married7615.54713.5
 Lives alone49025.334930.9
Illness related    
 Arthritis type    
  Inflammatory arthritis16333.310830.9
  OA27856.717750.7
  Both OA & inflammatory arthritis4910.06418.3
 Duration at wave 1, mean ± SD years4889.17 ± 8.7  
 Pain severity in last month    
  No days8617.67421.2
  A few days12024.511332.4
  Some days11723.98022.9
  Most days10922.25114.6
  All days5411.0318.9
 Fatigue in previous month    
  No days7615.55816.6
  A few days7214.78424.1
  Some days9519.47621.8
  Most days12124.77421.2
  All days12325.15716.3
Employment    
 Job sector    
  Business, finance, administration16233.18834.5
  Health, science, art17535.89336.5
  Sales, services10220.95120.0
  Trades, transportation, equipment operator5010.2239.0
 Psychosocial, mean ± SD score    
  AWS (observed range 6–30)46417.9 ± 5.925216.4 ± 5.3
  CES-D (observed range 0–48)48410.9 ± 10.03449.3 ± 8.5
  WALS (observed range 0–24)4746.4 ± 4.42557.3 ± 4.6

Changes in employment status among participants for whom data was available across ≥2 time points during followup (n = 363) are summarized in Table 2. Sixty-three percent of respondents remained employed throughout the study, 29.8% left the labor force at some point during followup and did not return, and 7.2% left employment and returned at a later time.

Table 2. Employment status changes during followup (n = 363)
Employment statusN (%)
Employed at all time points229 (63.1)
Left labor force and did not return during followup108 (29.7)
 Within the first 18 months42 (11.6)
 Between 18 and 36 months34 (9.4)
 Between 36 and 54 months32 (8.8)
Left labor force and returned at a later time26 (7.2)
 By 36th month of followup13 (3.6)
 By 54th month of followup13 (3.6)

Means and frequencies of work transitions at T2–T4 are presented in Table 3. These data represent changes to employment made after the onset of the study, allowing us to examine work transitions within a specific timeframe. Overall, 76.5% of participants reported ≥1 work transition during followup. Approximately half of the sample reported ≥1 job disruption at each time. The most common were arthritis-related work interruptions of ≥20 minutes (∼30% of all job disruptions at each time point), being unable to take on extra projects (15–20%), and arriving late or leaving early (10%). Problems with supervisors or coworkers (5–9% of job disruptions) and being unable to attend business trips (<5%) were least common. Arthritis-related absenteeism varied, with 27.9% and 20.1% of respondents reporting absenteeism at T2 and T4, respectively, and a total of 47.1% of participants reporting arthritis-related absenteeism during followup. At T2, 17.9% of respondents reported having reduced their work hours since their previous interview. This dropped to 12.4% and 12.9% at T3 and T4, respectively, for a total of 36.1% of respondents reporting arthritis-related reduced work hours. The percentage of respondents who reported changing jobs was 11.4%, 6.2%, and 5.2% at T2, T3, and T4, respectively, with a total of 20.9% changing jobs. At T2, 14.8% of respondents reported having left the labor force. At T3 and T4, 11.6% and 8.6% of respondents reported leaving employment, respectively. Overall, 36.9% of participants reported leaving employment during followup.

Table 3. Arthritis-related work transitions during followup*
 Time 2, 18 months (n = 413)Time 3, 36 months (n = 372)Time 4, 54 months (n = 349)Total (n = 363)
  • *

    Values are the number (percentage) unless otherwise indicated. Percentages at each time point are calculated based upon the available participants at each time. Total percentages are based on the number of participants for whom there were ≥2 time points of data available. Numbers indicate ≥1 work transition reported during followup.

  • Defined as an event. Respondents who returned to the labor force during followup are included because they left the labor force previously.

