rr1026 - validation of the hse manual handling …the aim of this research was to ascertain whether...

Prepared by the Health and Safety Laboratory for the Health and Safety Executive 2014

Health and Safety Executive

Validation of the HSE Manual handling Assessment Charts as predictors of work-related low back pain

RR1026Research Report

Andrew DJ Pinder PhD and Gillian A Frost MResHealth and Safety LaboratoryHarpur HillBuxtonDerbyshire SK17 9JN

The aim of this research was to ascertain whether HSE’s ‘Manual handling Assessment Charts’ (MAC tool) could be used to predict workers losing time from work due to low back pain (LBP). Results from the study suggest that as the ‘Hand distance from the lower back’ increased, the risk of lost time due to LBP increased. For each 10 cm increase, the rate of lost time increased by approximately 20%. No evidence of relationships between other risk factors in the MAC and lost time was found. There was no evidence that the rate of lost time due to LBP increased with either increasing total MAC lifting score or total MAC carrying score.

Due to imprecision in the model estimates (wide confidence intervals), the lack of statistically significant results, and the limitations of the data, it was decide that it would not be appropriate to alter the scoring system currently used in the MAC based on these data. Duty holders should be confident in carrying on using the MAC tool as the risk factors for LBP included were identified as important by earlier studies.

This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.


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First published 2014

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KEY MESSAGES

• We tested the ability of the risk factors in the MAC (the Health & Safety Executive’s ‘Manual handling Assessment Charts’) to predict workers losing time from work due to low back pain (LBP). We found that as the ‘Hand distance from the lower back’ increased, the risk of lost time due to LBP increased. For each 10 cm increase, the rate of lost time due to LBP increased by approximately 20%. We did not find evidence of relationships between the other risk factors in the MAC and lost time due to LBP.

• We looked to see if the boundaries for the individual MAC risk factors should be adjusted. We did not find evidence that required us to change the existing boundaries.

• We looked at how well the total MAC scores identified high-risk jobs. We did not find evidence that the rate of lost time due to LBP increased with either increasing MAC lifting score or MAC carrying score.

• We found it difficult to carry out our analysis because the MAC was designed for assessing individual tasks, not jobs made up of many tasks and because it uses broad risk categories, not accurate measurements. This means that our results are not very precise. A larger study might find relationships that we were not able to find.

• The findings of this project may have implications for the use of the MAC by HSE and local authority inspectors of health and safety, particularly in relation to guidance on enforcement action.

• Duty holders should be confident in carrying on using the MAC tool as the risk factors for LBP included were identified as important by earlier studies.

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EXECUTIVE SUMMARY

BACKGROUND

The Health & Safety Executive (HSE) and the Health & Safety Laboratory (HSL) developed the Manual handling Assessment Charts (‘MAC tool’) for use by inspectors of health and safety carrying out visits to workplaces, to help them identify high-risk workplace manual handling activities. It was subsequently made available to the public.

The MAC tool can be used to assess the risks posed by lifting, carrying and team manual handling activities. It was designed to help the user understand, interpret and categorise the level of risk of the various known risk factors associated with manual handling activities. It uses a traffic light colour coding score system to highlight high-risk manual handling tasks. Numerical weightings have been assigned to the different colour codes and the total score can be used to rank tasks by severity. All these features are designed to help the user prioritise interventions to reduce the exposure to risk factors.

HSL carried out an HSE-funded project to collect epidemiological data to test if the 1991 NIOSH Lifting Equation could be used to predict if workers carrying out manual handling would take time off work due to low back pain (LBP). HSL also began work on developing the MAC tool after the NIOSH equation project started but before HSL collected any data. HSL therefore made sure that the data collected could also be used to validate the MAC tool. HSL recruited 515 subjects and followed them for 18 months to record incident episodes of loss of time (absence or restricted duties) due to LBP (‘lost time’).

OBJECTIVES

The purpose of this study was to use the prospective data set collected by HSL to validate the 1991 NIOSH Lifting Equation to validate the MAC tool. The specific objectives were:

1. To test the ability of individual risk factors to predict increased risk of lost time (absence/restricted duties) due to LBP or increased risk of reports of LBP, with or without lost time.

2. To test for linear trends in parametric factors that predict increased risk of LBP.

3. To test the thresholds between colour bands for individual MAC risk factors.

4. To test the ability of the scoring system of the MAC to identify high-risk jobs by examining the weights given to individual risk factors.

METHODS

Poisson regression was used to analyse the time to event data by calculating rate ratios for the different MAC colour bands for each MAC variable. Rate ratios were adjusted for covariates measured at baseline, either that were of a priori interest or that were shown to be predictors of lost time.

MAIN FINDINGS • Data were analysed from 486 of the 515 people recruited. The crude incidence rate of lost

time due to LBP was 29.3 [95% confidence interval (CI) 22.1–38.8] cases per 1,000 person-days in the study.

• Most study participants (80%) were male, and had not experienced LBP in the 12 months before the start of the study. The mean age of participants at baseline was 38.9 (SD 10.4)

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years, and the median duration of employment was 4.8 (range 0.04–39.1) years. Of the 126 unique jobs held by the 486 study participants, 54 (43%) involved carrying operations, and 24 (19%) involved team-handling operations. Only 80 people in the study (16%) undertook team-handling operations, and they experienced only eight episodes of lost time due to LBP. Therefore, the MAC variables for team handling were not investigated.

OBJECTIVE 1: • There was evidence that ‘Hand distance from the lower back’ was associated with lost time

due to LBP; those coded as Red had more than twice the rate of lost time compared to those coded as Amber (adjusted RR 2.02, 95% CI 1.05–3.89). There was no evidence that any of the other risk factors were associated with lost time due to LBP.

OBJECTIVE 2: • There was a statistically significant increasing linear trend going from Green to Amber to

Red categories for ‘Hand distance from the lower back’. The rate of lost time due to LBP increased by 20% for each 10 cm increase in hand distance (RR 1.21, 95% CI 1.05–1.40). The evidence did not support a non-linear relationship between hand distance and lost time due to LBP. There was no evidence of a linear or non-linear relationship between ‘Carry distance’ (using the underlying values rather than the colour bands) and lost time due to LBP.

OBJECTIVE 3: • Various alternative categorisations were investigated for ‘Load weight/frequency’,

‘Maximum individual load weight’, ‘Maximum effort’, ‘Weighted mean load weight’, ‘Hand distance from the lower back, ‘Vertical lift region’ and ‘Carry distance’, but no evidence was found to support any of the alternatives proposed over the current categorisations.

OBJECTIVE 4: • There was no evidence that the rate of lost time due to LBP increased with either increasing

MAC lifting score or MAC carrying score. Due to imprecision in the model estimates (wide confidence intervals), the lack of statistically significant results, and the limitations of the data, it was decided that it would not be appropriate to alter the scoring system currently used in the MAC based on these data.

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CONTENTS PAGE

1. INTRODUCTION ..................................................................... 1 1.1 Context of the research 1 1.2 Earlier HSE research 5 1.3 Other relevant research 6 1.4 Research questions 6

2. METHODOLOGY .................................................................... 7 2.1 The design of the study 7 2.2 The sources of data and details on the sample and the response rate 8 2.3 Conversion of recorded data to MAC colour codes 10 2.4 Alternate boundaries for MAC variables 13 2.5 Composite weight/frequency indices 15 2.6 Job-level MAC variables 15 2.7 Analysis techniques 16

3. RESULTS.............................................................................. 20 3.1 Descriptive statistics 20 3.2 Tests of personal variables as risk factors of lost time due to LBP 21 3.3 Statistical analysis – lifting operations 22 3.4 Statistical analysis – carrying operations 25 3.5 Statistical analysis – team-handling operations 27

4. DISCUSSION / CONCLUSIONS........................................... 28 4.1 Predictive ability of individual risk factors 28 4.2 Testing parametric factors for linear trends 28 4.3 Testing colour band thresholds 28 4.4 Testing the scoring system 29 4.5 Limitations of the methodology 29 4.6 Potential interaction with a history of recent LBP 30 4.7 Future use of the MAC tool 31

5. ANNEX: TABLES OF DETAILED RESULTS....................... 32

6. REFERENCES ...................................................................... 52

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1. INTRODUCTION

1.1 CONTEXT OF THE RESEARCH

Many methods exist for assessing risk factors for low back pain (LBP) arising from manual handling operations. After consideration of their usefulness during workplace inspections, HSE and HSL(1-3) developed the Manual handling Assessment Charts (MAC tool) (Figure 1) for use by Health and Safety Inspectors carrying out visits to workplaces, to help identify high-risk workplace manual handling activities. It was subsequently made available to the public and has proved very popular with those responsible for managing risks from manual handling operations in the workplace (‘duty holders’). Features of the MAC are shown in Figure 2 to Figure 6.

Figure 1 The front cover of the MAC

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Figure 2 The lifting flowchart of the MAC

There have been few attempts to validate the various methods of assessing manual handling operations as predictors of harm to workers. HSL carried out an HSE-funded project to collect epidemiological data to validate the 1991 National Institute for Occupational Safety and Health (NIOSH) Lifting Equation(4) as a predictor of workers taking time off work due to LBP caused by manual handling(5-7). That project was designed to replicate a project already under way in the USA carried out by the Liberty Mutual Research Institute for Safety(8-16). HSL had begun work on the NIOSH lifting equation project, but had not started data collection when they also began work on developing the MAC tool. The opportunity was therefore taken to ensure that the data collected to validate the NIOSH lifting equation could also be used to validate the MAC tool.

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Figure 3 The first page of the MAC assessment guide for lifting operations

Figure 4 The second page of the MAC assessment guide for lifting operations

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The MAC tool(17) can be used to assess the risks posed by lifting, carrying and team manual handling activities. It was designed to help the user understand, interpret and categorise the level of risk of the various known risk factors associated with manual handling activities. It uses a traffic light colour coding score system to highlight high-risk manual handling tasks.

The different manual handling factors for each of the three types of manual handling operations that can be assessed with the MAC are presented as separate flow charts. Each flow chart leads the user through each factor of the manual handling operation, giving guidance on evaluating and grading the degree of risk. Each operation is supported by an assessment guide (for example, Figure 4) that gives more information on scoring each factor of the flow chart. Separate graphs (such as Figure 5) allow the user to consider the combined effects of load and frequency, both in lifting operations and in carrying operations. Numerical weightings have been assigned to the different colour codes and the total score can be used to rank tasks by severity (see Figure 6 for the score sheet). All these features are designed to help the user prioritise interventions to reduce the exposure to risk factors.

Figure 5 The MAC load weight/frequency graph for lifting operations

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Figure 6 The MAC score sheet

1.2 EARLIER HSE RESEARCH

The Liberty Mutual project(16) had intended to recruit 2000 subjects and follow them prospectively for 18 months. Problems with finding sufficient subjects in jobs that met their inclusion criteria resulted in them only recruiting 453 subjects. They experienced a high drop out rate and recorded a low number of incident cases. They were therefore unable to test the predictive ability of the NIOSH equation.

HSL sought to recruit 1000 subjects(18) but also had problems finding subjects in suitable jobs so the final cohort size was only 515. The annual drop out rate was 19.5%. Because of the time-consuming nature(19) of reducing task data to the form needed for analysis, the initial analysis(5, 6) was restricted to task data from a sub-group of 346 subjects. This showed that neither the maximum NIOSH Single Task Lifting Index (STLI) nor the Composite Lifting Index (CLI) predicted either loss of time (absence or restricted duties) from work due to LBP (‘lost time’), or reports of LBP with or without lost time(5, 6). It did find a statistically significant increase in the risk of lost time as the maximum horizontal hand distance increased (P=0.01). In addition, there was a statistically significant increase in risk with increasing vertical offset of the hands from 750 mm, but this ceased to be statistically significant after adjustment for weight, age, gender and LBP in the previous 12 months. The focus of the 1991 NIOSH equation is on the Recommended Weight Limit (RWL) and the ratio of the actual load handled to it (the Lifting Index). Values of individual risk factors are not assessed separately. However, the focus of the MAC is on individual risk factors, with the scoring system as a secondary consideration. It was therefore important to obtain the maximum statistical power by using task data from all the subjects in the study; this required additional work to prepare the data from the remaining 169 subjects for analysis. This also provided opportunity to re-analyse the NIOSH data using the complete data set(7).

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1.3 OTHER RELEVANT RESEARCH

A recent prospective study has demonstrated relationships between the peak STLI and incident cases of low back/gluteal pain lasting at least 24 hours in a sub-group of 258 subjects without a recent history of LBP at baseline(20). They reported, after adjustment for age, BMI and gender, a Hazard Ratio (HR) of 1.25 (95% CI 1.043–1.502, P=0.016). For the CLI they reported an HR of 1.13 that approached significance at the 95% level (95% CI 0.996–1.274, P=0.058). They also reported a significant trend, with the incidence of LBP increasing as the peak load moment (maximum horizontal hand distance × load) increased (peak load moment≤575 kg-cm vs peak load moment>1150 kg-cm, HR 2.77, 95% CI 1.49–5.15, P=0.001).

1.4 RESEARCH QUESTIONS

The purpose of this study was to make further use of the prospective data set collected by HSL to validate the 1991 NIOSH Lifting Equation by seeking to validate the MAC tool. The specific objectives were:

1. To test the ability of individual risk factors to predict increased risk of lost time (absence/restricted duties) due to LBP or increased risk of reports of LBP, with or without lost time.

2. To test for linear trends in parametric factors that predict increased risk of LBP.

3. To test the thresholds between colour bands for individual MAC risk factors.

4. To test the ability of the scoring system of the MAC to identify high-risk jobs by examining the weights given to individual risk factors.

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2. METHODOLOGY

2.1 THE DESIGN OF THE STUDY

This study was designed as further analysis of the data collected by HSL to evaluate the 1991 NIOSH lifting equation(6). That study protocol involved baseline measurements of the included jobs and completion of a baseline questionnaire by participants employed to perform those jobs. Follow-up of participants occurred every three months for 18 months to record incident cases of lost time due to LBP. The target of 1000 subjects was chosen(18) as a compromise between the need to keep the size of the study within manageable bounds and available resources, and the need to provide meaningful results. The NIOSH equation calculates the RWL by multiplying a ‘Load Constant’ by a series of multipliers with maximum values of 1.0(4). Therefore, the sample size required to test the ability of each of the constituent multipliers to predict lost time depends on the range of values the multiplier can have, with larger samples required to evaluate the multipliers with smaller ranges. It was acknowledged(18) that while the target number would be sufficient to evaluate the overall equation and might be sufficient to evaluate the more important individual factors within the equations, it definitely would not be sufficient to evaluate the least influential.

