International Journal of Disability, Community & Rehabilitation

Musculoskeletal disability, chronic disease and labour force participation in Australia

Maria Crotty, Lynne C. Giles, Ian D. Cameron and Peter M. Brooks

Introduction

There is growing evidence that individuals with chronic diseases have difficulty securing employment (Petrides, Petermann, Henrichs, Petzoldt, Rolver, Schidlmeier, Webber, & Wendt, 1995), are more likely to drop out of the labour force (Doeglas, Suurmeijer, Krol, Sanderman, Van Leewuwen, & Van Rijswijkl, 1995; Mitchell, 1990; Yelin, Henke, & Epstein, 1987) and rarely have workplace adjustments made to assist them (Baanders, Andries, Rijken, & Dekker, 2001). With the ageing of the workforce the number of workers with chronic disease and disability will rise and the challenge to accommodate these groups in the workforce will increase. Of the chronic diseases the relationship between arthritis and work disability has probably had the most attention. National surveys examining arthritis have found that among people reporting symptomatic arthritis more than half will report some type of work disability (Dunlop, Manheim, Yeline, Song, & Chang, 2003). More than 40% of 8.78 million people aged 51-61 reported work disability in the US 1992 Health and Retirement Study (Yelin, 1995). A recent Australian review of the costs of arthritis used the National Health Survey to make estimates and found that arthritis is responsible for nearly 1.8 million days of reduced activity and about 213 000 days off work or school each year in Australia (Access Economics, 2001). The indirect costs including loss of earnings and lost production following premature retirement were triple the direct costs ($6.72 billion). Little however is known about how this compares to the impact of other disabling chronic diseases in Australia and little is known about the relationship of labour force participation with pain.

More work is needed to promote public policies, which are responsive to the needs of people with chronic diseases and more work is needed to promote an integration of clinical and social approaches. Secondary analyses of national data sets while constrained by a limited range of variables and the method of collection (which is often self report) give a picture of labour market issues.

In this analysis, using the 1998 Survey of Disability, Ageing and Carers (SDAC), we sought to examine the impact of musculoskeletal disease upon labour force participation and determine the effect of comorbidity and pain on workforce participation in a national sample of working age adults.

Methods

Data from the 1998 Survey of Disability, Ageing and Carers were used (Australian Bureau of Statistics, 1999b, 1999c). The subset of participants aged 15-64 years (n=25 217) were considered in this study. The Confidentialised Unit Record File distributed by the Australian Bureau of Statistics was used as the data source in these analyses (Australian Bureau of Statistics, 1999a).

A binary variable for labour force participation was created from the responses to questions concerning employment status and job seeking. Persons in full or part-time employment or looking for full or part-time work were defined as in the labour force. Persons not in the labour force included those who had retired, had family considerations, were full-time students or did not need or want to work, or were not participating in the labour force due to health and disability. Because our primary interest was in work disability, our conceptualisation of labour force participation focused on those respondents who were engaged in or seeking full or part-time employment, and those who were not in the labour force due to health and disability. Thus the usable sample comprised 19 496 persons.

We considered a range of possible predictors of labour force participation that were used by Badley and Wang (1998). Sociodemographic variables that we considered included age group, gender, education (currently attending school, incomplete secondary education, complete secondary education, missing), residence (capital city, other location), and family composition (couple only, couple with dependent children, couple with independent children, living alone, other).

Chronic conditions considered in these analyses included arthritis and musculoskeletal conditions, cancer, circulatory disorders, depression, diabetes, neurological conditions (excluding stroke), respiratory disease, hearing or vision (sensory) conditions, and stroke. The total number of chronic conditions was also calculated, and classified as 0, 1, 2 or at least 3 chronic conditions. We hypothesized that pain would be an additional independent factor influencing workforce participation (Badley & Wang, 1998). Participants' responses to a question concerning chronic pain were used to derive a binary variable indicating the presence or absence of pain.

Arthritis and other musculoskeletal diseases were examined in greater detail than other conditions because of their much higher prevalence and importance as an area for intervention (Australian Bureau of Statistics, 1999b). We considered a number of other chronic conditions to assess the effect of comorbidity with arthritis and musculoskeletal disease upon labour force participation. For arthritis, participants were classified as i) having no chronic conditions, ii) having arthritis and no other chronic conditions, iii) having chronic conditions but not arthritis, or iv) having both arthritis and other chronic conditions. A similar definition was applied for musculoskeletal disease excluding arthritis.

