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Factors associated with sickness certification of injured workers by General Practitioners in Victoria, Australia

  • Rasa Ruseckaite1, 2, 6Email author,
  • Alex Collie1, 2,
  • Maatje Scheepers1, 4,
  • Bianca Brijnath3,
  • Agnieszka Kosny5, 2 and
  • Danielle Mazza3
BMC Public HealthBMC series – open, inclusive and trusted201616:298

https://doi.org/10.1186/s12889-016-2957-5

Received: 17 November 2015

Accepted: 14 March 2016

Published: 6 April 2016

Abstract

Background

Work-related injuries resulting in long-term sickness certification can have serious consequences for injured workers, their families, society, compensation schemes, employers and healthcare service providers. The aim of this study was to establish what factors potentially are associated with the type of sickness certification that General Practitioners (GPs) provide to injured workers following work-related injury in Victoria, Australia.

Methods

This was a retrospective population-based cohort study was conducted for compensation claims lodged by adults from 2003 to 2010. A logistic regression analysis was performed to assess the impact of various factors on the likelihood that an injured worker would receive an alternate/modified duties (ALT, n = 28,174) vs. Unfit for work (UFW, n = 91,726) certificate from their GP.

Results

A total of 119,900 claims were analysed. The majority of the injured workers were males, mostly age of 45-54 years. Nearly half of the workers (49.9 %) with UFW and 36.9 % with ALT certificates had musculoskeletal injuries. The multivariate regression analysis revealed that for most occupations older men (55-64 years) were less likely to receive an ALT certificate, (OR = 0.86, (95%CI, 0.81 – 0.91)). Workers suffering musculoskeletal injuries or occupational diseases were nearly twice or three times at higher odds of receiving an ALT certificate when compared to fractures. Being seen by a GP experienced with workers’ compensation increased the odds of receiving ALT certificate (OR = 1.16, (95%CI, 1.11 – 1.20)). Occupation and industry types were also important factors determining the type of certificate issued to the injured worker.

Conclusions

This study suggests that specific groups of injured workers (i.e. older age, workers with mental health issues, in rural areas) are less likely to receive ALT certificates.

Keywords

General practice Work injury Certification Return to work

Background

Work-related injuries and diseases can have serious consequences for injured workers, their families, society, compensation schemes, employers and healthcare service providers. Healthcare utilization and sick leave taken by injured workers create substantial costs for compensation schemes [1, 2]. Extended absence from work can also place injured workers and their families in a weaker financial position and increase social isolation [3, 4]. Unfortunately, long-term sickness absence is very high in many countries [5]; only about 50 % of those who are off work for more than 6 months return to their normal workplace duties [6, 7].

The importance of demographic, medical, economic, social and job-related factors influencing duration of disability and return to work (RTW) after illness has been examined previously [815]. For example, Heymans et al [9] showed that “moderate” or “poor” job satisfaction, higher pain intensity, and female gender predict longer work absence in workers suffering from lower back pain. Similarly, Oyeflaten et al [11] found that women, blue collar workers and those with previous long-term (mean 9.3 months, SD = 3.4) sick leave had a lower probability for RTW amongst workers with mental and musculoskeletal problems.

Although many studies have investigated factors that predict disability after work-related injuries, it is not yet known if the same factors determine the type of sickness certificate issued to injured workers by their General Practitioners (GPs). It is important to understand if these same factors apply to GP certification practices because GPs play a significant role in the RTW process in Australia, being the first point of contact with the healthcare system for many injured workers and the main “gatekeepers” to workers compensation and disability benefits [16].

In Australia injured workers are issued three types of certificates: unfit for work (UFW), alternate or modified duties (ALT) and fit for work [17]. A medical certificate should be original, contain the worker’s name, employer details, precise diagnosis, dates on which the examination took place and when it was issued, and also dates on which the worker was unfit [18]. If the worker is recommended ALT duties, the GP will then tick an appropriate box with opportunity for comment and further consultation outside the certificate itself.