Pinder and Frost(6) used logistic regression, and Cox regression to test for relationships between baseline variables and the incidence of lost time due to LBP. They also used Generalised Estimating Equations to test for relationships between baseline variables and reports of LBP at three-monthly follow-ups.

The first stage of the current study consisted of completing the coding of task data from the remaining subjects and ensuring that it was in the correct format for analysis. The second stage used Poisson regression to examine the ratios of rates of reporting LBP in different conditions. Poisson regression was chosen in preference to Cox regression because it makes fewer assumptions, is easier to use, and is easier to interpret.

2.1.1 Selection criteria

To be included in the original study, jobs had to involve manual handling as a regular daily activity, with each worker performing at least 25 lifts/lowers per day. They had to be expected to continue in their existing form for at least the 18 months of the follow-up period. Jobs that required substantial vehicle driving, the handling of people or team handling by teams of more than two people were excluded. Jobs involving either carrying or team handling in teams of two people were included in the study.

Both men and women employed in the jobs that qualified for the study were asked to participate. No age limits or health status restrictions were imposed except that women who were pregnant or who had had a baby in the previous six months were excluded, since pregnancy itself can cause LBP. Participants needed to be full-time employees, with at least one week of experience in the job, be expecting to stay in the job for the following 18 months and be willing to fill in the baseline questionnaire and a follow-up questionnaire every three months for 18 months.

2.1.2 Ethical approval of the study protocol and informed consent

The original study was approved by the HSE Research Ethics Committee (ETHCOM/REG/98/12) in May 2001. Survey Control approval for HSL as part of the UK Civil Service to carry out a survey of industry was obtained from the HSE Survey Control Liaison Officer in April 2001. Participating firms and individuals gave informed consent to

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participation and to the use of photographs of individuals and processes. Because no new data were being collected, no further ethical approval was needed for this study.

2.2 THE SOURCES OF DATA AND DETAILS ON THE SAMPLE AND THE RESPONSE RATE

2.2.1 Subject recruitment and baseline questionnaire completion

Subjects read the participant information sheet and gave informed consent to participation and separate consent to being videoed or photographed. It was emphasised that it was not necessary to video all participants.

Each subject filled in a four-page questionnaire at baseline. This asked for basic personal data (gender, date of birth, height, weight and handedness), job-related data (including hours worked, and history of LBP in the previous 12 months), health data (participation in exercise and smoking status), history of MSDs in the previous three months (a version of the Nordic Musculoskeletal Questionnaire) and six psychosocial factors. Five psychosocial scales (‘Influence on and control over work’, ‘Supervisor climate’, ‘Stimulus from the work itself’, ‘Relations with fellow workers’ and ‘Psychological work load’ were taken from the PAK (Psykosocial Arbetsmiljökartläggning) questionnaire(21). A sixth scale, ‘Management commitment to health and safety’, was added.

Subjects usually completed the baseline questionnaire in work time, usually in a group of up to 10 individuals supervised by the HSL researcher. This was often during a planned or natural break, or at the start of the work shift. The questionnaire was marked as ‘Confidential’ and had a heading or watermark stating that it should only be returned to HSL staff or HSL. Some questionnaires were left with workers with return envelopes. Where possible these were collected from the individuals concerned or returned to union or safety representatives or, as a last resort, management. Questionnaires and return envelopes were occasionally left for subjects to return by post. The questionnaires were not anonymous due to the need for repeated contact with the subjects and the need to link responses on the follow-up questionnaires to the baseline questionnaire and job measurements.

2.2.2 Baseline measurement of tasks involving manual handling

Where more than one subject was recruited in a job, task measurements were taken only from individuals who were willing to be videoed. The NIOSH equation does not take account of differences between individuals performing a task, but focuses solely on the parameters of the task. This is also true of the MAC tool. Therefore, it was felt that it was justified to limit the amount of data collected by assuming that intra-participant variation within a job was small enough to be disregarded, so it was only necessary to gather task data from one representative individual performing each manual handling operation. If a number of individuals rotated round workstations, measurements were made on the individual available at a workstation.

Video was used to record each manual handling task carried out as part of the job. The duration of videoing depended on the nature of the job, how many tasks made up the job, how much variability there was within it, and the frequency of handling, with high frequency tasks being videoed for shorter periods than lower frequency tasks. The aim was to gain a representative sample of the task so that inter-cycle variations in frequency would be averaged out. Linear dimensions required for the NIOSH analysis were measured with a metal tape measure. Angles were either measured with a goniometer or estimated from the video. The quality of the hand/ handle coupling was determined from the video. Variables unique to the MAC were coded directly from the video.

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Data were recorded on-site in a variety of ways, including on a hand-held computer, on paper and on video. Sketches of workplaces were made with measured dimensions. Measurements and verbal statements of measurements were recorded on video for later extraction. Discussions with managers and workers that occurred during filming of tasks were also recorded in this way. Where possible, weight information was taken from company records. Otherwise, weights were recorded either from direct weighing of the item on a set of calibrated electronic bathroom scales or directly from markings on the object.

The videos were collected with hand-held cameras without additional lighting, at 25 frames per second on either VHS-C or DV tapes. The tapes were then digitised to MPEG-1 format, including sound. Coding of the task variables and transcription of comments were carried out using The Observer software (Noldus Information Technology BV, The Netherlands) version 5.0 and then version XT. This allowed frame-by-frame control of the video to establish timings of events and so to allow the frequency of lift to be calculated by averaging over multiple cycles. Different manual handling operations were defined as Activities with the type ‘State Event’. Modifiers were used to distinguish multiple tasks carried out as part of the same job. Multiple individuals in a film segment were coded in separate data files. The type of manual handling operation (such as lifting) was coded at its beginning and either the end of the operation or the transition to another type of handling (such as carrying) was coded. This made explicit the complex nature of most actual handling operations. The cycle time for a task was defined as the duration between one event (such as the initiation of a lift) and the recurrence of the same event in the next cycle of the task. It therefore included any time between the end of the handling operation and the recurrence of the marker event in the next cycle.

2.2.3 Follow-up questionnaires

Every three months from the date of entry of a subject to the study, a covering letter and one page questionnaire were posted to the last known contact address of that person with a Freepost return envelope. Non-responders were reminded up to three times, starting two weeks after the initial follow-up letters, and then at weekly intervals. Where possible, this was done by telephone.

The first section of the follow-up questionnaire asked the individual to check their contact details. The second asked if he or she was still working in the previously reported job and work area and for the same company. The third section asked if any LBP had been experienced since the date of the previous response and three ‘Yes’ options were offered: ‘Work not affected’; ‘Put on light duties/restricted hours’; ‘Taken time off work’. Start and end dates were requested for the last two options. Dates were not requested for the ‘Work not affected’ option since multiple episodes could have occurred in the three months, and the ability of subjects to recall dates of pain episodes would be much less than their ability to recall losing time from work. The fourth section asked if the subject had been injured at work in the same period and offered the same response options, including asking for start and end dates for restricted duties or time off. For the ‘Work not affected’ response, it also asked for the date of injury. Additional questions asked for the type of injury and the body part injured.

2.2.4 Triangulation of follow-up data

To provide a secondary source of job change/absence data, each company was contacted to ask about job changes, absences due to LBP and injuries at work that they had records for the individuals participating in the study. This was done for two periods: the first nine months and the final nine months of the follow-up. Not all companies responded to these requests. Where there were discrepancies between these data and the data from individuals, judgements were made as to which was more reliable, taking into account the tendency of company responses to

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fail to answer some questions. This proved to be an invaluable means of identifying individuals who had changed job but had failed to respond to follow-up questionnaires.

HSE databases of reports made to HSE under RIDDOR (1995) (Reporting of Injuries, Deaths and Dangerous Occurrences Regulations, 1995) were searched by name and study dates for all participants. The reports retrieved showed that some reports of LBP received from individuals were due to other causes, such as vehicle impacts, not manual handling. It was noted that many over three day absences notified by individuals were not found in the RIDDOR database.

2.3 CONVERSION OF RECORDED DATA TO MAC COLOUR CODES

The data collected(6) for the evaluation of the NIOSH equation(4) were recorded as actual dimensions. It was therefore necessary to define boundaries for each variable to convert them into the MAC colour bands and MAC scores. Following the NIOSH approach(4), the hand position was defined as the point mid-way between the left and right hands, and the position of the low back was approximated as being above the mid-point between the ankles.

2.3.1 Categorisation of task types

To be included in the study, jobs had to include manual handling and the attempt was made to capture all tasks involving manual handling that were performed regularly as part of the job. Jobs could also include carrying and/or team handling. In the MAC, a task is only classed as a carrying operation if the carrying distance is at least two metres. In other words, a task that involves a carry distance of less than 2 m is treated as only involving lifting. Therefore, any task that had a carry distance of less than 2 m was classified as a lifting operation. The number of workers performing a lifting operation was recorded and any two-person tasks were classified as team handling operations.

2.3.2 Load weight/frequency for lifting operations

The boundaries in the ‘Load weight/frequency chart for lifting operations’ were defined (Table 1) by reference to maximum acceptable weights for lifting reported in Tables 2 and 3 of Snook and Ciriello(22). These were data for maximum acceptable weights of lift in the floor to knuckle region, with a compact load (width 340 mm) with handles being lifted over a 760 mm range close to the body. An upper weight limit of 50 kg was imposed(2). Comparison of these values with the printed graph in the MAC revealed discrepancies that appear to have crept in during the production of the final version of the MAC. Almost all of these were only 1 kg; the largest was 3 kg (Table 1). For the analysis, the values in the printed graph were used.

Table 1 Actual (intended) values for the ‘Load weight/frequency graph for lifting operations’

One lift every (lifts per hour) Bound-ary

Accept-able to

Day (0) 30 min-utes (2)

5 min-utes (20)

2 min-utes (30)

1 minute (60)

14 sec-onds (250)

9 sec-onds (400)

5 sec-onds (720)

Green/ Amber

50% of females

23 kg 19 kg (17 kg)

17 kg (16 kg)

15 kg 14 kg 13 kg 12 kg 10 kg

Amber/ Red

50% of males

45 kg (44 kg

39 kg (38 kg)

38 kg (37 kg)

34 kg 30 kg 22 kg 19 kg 17 kg (16 kg)

Red/ Purple

10% of males

50 kg 50 kg 50 kg 50 kg 47 kg (44 kg)

32 kg (33 kg)

28 kg (29 kg)

24 kg

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A look up table was created to convert values of weight and frequency into the MAC colour band. Linear interpolation was used to determine the boundary points between the points defined in Table 1.

2.3.3 Load weight/frequency for carrying operations

The principles adopted for lifting operations were used to convert the measured load weights and frequencies for carrying operations to colour bands using the boundaries in Table 2. The boundaries had been defined by reference to data from Table 10 of Snook and Ciriello(22) for maximum acceptable weights of carry over distances of 2.1 m at approximately elbow height (111 cm above floor level for males and 105 cm above floor level for females). The boxes had handles and were carried close to the body. Again, an upper weight limit of 50 kg was imposed(2). Again, there are discrepancies between these values and the printed graph in the MAC, but all were of 1 kg.

Table 2 Actual (intended) values for the ‘Load weight/frequency graph for carrying operations’

One carry every (carries per hour) Boundary Acceptable

to Day (0) 30 minutes

(2) 5 minutes (12)

2 minutes (30)

1 minute (60)

12 seconds (300)

Green/ Amber

50% of females

25 kg 19 kg (18 kg)

19 kg (18 kg)

19 kg (18 kg)

19 kg (18 kg)

16 kg

Amber/ Red

50% of males

44 kg 38 kg 32 kg (33 kg)

30 kg 30 kg 25 kg

Red/ Purple

10% of males

50 kg 50 kg 49 kg (48 kg)

42 kg (43 kg)

42 kg (43 kg)

35 kg

2.3.4 Hand distance from low back

The MAC provides photographs to help the user categorise this variable. The original subject of the photographs replicated the postures, markers were placed on the floor to indicate the positions of the hands and the mid-ankles and the distances measured (Table 3). Boundaries between the categories were defined as rounded values of the mid-points between the measured values (Table 4). These were used to convert the measured values of the horizontal distance of the hands form the mid-ankles, H, from the NIOSH study into MAC colour bands. As H was measured at both the origin and destination of the lift, the larger value was used to determine the MAC colour band.

Table 3 Measured values of ‘Hand distance from the lower back’ for the postures in the MAC assessment guide for lifting operations

Hand distance from the lower back category

Arm/back posture Measured horizontal distance from mid ankles

Close (Green) Upper arms vertical 372 mm Moderate (Amber) Trunk bent forward 435 mm Moderate (Amber) Upper arms angled 533 mm Far (Red) Upper arms angled & trunk bent 576 mm

Table 4 Numerical boundaries for ‘Hand distance from low back’ colour bands Hand distance from the lower back boundary

Horizontal distance from mid ankles

Green/Amber 400 mm Amber/Red 550 mm

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2.3.5 Vertical lift region

The assessment guide for the MAC defines the colour bands for the ‘Vertical lift region’ in terms of floor level and anthropometric landmarks – knee height, elbow height, and head height. The NIOSH equation defines 750 mm as the optimal lift height, linking it to the 50th percentile male knuckle height. Therefore boundary values (Table 5) were taken from 50th percentile male data in Table 10.2 of Pheasant and Haslegrave(23) rounded to the nearest 25 mm. Both origin and destination heights had been recorded so both were converted to MAC colour bands and the worst-case value used.

Table 5 Numerical boundaries for Vertical lift region

Vertical lift region boundary

Mid-hand position Height above floor level

Red/Amber Head height 1750 mm Amber/Green Elbow height 1100 mm Green/Amber Knee height 550 mm Amber/Red Floor level 0 mm

2.3.6 Trunk twisting and sideways bending

The assessment guide for the MAC does not give precise boundaries to distinguish a neutral, symmetrical, trunk posture from ones that are twisted and/or bent sideways. It was therefore decided to adopt the figure of 20° (Table 6) that the QEC (Quick Exposure Check)(24) used for the neutral/non-neutral boundaries for these aspects of trunk posture, following the precedents set in the creation of RULA (Rapid Upper Limb Assessment)(25) and in the case-referent study by Punnett et al.(26).