Analyses

All analyses were weighted, to take into account the unequal probability of selection for a person into the 1998 SDAC. The weights were derived from those supplied by the Australian Bureau of Statistics, and corrected to sum to 19 496.

Frequency distributions were initially tabulated for all variables. The prevalence of arthritis, musculoskeletal disease and chronic conditions among labour force participants and non-participants was plotted. The relationship between sociodemographic and health variables and labour force participation was examined using chi-square tests of association. Separate logistic regression analyses, controlling for the sociodemographic variables, were used to estimate the odds ratio (OR) of labour force participation associated with arthritis, musculoskeletal disease, and the number of chronic conditions respectively. The analyses were repeated including pain to assess its effect on the models.

Results

A total of 18 511 (94.9%) respondents were either engaged in or seeking full or part-time employment, while 985 (5.1%) were not in the labour force because of their health or disability. Table 1 summarizes labour force participation against the sociodemographic variables and pain. Chi-square tests of association showed that the relationship between labour force participation and each of these variables was significant (P<0.001), with the exception of gender, where a non-significant result (P=0.419) was found. Labour force participation generally declined with age, so that just over three quarters of the sample aged 60-64 were participants in the labour force, compared with almost 100 per cent of younger respondents. There was a strong relationship between non-participation and pain. More than one quarter of those with chronic pain were not participating in the labour force.

Table 1: Summary of labour force participation for sociodemographic variables and pain

	in labour force		not in labour force
	n	%b	n	%c	%d	Total	P-value
Age last birthday							<0.001
15-34 years	8 400	45.4	139	14.1	1.6	8 539
35-49 years	6 879	37.2	308	31.3	4.3	7 187
50-54 years	1 728	9.3	187	18.9	9.7	1 915
55-59 years	998	5.4	205	20.8	17.0	1 203
60-64 years	505	2.7	147	14.9	22.5	652
Gender							0.419
Male	10 372	56.0	539	54.7	4.9	10 911
Female	8 139	44.0	446	45.3	5.2	8 585
Education							<0.001
Did not complete secondary	10 090	54.5	805	81.7	7.4	10 895
Completed secondary	8 421	45.5	180	18.3	2.1	8 601
Residential location							<0.001
Capital city	12 218	66.0	585	59.4	4.6	12 803
Balance of state	6 293	34.0	400	40.6	6.0	6 693
Family Composition							<0.001
Couple only	3 554	19.2	254	25.8	6.7	3 808
Couple with dependent children	5 924	32.0	110	11.2	1.8	6 034
Couple with independent children	3 535	19.1	166	16.8	4.5	3 700
Lives alone	1 434	7.7	202	20.5	12.4	1 636
Other	4 064	22.0	253	25.7	5.9	4 318
Pain							<0.001
Absent	17 085	92.3	411	41.7	2.3	17 496
Present	1 426	7.7	574	58.3	28.7	2 000

a: reported not in labour force due to health or disability

b: % based on those in labour force

c: % based on those not in labour force

d: row-specific %

Table 2 shows 74.9% of the participants who were in the labour force had none of the chronic conditions we considered, whereas only 8.4% of the participants, who were not in the labour force for health or disability reasons, reported none of the chronic conditions. Of the participants not in the labour force, 40.7% were affected by a musculoskeletal disease (excluding arthritis) and at least one other condition, while 12.8% were affected by musculoskeletal disease alone. A total of 25.7% of the non-participants in the labour force had arthritis and at least once other condition, compared with only 2.5% of the labour force participants having this level of morbidity. Musculoskeletal disease and arthritis were the most commonly reported conditions.

Table 2: Summary of labour force participation for selected chronic conditions included in 1998 Survey of Disability, Ageing and Carers

	in labour force		not in labour force due to disability
Condition	n	%b	n	%c	row %d
no conditions	13 863	74.9	83	8.4	0.6