Our recent analysis [16, 19] of administrative sickness certification data in the state of Victoria showed that the majority of workers receive UFW certificates, while only one third are certified as being able to RTW on alternate duties. To understand this discrepancy, we conducted a cohort analysis of administrative claims data to compare and contrast UFW versus ALT certificates. The aim of the present analysis was to establish whether demographic, occupational, industry, medical (GP caseload of injured workers), injury and socio-economic factors can be associated with the type of sickness certificate issued by a GP to a worker following a work-related injury or disease.

Methods

Study design and Settings

The state of Victoria in Australia had a working population of approximately 2.8 million as at June 2011 [20]. Employers in the state are required to maintain workers’ compensation insurance through the WorkSafe Victoria (WSV) unless they are able to self-insure, obtain insurance through the national workers’ compensation scheme, or if they are a sole trader. The WSV system provides coverage for approximately 85 % of the Victorian labour force. All injuries and illnesses that exceed the pecuniary threshold for healthcare expenses or have required more than 10 days work absence are required to be lodged with the WSV via one of six private insurers.

The Victorian workers compensation system requires production of a medical certificate in order to accept a compensation claim. Certificates can be submitted by GPs and physical therapists or by hospital-based medical practitioners. The medical certificate contains information that include the practitioner’s recommendation regarding fitness to work (UFW, ALT, fit for work) and the start and end date of the certificate [16]. There are statutory limits for the duration of UFW certificates defined in the state’s workers compensation regulations. Initial medical certification for a workers compensation claim can be of up to 14 days duration whilst subsequent certificates can be of up to 28 days duration.

This study was a retrospective population-based cohort study, for which the authors accessed the Compensation Research Database (CRD) established at the Institute for Safety Compensation and Recovery Research (ISCRR) at Monash University, Melbourne, Australia. The CRD contains de-identified case-level administrative data received from the WSV between years 1986-2012 [21, 22]. The CRD only contains details of sickness certificates issued for injuries sustained in the workplace, as periods of sick leave caused by pre-existing non-work related health problems are not recorded.

More detailed information on this dataset is provided elsewhere [16].

Study sample

All data for accepted compensation claims lodged by working age adults (15 - 65 years) with a date of injury between 1 Jan 2003 and 31 Dec 2010 were extracted from the database (n = 217,076). Claims were excluded if:
  • The claim was accepted prior to 2003, as there were no adequate data on sickness certificates available.

  • The claim was for healthcare expenses only (i.e., the claim did not meet the 10 day work absence threshold, therefore no sickness certificate was issued) (n = 78,086, 35.6 %);

  • The initial sickness certificate was written by a health practitioner other than a GP (n = 5439, 2.5 %);

  • The information on duration of certificates contained logical errors, such as certification date prior to injury date and similar (n = 82, 0.04 %).

  • Claims that had no sickness certificates associated with it (n = 9654, 4.4 %)

  • Worker was issued a “fit for work” certificate or recommended a full RTW (n = 3915, 1.8 %). More specific and detailed inclusion/exclusion details are published elsewhere [16, 19].

In this study only the initial sickness certificates were included in the analysis, since in this database information recorded about subsequent certificates may be incorrect or missing. Sickness certificates of all individual claimants were organised into two pre-defined categories: (1) UFW certificates where GPs recommended a complete absence from work (n = 91,726) and (2) certificates where the GP recommended a RTW with ALT duties (n = 28,174).

Following several consultations within the research team, which included GPs, six categories of the most frequent worker conditions (injuries and diseases described by the Type of Occurrence Classification System (TOOCS) Third Edition (http://www.safeworkaustralia.gov.au/sites/SWA) to code injury and disease types) for issuing sickness certificates were included in the analysis: (1) fractures, (2) musculoskeletal diseases (MSD), (3) other traumatic injuries, (4) back pains and strains, (5) mental health conditions (MHC) including work-related stress and post-traumatic stress disorders, and (6) other diseases [16]. The TOOCS system is designed to code both injuries and diseases, and identifies the most serious injury or disease reported on the initial claim for workers’ compensation and allocates an appropriate code from the Nature of Injury/Disease Classification. If more than one injury or disease is reported, the most serious injury or disease that is likely to have the most adverse effect on the worker’s life is selected [16].