Table 6 Numerical boundaries for trunk twisting and sideways bending Trunk twisting/sideways bending boundaries

Angle of deviation from symmetrical posture

No trunk twisting/trunk twisting ±20° No sideways flexion/sideways flexion 20°

2.3.7 Other MAC variables

Table 7 lists the other MAC variables and how they were coded. It also indicates any equivalents in the NIOSH equation.

Table 7 Coding of other MAC variables MAC variable How coded NIOSH equation

equivalent Postural constraints Coded directly from video None Grip on the load Recoded directly from the

equivalent NIOSH category Coupling

Floor surface Coded directly from video None Other environmental factors (extreme temperature, strong air movements, extreme lighting)

Coded directly from video None

Carry distance Coded directly from video None Obstacles en route (carrying only) Coded directly from video None Communication, coordination and control (team-handling only)

Coded directly from video None

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2.4 ALTERNATE BOUNDARIES FOR MAC VARIABLES

In order to explore the effect of changing the boundaries between colour bands for MAC variables, alternate boundaries were tested for the ‘Load weight/frequency graph for lifting operations’ and for the ‘Vertical lift region’.

2.4.1 Load weight/frequency for lifting operation alternate boundaries

The approach adopted when testing alternate load weight/frequency boundaries was to reduce the values used for the boundaries so that they represented weights acceptable to greater proportions of the population. This increased the severity of the classification of load weight/ frequency combinations. The Purple/Red boundary was changed from acceptable to 10% of males to acceptable to 25% of males. The Amber/Green boundary was changed from acceptable to 50% of females to acceptable to 75% of females. Examination of the figures given by Snook and Ciriello(22) showed that weights acceptable to 25th percentile females and to 75th percentile males were both intermediate between the alternative Purple/Red and Amber/Green boundaries (Table 8). Therefore, two alternate charts were defined (Table 9), with one using the 25th percentile female values for the Red/Amber boundaries and the other using the 75th percentile male values. The charts are illustrated in Figure 7 and Figure 8.

Table 8 Alternate colour band boundaries for ‘Load weight/frequency for lifting operations’

One lift every (lifts per hour) Bound-ary

Accept-able to

Day (0) 30 min-utes (2)

5 min-utes (12)

2 min-utes (30)

1 min-ute (60)

14 sec-onds (250)

9 sec-onds (400)

5 sec-onds (720)

Green/ Amber

75% of females

19 kg 14 kg 13 kg 13 kg 12 kg 11 kg 10 kg 8 kg

Amber/ Red

25% of females


Amber/ Red

75% of males


Red/ Purple

25% of males


Table 9 Alternate boundaries for ‘Load weight/frequency graph for lifting operations’ Boundary Original Alternative 1 Alternative 2 Purple/Red Acceptable to 10% of

males Acceptable to 25% of males

Acceptable to 25% of males

Red/Amber Acceptable to 50% of males

Acceptable to 25% of females

Acceptable to 75% of males

Amber/Green Acceptable to 50% of females



14

Day (0) 30 minutes (2) 5 minutes (12) 2 minutes (30) 1 minute (60) 14 seconds (250) 9 seconds (400) 5 seconds (720)

(lifts pe r hour)One lift eve ry

0

10

20

30

40

50

0

10

20

30

40

50

Wei

ght

of lo

ad (

kg)

R/P boundary: acceptable to 25% of m ale s A/R boundary: Acceptable to 25% of fe males G/A boundary: Acceptable to 75% of fe males

Figure 7 Load weight/frequency for lifting operations (with the Red/Amber boundary

set as acceptable to 25% of females - alternative 1)

Day (0) 30 minutes (2) 5 minutes (12) 2 minutes (30) 1 minute (60) 14 seconds (250) 9 seconds (400) 5 seconds (720)

(lifts pe r hour)One lift eve ry

0

10

20

30

40

50

0

10

20

30

40

50

Wei

ght

of lo

ad (

kg)

R/P boundary: acceptable to 25% of m ale s A/R boundary: acceptable to 75% of m ale s G/A boundary: Acceptable to 75% of fe males

Figure 8 Load weight/frequency for lifting operations (with the Red/Amber boundary

set as acceptable to 75% of males - alternative 2)

2.4.2 Vertical lift region alternate boundaries

The boundaries for the ‘Vertical lift region’ are defined in terms of landmarks on the body. Alternate landmarks were defined by extending the range of the Green zone from between elbow height and knee height to between shoulder height and mid-lower leg height. These landmarks relate to the boundaries used in the risk filter in Appendix 1 of the HSE guidance on the 1992 Manual Handling Operations Regulations(27). Again, the numerical values of the landmarks were defined (Table 10) using values for 50th percentile males(23).

15

Table 10 Original and alternate boundaries for ‘Vertical lift region’ Boundary Original

landmarks Height above floor Alternate

landmarks Height above floor

Red/Amber Head height 1750 mm Head height 1750 mm Amber/Green Elbow height 1100 mm Shoulder height 1450 mm Green/Amber Knee height 550 mm Mid lower leg

height 275 mm

Amber/Red Floor level 0.01mm Floor level 0.01 mm

2.5 COMPOSITE WEIGHT/FREQUENCY INDICES

Since most of the jobs in the study involved multiple tasks with different parameters, it was necessary to find a method of scoring total daily exposure for the MAC load weight/frequency graphs for jobs involving varying weights and frequencies. Therefore, three variables were defined (Table 11) in order to capture both the worst-case and the average demands, and the MAC colour band was obtained for each of these variables.

Table 11 Summary load weight/frequency variables for lifting operations Maximum individual

load weight Maximum effort Weighted mean load

weight Abbreviation MILW MEFF WMLW Definition The weight of the

heaviest item lifted The weight handled in the task with the greatest value of load × frequency

Average effect taking all tasks and frequencies into account

Meaning The worst single load The task that moves weight most rapidly

The overall demands on the worker

Associated frequency The frequency at which this load is lifted

The frequency at which this load is lifted

Overall frequency of handling

Multiple values NA Take the worst-case colour band.

NA

2.6 JOB-LEVEL MAC VARIABLES

The MAC was designed as a tool for assessing risk of manual handling operations at the level of individual tasks. It therefore focuses attention on individual risk factors in order to facilitate identification of possible changes to reduce exposure to those risk factors. It was not designed to assess the risk to an individual from all the manual handling tasks carried out by that person.

It would not be possible to analyse the data by using parameters of individual tasks to predict the incidence of LBP, because if an individual performs multiple tasks it would be impossible to disentangle the effects of a specific task. Therefore, it was necessary to summarise the task-level MAC variables to give measures of exposure at the job level. Following the general approach of the MAC, that considers ‘worst-case scenarios’ when assessing individual risk factors, the worst-case scenario taken over all tasks within a job was identified for each MAC variable. Thus, if two tasks were coded Amber for ‘Postural constraints’ and one Red, then the Red was taken as the job-level colour.

One limitation of using the worst-case scenario approach is that a job involving just one Red task out of nine tasks is grouped with jobs where all tasks were coded Red. Therefore, alternative job-level variables were generated for each MAC variable, using the proportion of tasks within the job that were colour coded Red/Purple for each MAC variable, i.e. fell into the most severe categories. (The Red and Purple categories were combined because only the load weight/frequency factor makes use of the Purple category.) In this approach, a job with five out

16

of six tasks coded as Red is considered riskier than one that has just one of six tasks coded Red. However, though this approach takes into account the number of tasks that make up the job, it does not take into account the number of repetitions of each task, i.e. it is not a time-weighted average.

2.7 ANALYSIS TECHNIQUES

2.7.1 Use of incidence data

The follow-up questionnaires allowed individuals to report suffering LBP that had not resulted in time off work or time at work on restricted duties, as well as incident episodes that involved losing time from normal duties at work. This meant that individuals could make multiple reports of suffering LBP within the different three-month intervals between follow-ups. As most of these reports were not related to lost time, specific dates were not reported. They therefore constituted ‘repeated panel data’, which can be analysed using Generalised Estimating Equations(28). An initial examination of this approach showed that it did not add information to the results already obtained and so it was not pursued any further. Therefore, all the reported results refer to the analysis of the incidence of lost time using Poisson regression.

2.7.2 Task level worst-case scenarios

In many tasks, one or both of the variables indicating hand height or horizontal reach varied systematically, for example, when items were being added to a stack. In order to simplify the analysis for the NIOSH equation in situations where this occurred, the varying values were replaced by a ‘Single Equivalent Value’ (SEV) that had the same lifting index as the CLI for the sequence of lifts. Because the MAC has no equivalent to the CLI, it was necessary to identify the maximum contributing value of the hand height or hand distance from low back in order to identify the true worst-case scenario.

2.7.3 Use of multiple charts

The selection criteria for inclusion of a job within the study demanded that all tasks assessed involve manual lifting or lowering. Therefore, all tasks were assessed with the lifting operations flowchart, but only some tasks also involved carrying operations, and even fewer were team-handling operations (Table 12). Therefore, the job-level data required for the carrying operation and team-handling operation flowcharts were based on just the carrying and team-handling tasks within the job, and the sample sizes were consequently reduced. This meant that the same job could have two different values for job-level MAC variables, depending on the type of task being considered.

2.7.4 Outcome of interest

The follow-up questionnaire asked about experience of LBP in the previous three months. Three options for reporting LBP were given: ‘Work not affected’, ‘Put on light duties/restricted hours at work’ and ‘Taken time off work’. The questionnaire also asked for dates of episodes of restricted duties or time off. Because of small numbers reporting restricted duties, and because absences were often followed by periods back at work on restricted duties, the final two categories were combined to create a category of ‘lost time’. This was used as the outcome of interest. Because time to event data and durations were available for this category, it was possible to carry out longitudinal analysis of the data.

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t=0 t=140 t=180 t=400

Absent due to LBP

Not at risk At risk At risk

2.7.5 Analysis using Poisson regression

Poisson regression was used to investigate the associations between variables in the MAC and lost time. This technique was chosen since it models the rate of the outcome, taking into account the length of follow-up for each person and therefore allows for unequal follow-up times. This method also allows for multiple events per person, time ‘not at risk’, such as when absent due to an incident episode, and changing exposure status (such as a change in job).

Figure 9 illustrates how incident episodes were dealt with in this study. After 140 days in the study, a participant began an episode of lost time, which lasted for 40 days. By definition a participant cannot begin an episode of lost time during an existing episode, so this individual could not be ‘at risk’ for those 40 days. Once the episode of lost time finished, the participant was again at risk of a further lost time episode until day 400 when they dropped out of the study. The person-days at risk (360 days) were therefore fewer than the days in the study – the duration of follow-up (400 days).

Figure 9 Example set-up for Poisson regression analysis

In this example, there would be three records for the subject:

• Lost time after 140 days at risk;

• Not at risk for 40 days;

• No lost time after 220 days at risk.

The dependent variable was the variable for whether or not lost time had occurred during an ‘at risk’ period, with person-days at risk entered as the offset variable for that period.

The output from Poisson regression is presented in terms of rate ratios (RRs). When the variable of interest is categorical (for example, smoker or non-smoker), the RR compares the incidence rate experienced by a comparison group (for example, smokers) to that experienced by the reference group (for example, non-smokers). An RR greater than 1 indicates that the rate in the comparison group is greater than that in the reference group; an RR less than 1 indicates that the rate is less than that in the reference group. When investigating continuous variables, the RR represents the change in rate for a unit increase in the continuous variable.

Robust variance estimation was used throughout to account for possible clustering due to multiple events/records per participant. Separate analyses were performed for lifting operations, carrying operations, and team-handling operations, with the same methods followed for each. All analyses were performed in Stata SE version 11.2 for Windows1.

1 StataCorp. Stata statistical software SE version: Release 11.2. College Station, TX: StataCorp LP; 2011.

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Multi-level Poisson regression was used to test personal variables. To do this, job-level MAC variables were entered into separate Poisson regression models. Rate ratios were calculated both with and without adjustment for covariates. The personal factors adjusted for were:

• Age;

• History at baseline of recent (within the previous 12 months) LBP;

• Gender;

• Regular exercise;

• Supervisor climate;

• The number of tasks in a job.

The personal variables adjusted for were identified as independent predictors of lost work time due to LBP through multi-variable Poisson regression. Two other factors that were adjusted for were:

• The occurrence of carrying tasks within the job (except in the analysis of carrying operations);

• The occurrence of team handling within the job (except in the analysis of team-handling operations).

All MAC variables were entered as a series of indicator variables, the significance of which was tested using a joint Wald test. Reference categories were chosen to be the lowest category. RRs can be imprecise when the number of observed cases is small. Therefore, categories with fewer than five observed cases were combined with the ‘worst’ adjacent category where possible. Since Green is designed to reflect ‘no’ or ‘low’ risk, it was not appropriate to combine this with any other category so Amber was often combined with Red. Where combination was not possible, RRs and 95% confidence intervals were not reported. A score test was used to test for trend across categories, which involved assigning a score to the categories of the MAC variable (for example, Green=0, Amber=1, Red=2, etc) and entering this into the model as a continuous variable. Even if categories were combined to avoid categories with few observed cases, the score test for trend used the original categories. So, if the categories had been combined to give Green, Amber, and Red/Purple for the analysis, the original scores (0=Green, 1=Amber, 2=Red, and 3=Purple) would be used in the score test for trend. A continuous test for trend rather than a score test for trend was performed where appropriate – that is, for the number of Reds/Purples, the total score, and the proportion of Reds/Purples). This uses the continuous variable rather than assigning scores to the categorised variable.

The values underlying the MAC colour codes for ‘Hand distance from the lower back’ for lifting operations, and ‘Carry distance’ for carrying operations, were investigated for non-linear relationships with the incidence of lost time due to LBP. The Akaike Information Criterion (AIC) was used to compare linear, quadratic and cubic Poisson regression models. When comparing models, the lower AIC value indicates the better fitting model, with a difference greater than 2.0 indicating a marked preference for the model with the smaller AIC.

The alternative categorisations for various MAC variables were also investigated and compared to the original categories using the AIC.