Arthritis+/-chronic conditions
arthritis only	336	1.8	31	3.2	8.5
chronic conditions only	3 842	20.8	619	62.8	13.9
arthritis and chronic conditions	471	2.5	253	25.7	34.9
Cancer+/-chronic conditions
cancer only	40	0.2	11	1.1	21.6
chronic conditions only	4 577	24.7	858	87.0	15.8
cancer and chronic conditions	31	0.2	34	3.5	52.3
Circulatory+/-chronic conditions
circulatory only	454	2.5	19	1.9	4.0
chronic conditions only	3 787	20.5	626	63.5	14.2
circulatory and chronic conditions	408	2.2	258	26.2	38.7
Depression+/-chronic conditions
depression only	68	0.4	19	1.9	22.0
chronic conditions only	4 497	24.3	791	80.3	15.0
depression and chronic conditions	83	0.4	92	9.3	52.6
Diabetes+/-chronic conditions
diabetes only	111	0.6	5	0.5	4.2
chronic conditions only	4 433	23.9	810	82.2	15.5
diabetes and chronic conditions	104	0.6	88	8.9	45.7
Musculoskeletal (exc arthritis)+/-chronic conditions
musculoskeletal only	1 172	6.3	126	12.8	9.7
chronic conditions only	2 704	14.6	376	38.1	12.2
musculoskeletal and chronic conditions	772	4.2	401	40.7	34.2
Musculoskeletal (inc arthritis)+/-chronic conditions
musculoskeletal only	1 644	8.9	211	21.5	11.4
chronic conditions only	2 163	11.7	259	26.3	10.7
musculoskeletal and chronic conditions	841	4.5	432	43.8	33.9
Neurological+/-chronic conditions
neurological only	189	1.0	60	6.1	24.1
chronic conditions only	4 264	23.0	665	67.4	13.5
neurological and chronic conditions	195	1.1	178	18.1	47.7
Respiratory+/-chronic conditions
respiratory only	698	3.8	24	2.5	3.3
chronic conditions only	3 626	19.6	718	72.8	16.5
respiratory and chronic conditions	324	1.7	161	16.3	33.2
Sensory+/-chronic conditions
sensory only	358	1.9	6	0.6	1.7
chronic conditions only	3 945	21.3	749	76.0	16.0
sensory and chronic conditions	345	1.9	148	15.0	30.0
Stroke+/-chronic conditions
stroke only	16	0.1	3	0.3	17.6
chronic conditions only	4 594	24.8	847	86.0	15.6
stroke and chronic conditions	38	0.2	52	5.3	57.9
# Chronic conditions
0	14 430	78.0	155	15.7	1.1
1	3 233	17.5	348	35.3	9.7
2	693	3.7	283	28.8	29.0
3+	154	0.8	199	20.2	56.3

b: % based on those in labour force

c: % based on those not in labour force

d: row-specific %

Logistic regression models were fitted to ascertain the odds ratio of not being in the labour force associated with arthritis, musculoskeletal disease and the number of chronic conditions. Age group, education and family composition were included in all models. A model containing only age group, education, family composition and pain was also fitted, and this showed the OR of not participating in the labour force was 11.1 times greater (95% CI 9.6-12.9) for those participants with pain than those participants without pain.

The risk of not participating in the labour force was much greater for arthritis or musculoskeletal disease in conjunction with other chronic conditions than for arthritis or musculoskeletal disease alone. Table 3 shows that after controlling for pain, age group, education and family composition, the odds ratio for not participating in the labour force associated with arthritis plus other chronic conditions was 17.3 (95% CI 12.7-23.4). The odds ratio associated with musculoskeletal disease (excluding arthritis) plus other chronic conditions was similarly large (OR=22.6; 95% CI=17.0-30.0).

Table 3: Odds ratio of labour force non-participation for arthritis, musculoskeletal conditions and number of chronic conditions

Excluding pain from the model

Including pain in the model

ORa

95% CI

ORa

95% CI

Arthritis+/-chronic conditions

no conditions (referent category)

1.0

1.0

arthritis only

8.3

5.3 - 12.8

4.0

2.5 - 6.3

chronic conditions only

19.2

15.1 - 24.2

11.7

9.1 - 15.0

arthritis and chronic conditions

42.7

32.3 - 56.4

17.3

12.7 - 23.4

Musculoskeletal conditions

no conditions (referent category)

1.0

1.0

musculoskeletal only

14.5

10.9 - 19.4

6.9

5.0 - 9.4

chronic conditions only

15.0

11.7 - 19.2

11.2

8.6 - 14.4

musculoskeletal and chronic conditions

50.4

39.0 - 65.2

22.6

17.0 - 30.0

# Chronic conditions

0 conditions (referent category)

1.0

1.0

1 conditions

7.4

6.1 - 9.0

4.6

3.7 - 5.7

2 conditions

22.0

17.6 - 27.4

11.1

8.6 - 14.2

>3 conditions

62.8

47.5 - 83.1

26.9

19.7 - 36.7

a: All models adjusted for age group, education, family composition. Gender and residential location not significant covariates in any analysis so excluded from final models.