Statistical analysis

Both univariate and multivariate logistic regression analysis was performed to assess the impact of a number of factors on the likelihood that an injured worker would receive an ALT certificate from their GP. In the present study, the model predicted ALT (i.e. ALT certificate was set as 1 and UFW as 0). The model consisted of demographic (age group, gender, residential location), occupational (occupation group and employer segment size), industry type, medical (GP caseload of injured workers), injury type and socio-economic factors each with two or more levels (see Table 1). Employer segment size is based on the employer’s annualised remuneration and is grouped into small - < $1 M, medium $1 M - $20 M, large - > $20 M and government.
Table 1

Risk factors investigated in the present study

Variable

Description

Age group

Age groups in 10 year age bands as per the Australian Bureau of Statistics (www.abs.gov.au);

Gender

Male/Female

Worker condition

Worker condition at the initiation of claim

Postcode

Local government area postcode transformed to the residential location: metro, rural, interstate, missing or unknown.

GP caseload

The GP caseload was calculated by adding the number of claims for each GP provider and dividing into four groups based on consultation with GP’s on what was considered low and high caseloads for a provider. Group 1 with 13 claims per provider (c/p) were considered low, group 2 with 14 – 26 c/p was low-medium, group 3 with 27 – 48 c/p was high-medium and group 4 with 49+ c/p was considered a high caseload (over the eight year period from 2003-2010).

Occupation group

The major occupation group for the claimant based on the Australian and New Zealand Standard Classification of Occupations (ANZSCO).

Employer segment size

This variable reflects the size of the employer where the injury took place. The segment size is classified into four groups determined by the organisation’s annual remuneration; <$1 M (Small); $1 M - $20 M (Medium); >$20 (Large); Government (Government).

Industry group

The major workplace industry group code based on the Australian and New Zealand Standard Industrial Classification (ANZSIC) 2006 codes.

Socio-economic Index (SEIFA)

The “Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) - 2011 State Score”, refers to a classification by the Australian Bureau of Statistics that ranks areas in Australia according to relative socio-economic advantage and disadvantage based on information from the five-yearly Census. All areas are ordered from the lowest (10 % assigned 1) to the highest (10 % assigned 10) decile number. Each area is divided into 10 groups and assigned a decile number, each decile subsequently then have an equal number of areas not necessarily people

All factors had statistically significant contributions and were added to the multivariate model (Table 3). For the univariate analyses, all cases were included except for the Socio-Economic Indexes for Areas (SEIFA) [23] variable, which was missing for 241 cases. In the multivariate model these 241 (0.2 %) cases from the SEIFA variable were removed. The final sample for the multivariate model included 91,541 UFW cases and 28,118 ALT cases.

Cox and Snell [24] and Nagelkerke [25] pseudo R2 provides an indication of how well the fit of the model is relative to a ‘null’ model with no risk factors. The Nagelkerke R2 allows for the R2 to potentially reach 1.0, a correction to Cox and Snell that do not allow this [26].

All statistical analyses were conducted using the Statistical Package for Social Sciences (SPSS v.21). All statistical tests were conducted at the two-sided p < 0.05 level of significance. Study approval was obtained from Monash University Human Research Ethics Committee.

Results

General findings

A total of 119,900 claims with initial sickness certificates were included in this study. A descriptive summary of the variables is provided in Table 2 which outlines the number and proportion (%) of sickness certificates within each risk factor. The majority of the injured workers in both ALT and UFW categories were men, mostly between 45-54 years of age. Nearly half (49.9 %) of injured workers with UFW and 36.9 % of ALT certificates suffered from MSD. The most common occupation in the study sample was labourer, the most common industry – manufacturing, and the most common location of all injured workers was the metropolitan area of the state capital city.
Table 2

Profile of alternate duties and unfit for work certificates by category in Victoria, 2003-2010

 