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2.7.6 Identification of personal variables that were independent risk factors of lost time due to LBP

Poisson regression was also used to identify individual-level variables that were independent risk factors of lost time due to LBP. Personal variables, lifestyle factors and work characteristics were entered into separate Poisson regression models one at a time. Both crude (unadjusted) rate ratios and rate ratios adjusted for age at entry, gender and LBP experience before the study as a priori risk factors. All variables were entered as a series of indicator variables, the significance of which was tested using a joint Wald test. Reference categories were chosen to be the lowest category. Where appropriate, variables were also entered as continuous, rather than categorical, variables.

Only those variables found to be statistically significant (P<0.05) in the adjusted analyses were considered for inclusion into the multi-variable Poisson regression model. The base multi-variable model included age at entry, gender and LBP experience before the study as a priori risk factors. Variables were then included, one at a time, using a forward selection procedure. The final multi-variable model gave the independent risk factors to be adjusted for in the analysis of the MAC.

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3. RESULTS

3.1 DESCRIPTIVE STATISTICS

Altogether, 515 people completed the baseline questionnaire. No follow-up information was obtained for 17 of these. A further seven people were excluded from analysis since their jobs were too complex to analyse. Five others were excluded due to their jobs involving little or no manual handling. The remaining 486 people (94.4% of the 515 recruited) were included in the analysis. Table 12 shows the number of people included in the analysis of each of the three flowcharts. Since all tasks required lifting/lowering, this analysis included all 486 people (100%). Of these, 227 people (47%) took part in carrying tasks, and 80 people (16%) performed team-handling tasks. Just 57 people (12%) had performed both carrying and team-handling operations (not necessarily at the same time) during follow-up.

Table 12 Number of participants, follow-up time, and number of episodes of lost time due to LBP

Total – all performed lifting operations

Performed carrying operations

Performed team-handling operations

People 486 227 80 Follow-up/observation time (days) 223,472 Episodes of lost time due to LBP 65 25 8 Person-days at risk 221,782 106,410 36,127 Crude incidence rate, per 100,000 person-days (95% CI)

29.3 (22.1–38.8) 23.5 (15.4–35.8) 22.1 (9.8–50.1)

Note: ‘lost time’ refers to work lost due to being placed on restricted duties or absence from work.

050

100

150

200

250

Freq

uenc

y

0 200 400 600Total follow-up/observation time (days)

Figure 10 Histogram of follow-up/observation time for all 486 participants

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Total follow-up time was 223,472 days, with a median of 549 days (approximately 18 months) and a range of 13 to 644 days. The distribution of duration of follow-up is provided in Figure 10, which shows that the rate at which individuals dropped out was relatively stable over the duration of the study. Altogether, there were 65 episodes of lost work time due to LBP experienced by 52 people during follow-up, with two individuals reporting three episodes each. The crude incidence rate of lost work time due to LBP was 29.3 [95% confidence interval (CI) 22.1–38.8] cases per 1,000 person-days, and this was similar for those who had performed carrying operations, and those who had performed team-handling operations (Table 12).

Table 19 and Table 20 in the Annex shows the characteristics of the study participants grouped by the type of operation they performed. Most study participants (80%) were male, and had not experienced LBP in the 12 months before the start of the study. The mean age of participants at baseline was 38.9 (SD 10.4) years, and the median duration of employment was 4.8 (range 0.04–39.1) years. There was evidence that the subgroups that performed carrying and team handling operations differed to the whole group of study participants. In particular, the proportion of women was greater among those who had performed carrying operations at some stage during follow-up compared to those who had not (26% versus 14%, P=0.002). Those who performed carrying operations tended to be slightly older (P=0.026), shorter (P=0.036), have been employed for a greater length of time (P<0.001), have a shorter journey to work (P=0.035), and have lower influence and control over work (P=0.005). Also, a greater proportion of those who performed carrying operations had experienced some form of musculoskeletal trouble in the three months before the study, compared to those who had not performed carrying operations (76% versus 68%, P=0.046). There were similar differences between those who had undertaken team-handling operations and those who had not. The proportion of women was greater among those who had performed team-handling operations compared to those who had not (38% versus 16%, p<0.001). Those who took part in team-handling operations tended to weigh slightly less (P=0.038), be slightly shorter (P=0.029), and have a smaller proportion of smokers (29% versus 42%, P=0.031) than those who had not.

The jobs held by the participants are described in Table 21 in the Annex in terms of their MAC colour coding. In total, there were 126 unique jobs held by the 486 study participants during the study period. Of those jobs, 54 (43%) involved carrying operations, and 24 (19%) involved team-handling operations. The number of tasks per job ranged from one to 67, with a median of four. The distribution of jobs across MAC categories tended to be very unbalanced, with one category holding the majority of jobs (over 60% in most cases). When considering lifting operations, for load weight/frequency, postural constraints, floor surface and other environmental factors, the worst-case scenario over all tasks was Green for the majority of jobs. From a regulatory perspective, this is encouraging. However, the worst-case scenario was Red for ‘Hand distance from the lower back’, and ‘Grip on the load’ for the majority of lifting jobs. Similarly, the worst-case scenario for just the carrying tasks in a job was Red for ‘Grip on the load’ in the majority of jobs. The worst-case scenario over just the team-handling tasks in a job was Red for the majority of jobs for ‘Grip on the load’, and for ‘Trunk twisting/sideways bending’.

3.2 TESTS OF PERSONAL VARIABLES AS RISK FACTORS OF LOST TIME DUE TO LBP

Table 22 in the Annex shows the crude and adjusted RRs of lost time due to LBP for personal variables, lifestyle factors and work characteristics. After adjustment for age, gender and previous LBP experience, both regular exercise and supervisor climate were statistically significantly associated with lost time due to LBP. Therefore, both were included in the multi-variable Poisson regression model.

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Table 13 shows the final multi-variable Poisson regression model, which included both regular exercise and supervisor climate. All variables in the model would be adjusted for in the analysis of the MAC.

Table 13 Multivariable Poisson regression model for the relationship between personal variables and lost time due to LBP

Variable (Participants=465; jobs=126) Cases Days at risk Rate Ratio (95% CI)/P-value Sex Male 57 173,763 1.00 Ref Female 4 39,627 --- --- LBP experienced during previous 12 months No 18 119,921 1.00 Ref Yes: work unaffected 17 66,195 1.57 (0.75–3.31) Yes: lost work time 26 27,274 5.91 (3.16–11.06)*** Wald test P<0.001*** Age (years), continuous 61 216,390 1.00 (0.97–1.04) Wald test P=0.861 Exercise regularly No 33 87,510 1.00 Ref Yes 28 125,880 0.49 (0.29–0.84)** Wald test P=0.009** Supervisor climate, continuous 61 216,390 0.94 (0.89–0.99)* Wald test P=0.016*

Ref, reference category *, statistically significant with P<0.05; **, statistically significant with P<0.01; ***, statistically significant with P<0.001.

3.3 STATISTICAL ANALYSIS – LIFTING OPERATIONS

3.3.1 Overall results

Table 23 in the Annex shows the crude and adjusted RRs associated with the individual MAC variables in the lifting operation flowchart. Those whose worst-case scenario was Red for ‘Hand distance from the lower back’ had twice the rate of lost time due to LBP than those with Amber (adjusted RR 2.02, 95% CI 1.05–3.89), and the score test for trend suggested that the rate increased linearly from Green to Amber to Red (P=0.017). There were no other statistically significant results when considering the worst-case scenario variables.

Table 24 in the Annex shows the crude and adjusted RRs associated with the MAC variables based on the proportion of tasks coded Red or Purple within the job. The ‘<1%’ category corresponds to all tasks within a job coded as Green or Amber, and the ‘>99%’ category corresponds to all tasks coded as Red. For variables that have just two categories (‘<1%’ and ‘1 %+’), this is equivalent to comparing the combined Green and Amber category to the combined Red and Purple category in Table 23 in the Annex. The continuous test for trend was statistically significant for ‘Hand distance from the lower back’, providing evidence that the rate of lost time due to LBP increased with the proportion of tasks coded Red (P=0.033). This suggested that even if it was not possible to remove all Red tasks from a job, removing some could decrease the risk of lost time due to LBP. There were no other statistically significant results observed.

3.3.2 Load weight/frequency variables

Load weight/frequency, maximum individual load weight, maximum effort and weighted mean load weight were all coded using the chart in the MAC and with the alternative graphs for Load

23

weight/frequency for lifting operations (Figure 7 and Figure 8). Table 25 in the Annex compares the results obtained from the three graphs. None of the categorisations resulted in any of the variables being statistically significantly associated with lost time due to LBP (all Wald tests, and score tests for trend were not statistically significant). In addition to this, none of the alternative categorisations resulted in a better fitting model than the original categorisations according to the AIC; for example, the difference between the lowest and highest AIC was less than 2.0 for the load weight/frequency (486.28 versus 486.93). Therefore, there was no evidence to support altering the chart currently used in the MAC to either of the alternatives.

3.3.3 Hand distance from the lower back

Table 14 shows the results of the investigation into whether the relationship between ‘Hand distance from the lower back’ and lost time due to LBP was non-linear. The linear trend was statistically significant, and for every 10 cm increase in ‘Hand distance from the lower back’, the rate of lost time increased by around 20% (adjusted RR 1.21, 95% CI 1.05–1.40). Neither the quadratic nor the cubic terms in the quadratic and cubic models were statistically significant. The AIC indicated that the quadratic model did not improve the model fit (475.96 versus 477.61) and the cubic model made the model fit worse with a difference greater than 2.0 (475.96 versus 478.69). Since the linear model remained the model with the lowest AIC, there was no evidence that the relationship between ‘Hand distance from the lower back’ and the incidence of lost time was non-linear, and no obvious alternative categorisations were identified.

Table 14 Relationship between ‘Hand distance from the lower back’ and incidence of lost time due to LBP, estimated using Poisson regression

Model Adjusted Rate Ratio a

(95% CI) AIC

Model 1 – Linear 475.96 Hand distance from the lower back (10 cm) 1.21 (1.05–1.40)* Model 2 – Quadratic 477.61 Hand distance from the lower back (10 cm) 0.93 (0.34–2.51) Hand distance from the lower back (10 cm) squared 1.00 (1.00–1.01) Model 3 – Cubic 478.69 Hand distance from the lower back (10 cm) 7.12 (0.10–509.2) Hand distance from the lower back (10 cm) squared 0.97 (0.92–1.03) Hand distance from the lower back (10 cm) cubed 1.00 (1.00–1.00)

a: adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, if the job involved carrying, and if the job involved team-handling; Ref, reference category; *, statistically significant with P<0.05.

Figure 11 shows the distribution of the worst-case scenario values of ‘Hand distance from the lower back’ over all 126 jobs. This shows the unequal distribution of jobs across categories of the MAC variable, with most jobs having a worst-case scenario that was Red. An alternative categorisation was therefore suggested, based on an even distribution of jobs across colour bands so that a third were coded as Green, a third as Amber, and the final third as Red. The cut-offs for the alternative categorisation were 50 cm and 75 cm, instead of the current 40 cm and 55 cm (Table 4). The two categorisations for ’Hand distance from the lower back’ are compared in Table 15. The alternative categorisation resulted in a more balanced distribution of cases and days at risk across groups as expected, and the score test for trend remained statistically significant (P=0.032). However, the AIC did not favour one categorisation above the other, since the difference between the two AICs was less than 2.0. Therefore, the data did not support changing the current categories for ‘Hand distance from the lower back’ to the alternative.

24

05

1015

Freq

uenc

y

20 40 60 80 100 120Hand distance from the lower back (cm)

Green Amber RedJobs currently coded as:

Figure 11 Distribution of jobs for hand distance from the lower back, based on worst-

case scenario over all tasks within a job Table 15 Comparison of two categorisations of ‘Hand distance from the lower back’

Hand distance from the lower back (cm)

Cases Days at risk Adjusted Rate Ratio a

(95% CI) AIC or P-value

Current categorisation AIC=479.62 Green: <40 0 4,346 NA NA Amber: 40- 12 57,704 1.00 Ref Red: 55+ 49 149,473 2.02 (1.05–3.89)* Wald test P=0.035* Score test for trend P=0.017* Categorisation based on distribution of jobs AIC=479.97 1st third: <50 12 52,283 1.00 Ref 2nd third: 50- 14 66,338 1.13 (0.53–2.45) 3rd third: 75+ 35 92,902 2.24 (1.05–4.77)* Wald test P=0.053 Score test for trend P=0.032*

a, adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, if the job involved carrying, and if the job involved team-handling using separate Poisson regression models; Ref, reference category; *, statistically significant with P<0.05.

3.3.4 Vertical lift region

The alternative categorisation for the ‘Vertical lift region’ was based on different body landmarks. The results of comparing this and the current categorisation are shown in Table 16. The alternative categorisation resulted in a more even distribution of cases and days at risk across categories, with some jobs originally coded as Amber moving to the Green category.

25

However, the ‘Vertical lift region’ was not statistically significantly associated with incidence of lost time due to LBP for either of the categorisations, and there was no evidence that using the alternative categorisation improved the model fit (since the difference between the two AICs was less than 2.0). Therefore, there was no evidence to support a change in categorisation for ‘Vertical lift region’.

Table 16 Comparison of two categorisations for ‘Vertical lift region’ Vertical lift region (cm) Cases Days at

riskAdjusted

Rate Ratio a(95% CI) AIC or P-

value Current categorisation AIC=485.75Green: Above knee and below elbow height (55–109.9)

0 556 NA NA

Amber: Below knee or above elbow height (0.001–54.9 or 110–174.9)

48 177,836 1.00 Ref

Red: Floor level or below or above head height (<0.001 and/or 175+)

13 33,131 1.25 (0.33–4.79)

Wald test P=0.747 Score test for trend P=0.721 Alternative categorisation AIC=485.08Green: Above mid lower leg and below shoulder height (27.5–144.9 cm)

12 53,665 1.00 Ref

Amber: Below mid lower leg or above shoulder height (0.001–27.4 or 145–174.9)

36 124,727 1.36 (0.72–2.58)

Red: Floor level or below or above head height (<0.001 or 175+)

13 33,131 1.61 (0.38–6.78)

Wald test P=0.619 Score test for trend P=0.384

a, adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, if the job involved carrying, and if the job involved team-handling using separate Poisson regression models; Ref, reference category

3.4 STATISTICAL ANALYSIS – CARRYING OPERATIONS

Table 26 in the Annex shows the crude and adjusted RRs associated with the MAC variables as they currently stand for the carrying operation MAC. The score test for trend was statistically significant for ‘Hand distance from the lower back’ (P=0.031), suggesting that rate of lost time due to LBP increased linearly from Green to Amber to Red. However, this was no longer statistically significant when other risk factors were taken into account (P=0.123). There were no other statistically significant results when considering the worst-case scenario MAC variables for carrying operations. However, it is worth noting that only 227 people (57%) undertook carrying tasks, and so the ability of the analysis to detect associations if they truly existed would be reduced.