The odds ratio for not being in the labour force increased as the number of chronic conditions rose. After controlling for the sociodemographic variables and pain, those with one chronic condition had an odds ratio of 4.6 (95% CI 3.7-5.7) of not being in the labour force compared to those with no chronic conditions (Table 2). The corresponding estimates for two chronic conditions (OR=11.1; 95% CI 8.6-14.2) and at least three chronic conditions (OR=26.9; 95% CI 19.7-36.7) suggested a non-linear increase in the odds ratio with more chronic conditions.

Discussion

In this report we demonstrate the problematic labour market position of older Australians with chronic disease. This analysis of the Australian Disability Survey confirms that in 1998 those out of the labour force were more likely to have a chronic disease and that the more chronic diseases an individual had, the greater the chance he/she would not be working. In our analyses the negative association between multiple chronic health problems and labour force participation was marked even after controlling for sociodemographic variables (including age) and pain.

In 1998 the level of risk of non-participation in the labour force increased from five times as likely with one chronic disease to 27 times more likely with three or more conditions. Comorbidity has been shown in studies of older adults to be strongly related to disability and functional decline (Guralnik, LaCroix, Abbott, Berkman, Satterfield, Evans, & Wallace, 1993; Verbrugge, Lepkowski, & Imanaka, 1989) but there is little understanding of the effect of various diseases, combinations of diseases and the effects of buffers (such as autonomy, work place adjustments). Other work on chronic musculoskeletal conditions suggests that age, education and job status in the labour forces are more important determinants of work status than disease factors (Frank & Chamberlain, 2001; Yelin, Meenan, Nevitt, & Epstein, 1980) and that the disease itself may not matter as much as the disability associated with it (Badley & Wang, 1998). A population survey in Sweden found that the number of health problems interacted with quality of life regardless of age but the joint burden of unemployment and multiple chronic health problems in working age survivors was substantial (Michelson, Bolund, & Brandberg, 2000).

We confirmed that common conditions such as musculoskeletal disease and arthritis have a large impact on work disability and this impact increases if a comorbid condition is present. The social and economic costs are high but importantly these results raise questions about the future lack of labour as the population ages and develop chronic diseases. Our current approach to disability management in the workforce seems unable to accommodate those with chronic disease. There is ample evidence that outcomes of vocational rehabilitation are better in those still employed than in those who have left their job because of ill health so effective strategies and interventions are needed (Marnetoft, Selander, Bergroth, & Ekholm, 2001).

There is evidence that job adjustments can enhance the labour force participation of the chronically ill (Teasell & Bombardier, 2001) but there is also evidence that these adjustments are rarely made. In a representative sample of Dutch patients with chronic diseases who were working 85% reported that no work adjustments had been made (Baanders, Andries, Rijken, & Dekker, 2001). In those where adjustments had been made material adjustments i.e. alterations in equipment and furniture were less common than non-material adjustments i.e. working hours or types of work. It appears that greater implementation of work adjustments would be particularly helpful for workers with more physically demanding jobs e.g. blue collar workers who may have less access to flexible conditions. A previous Australian study on 9 167 workers found that one in ten workers rated their health as poor. Blue collar workers were more likely to rank their health as poor. However this group was least likely to take time off when compared with professionals as they often have less access to sick leave (Korda, Strazdines, Broom, & Lim, 2002).

Restoring the ability to work is an important goal of rehabilitation in treating people with chronic disease but identification of those at risk of early withdrawal from the workforce is poorly understood. For example conflicting evidence exists on early versus delayed vocational intervention stemming in part from varying definitions of vocational rehabilitation with some investigators distinguishing medical rehabilitation from vocational rehabilitation (Marnetoft, Selander, Bergroth, & Ekholm, 1999; Saxon, Spitznagel, & Shellhorn-Schutt, 1983). Returning those with musculoskeletal conditions to work is known to be complex and optimised by involvement of a variety of professionals but the individual's ability to influence his/her own vocational rehabilitation is felt to be central (Selander, Marnetoft, Bergroth, & Ekholm, 2002). Recent work on screening tools for clinicians such as the Work Limitations Questionnaire (Lerner, Amick, Rogers, Malspeis, Bungay, & Cynn, 2001) and the Work Instability Scale (Gilworth, Chamberlain, Harvey, Woodhouse, Smith, Smyth, & Tennant, 2003) aim to identify patients with a mismatch between functional abilities and the demands of the job. Such instruments may assist identification of individuals likely to need detailed work assessments but there is limited information on interventions likely to reduce premature work cessation.