Alternate duties certificates

Unfit for work certificates

Total Certificates

Factors

N

Row %

N

Row %

N

Row %

Total Claims

28,174

23.5

91,726

76.5

119,900

100

Age Group

 15 – 24 years

2827

22.4

9793

77.6

12,620

100

 25 – 34 years

5551

24.7

16,956

75.3

22,507

100

 35 – 44 years

7279

23.5

23,643

76.5

30,922

100

 45 – 54 years

8307

23.7

26,714

76.3

35,021

100

 55 – 64 years

4210

22.4

14,611

77.6

18,821

100

Gender

 Male

18,950

24.3

58,891

75.7

77,841

100

 Female

9224

21.9

32,835

78.1

42,059

100

Worker Condition

 Fractures

2040

17.3

9756

82.7

11,796

100

 MSD

14,062

29.3

33,884

70.7

47,946

100

 Other traumatic injuries

3402

18.2

15,320

81.8

18,722

100

 Back pains & strains

4200

21.0

15,765

79.0

19,965

100

 MHC

608

4.9

11,871

95.1

12,479

100

 Other diseases

3862

42.9

5130

57.1

8992

100

Local Government Area

 Metro

18,686

24.6

57,367

75.4

76,053

100

 Rural

7214

20.9

27,233

79.1

34,447

100

 Interstate

2274

24.2

7126

75.8

9400

100

GP caseload

 1 – 13 Claims/provider

6622

22.4

22,941

77.6

29,563

100

 14 – 26 Claims/provider

6654

22.1

23,389

77.9

30,043

100

 27 – 48 Claims/provider

6763

22.1

23,824

77.9

30,587

100

 49 + Claims/provider

8135

27.4

21,572

72.6

29,707

100

Occupation

 Managers

1428

22.5

4909

77.5

6337

100

 Professionals

2393

19.0

10,205

81.0

12,598

100

 Technicians & trades

6473

24.7

19,688

75.3

26,161

100

 Community & personal service

2792

17.1

13,532

82.9

16,324

100

 Clerical & admin

978

21.4

3593

78.6

4571

100

 Sales workers

926

23.6

2997

76.4

3923

100

 Machinery operators & drivers

5625

26.7

15,463

73.3

21,088

100

 Labourers

7559

26.2

21,339

73.8

28,898

100

Employer Segment Size

 Small

6190

19.3

25,916

80.7

32,106

100

 Medium

12,576

25.4

36,876

74.6

49,452

100

 Large

7851

28.6

19,570

71.4

27,421

100

 Government

1557

14.3

9364

85.7

10,921

100

Industry

 Manufacturing

7733

31.0

17,232

69.0

24,965

100

 Wholesale trade

2261

29.4

5442

70.6

7703

100

 Mining

105

28.3

266

71.7

371

100

 Electricity, gas, water & waste

296

26.8

808

73.2

1104

100

 Professional scientific & technical services

581

25.4

1705

74.6

2286

100

 Information media & telecommunications

207

24.8

628

75.2

835

100

 Retail trade

1588

24.3

4957

75.7

6545

100

 Transport, postal & warehousing

2166

22.5

7481

77.5

9647

100

 Construction

2775

21.5

10,115

78.5

12,890

100

 Administrative & support services

818

21.3

3027

78.7

3845

100

 Rental hiring & real estate services

221

20.9

835

79.1

1056

100

 Arts & recreation services

618

20.8

2356

79.2

2974

100

 Accommodation & food services

765

20.3

3004

79.7

3769

100

 Financial & insurance services

127

18.9

544

81.1

671

100

 Healthcare & social assistance

3358

18.8

14,497

81.2

17,855

100

 Education & training

1156

18.0

5261

82.0

6417

100

 Agriculture, forestry & fishing

541

17.4

2577

82.6

3118

100

 Public administration & safety

1297

16.7

6451

83.3

7748

100

 Other services

11,561

25.6

4540

74.4

16,101

100

Socio-Economic Index

 Lowest 10 % (0-10 %)

3232

25.7

9359

74.3

12,591

100

 Lowest 11-20 %

1626

21.1

6078

78.9

7704

100

 Lowest 21-30 %

2064

23.5

6728

76.5

8792

100

 Lowest 31-40 %

2853

22.9

9590

77.1

12,443

100

 Lowest 41-50 %

2982

23.6

9658

76.4

12,640

100

 Highest 51-60 %

3396

23.5

11,033

76.5

14,429

100

 Highest 61-70 %

4208

24.5

12,998

75.5

17,206

100

 Highest 71-80 %

2827

23.1

9402

76.9

12,229

100

 Highest 81-90 %

3717

23.5

12,125

76.5

15,842

100

 Highest 10 % (91-100 %)