Table 27 in the Annex shows the crude and adjusted RRs associated with the MAC variables based on the proportion of carrying tasks coded Red or Purple. Due to the reduced number of participants included in this analysis, many variables did not have sufficient numbers of cases to estimate RRs. There were no statistically significant results.

Table 17 shows the results of the investigation into whether the relationship between ‘Carry distance’ and lost time due to LBP was non-linear. There was no evidence of a linear or non-linear relationship with ‘Carry distance’, since no RRs were statistically significant. The difference in AICs indicated that the model that included a quadratic term for ‘Carry distance’ fitted the data better than the model with just a linear term. However, the quadratic term was not statistically significant, so there was insufficient evidence to conclude that the relationship is non-linear.

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Table 17 Relationship between ’Carry distance’ and incidence of lost time due to LBP, estimated using Poisson regression

Model Adjusted Rate Ratio a

(95% CI) AIC

Model 1 – Linear 208.28 Carry distance (m) 0.99 (0.92–1.06) Model 2 – Quadratic 205.64 Carry distance (m) 0.72 (0.47–1.09) Carry distance (m) squared 1.01 (1.00–1.01) Model 3 – Cubic 207.46 Carry distance (m) 0.61 (0.29–1.30) Carry distance (m) squared 1.02 (0.97–1.08) Carry distance (m) cubed 1.00 (1.00–1.00)

a, adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, and if the job involved team-handling; Ref, reference category.

05

1015

20Fr

eque

ncy

0 10 20 30 40 50Carry distance (m)

Green Amber RedJobs currently coded as:

Figure 12 Distribution of jobs for ‘Carry distance’, based on worst-case scenario over

all carrying tasks within a job

Figure 12 shows the distribution of values of the worst-case scenarios for ‘Carry distance’ over all 54 jobs that involved carrying. This shows the highly skewed nature of the data, with some carries involving extreme (greater than 20 m) distances. Two alternative categorisations for ‘Carry distance’ were suggested based on the distribution of jobs. The first was based on thirds of the distribution, so that a third of jobs were coded as Green, a third as Amber, and the final third as Red. Due to there being a few extreme distances, it was suggested that the Red category might need to be divided into a Red and Purple category. The second alternative categorisation was therefore based on thirds for the Green and Amber categories, but the Red and Purple categories were based on sixths. This meant that a third of the jobs were coded Green, a third Amber, a sixth Red, and the final sixth as Purple. The proposed alternative categorisations and

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the original categorisation used in the MAC are compared in Table 18. The differences in AICs favoured the original categorisation of ‘Carry distance’ over the categorisation that introduced a fourth (Purple) category, but there was no evidence that the original categorisation fitted the data substantially better than the categorisation based on tertiles. In addition, there was no statistically significant association between ‘Carry distance’ and lost time due to LBP for any of the categorisations used. Therefore, there was no evidence to support changing the current categories used in the carrying operation flowchart for ‘Carry distance’ to either of the alternatives.

Table 18 Comparison of alternative categorisations to current categorisation for ‘Carry distance’, based on the distribution of jobs

Carry distance (m) Cases Days at risk

Adjusted Rate Ratio a


Current categorisation AIC=206.99 Green: 2- 11 32,383 1.00 Ref Amber: 4- 9 50,399 0.43 (0.13–1.49) Red: 10+ 2 19,962 --- --- Wald test P=0.335 Score test for trend P=0.181 Categorisation based on distribution of jobs AIC=208.28 1st tertile: 2- 9 29,875 1.00 Ref 2nd tertile: 3.25- 6 23,346 0.67 (0.16–2.72) 3rd tertile: 5+ 7 49,523 0.42 (0.09–2.03) Wald test P=0.544 Score test for trend P=0.278 Categorisation based on distribution of jobs AIC=210.16 1st tertile: 2- 9 29,875 1.00 Ref 2nd tertile: 3.25- 6 23,346 0.67 (0.16–2.79) 5th sextile: 5- 5 29,561 0.46 (0.08–2.59) 6th sextile: 10+ 2 19,962 --- --- Wald test P=0.725 Score test for trend P=0.273

a, adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, and if the job involved team-handling using separate Poisson regression models; Ref, reference category.

3.5 STATISTICAL ANALYSIS – TEAM-HANDLING OPERATIONS

Only 80 people in the study (16%) undertook team-handling operations, and they experienced just eight episodes of lost time due to LBP. The ability to detect true associations between MAC variables and lost time due to LBP based on these data would therefore be severely restricted. Therefore, the MAC variables for team handling were not investigated. The cases and days at risk are reported in Table 28 in the Annex.

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4. DISCUSSION / CONCLUSIONS

4.1 PREDICTIVE ABILITY OF INDIVIDUAL RISK FACTORS

The first objective set out in the project proposal was:

• To test the ability of individual risk factors to predict increased risk of lost time (absence/restricted duties) due to LBP or increased risk of reports of LBP, with or without lost time.

4.1.1 Lifting operations

There was evidence that the ‘Hand distance from the lower back’ was associated with lost time due to LBP; those coded as Red had over twice the rate of lost time compared to those coded as Amber for ‘Hand distance from the lower back’ (adjusted RR 2.02, 95% CI 1.05–3.89).

4.1.2 Carrying operations

There was no evidence that any of the carrying risk factors were associated with lost time due to LBP. However, fewer people performed carrying tasks, and so the ability of the analysis to detect associations if they truly existed was less than in the analysis of jobs involving lifting.

4.1.3 Team-handling operations

Only 80 people (16%) undertook team-handling operations and they experienced only eight episodes of lost time due to LBP. The ability to detect associations between MAC variables and lost time due to LBP using these data, if they truly existed, was severely restricted, and so this was not investigated further.

4.2 TESTING PARAMETRIC FACTORS FOR LINEAR TRENDS

The second objective was:

• To test for linear trends in parametric factors that predict increased risk of LBP.


There was a statistically significant increasing linear trend going from Green to Amber to Red categories for ‘Hand distance from the lower back’ (score test for trend P=0.017). Investigation of the underlying values of the hand distance from the lower back (rather than the colour codes) showed evidence that the rate of lost time due to LBP increased by, on average, 20% for each 10 cm increase in hand distance (RR 1.21, 95% CI 1.05–1.40). The evidence did not support a non-linear relationship between hand distance and lost time due to LBP.


There was no evidence of a linear or non-linear relationship between ‘Carry distance’ (using the underlying values rather than the colour bands) and lost time due to LBP.

4.3 TESTING COLOUR BAND THRESHOLDS

The third objective was:

• To test the thresholds between the colour bands for individual MAC risk factors.

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Various alternative categorisations were investigated for ‘Load weight/frequency’, ’Maximum individual load weight’, ’Maximum effort’, ’Weighted mean load weight’, ’Hand distance from the lower back, and ‘Vertical lift region’, but no evidence was found to support any of the alternatives proposed over the current categorisations.


Alternative categorisations were investigated for ‘Carry distance’, but there was no evidence to support the alternatives proposed over the current categorisation.

4.4 TESTING THE SCORING SYSTEM

The fourth objective was:

• To test the ability of the scoring system of the MAC to identify high-risk jobs by examining the weights given to individual risk factors.


There was no evidence that the rate of lost time due to LBP increased with increasing MAC lifting score.


There was no evidence that the MAC score for carrying operations was associated with the rate of lost time due to LBP.

Due to imprecision in the model estimates (wide confidence intervals), the lack of statistically significant results, and the limitations of the data discussed below, it was decided that it would not be appropriate to alter the scoring system currently used in the MAC based on these data.

4.5 LIMITATIONS OF THE METHODOLOGY

4.5.1 Summarising task-level data at the job-level

The MAC tool was designed for use at the task-level, and so it was necessary to summarise the task-level data to the job-level for the analysis. However, information is lost in doing this, which could result in potential associations being weakened or attenuated.

4.5.2 Variability of jobs

The nature of most of the jobs included in the study was that they included multiple tasks and often parameters such as the origin of, or destination for, a lift varied systematically within otherwise identical tasks. Variability of tasks is seen as beneficial by ergonomists(29) since it may have protective effects by reducing localised stresses on at-risk tissues in the body. Dempsey(12) discussed the complexities that arise from task variability when collecting data for epidemiological studies of manual handling and LBP.

4.5.3 Unequal distribution of jobs

There tended to be an extremely unequal distribution of jobs across categories of the MAC when defining the job-level variables based on the worst-case scenario, such that some categories did not have any jobs in them. This unequal distribution of jobs leads to an unequal

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distribution of cases and days at risk, which can affect the ability of the statistical analysis to detect associations if they truly exist (reduced statistical power).

4.5.4 Missing data

Missing values in the personal variables meant that 21 people were not included in the analysis when adjustment for personal variables was made. ‘Multiple imputation’ is a technique that can be used to fill in the missing values when values are missing completely at random (MCAR). If this is not the case, the results from analyses that use imputed data can be subject to bias. Because data were missing from variables such as body weight that are personally sensitive, it was decided not to use multiple imputation in this instance.

4.6 POTENTIAL INTERACTION WITH A HISTORY OF RECENT LBP

As noted previously, the study by Boda et al.(20) reported a relationship between the NIOSH peak STLI Index and the risk of low back / gluteal pain in a subgroup of workers free from LBP for at least 90 days at baseline. By contrast, the original HSL analysis of the 1991 NIOSH equation(5, 6) had found no such relationship in a cohort containing workers with and without a history of recent LBP (defined, as in this study, as in the previous 12 months). These differing findings suggested that the ability of the NIOSH equation to predict LBP may depend on the recent history of LBP – that is, there could be an interaction present. Re-analysis(7) has shown that there was a statistically significant interaction (p=0.030) between history of recent LBP and the NIOSH STLI. The adjusted HR (HR=1.37 (95% CI 1.05-1.78)) for STLI among those without recent LBP at baseline was statistically significant. For those without previous LBP it was not (HR=0.88 (95% CI 0.64-1.20).

This raises complex issues as to why a tool such as the NIOSH equation should be able to predict lost time due to LBP in individuals without recent LBP but not in those who have suffered LBP in the previous 12 months. It also raises issues of whether this interaction also applies to other tools such as the MAC and the implications of such interactions for the usefulness of methods of assessing risk.

Key to these issues is the natural history of LBP, which often occurs for short periods with spontaneous recovery. As far back as 1969, Rowe commented that LBP is “characteristically intermittent, episodic and recurrent and can meaningfully be studied only as a continuum which stretches through the active years of a man’s life”(30). Troup et al.(31) suggested that some backs have a phase where they are liable to cause pain that can last a year or two and noted that return to work “does not indicate relief of symptoms, let alone full recovery”. Ferguson et al.(32) found that patients with long-term muscular LBP generated increase spine loading due to increased levels of muscle co-activity, meaning they were at greater risk than symptom-free workers. They suggested that a symptomatic worker with muscular LBP returning to work might need to be on light duties for a minimum of three months. In a cohort of nursing students followed throughout training(33), the recent and recurrent history of LBP was a more important risk factor for new episodes of LBP than just a history of previous LBP.

Given the recurrent nature of LBP, it is necessary to exclude individuals with a history of LBP from studies trying to elucidate risk factors for first onset of LBP(34). However, such selection of subjects is more problematic in studies examining the ability of risk assessment methods to predict LBP or lost time due to LBP since these are designed for use at the task level and do not distinguish between differential risks to individuals with or without existing LBP. A study of a sample of 32-33 year olds from the UK 1958 birth cohort(35) excluded 43% who had previously suffered back pain and the authors raised the possibility that the risk factors they identified (psychological distress 10 years previously, and smoking) do not apply to individuals with the most “frail” backs.

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Troup et al.(36) found that using a person’s history of LBP in combination with a battery of anthropometric measures, measures of isometric and psychophysical strength and respiratory function was effective in predicting individuals that remained free of LBP and individuals that continued to have chronic pain. However, their discriminant analysis model tending to underestimate occurrences of cases of LBP and was poor at predicting transition to other states.

It therefore seems that the structures of the back of an individual that has recently recovered from an episode of LBP are at increased risk of generating pain than in individuals without a history of problems. It can therefore be speculated that this increased risk correlates or overlaps with the increased risk from manual handling risk factors and that individuals with histories of recent LBP are as likely to suffer new episodes when not exposed to manual handling as when exposed to it.

It is therefore possible that, like the NIOSH equation, the predictive ability of the MAC could be different for individuals with no history of LBP and individuals with recent LBP. However, the limitations of the data, particularly the unequal distribution of jobs across MAC colour bands, meant that it was not possible in this study to investigate this issue in more detail.

4.7 FUTURE USE OF THE MAC TOOL

The limitations of the sample size, the complexity of real jobs that rarely consist of a single repetitive task and the difficulties caused by the broad risk categories the MAC uses in preference to accurate measurements means that the results reported here are not very precise. It would therefore be wrong to conclude that the MAC is not a useful method of assessing risk arising from manual handling operations.

The findings of this project may have implications for the use of the MAC by HSE and local authority inspectors of health and safety, particularly in relation to guidance on enforcement action.

Duty holders should be confident in carrying on using the MAC tool as the risk factors for LBP included were identified as important by earlier studies.