Several caveats need to be considered when interpreting these results. National surveys rely on self-reported diagnoses which lack diagnostic accuracy and may overestimate the prevalence of some conditions. Nevertheless they are important for policy makers as many persons with musculoskeletal disease do not see a health care provider so self report data are necessary to access full burden of disease. Perhaps more importantly cross sectional analyses such as these ignore "blurred transitions" where individuals have repeated labor force exits and entrances or a combination of retirement and work. There is evidence that there is a growing diversity of late life work participation, with fewer than half of recent US samples making "crisp" exits (Mutchler, Burr, Pienta, & Massagli, 1997). The role of illness in determining the pattern of departure is poorly understood and longitudinal analyses are needed to understand the patterns.

The disability burden of arthritis and musculoskeletal diseases has been characterised (Verbrugge & Juarez, 2001) as high volume but low severity. However the impact on the ability to work is substantial. These data support the importance of musculoskeletal disease as a determinant of disability in the community and the inclusion of these conditions as Australia's seventh National Health Priority. Musculoskeletal disease increases with age and will present challenges in developing strategies to maintain these individuals in the workforce. As working populations age, employers and governments will be confronted with reduced work performance, absenteeism and early departure from the workforce. At present there is a limited evidence base on interventions and strategies to reduce disability and pain in the workplace setting. Our data confirm the profound impact on the labour force participation that musculoskeletal conditions have and emphasise the importance of identifying other chronic diseases in persons with musculoskeletal disease in the workforce.

References

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Michelson, H., Bolund, C., & Brandberg, Y. (2000). Multiple chronic health problems are negatively associated with health related quality of life (HRQoL) irrespective of age. Quality of Life Research, 9(10), 1093-1104.

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Contact for correspondence

Maria Crotty PhD, FAFRM (RACP), Professor, Department of Rehabilitation and Aged Care, Flinders University, GPO Box 2100 South Australia 5001; Ph 61882751103; Email maria.crotty@flinders.edu.au

Lynne C Giles MPH, AStat, Clinical Epidemiologist and Research Manager, Department of Rehabilitation and Aged Care, Flinders University, GPO Box 2100 South Australia 5001; Ph 61882751103; Email lynne.giles@rgh.sa.gov.au

Ian D Cameron PhD, FAFRM (RACP), Associate Professor and Head, Rehabilitation Studies Unit, University of Sydney, PO Box 6, Ryde, New South Wales 1680; Ph +61298089236; Email: ianc@mail.usyd.edu.au

Peter M Brooks MB BS, FRACP, FAFRM, FAFPHM, FRCP (Edin), Executive Dean Health Sciences, University of Queensland, Herston Queensland 4029; Ph +61733655106; Email: p.brooks@mailbox.uq.edu.au

Keywords

Chronic disease, arthritis, musculoskeletal diseases, employment

Author Biographies

Professor Maria Crotty MPH, PhD, FAFRM (RACP), FAFPHM is a clinical academic and specialist in rehabilitation medicine. She was appointed Professor in 2001 and from 2001-2003 she was Director of both aged care and rehabilitation services across three public hospitals in Southern Adelaide.

Currently the Director of the Flinders University Rehabilitation Studies Unit and coordinates the Postgraduate Program in Clinical Rehabilitation, Professor Crotty is also Director of Rehabilitation Services at Repatriation General Hospital.

International Journal of Disability, Community & Rehabilitation
Volume 3, No. 2 Canada
www.ijdcr.ca
ISSN 1703-3381

yes

	Excluding pain from the model		Including pain in the model
	ORa	95% CI	ORa	95% CI
Arthritis+/-chronic conditions
no conditions (referent category)	1.0		1.0
arthritis only	8.3	5.3 - 12.8	4.0	2.5 - 6.3
chronic conditions only	19.2	15.1 - 24.2	11.7	9.1 - 15.0
arthritis and chronic conditions	42.7	32.3 - 56.4	17.3	12.7 - 23.4
Musculoskeletal conditions
no conditions (referent category)	1.0		1.0
musculoskeletal only	14.5	10.9 - 19.4	6.9	5.0 - 9.4
chronic conditions only	15.0	11.7 - 19.2	11.2	8.6 - 14.4
musculoskeletal and chronic conditions	50.4	39.0 - 65.2	22.6	17.0 - 30.0
# Chronic conditions
0 conditions (referent category)	1.0		1.0
1 conditions	7.4	6.1 - 9.0	4.6	3.7 - 5.7
2 conditions	22.0	17.6 - 27.4	11.1	8.6 - 14.2
>3 conditions	62.8	47.5 - 83.1	26.9	19.7 - 36.7