1213

21.0

4570

79.0

5783

100

MSD musculoskeletal disorders, MHC mental health conditions

Individual variable Univariate and Multivariate analysis

Table 3 summarizes the contributions of each risk factor in the univariate and multivariate model. Univariate analysis (step 1) for all nine category variables was conducted to identify significant individual predictors. The nine category variables were then added into the multivariate model (step 2).
Table 3

Odds ratio and significance of factors associated with the type of GP certificate being issued (Unfit for work vs. Alternate duties, where Alternate duties is the outcome)

 

Univariate model

Multivariate model

Factors

Odds Ratio

CI at 95 %

Odds Ratio

CI at 95 %

Age Group

 15 – 24 years [REF]

1

 

1

 

 25 – 34 years

1.13a

1.07 – 1.19

1.03

0.97 – 1.09

 35 – 44 years

1.06

1.01 – 1.12

0.95

0.91 – 1.00

 45 – 54 years

1.39a

1.02 – 1.13

0.96

0.91 – 1.01

 55 – 64 years

1.32

0.94 – 1.05

0.86a

0.81 – 0.91

Gender

 Male [REF]

1

 

1

 

 Female

0.87a

0.84 – 0.89

1.06a

1.02 – 1.10

Worker Condition

 Fractures [REF]

1

 

1

 

 MSD

1.95a

1.88 – 2.09

1.89a

1.79 – 1.99

 Other traumatic injuries

1.06

1.00 – 1.12

0.99

0.93 – 1.05

 Back pains and strains

1.27a

1.20 – 1.35

1.19a

1.12 – 1.27

 MHC

0.24a

0.22 – 0.26

0.25a

0.22 – 0.27

 Other diseases

3.60a

3.37 – 3.83

3.32a

3.11 – 3.54

Local Government Area

 Metro [REF]

1

 

1

 

 Rural

0.81a

0.78 – 0.83

0.91a

0.87 – 0.94

 Interstate

0.98

0.93 – 1.00

0.95

0.91 – 1.01

GP caseload

 1 – 13 Claims/provider [REF]b

1

 

1

 

 14 – 26 Claims/provider

0.98

0.94 – 1.02

0.93a

0.90 – 0.97

 27 – 48 Claims/provider

0.98

0.94 – 1.02

0.89a

0.85 – 0.92

 49 + Claims/provider

1.30a

1.25 – 1.35

1.16a

1.11 – 1.20

Occupation

 Managers [REF]

1

 

1

 

 Professionals

0.80a

0.74 – 0.86

0.83a

0.76 – 0.91

 Technicians & trades

1.13a

1.05 – 1.21

0.91

0.84 – 0.97

 Community & personal service

0.71a

0.66 – 0.76

0.80a

0.73 – 0.87

 Clerical & admin

0.93

0.85 – 1.02

0.96

0.87 – 1.07

 Sales workers

1.06

0.96 – 1.16

0.92

0.83 – 1.02

 Machinery operators & drivers

1.25a

1.17 – 1.33

0.91

0.84 – 0.97

 Labourers

1.21a

1.14 – 1.29

0.91

0.85 – 0.97

Employer Segment Size [REF]

 Small

1

 

1

 

 Medium

1.42a

1.38 – 1.47

1.38a

1.33 – 1.43

 Large

1.68a

1.68 – 1.74

1.86a

1.78 – 1.94

 Government

0.69a

0.65 – 0.73

1.24a

1.14 – 1.34

Industry

 Agriculture, forestry & fishing [REF]

1

 

1

 