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5. ANNEX: TABLES OF DETAILED RESULTS

Table 19 Characteristics of the study participants at baseline (i)

All - performed lifting operations

Also performed carrying operations Also performed team-handling operations

No Yes P-value (No vs. Yes)


Total 486 259 227 406 406 80 80 Sex, n (%) 0.002**

g <0.001*** g

Male 391 (80%) 222 (86%) 169 (74%) 341 (84%) 50 (63%) Female 95 (20%) 37 (14%) 58 (26%) 65 (16%) 30 (38%) LBP experienced during 12 months before study, n (%) 0.451

g 0.152 g

No 279 (57%) 147 (67%) 132 (58%) 226 (56%) 53 (66%) Yes: work unaffected 146 (30%) 83 (32%) 63 (28%) 129 (32%) 17 (21%) Yes: lost work time 61 (13%) 29 (11%) 32 (14%) 51 (13%) 10 (13%) Age (years), mean (standard deviation) a

38.9 (10.4) 37.9 (10.7) 40.0 (10.0) 0.026* h 38.9 (10.5) 39.0 (10.0) 0.925 h

Weight (kg), mean (SD) b 80.1 (14.1) 80.5 (13.6) 79.6 (14.6) 0.510 h 80.7 (13.9) 77.1 (14.7) 0.038* h Height (kg), mean (SD) c 1.74 (0.09) 1.75 (0.09) 1.73 (0.09) 0.036* h 1.75 (0.09) 1.72 (0.10) 0.029* h BMI (kg/m2), mean (SD) d 26.3 (3.9) 26.3 (4.0) 26.4 (3.8) 0.816 h 26.4 (3.9) 25.9 (3.9) 0.303 h Weekly working hours, mean (SD)

40.8 (6.0) 41.2 (7.0) 40.3 (4.5) 0.111 h 40.6 (6.1) 41.7 (5.3) 0.122 h

Length of employment (years), median (range) e

4.8 (0.04–39.1) 4.5 (0.04–36.0) 5.9 (0.1–39.1) <0.001*** i 4.8 (0.04–36.4) 5.2 (0.2–39.1) 0.247 i

Daily driving time (min-utes), median (range)

25 (0–270) 30 (0–240) 20 (0–270) 0.035* i 30 (0–270) 20 (0–120) 0.308 i

Exercise regularly, n (%) 0.356 g 0.722

g No 197 (41%) 100 (39%) 97 (43%) 166 (41%) 31 (39%) Yes 289 (59%) 159 (61%) 130 (57%) 240 (59%) 49 (61%) Current smoker, n (%) 0.153

g 0.031* g

No 294 (60%) 149 (58%) 145 (64%) 237 (58%) 57 (71%) Yes 192 (40%) 110 (42%) 82 (36%) 169 (42%) 23 (29%)

*, statistically significant with P<0.05; **, statistically significant with P<0.01; ***, statistically significant with P<0.001. a, one missing value, total n=485; b, 10 missing values, total n=476; c, 4 missing values, total n=482; d, 14 missing values, total n=472; e, 7 missing values, total n=479 g, Chi-squared test; h, T-test; i, Wilcoxon rank-sum test.

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Table 20 Characteristics of the study participants at baseline (ii) All- performed

lifting operations Also performed carrying operations Also performed team-handling operations



Any musculoskeletal trouble in the previous 3 months 0.046* b 0.098

b No 139 (29%) 84 (32%) 55 (24%) 110 (27%) 29 (36%) Yes 347 (71%) 175 (68%) 172 (76%) 296 (73%) 51 (64%) Psychosocial variables, mean (SD) a Influence on & control over work

-0.42 (4.84) 0.18 (4.80) -1.09 (4.81) 0.005** c -0.41 (4.85) -0.45 (4.83) 0.946 c

Supervisor climate

1.13 (5.02) 1.18 (5.34) 1.07 (4.65) 0.812 c 0.95 (5.13) 2.03 (4.32) 0.087 c

Stimulus from the work itself

-0.02 (5.22) 0.09 (5.39) -0.13 (5.03) 0.655 c 0.01 (5.21) -0.17 (5.31) 0.781 c

Relations with fellow workers

3.69 (4.27) 3.50 (4.51) 3.90 (3.99) 0.310 c 3.60 (4.31) 4.13 (4.31) 0.322 c

Psychological work load

1.00 (4.77) 0.92 (4.99) 1.09 (4.53) 0.712 c 0.87 (4.84) 1.64 (4.37) 0.201 c

Management commitment to health & safety

2.12 (5.03) 1.96 (5.21) 2.30 (4.83) 0.473 c 2.02 (5.00) 2.65 (5.21) 0.315 c

Note: psychosocial variables ranged from -10 to +10. *, statistically significant with P<0.05; **, statistically significant with P<0.01; ***, statistically significant with P<0.001. a, 20 missing values, total=466 b, Chi-squared test; c, T-test

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Table 21 MAC variables for study jobs, based on the worst-case scenario over tasks within a job MAC variable and colour band

Description Jobs involving lifting (worst-case scenario over all tasks)

Jobs involving carrying (worst-case scenario over carrying tasks only)

Jobs involving team handling (worst-case scenario over team-handling tasks only)

Total jobs 126 (100%) 54 (43%) 24 (19%) Total tasks per job, median (range) Load weight/frequency Green Read from chart 94 (75%) Amber Read from chart 28 (22%) Red Read from chart 3 (2%) Purple Read from chart 1 (1%) Hand distance from the lower back Green CLOSE: upper arm vertical/trunk upright 5 (4%) 8 (15%) 6 (25%) Amber MODERATE: upper arm angled or trunk bent forward 40 (32%) 25 (46%) 13 (54%) Red FAR: upper arm angled and trunk bent forward 81 (64%) 21 (39%) 5 (21%) Vertical lift region a Green Above knee and/or below elbow height 1 (1%) 0 (0%) Amber Below knee and/or above elbow height 113 (90%) 22 (92%) Red Floor level or below and/or above head height 12 (10%) 2 (8%) Trunk twisting/sideways bending Green Little or no twisting or sideways bending 33 (26%) 7 (29%) Amber Trunk twisting OR sideways bending 58 (46%) 5 (21%) Red Trunk twisting AND sideways bending 35 (28%) 12 (50%) Postural constraints Green None 87 (69%) 37 (69%) 21 (88%) Amber Restricted 37 (29%) 17 (31%) 3 (13%) Red Severely restricted 2 (2%) 0 (0%) 0 (0%) Grip on the load Green Good 1 (1%) 2 (4%) 0 (0%) Amber Reasonable 2 (2%) 2 (4%) 0 (0%) Red Poor 123 (98%) 50 (93%) 24 (100%) Floor surface Green Dry and in good condition 90 (71%) 41 (76%) 19 (79%) Amber Dry but in poor conditions or uneven 0 (0%) 0 (0%) 0 (0%) Red Contaminated, wet, sloping or unstable 36 (29%) 13 (24%) 5 (21%)

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MAC variable and colour band

Description Jobs involving lifting (worst-case scenario over all tasks)

Jobs involving carrying (worst-case scenario over carrying tasks only)

Jobs involving team handling (worst-case scenario over team-handling tasks only)

Other environmental factors Green No factors present 94 (75%) 45 (83%) 21 (88%) Amber One factor present 31 (25%) 9 (17%) 3 (13%) Red Two or more factors present 1 (1%) 0 (0%) 0 (0%) Carry load weight/frequency Green Read from chart 48 (89%) Amber Read from chart 5 (5%) Red Read from chart 0 (0%) Purple Read from chart 1 (2%) Asymmetrical trunk/load Green Load symmetrical in front/two hands 13 (24%) Amber Asymmetrical or offset load/hands 36 (67%) Red One-handed to side or twisting/back bent 5 (9%) Carry distance (m) Green 2- 19 (35%) Amber 4- 23 (43%) Red 10 m+ 12 (22%) Obstacles en route Green No obstacles OR carry route is flat 34 (63%) Amber Steep slope OR Trip hazards OR steps 19 (35%) Red Ladders 1 (2%) Team load weight Green 2 person <35 kg; 3 person <40 kg 24 (100%) Amber 2 person 35–50 kg; 3 person 40–75 kg; 4 person 40–100 kg 0 (0%) Red 2 person 50–85 kg; 3 person 75–125 kg; 4 person 100–170 kg 0 (0%) Purple 2 person >85 kg; 3 person 75–125 kg; 4 person 100–170 kg 0 (0%) Communication, co-ordination and control Green Good 18 (75%) Amber Reasonable 3 (13%) Red Poor 5 (13%)

Data are numbers with percentages in parentheses, unless otherwise specified. a, 21 missing values for lifting jobs, total n=105, 1 missing value for team-handling jobs, total n=23.

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Table 22 Crude and adjusted RRs of lost time due to LBP for personal variables, estimated using separate Poisson regression models Personal variable Crude analysis (participants=486; jobs=126) Adjusted analysis (participants=485; jobs=126) Cases Days at risk Rate Ratio (95% CI)/P-

value Cases Days at risk Adjusted

Rate Ratio a(95% CI) /P-value

Gender Male 57 177,896 1.00 Ref 57 177,329 1.00 Ref Female 8 43,886 0.57 (0.27–1.18) 8 43,886 0.56 (0.26–1.19) Wald test P=0.131 P=0.133 LBP in 12 months before study No 18 125,104 1.00 Ref 18 124,537 1.00 Ref Yes: work unaffected 19 68,340 1.93 (0.93–4.02) 19 68,340 1.91 (0.92–3.96) Yes: lost work time 28 28,338 6.87 (3.65–12.93)*** 28 28,338 6.78 (3.61–12.74)*** Wald test P<0.001*** P<0.001*** Age (years) b <30 11 40,757 1.00 Ref 30- 22 80,971 1.01 (0.48–2.09) 40+ 32 99,487 1.19 (0.59–2.42) Wald test P=0.835 Continuous variable 65 221,215 1.01 (0.98–1.04) 65 221,215 1.00 (0.97–1.04) Wald test P=0.517 P=0.859 Weight (kg) c <70 16 57,405 1.00 Ref 16 57,405 1.00 Ref 70- 17 57,309 1.06 (0.48–2.37) 17 57,309 0.79 (0.38–1.67) 80- 20 53,411 1.34 (0.64–2.81) 20 52,844 1.10 (0.55–2.21) 90+ 11 48,622 0.81 (0.36–1.81) 11 48,622 0.63 (0.28–1.40) Wald test P=0.665 P=0.498 Continuous variable 64 216,747 1.00 (0.99–1.02) 64 216,180 1.00 (0.98–1.02) Wald test P=0.687 P=0.751 Height (m) d <1.70 16 58,952 1.00 Ref 16 58,952 1.00 Ref 1.70- 15 43,783 1.26 (0.55–2.90) 15 43,783 0.93 (0.41–2.12) 1.75- 13 48,487 0.99 (0.48–2.05) 13 48,487 0.65 (0.30–1.43) 1.80- 12 41,644 1.06 (0.41–2.73) 12 41,644 0.79 (0.29–2.16) 1.85+ 9 26,786 1.24 (0.53–2.89) 9 26,219 0.89 (0.37–2.14) Wald test P=0.965 P=0.835 Continuous variable 65 219,652 1.72 (0.10–28.36) 65 219,085 0.30 (0.01–10.58)

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Personal variable Crude analysis (participants=486; jobs=126) Adjusted analysis (participants=485; jobs=126) Cases Days at risk Rate Ratio (95% CI)/P-



Wald test P=0.706 P=0.509 BMI (kg/m2) e Normal (18.5–24.9) 24 85,582 1.00 Ref 24 85,582 1.00 Ref Overweight (25.0–29.9) 31 91,973 1.20 (0.65–2.23) 31 91,406 0.97 (0.54–1.74) Obese (30+) 9 37,062 0.87 (0.38–1.96) 9 37,062 0.99 (0.45–2.19) Wald test P=0.694 P=0.994 Continuous variable 64 214,617 1.01 (0.95–1.07) 64 214,050 1.00 (0.93–1.08) Wald test P=0.824 P=0.970 Weekly working hours <40 25 101,731 1.00 Ref 25 101,731 1.00 Ref 40+ 40 120,051 1.36 (0.77–2.39) 40 119,484 1.28 (0.72–2.26) Wald test P=0.294 P=0.401 Continuous variable 65 221,782 1.04 (1.00–1.09) 65 221,215 1.03 (0.99–1.07) Wald test P=0.063 P=0.125 Length of employment (years) f <1 11 25,230 1.00 Ref 11 25,230 1.00 Ref 1- 17 81,570 0.48 (0.19–1.19) 17 81,003 0.42 (0.18–0.97)* 5- 11 41,580 0.61 (0.22–1.65) 11 41,580 0.37 (0.14–0.98)* 10+ 24 71,217 0.77 (0.30–1.96) 24 71,217 0.56 (0.22–1.40) Wald test P=0.335 P=0.141 Continuous variable 63 219,597 1.02 (0.98–1.05) 63 219,030 1.01 (0.97–1.05) Wald test P=0.365 P=0.721 Daily driving time (minutes) 0 4 20,642 --- --- 4 20,642 --- --- 1- 30 93,179 1.00 Ref 30 92,612 1.00 Ref 30- 19 69,577 0.85 (0.44–1.64) 19 69,577 0.65 (0.34–1.25) 60+ 12 38,384 0.97 (0.43–2.19) 12 38,384 0.80 (0.39–1.66) Wald test P=0.789 P=0.636 Continuous variable 65 221,782 1.00 (0.99–1.01) 65 221,512 1.00 (0.98–1.01) Wald test P=0.976 P=0.509 Exercise regularly No 33 89,335 1.00 Ref 33 89,335 1.00 Ref Yes 32 132,447 0.65 (0.37–1.14) 32 131,880 0.57 (0.33–0.96)*