Job disruptions, %
 None46.255.454.9
 120.718.618.4
 2–322.416.218.4
 ≥410.69.88.2
Job disruptions, mean ± SD1.28 ± 1.71.07 ± 1.61.04 ± 1.61.15 ± 1.6
Absenteeism115 (27.8)85 (22.8)70 (20.1)171 (47.1)
Reduced work hours74 (17.9)46 (12.4)45 (12.9)131 (36.1)
Changed jobs47 (11.4)23 (6.2)18 (5.2)76 (20.9)
Left labor force61 (14.8)43 (11.6)30 (8.6)134 (36.9)
Total arthritis-related work transitions271 (65.6)192 (51.6)186 (53.3)316 (76.5)

The coefficient estimates for the multivariate longitudinal analyses of each work transition using GEEs are presented in Table 4. Analyses included the independent variables as well as work transitions from preceding waves. Men reported more job disruptions than women (P < 0.05). Workplace activity limitations predicted later job disruptions (P < 0.05), as did perceptions of arthritis-work spillover (P < 0.001), previous reports of arthritis-related absenteeism (P < 0.01), and changing jobs (P < 0.05). Absenteeism was reported less often in the health/science sector compared with business/administration (P < 0.05). Perceptions of arthritis-work spillover (P < 0.05), depressive symptoms (P < 0.01), and reports of previous job disruptions (P < 0.001) were also associated with later absenteeism. Older adults (P < 0.05), those reporting greater arthritis-work spillover (P < 0.05), or those who previously changed jobs (P < 0.01) were more likely to report reduced work hours. Individuals with postgraduate education were significantly more likely to change jobs (P < 0.01). Changing jobs was also related to previously reducing work hours (P < 0.05). Finally, leaving the labor force was associated with being older (P < 0.001), a lower education level (P < 0.01), having less control over one's work schedule (P < 0.05), being employed in the health/sciences sector (P < 0.05), and reporting previous job disruptions (P < 0.05) and reductions to work hours (P < 0.01).

Table 4. Longitudinal analyses of the association of demographic, illness, work context, psychological, and previous work transitions with subsequent arthritis-related work transitions using generalized estimation equations*
VariablesProductivity lossesWork changesLeft labor force
Job disruptionsAbsenteeismReduced hoursChanged jobs
  • *

    Values are the b (95% confidence interval). Only independent variables significantly associated with work transitions are shown. See Table 1 for definitions.

  • P < 0.05.

  • P < 0.001.

  • §

    Reference category = elementary or secondary education.

  • P < 0.01.

  • #

    Reference category = inflammatory arthritis or both OA and inflammatory arthritis.

  • **

    Reference category = business, finance, administration.

Demographic     
 Age  0.03 (0.01, 0.06) 0.06 (0.03, 0.09)
 Female−0.31 (−0.57, −0.06)    
 Living alone     
 Education§     
  Some postsecondary     
  Postsecondary     
  Postgraduate   1.60 (0.43, 2.80)−1.41 (−2.20, −0.61)
Illness related     
 Arthritis duration     
 OA diagnosis#     
 Previous fatigue     
 Previous pain     
Work context     
 Previous schedule control    −0.19 (−0.33, −0.04)
 Previous WALS0.03 (0.00, 0.05)    
 Occupation**     
 Health, science, art, sports −0.48 (−0.94, −0.03)  0.70 (0.17, 1.24)
 Sales and services     
 Trades, transportation, equipment     
Psychological     
 Previous AWS0.07 (0.05, 0.10)0.04 (0.00, 0.09)0.06 (0.01, 0.11)  
 Previous CES-D 0.03 (0.01, 0.05)   
Previous work transitions     
 Previous job disruptions0.22 (0.10, 0.35)  0.16 (0.02, 0.31)
 Previous absenteeism0.26 (0.08, 0.45)   
 Previous reduced hours  0.75 (0.18, 1.36)0.74 (0.25, 1.23)
 Previous change of job0.26 (0.04, 0.47) 0.74 (0.26, 1.23) 

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

This research examined arthritis-related work transitions reported over 4.5 years. The findings extend previous studies by capturing dimensions of participation in paid work beyond the categories of employed or not employed. Although >60% of respondents remained working throughout the study, work transitions were common, being reported by three-quarters of participants. Moreover, these changes were often related to subsequently making other work transitions, including leaving employment. As such, this research sheds light on a process of diverse employment changes that may occur in the working lives of many people with arthritis.