 Mining

1.88a

1.47 –2.40

1.53a

1.18 – 1.97

 Manufacturing

2.13a

1.94 – 2.35

1.54a

1.39 – 1.71

 Electricity, gas, water & waste

1.74a

1.48 – 2.05

1.31a

1.11 – 1.55

 Construction

1.30a

1.18 – 1.44

1.08

0.97 – 1.20

 Wholesale trade

1.97a

1.78 – 2.19

1.49a

1.33 – 1.66

 Retail trade

1.52a

1.36 – 1.70

1.21a

1.07 – 1.36

 Accommodation & food services

1.21a

1.07 – 1.37

1.05

0.92 – 1.19

 Transport, postal & warehousing

1.37a

1.24 – 1.53

1.06

0.95 – 1.19

 Information media & telecommunications

1.57a

1.30 – 1.88

1.15

0.95 – 1.40

 Financial & insurance services

1.11

0.89 – 1.97

0.93

0.73 – 1.17

 Rental hiring & real estate services

1.26a

1.05 – 1.50

1.19

0.99 – 1.43

 Professional scientific & technical services

1.62a

1.42 – 1.85

1.37a

1.19 – 1.58

 Administrative & support services

1.28a

1.14 – 1.45

1.06

0.93 – 1.20

 Public administration & safety

0.95

0.85 – 1.06

1.00

0.88 – 1.14

 Education & training

1.04

0.93 – 1.17

1.12

0.98 – 1.27

 Healthcare & social assistance

1.10

0.99 – 1.21

0.86

0.77 – 0.97

 Arts & recreation services

1.24a

1.09 – 1.42

0.99

0.86 – 1.13

 Other services

1.63a

1.46 – 1.82

1.34a

1.20 – 1.51

Socio-Economic Index

 Lowest 10 % (0-10 %) [REF]

1

 

1

 

 Lowest 11-20 %

0.77a

0.72 – 0.82

0.96

0.90 – 1.04

 Lowest 21-30 %

0.88a

0.83 – 0.94

0.99

0.93 – 1.06

 Lowest 31-40 %

0.86a

0.81 – 0.91

0.97

0.91 – 1.03

 Lowest 41-50 %

0.89a

0.84 – 0.94

1.00

0.94 – 1.07

 Highest 51-60 %

0.89a

0.84 – 0.94

0.98

0.93 – 1.04

 Highest 61-70 %

0.93

0.88 – 0.98

1.04

0.98 – 1.10

 Highest 71-80 %

0.87a

0.82 – 0.93

1.02

0.96 – 1.09

 Highest 81-90 %

0.88a

0.84 – 0.93

1.05

0.98 – 1.11

 Highest 10 % (91-100 %)

0.76a

0.71 – 0.82

0.95

0.88 – 1.03

MSD musculoskeletal disorders, MHC mental health conditions

adenotes p < 0.05

bper eight year period

The full multivariate model containing all nine category variables (inclusive of the variables within each category) was statistically significant, X 2 (52, N = 120,186) = 8636.976, indicating ability to distinguish between injured workers who receive an ALT and UFW certificate. The model explained between 7 % (Cox and Snell R Square) and 10.5 % (Nagelkerke R Square) of the variance in certificate type.

Compared to younger workers aged 15-24, there was a significantly reduced likelihood of workers in the 55-64 age-category receiving an ALT certificate from their GP OR = 0.86 (by 14 %), (95%CI, 0.81 – 0.91). Compared to men, women were at a slightly increased (0.62 %) chance of being issued ALT certificates, OR = 1.06 (95%CI, 1.02 – 1.10).

Taking other variables into account, worker condition was a significant risk factor. Table 3 shows that workers with MSD, OR = 1.89, (95%CI 1.79 – 1.99), and other diseases, OR = 3.32, (95%CI, 3.11 – 3.54), were three times more likely to receive an ALT certificate than those with fractures, whereas workers with MHC, OR = 0.25, (95%CI, 0.22 – 0.27), were less likely to receive an ALT certificate than those with fractures, MSDs and other diseases.

Worker’s area of residence was also an important risk factor. Compared to workers from metropolitan areas, there was a significantly reduced likelihood of injured workers from a rural area, OR = 0.91, (95%CI, 0.87 – 0.94), and interstate, OR = 0.95, (95 % CI, 0.91-1.01) receiving an ALT certificate from their GP.