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Wald test P=0.136 P=0.034* Current smoker No 32 134,562 1.00 Ref 32 113,995 1.00 Ref Yes 33 87,220 1.59 (0.91–2.79) 33 87,220 1.56 (0.88–2.78) Wald test P=0.105 P=0.126 Any musculoskeletal trouble in the previous 3 months No 8 61,047 1.00 Ref 8 61,047 1.00 Ref Yes 57 160,735 2.71 (1.05–7.01)* 57 160,168 1.59 (0.54–4.71) Wald test P=0.040* P=0.399 Influence on & control over work g [-10, -5) 17 35,279 1.00 Ref 17 35,279 1.00 Ref [-5, 0) 22 63,441 0.72 (0.33–1.58) 22 63,441 0.74 (0.35–1.57) [0, 5) 17 79,104 0.45 (0.20–0.97)* 17 78,537 0.58 (0.27–1.22) [5, 10] 5 36,133 0.29 (0.09–0.94)* 5 36,133 0.41 (0.13–1.29) Wald test P=0.082 P=0.359 Continuous variable 61 213,957 0.92 (0.86–0.98)** 61 213,390 0.94 (0.88–1.01) Wald test P=0.007** P=0.073 Supervisor climate g [-10, -5) 13 22,244 1.00 Ref 13 22,244 1.00 Ref [-5, 0) 21 55,782 0.64 (0.28–1.47) 21 55,781 0.59 (0.27–1.30) [0, 5) 19 79,963 0.41 (0.18–0.92)* 19 79,963 0.45 (0.22–0.92)* [5, 10] 8 55,968 0.24 (0.09–0.70)** 8 55,401 0.31 (0.12–0.80)* Wald test P=0.034* P=0.063 Continuous variable 61 213,957 0.92 (0.87–0.98)* 61 213,390 0.94 (0.89–0.99)* Wald test P=0.012* P=0.030* Stimulus from the work itself g [-10, -5) 15 36,733 1.00 Ref 15 36,733 1.00 Ref [-5, 0) 13 58,264 0.55 (0.24–1.25) 13 58,264 0.68 (0.31–1.47) [0, 5) 24 72,655 0.81 (0.36–1.80) 24 72,088 1.12 (0.53–2.38) [5, 10] 9 46,305 0.48 (0.17–1.37) 9 46,305 0.85 (0.31–2.34) Wald test P=0.354 P=0.500 Continuous variable 61 213,957 0.96 (0.91–1.02) 61 213,390 0.99 (0.93–1.06) Wald test P=0.230 P=0.848

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Relations with fellow workers g [-10, -5) 2 5,154 --- --- 2 5,154 --- --- [-5, 0) 13 32,873 1.00 Ref 13 32,873 1.00 Ref [0, 5) 22 76,097 0.73 (0.34–1.56) 22 76,097 0.95 (0.45–2.00) [5, 10] 24 99,833 0.61 (0.28–1.31) 24 99,266 1.07 (0.48–2.39) Wald test P=0.633 P=0.952 Continuous variable 61 213,957 0.97 (0.90–1.05) 61 213,390 1.02 (0.94–1.10) Wald test P=0.460 P=0.706 Psychological work load g [-10, -5) 7 19,103 1.00 Ref 7 19,103 1.00 Ref [-5, 0) 20 59,336 0.92 (0.38–2.20) 20 59,336 0.84 (0.38–1.86) [0, 5) 24 85,943 0.76 (0.34–1.69) 24 85,943 0.93 (0.46–1.90) [5, 10] 10 49,575 0.55 (0.20–1.53) 10 49,008 0.70 (0.26–1.85) Wald test P=0.656 P=0.894 Continuous variable 61 213,957 0.95 (0.90–1.01) 61 213,390 0.98 (0.93–1.04) Wald test P=0.109 P=0.516 Management commitment to health & safety g [-10, -5) 9 17,182 1.00 Ref 9 17,182 1.00 Ref [-5, 0) 12 44,852 0.51 (0.18–1.43) 12 44,852 0.63 (0.24–1.62) [0, 5) 26 74,722 0.66 (0.25–1.74) 26 74,722 0.79 (0.33–1.85) [5, 10] 14 77,201 0.35 (0.12–0.98)* 14 76,634 0.54 (0.21–1.40) Wald test P=0.184 P=0.570 Continuous variable 61 213,957 0.95 (0.90–1.00) 61 213,390 0.97 (0.921–1.02) Wald test P=0.068 P=0.258

Ref, reference category; *, statistically significant with P<0.05; **, statistically significant with P<0.01; ***, statistically significant with P<0.001. a, adjusted for age (continuous), previous LBP experience, and gender b, 1 missing value, total n=485 for crude analysis. c, 10 missing values, total n=476 for crude analysis and total n=475 for adjusted analysis. d, 4 missing values, total n=482 for crude analysis and total n=481 for adjusted analysis. e, 14 missing values, total n=472 for crude analysis and total n=471 for adjusted analysis. f, 7 missing values, total n=479 for crude analysis and total n=478 for adjusted analysis. g, 20 missing values, total n=466 for crude analysis and total n=465 for adjusted analysis.

40

Table 23 Crude and adjusted rate ratios of lost time due to LBP for MAC lifting variables based on worst-case scenarios over all tasks within a job, estimated using separate Poisson regression models

MAC variable (worst-case scenario) Crude analysis (participants=486; jobs=126) Adjusted analysis a (participants=465; jobs=126) Cases Days at risk Rate Ratio (95% CI)/P-

value Cases Days at risk Rate Ratio (95% CI)/P-

value Load weight/frequency (lifting) Green 52 167,711 1.00 Ref 48 160,022 1.00 Ref Amber/Red/Purple 13 52,204 0.80 (0.41–1.59) 13 51,501 1.11 (0.54–2.27) Wald test P=0.530 P=0.785 Score test for trend P=0.613 P=0.696 Hand distance from the lower back Green 0 4,346 NA NA 0 4,346 NA NA Amber 12 57,960 1.00 Ref 12 57,704 1.00 Ref Red 53 157,609 1.63 (0.84–3.15) 49 149,473 2.02 (1.06–3.89)* Wald test P=0.150 P=0.035* Score test for trend P=0.063 P=0.017* Vertical lift region Green 0 556 NA NA 0 556 NA NA Amber 52 184,600 1.00 Ref 48 177,836 1.00 Ref Red 13 34,759 1.33 (0.64–2.76) 13 33,131 1.25 (0.33–4.79) Wald test P=0.449 P=0.747 Score test for trend P=0.417 P=0.721 Trunk twisting/sideways bending Green 17 48,631 1.00 Ref 16 47,548 1.00 Ref Amber 28 100,149 0.80 (0.39–1.64) 25 95,375 1.04 (0.49–2.20) Red 20 71,135 0.80 (0.37–1.76) 20 68,600 0.67 (0.28–1.59) Wald test P=0.812 P=0.483 Score test for trend P=0.618 P=0.360 Postural constraints Green 38 137,451 1.00 Ref 34 131,594 1.00 Ref Amber/Red 27 82,464 1.18 (0.67–2.11) 27 79,929 1.20 (0.63–2.28) Wald test P=0.566 P=0.588 Score test for trend P=0.294 P=0.234 Grip on the load Green 0 138 NA NA 0 138 NA NA Amber 0 4,864 NA NA 0 4,864 NA NA

41



value Red 65 214,913 NA NA 61 206,521 NA NA Floor surface Green 55 167,962 1.00 Ref 51 160,893 1.00 Ref Amber 0 0 NA NA 0 0 NA NA Red 10 51,953 0.59 (0.28–1.22) 10 50,630 0.76 (0.34–1.68) Wald test P=0.156 P=0.497 Score test for trend P=0.156 P=0.497 Other environmental factors Green 54 170,474 1.00 Ref 50 162,916 1.00 Ref Amber/Red 11 49,441 0.70 (0.35–1.40) 11 48,607 0.76 (0.37–1.55) Wald test P=0.318 P=0.444 Score test for trend P=0.499 P=0.672 Maximum individual load weight Green 48 154,321 1.00 Ref 44 146,714 1.00 Ref Amber/Red/Purple 17 65,594 0.83 (0.45–1.54) 17 64,809 1.14 (0.59–2.18) Wald test P=0.558 P=0.698 Score test for trend P=0.628 P=0.624 Maximum effort Green 59 201,542 1.00 Ref 55 193,404 1.00 Ref Amber/Red/Purple 6 18,373 1.12 (0.32–3.84) 6 18,119 1.53 (0.45–5.22) Wald test P=0.862 P=0.497 Score test for trend P=0.742 P=0.750 Weighted mean load weight Green 55 185,303 1.00 Ref 51 177,065 1.00 Ref Amber 10 34,612 0.97 (0.48–1.99) 10 34,458 0.98 (0.47–2.06) Red 0 0 NA NA 0 0 NA NA Wald test P=0.941 P=0.959 Score test for trend P=0.941 P=0.958 Total number of Reds/Purples for individual task 0–1 12 42,736 1.00 Ref 12 41,323 1.00 Ref 2 25 91,384 0.97 (0.43–2.22) 21 87,737 0.90 (0.41–1.97) 3–4 28 85,795 1.16 (0.51–2.64) 28 82,463 1.28 (0.57–2.88) Wald test P=0.842 P=0.590

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value Continuous test for trend P=0.560 P=0.362 Total MAC score for individual task <10 11 40,569 1.00 Ref 11 40,190 1.00 Ref 10- 19 70,949 0.99 (0.43–2.29) 15 67,449 1.06 (0.44–2.59) 12- 9 25,793 1.29 (0.45–3.70) 9 24,193 1.31 (0.49–3.47) 14- 13 37,078 1.29 (0.49–3.40) 13 34,868 1.01 (0.26–3.92) 16+ 13 45,526 1.05 (0.42–2.66) 13 44,823 1.65 (0.63–4.36) Wald test P=0.954 P=0.814 Continuous test for trend P=0.604 P=0.221

Ref, reference category; NA, not applicable. *, statistically significant with P<0.05; **, statistically significant with P<0.01; ***, statistically significant with P<0.001. a, Adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, if the job involved carrying, and if the job involved team handling.

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Table 24 Crude and adjusted rate ratios of lost time due to LBP for the proportion of Reds/Purples observed for each MAC lifting variable, estimated using separate Poisson regression models

MAC variable (Proportion of tasks with Red/Purple colour coding)

Crude analysis (participants=486; jobs=126) Adjusted analysis a (participants=465; jobs=126)

Cases Days at risk Rate Ratio (95% CI)/P-value


Load weight/frequency (lifting) <1% 62 211,514 1.00 Ref 58 203,145 1.00 Ref 1%+ 3 8,701 --- --- 3 8,378 --- --- Hand distance from the lower back <1% 12 62,306 1.00 Ref 12 62,050 1.00 Ref 1–24% 15 42,596 1.83 (0.79–4.22) 13 39,065 2.53 (0.54–11.76) 25–49% 7 54,225 0.67 (0.27–1.69) 5 51,093 0.79 (0.30–2.12) 50–74% 20 33,361 3.11 (1.40–6.91)** 20 32,289 3.63 (1.66–7.98)** 75–99% 5 7,846 3.31 (1.04–10.52)* 5 7,846 2.79 (0.77–10.08) 99%+ 6 19,581 1.59 (0.56–4.54) 6 19,180 1.62 (0.58–4.55) Wald test P=0.007** P=0.006** Continuous test for trend P=0.077 P=0.033* Vertical lift region <1% 52 185,156 1.00 Ref 48 178,392 1.00 Ref 1%+ 12 34,759 1.33 (0.64–2.77) 13 33,131 1.25 (0.33–4.80) Wald test P=0.444 P=0.746 Continuous test for trend P=0.355 P=0.569 Trunk twisting/sideways bending <1% 45 148,780 1.00 Ref 41 142,923 1.00 Ref 1–24% 12 35,942 1.10 (0.52–2.33) 12 33,407 0.69 (0.20–2.39) 25%+ 8 35,193 0.75 (0.31–1.81) 8 35,193 0.63 (0.28–1.41) Wald test P=0.761 P=0.477 Continuous test for trend P=0.300 P=0.144 Postural constraints <1% 61 216,708 1.00 Ref 58 208,316 1.00 Ref 1%+ 4 3,207 --- --- 4 3,207 --- --- Grip on the load <1% 0 5,002 NA NA 0 5,002 NA NA 1–24% 9 19,489 1.00 Ref 9 19,489 1.00 Ref

44

MAC variable (Proportion of tasks with Red/Purple colour coding)

Crude analysis (participants=486; jobs=126) Adjusted analysis a (participants=465; jobs=126)



25–74% 15 41,834 0.78 (0.29–2.09) 15 41,834 3.62 (0.17–76.49) 75–99% 5 21,464 0.50 (0.12–2.06) 5 21,464 2.57 (0.13–49.82) >99% 36 132,123 0.59 (0.25–1.41) 36 132,126 2.35 (0.10–53.78) Wald test P=0.590 P=0.617 Continuous test for trend P=0.456 P=0.744 Floor surface <1% 55 167,962 1.00 Ref 51 160,893 1.00 Ref 1%+ 10 51,953 0.59 (0.28–1.22) 10 50,630 0.76 (0.34–1.68) Wald test P=0.156 P=0.497 Continuous test for trend P=0.125 P=0.457 Other environmental factors <1% 64 219,804 1.00 Ref 60 211,412 1.00 Ref 1%+ 1 111 --- --- 1 111 --- ---

Ref, reference category; NA, not applicable. *, statistically significant with P<0.05; **, statistically significant with P<0.01; ***, statistically significant with P<0.001. a, Adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, if the job involved carrying, and if the job involved team-handling.