Research has assessed productivity losses by taking measure of absenteeism (25, 26, 44–46) and more recently by examining presenteeism (i.e., at-work productivity losses) (22, 24–26). Job disruptions such as being unable to work at particular times and difficulties with coworkers have also been characterized as potential productivity problems, although these have been assessed less frequently (40). In the current research, approximately half of the participants reported ≥1 job disruption, most frequently in the form of work interruptions, being unable to take on extra projects, and arriving late or leaving early. A difficulty facing researchers measuring this type of productivity loss is that job disruptions are difficult to quantify in terms of their frequency, duration, and cost. Also, it is unclear whether some job disruptions or constellations of problems are more important than others in predicting future work transitions. Additional research measuring the productivity impact of job disruptions is needed to assess their full impact.

Over the duration of the study, nearly half of the respondents reported arthritis-related absenteeism, more than one third had reduced their work hours, and approximately 1 in 5 had changed jobs. At each successive wave, fewer respondents reported these work transitions. It is likely that this is because some work transitions constitute relatively rare events; having reduced their hours or changed jobs, respondents are less likely to do so again within a short time. Overall, however, the ongoing impact of arthritis on employment is notable. Assessing a range of arthritis-related work place outcomes can enhance our understanding of the impact of the disease on employment and deserves increased attention in future research.

Similar to other research, older workers in the current study were more likely to reduce work hours or leave employment than younger workers (42, 43). Financial resources make these work transitions more likely with age. It is not clear whether the changes were desired. Research with older workers found reduced hours were a preferred option prior to retirement (42). In this study, reducing hours was associated with perceptions of spillover (i.e., difficulty balancing the demands of work and caring for arthritis). Additional research is needed to examine whether reduced hours is an adverse job status among some groups or a work transition that facilitates employment and other lifestyle goals. This also has implications for studies estimating the indirect costs of arthritis because they have typically included reduced hours as an undesired productivity loss.

Men reported more arthritis-related job disruptions than women. There were no other sex differences in work transitions. As noted, we lacked comprehensive data examining job disruptions. However, in a large Canadian study of arthritis disability, men were more likely to report work place limitations, whereas women were more likely to leave the labor force (52). It may be that financial considerations, such as being the main or sole income earner, result in men remaining employed despite job disruptions. Differences may also relate to the types of jobs men and women occupy.

Similar to previous studies, education acted as a resource (10). Individuals with greater education were more likely to change jobs and less likely to leave employment. This suggests that arthritis work place interventions need to consider skill training and education that may enable people with arthritis to remain working in a different job or occupation, as well as work place accommodations and arthritis therapies to treat the disease.

Illness variables, including diagnosis of OA or inflammatory arthritis, were not related to later work transitions. However, it is inappropriate to conclude that disease factors have no relationship to work transitions. Instead, the findings point to the need for investigation of the mechanisms of the impact of arthritis on work transitions. Specifically, although direct effects of chronic, severe pain and fatigue on work transitions were not found, symptoms may act indirectly by increasing the likelihood of work place activity limitations or by changing perceptions of work, such as increased role conflict or spillover. Activity limitations and changed perceptions may in turn result in decisions to make work transitions. Some evidence for this exists elsewhere. The effects of symptoms on participation restrictions have been found to be mediated by activity limitations and depression (53), and fatigue and activity limitations were important predictors of perceived spillover in other research (18). Studies testing complex models of the moderating and mediating effects of illness, disability, and psychosocial variables are needed.

The absence of pain and fatigue as predictors of work transitions may also relate to measurement. Similar to other studies, we asked participants about pain and fatigue in the previous month. However, individuals with arthritis reported considerable variability in symptoms, making it unlikely that symptoms at a particular point influenced work transitions assessed 18 months later. This creates difficulties for researchers interested in long-term changes in employment. Reviews of research examining work disability in RA also found inconsistent results for illness variables (10). More frequent assessment of disease factors or additional measures capturing symptom variability may be needed.