An analysis of GP caseloads showed that GPs with the highest case load (i.e. 49 and more claims per provider over the eight year period), OR = 1.16, (95%CI, 1.11 – 1.20), were more likely (by ~16 %) to issue an ALT certificate to an injured worker than those GPs who saw less than 13 injured workers over eight years.

In terms of worker occupation, compared to managers, only professionals, OR = 0.83, (95%CI, 0.76 – 0.91) and community and personal service workers, OR = 0.80, (95%CI, 0.73 – 0.87) were significantly less likely (by 17 and 20 %) to receive an ALT certificate.

Employer segment size was a significant risk factor associated with an ALT certificate. Workers from medium (OR = 1.38 by ~38 %), (95%CI, 1.33- 1.43), large (OR = 1.86 (by ~86 %), 95 % CI, 1.78-1.94) and government size organizations (OR = 1.24 (by ~24 %), 95 % CI, 1.14-1.34) were more likely to receive ALT certificates than those from small organizations.

When considering industry, injured workers from mining, OR = 1.53, (95%CI, 1.18 – 1.97), manufacturing, OR = 1.54, (95%CI, 1.39 – 1.71), wholesale trade, OR = 1.49, (95%CI, 1.33 – 1.66), professional scientific and technical services, OR = 1.37, (95%CI, 1.19 –1.58) and other not elsewhere classified industries, OR = 1.34, (95%CI, 1.20–1.51) were significantly more likely (by ~40 % - 50 %) to receive an ALT certificate compared to injured workers from the agriculture, forestry and fishing industry. Taking other variables into account, SEIFA was not associated with ALT certificate at all (Table 3).

Discussion

General findings

The results of the current study clearly indicate that older workers, those with MHCs and those living rurally are more likely to receive UFW certificates than workers with physical injuries, workers living in metropolitan areas and workers visiting GPs with a higher injured worker case load. The latter are more likely to receive an ALT certificate. It is yet unknown why certain factors are associated with ALT certificates; however assumptions can be made based on existing literature, which show that older workers are less likely to RTW because they may have childcare and family responsibilities, are closer to retirement and may recover more slowly from an injury because of age and other existing health issues [2729]. Older workers (between the age of 55 and 64 years) also seem to have more difficulty adapting to modified duties [11, 30]. In contrast, younger adults have been shown to have more favourable employment outcomes after injury [4, 12, 31].

We also found that workers suffering from physical injuries and other diseases were more likely to receive ALT certificates than workers with MHCs. It could be that GPs are more inclined to recommend modified duties and earlier RTW to such workers with physical conditions because they are familiar with interventions and type of modified duties available at workplaces that would be appropriate for such conditions [32]. Moreover, there is still a stigma associated with MHC and health professionals may perceive injured workers with mental illness as having poorer health outcomes than they really have [16, 33]. Studies also show that when it comes to MHC claims GPs grapple with issues such as diagnostic uncertainty, conflicting medical opinions, poor communication between professionals and secondary concerns related to pain management, lack of motivation by the injured worker to RTW and sourcing appropriate care services [3436]. It is also possible that accommodations for MHC are absent in workplaces and as such GPs may be reluctant to suggest a return to work.

In terms of occupation, manual workers are less likely to receive ALT certificates than managers. This suggests that working on alternate or restricted duty appears to be a viable option mainly in managerial positions, whereas manual labour occupations have been associated with more severe disabilities of longer duration, probably associated with UFW rather with modified duties [37, 38]. On the other hand, research also shows that occupation does not determine the type of sickness certificate [39], and that may be why the odds of receiving ALT certificate across other occupations are very similar (Table 3).

As opposed to the findings reported by Shiel et al [15], demonstrating that GP and general practice factors had no significant impact on likelihood of a ‘may be fit’ note being issued, we found that those workers who see GPs with a higher caseload of injured workers are more likely to receive ALT certificates. This suggests that GPs with higher caseload of injured workers are familiar with the workers’ compensation system, have a positive attitude towards RTW and modified tasks and therefore more likely to recommend ALT duties [4042]. This finding also suggests that in order to achieve improved certification (i.e. higher proportion of ALT certificates) systems may want to steer injured workers towards more “experienced” GPs.

Employer segment size stood out as an important risk factor associated with ALT certificate. Injured workers from large enterprises were nearly twice as likely to receive ALT certificates as those who work for small size organizations. This corresponds to previous findings [12, 37] that showed working for larger companies was positively associated with return to work. Larger organisations are able to employ specialists in disability management [43], provide more information about modified duties and RTW and have greater flexibility in allowing workers to return to modified jobs [37]. Larger workplace size has been associated with a shorter duration of absence following a physical work injury because of an increased ability of larger workplaces to offer accommodations or alternate duties [44].

In terms of industry, workers from mining, manufacturing, electricity, gas, water and waste as well as wholesale trade industries are more likely (up to 50 %) to receive ALT certificates than workers from agriculture, forestry and fishing. Literature on industry as a predictor of RTW is scarce; however it is known that being a blue collar worker (i.e. performing manual labour) is associated with longer duration off work when compared to those workers who perform professional jobs [11]. While physically demanding occupations and employment in goods producing industries have been associated with slower RTW for physical injuries [45], studies on mental health claims have reported longer duration off work in government and educational industries compared to other industry sectors [46].

Study limitations and strengths

To the best of our knowledge, this is the first study that explores the factors associated with the type of sickness certificate issued by a GP in Australia. In this study we were able to examine almost all the predicting factors previously reported in the literature.

There are several limitations to our analyses. First, in this study we analysed the initial sickness certificates only. Consequently, we could not ascertain for how long UFW certificates were issued and when (and/or if) the changeover to ALT certificates occurred, thus facilitating RTW. Second, we were unable to analyse other important factors, such as comorbidities, a previous history of sickness certification, expectations of sickness absence and motivation as this information was not available from the data collected. The opportunity to include these explanatory variables would have increased the robustness of the model. Finally, data from administrative datasets are subject to entry errors, miscoding and misclassification, which we could not control for.

Practical applications

It is known that extended periods of sickness can negatively affect injured workers, their family, employers and lead to increased compensation schemes. Workers might have poorer health outcomes and require an increased number of health interventions, which are associated with higher compensation costs [47-49]. From a policy perspective, this study suggests that efforts to target specific groups of injured workers (i.e. older age, workers with MHCs in rural areas) and employers (e.g. smaller companies) could increase the awareness of benefits of modified and alternate duties and facilitate RTW for groups that are otherwise less likely to RTW.

Conclusions

The findings of this study suggest that seeing a GP with a higher caseload of injured workers (Table 3) increases the odds of receiving an ALT certificate. Such GPs perhaps are more experienced and familiar with work related injuries and compensation schemes. Perhaps they are also aware of RTW benefits; therefore they recommend ALT duties more frequently. Ultimately, it will be necessary to target specific workers’ groups, where ALT duties might be implemented and; therefore, interventions will need to be trialled and modified. Further research and more rigorous study designs are needed to determine what interventions and practice guidelines would be mostly effective to improve GP sickness certification practices, RTW and health outcomes of injured workers.

Ethics approval

Monash University Human Research Ethics Committee, Melbourne, Australia.

Availability of data and materials

Access to the CRD is publicly available for researchers to use, under strict guidelines approved by the compensation authorities and the Monash University Human Research Ethics Committee. Information about the CRD data can be found at http://www.iscrr.com.au/evidence-data-and-research/using-data/compensation-research-database-crd. For further information on this database, or to request a data extract for research, please review the ISCRR data access policy and email CRD@iscrr.com.au.

Declarations

Funding

This project was funded by the WorkSafe Victoria and the Transport Accident Commission via the Institute for Safety Compensation and Recovery Research (ISCRR).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute for Safety, Compensation and Recovery Research, Monash University
(2)
Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine Nursing and Health Sciences, Monash University, The Alfred Hospital
(3)
Department of General Practice, School of Primary Care, Faculty of Medicine Nursing and Health Sciences, Monash University
(4)
Monash Injury Outcomes Unit, Monash Injury Research Institute, Monash University
(5)
Institute for Work & Health
(6)
School of Public Health & Preventive Medicine, Monash University, The Alfred Centre, Alfred Hospital

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Copyright

© Ruseckaite et al. 2016

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