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Table 25 Comparison of alternative categorisations for load weight/frequency, maximum individual load weight, maximum effort, and weighted mean load

weight obtained using different load weight/frequency charts MAC variable (worst-case scenario) and chart



Load weight/frequency original chart AIC=486.28 Green 48 160,022 1.00 Ref Amber 10 43,123 1.01 (0.45–2.28) Red 3 6,469 2.03 (0.67–6.11) Purple 0 1,909 NA NA Wald test P=0.447 Score test for trend P=0.696 Load weight/frequency alternative chart 1 AIC=486.66 Green 29 93,064 1.00 Ref Amber 22 78,069 0.89 (0.43–1.85) Red 10 38,481 1.29 (0.54–3.07) Purple 0 1,909 NA NA Wald test P=0.781 Score test for trend P=0.799 Load weight/frequency alternative chart 2 AIC=486.93 Green 29 93,064 1.00 Ref Amber 27 103,100 0.96 (0.50–1.86) Red 5 13,450 1.36 (0.46–4.01) Purple 0 1,909 NA NA Wald test P=0.834 Score test for trend P=0.887 Maximum individual load weight original chart AIC=486.24 Green 44 146,714 1.00 Ref Amber 14 56,431 1.07 (0.53–2.14) Red 3 6,469 2.07 (0.68–6.28) Purple 0 1,909 NA NA Wald test P=0.438 Score test for trend P=0.624 Maximum individual load weight alternative chart 1 AIC=486.66 Green 29 93,064 1.00 Ref Amber 22 78,069 0.89 (0.43–1.85) Red 10 38,481 1.29 (0.54–3.07) Purple 0 1,909 NA NA Wald test P=0.781 Score test for trend P=0.799 Maximum individual load weight alternative chart 2 AIC=487.1.2 Green 29 93,064 1.00 Ref Amber 27 101,610 0.98 (0.50–1.89) Red 5 14,940 1.27 (0.43–3.76) Purple 0 1,909 NA NA Wald test P=0.901 Score test for trend P=0.929 Maximum effort original chart AIC=483.84 Green 55 193,404 1.00 Ref Amber 3 5,509 --- --- Red/Purple 3 7,017 --- --- Purple 0 5,593 NA NA Wald test P=0.510 Score test for trend P=0.750

46

MAC variable (worst-case scenario) and chart



Maximum effort alternative chart 1 AIC=485.42 Green 46 171,833 1.00 Ref Amber 12 24,591 1.53 (0.27–8.67) Red 3 9,442 --- --- Purple 0 5,567 NA NA Wald test P=0.852 Score test for trend P=0.917 Maximum effort alternative chart 2 AIC=485.21 Green 46 171,833 1.00 Ref Amber 12 26,652 1.20 (0.20–7.05) Red 3 7,381 --- --- Purple 0 5,567 NA NA Wald test P=0.772 Score test for trend P=0.983 Weighted mean load weight original chart AIC=484.10 Green 51 177,065 1.00 Ref Amber 10 34,458 0.98 (0.47–2.06) Red 0 0 NA NA Purple 0 0 NA NA Wald test P=0.959 Score test for trend P=0.958 Weighted mean load weight alternative chart 1 AIC=485.17 Green 39 151,843 1.00 Ref Amber 19 45,568 1.36 (0.61–3.02) Red 3 14,112 --- --- Purple 0 0 NA NA Wald test P=0.671 Score test for trend P=0.869 Weighted mean load weight alternative chart 2 AIC=485.70 Green 39 151,843 1.00 Ref Amber 19 51,559 1.12 (0.51–2.48) Red 3 8,121 --- --- Purple 0 0 NA NA Wald test P=0.799 Score test for trend P=0.559

a: adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, if the job involved carrying, and if the job involved team-handling using separate Poisson regression models; Ref, reference category.

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Table 26 Crude and adjusted rate ratios of lost time due to LBP for MAC carrying variables based on the worst-case scenario over all carrying tasks within a job, estimated using separate Poisson regression models

MAC variable (worst-case scenario) Crude analysis (participants=227; jobs=54) Adjusted analysis (participants=220; jobs=54) Cases Days at risk Rate Ratio (95% CI)/P-


Rate Ratio a(95% CI)/P-value

Load weight/frequency (carrying) Green 25 96,461 NA NA 22 92,795 NA NA Amber/Red/Purple 0 9,494 NA NA 0 9,949 NA NA Hand distance from the lower back Green 1 16,392 NA NA 1 16,392 NA NA Amber 9 46,445 1.00 Ref 9 45,105 1.00 Ref Red 15 43,573 1.99 (0.79–5.02) 12 41,247 1.55 (0.62–3.92) Wald test P=0.145 P=0.351 Score test for trend P=0.031* P=0.123 Asymmetrical trunk load Green 11 25,673 1.00 Ref 9 24,233 1.00 Ref Amber/Red 14 80,737 0.40 (0.17–0.94)* 13 78,511 0.65 (0.21–1.97) Wald test P=0.036* P=0.445 Score test for trend P=0.259 P=0.817 Postural constraints Green 19 78,274 1.00 Ref 16 74,608 1.00 Ref Amber 6 28,136 0.88 (0.32–3.40) 6 28,136 0.72 (0.27–1.89) Red 0 0 NA NA 0 0 NA NA Wald test P=0.800 P=0.505 Score test for trend P=0.800 P=0.505 Grip on the load Green 0 1,167 NA NA 0 1,167 NA NA Amber/Red 25 105,243 NA NA 22 101,577 NA NA Floor surface Green 21 87,329 1.00 Ref 18 84,224 1.00 Ref Amber 0 0 NA NA 0 0 NA NA Red 4 19,081 --- --- 4 18,520 --- --- Other environmental factors Green 24 98,081 1.00 Ref 21 94,415 1.00 Ref Amber 1 8,329 --- --- 1 8,329 --- ---

48

MAC variable (worst-case scenario) Crude analysis (participants=227; jobs=54) Adjusted analysis (participants=220; jobs=54) Cases Days at risk Rate Ratio (95% CI)/P-


Rate Ratio a(95% CI)/P-value

Red 0 0 NA NA 0 0 NA NA Carry distance Green 12 33,380 1.00 Ref 11 32,383 1.00 Ref Amber/Red 13 73,030 0.50 (0.21–1.15) 11 70,361 0.41 (0.12–1.34) Wald test P=0.104 P=0.140 Score test for trend P=0.108 P=0.181 Obstacles en route Green 13 64,184 1.00 Ref 12 62,387 1.00 Ref Amber/Red 12 42,226 1.40 (0.60–3.27) 10 40,357 1.33 (0.49–3.61) Wald test P=0.432 P=0.577 Score test for trend P=0.460 P=0.587 Total number of Reds/Purples for individual task 0–1 12 51,748 1.00 Ref 12 50,969 1.00 Ref 2–4 13 54,662 1.03 (0.44–2.37) 10 51,775 1.08 (0.41–2.84) Wald test P=0.953 P=0.880 Continuous test for trend P=0.871 P=0.596 Total MAC score for individual task 0–7 8 41,078 1.00 Ref 8 40,299 1.00 Ref 8–9 7 31,556 1.14 (0.40–3.24) 5 29,794 1.03 (0.32–3.29) 10–19 10 33,776 1.52 (0.58–3.99) 9 32,651 1.40 (0.51–3.84) Wald test P=0.691 P=0.774 Continuous test for trend P=0.917 P=0.813

Ref, reference category; NA, not applicable. *, statistically significant with P<0.05; **, statistically significant with P<0.01. a, adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, and if the job involved team-handling.

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Table 27 Crude and adjusted rate ratios of lost time due to LBP for the proportion of Reds/Purples observed for each MAC carrying variable, estimated using separate Poisson regression models

Crude analysis (participants=227; jobs=54) Adjusted analysis (participants=220; jobs=54) MAC variable (Proportion of tasks with Red/Purple colour coding) Cases Days at risk Rate Ratio (95% CI) Cases Days at risk Adjusted

Rate Ratio a(95% CI)

Load weight/frequency (carrying) <1% 25 104,501 NA NA 22 100,835 NA NA 1%+ 0 1,909 NA NA 0 1,909 NA NA Hand distance from the lower back <1% 10 62,837 1.00 Ref 10 61,497 1.00 Ref 1–49% 9 22,729 2.49 (1.02–6.06)* 6 20,466 1.68 (0.55–5.16) 50%+ 6 20,844 1.81 (0.56–5.89) 6 1.97 (0.61–6.43) Wald test P=0.127 P=0.445 Continuous test for trend P=0.372 P=0.304 Asymmetrical trunk/load <1% 23 101,718 1.00 Ref 20 98,052 1.00 Ref 1%+ 2 4,692 --- --- 2 4,692 --- --- Postural constraints <1% 25 106,410 NA NA 22 102,744 NA NA 1%+ 0 0 NA NA 0 0 NA NA Grip on the load <1% 2 6,371 --- --- 2 6,371 --- --- 1–49% 8 21,458 1.00 Ref 5 19,220 1.00 Ref 50%+ 15 78,581 0.51 (0.21–1.24) 15 77,153 0.94 (0.31–2.87) Wald test P=0.139 P=0.911 Continuous test for trend P=0.235 P=0.647 Floor surface <1% 21 87,329 1.00 Ref 18 84,224 1.00 Ref 1%+ 4 19,081 --- --- 4 18,520 --- --- Other environmental factors <1% 25 106,410 NA NA 22 102,744 NA NA 1%+ 0 0 NA NA 0 0 NA NA Carry distance <1% 22 84,472 1.00 Ref 20 82,782 1.00 Ref 1%+ 3 21,938 --- --- 2 19,962 --- ---

50

Crude analysis (participants=227; jobs=54) Adjusted analysis (participants=220; jobs=54) MAC variable (Proportion of tasks with Red/Purple colour coding) Cases Days at risk Rate Ratio (95% CI) Cases Days at risk Adjusted

Rate Ratio a(95% CI)

Obstacles en route <1% 25 105,847 NA NA 22 102,181 NA NA 1%+ 0 563 NA NA 0 563 NA NA

Ref, reference category; NA, not applicable. *, statistically significant with P<0.05; a, for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, and if the job involved team handling.

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Table 28 Cases and days at risk of lost time for MAC team-handling variables based on the worst-case scenario over all team-handling tasks within a job

MAC variable Crude analysis (participants=80; jobs=24)

Adjusted analysis (participants=76; jobs=24) a

Cases Days at risk Cases Days at riskLoad weight Green 8 36,127 7 34,496 Amber/Red/Purple 0 0 0 0 Hand distance from the lower back Green 5 17,710 4 16,713 Amber/Red 3 15,598 3 15,342 Vertical lift region Green 0 0 0 0 Amber/Red 8 36,127 7 34,496 Trunk twisting/sideways bending Green 5 21,330 4 19,699 Amber/Red 3 14,797 3 14,797 Postural constraints Green 6 32,252 5 30,621 Amber 2 3,875 2 3,875 Red 0 0 0 Grip on the load Green 0 0 0 0 Amber/Red 8 36,127 7 34,496 Floor surface Green 8 28,637 7 27,006 Amber 0 0 0 0 Red 0 7,490 0 7,490 Other environmental factors Green 6 30,770 5 29,139 Amber/Red 2 5,357 2 5,357 Communication, co-ordination and control Green 7 30,213 6 28,582 Amber/Red 1 5,914 1 5,914 Red 0 0 0 0 Total number of Reds/Purples for individual task c 1 6 18,597 5 17,344 2–3 2 17,530 2 17,152 Total MAC score for individual task c 5–9 6 19,567 5 18,314 10–13 2 16,560 2 16,182

Ref, reference category; CI, confidence interval; NA, not applicable. a, adjusted for age, previous LBP experience, gender, regular exercise, supervisor climate, number of tasks in job, and if the job involved carrying.

52

6. REFERENCES

1 HSE and HSL (2003). Manual Handling Assessment Charts, INDG383. (Sudbury Suffolk: HSE Books), INDG383. Available at http://www.hse.gov.uk/pubns/indg383.pdf.

2 Monnington, S.C., Pinder, A.D.J. and Quarrie, C. (2002). Development of an inspection tool for manual handling risk assessment. (Sheffield: Health and Safety Laboratory), HSL/2002/30. Available at http://www.hse.gov.uk/research/hsl_pdf/2002/hsl02-30.pdf .

3 Monnington, S.C., Quarrie, C.J., Pinder, A.D.J. and Morris, L.A. (2003). Development of Manual Handling Assessment Charts (MAC) for Health and Safety Inspectors. In: McCabe, P.T. (Ed.) Contemporary Ergonomics 2003 (London: Taylor & Francis), pp. 3-8.

4 Waters, T.R., Putz-Anderson, V. and Garg, A. (1994). Applications Manual for the Revised NIOSH Lifting Equation. (Cincinnati, Ohio: NIOSH), DHHS (NIOSH) Publication No. 94-110.

5 Pinder, A.D.J. and Frost, G.A. (2009). The 1991 NIOSH Lifting Equation does not predict low back pain. In: Marley, R.J., Kumar, A.R., Ware, B.F., and Lockhart, T.E. (Eds.) Proceedings of the XXIst Annual International Occupational Ergonomics and Safety Conference, Dallas, Texas, USA, 11-12 June 2009 International Society for Occupational Ergonomics and Safety), pp. 229-235.

6 Pinder, A.D.J. and Frost, G.A. (2011). Prospective evaluation of the 1991 NIOSH Lifting Equation. (Sudbury, Suffolk: HSE Books), RR901, 204 pages. Available at http://www.hse.gov.uk/research/rrhtm/rr901.htm .

7 Pinder, A.D.J. and Frost, G.A. (2012). Evaluation of the ability of the 1991 NIOSH Lifting Equation to predict loss of time from work due to low back pain: an 18 month prospective cohort study of industrial workers. Ergonomics, In preparation

8 Ayoub, M.M. (1996). Field Evaluation of NIOSH Lifting Equations. Revised Protocol. (Lubbock, Texas: Institute for Ergonomics Research, Texas Tech University), 63 pages.

9 Dempsey, P.G. and Westfall, P.H. (1997). Developing explicit risk models for predicting low-back disability. A statistical perspective. International Journal of Industrial Ergonomics, 19, (6), 483-497.

10 Dempsey, P.G. and Fathallah, F.A. (1999). Application issues and theoretical concerns regarding the 1991 NIOSH equation asymmetry multiplier. International Journal of Industrial Ergonomics, 23, (3), 181-191.

11 Dempsey, P.G. (2001). Field investigation of the usability of the Revised NIOSH Lifting Equation. In: Proceedings of the Human Factors and Ergonomics Society 45th Annual Meeting (Santa Monica, CA: The Human Factors and Ergonomics Society), Volume 2, pp. 972-976.

12 Dempsey, P.G. (2002). Usability of the revised NIOSH lifting equation. Ergonomics, 45, (12), 817-828.

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Published by the Health and Safety Executive 12/14



RR1026

www.hse.gov.uk

The aim of this research was to ascertain whether HSE’s ‘Manual handling Assessment Charts’ (MAC tool) could be used to predict workers losing time from work due to low back pain (LBP). Results from the study suggest that as the ‘Hand distance from the lower back’ increased, the risk of lost time due to LBP increased. For each 10 cm increase, the rate of lost time increased by approximately 20%. No evidence of relationships between other risk factors in the MAC and lost time was found. There was no evidence that the rate of lost time due to LBP increased with either increasing total MAC lifting score or total MAC carrying score.

Due to imprecision in the model estimates (wide confidence intervals), the lack of statistically significant results, and the limitations of the data, it was decide that it would not be appropriate to alter the scoring system currently used in the MAC based on these data. Duty holders should be confident in carrying on using the MAC tool as the risk factors for LBP included were identified as important by earlier studies.

This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

rr1026 - validation of the hse manual handling …the aim of this research was to ascertain whether...

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