Work context factors were associated with work transitions. Greater work place activity limitations were related to later reports of job disruptions. Assessments of activity limitations may be ideal for capturing specific productivity problems (e.g., difficulties with the schedule of work) rather than broad work transitions (e.g., changing jobs). Similar to disease symptoms, however, activity limitations may be part of a complex causal pathway, indirectly affecting other variables. In this study, workplace activity limitations were related to job disruption, which in turn predicted later absenteeism and leaving employment.

Participants working in the health/sciences sector were less likely to report absenteeism, but more likely to leave employment than those in business/administration. The former group included health professionals (e.g., nurses) and teachers, and comprised the largest of the job sectors examined. These occupations can be physically demanding for individuals with arthritis (e.g., lifting, standing for long periods). Moreover, the specialized training and nature of these jobs may make work transitions such as reduced hours and changing jobs less viable. Less control over the work schedule was also associated with leaving employment. This highlights the need for researchers to work in concert with employers to develop work place practices that impart greater flexibility and control to workers.

Psychological perceptions, especially arthritis-work spillover, were important in understanding productivity losses such as job disruptions and absenteeism, as well as in reducing hours. To date, employment studies have emphasized the impact of arthritis on work. Arthritis-work spillover underscores the importance of perceived role stress and role balance. The relationship of arthritis-work spillover to work transitions suggests that it may be an important factor in the early identification of work place problems that may lead to later work transitions. As such, future research may want to examine its potential for screening individuals for inclusion in work place interventions.

Of interest are the interrelationships among work transitions. Measures of productivity loss (job disruptions, absenteeism) were related in the current study. Job disruptions were also associated with subsequently leaving employment. Work changes (i.e., reduced hours, changing jobs) were interrelated, and reduced hours were associated with leaving employment. These findings suggest conceptual links among the different types of work transitions, and point to the importance of examining patterns of work transitions and their accumulation over time. Work transitions were not independent of one another and may form a hierarchy of changes that occur as individuals with arthritis struggle to remain employed. It was noteworthy that absenteeism and changing jobs were not directly associated with leaving employment. Absenteeism may be an episodic problem that affects working insofar as it may create job disruptions. Changing jobs may actually enable individuals to remain employed. In this sample, changing jobs was not associated with negative psychological perceptions or work context factors, but occurred among those with greater education. At the same time, if changing jobs is associated with reducing hours, it may subsequently relate to leaving employment.

Several limitations in this research need to be addressed. Attention to measurement of work transitions such as job disruptions and changing jobs would increase understanding of work transitions and their interrelationships. Research into the measurement and short- and long-term impacts of arthritis disease factors is also important given the variability of symptoms experienced by working individuals. Although our recruitment strategy enabled us to generalize beyond a clinical sample and was comparable with other samples in terms of demographic and disease characteristics, the sample was purposive. Research comparing diverse subgroups of arthritis, as well as comparing those with arthritis with those with no chronic health problems, is needed to replicate and extend the findings. Finally, assessing perceptions of work transitions would allow examination of whether work transitions were viewed as adverse employment options or were proactive efforts to remain employed.

In conclusion, this study highlights the diverse work transitions made by individuals with arthritis and underscores insights to be gained from using a multifaceted approach to measurement of participation in employment. The study also emphasized the interrelationships among productivity, work changes, and employment loss, as well as the role of other factors in predicting work transitions. A greater understanding of work transitions may enable new intervention efforts to target work changes that signal impending difficulties with remaining employed.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES

Dr. Gignac had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study design. Gignac, Lacaille, Anis, Badley.

Acquisition of data. Gignac.

Analysis and interpretation of data. Gignac, Cao, Lacaille, Anis, Badley.

Manuscript preparation. Gignac, Cao, Lacaille, Anis, Badley.

Statistical analysis. Gignac, Cao.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES