RELATIONSHIP BETWEEN JOB PHYSICAL ...

RELATIONSHIP BETWEEN JOB PHYSICAL DEMANDS AND OCCUPATIONAL LOW BACK PAIN IN A 90-DAY PAIN FREE COHORT

by

Sruthi Vasudev Boda

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy in Engineering

at The University of Wisconsin-Milwaukee December 2011

UMI Number: 3510668

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RELATIONSHIP BETWEEN JOB PHYSICAL DEMANDS AND OCCUPATIONAL LOW BACK PAIN IN A 90-DAY PAIN FREE COHORT

by

Sruthi Vasudev Boda

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy in Engineering

at The University of Wisconsin-Milwaukee December 2011

Ip/^/aoil Major Professor

Date

Graduate School Approval

Date

ABSTRACT RELATIONSHIP BETWEEN JOB PHYSICAL DEMANDS AND OCCUPATIONAL LOW BACK PAIN IN A 90-DAY PAIN FREE COHORT by Sruthi Vasudev Boda

The University of Wisconsin-Milwaukee, 2011 Under the Supervision of Professor Arun Garg

Low back pain (LBP) afflicts a vast majority of the working population in the United States with enormous human and economic costs. The risk factors for future LBP in individuals with past LBP remain unclear. This research was undertaken to study the relationship between quantified job physical demands and future occupational LBP (OLBP) in industrial workers with a history of LBP, while adjusting for significant covariates. One hundred and thirty industrial workers with past LBP (but pain free for at least 90 days at baseline) were studied prospectively for 4.5 years. Baseline job, individual and psychosocial data were collected. Workers were followed to observe changes in LBP status and job physical demands. LBP outcomes studied included OLBP lasting at least one day (OLBP-A), OLBP-A with medication use (OLBP-M), OLBP-A resulting in healthcare provider visit (OLBP-H) and OLBP-A resulting in lost workdays or light/restricted duty (OLBP-L). Quantified job physical demands for each worker included Lifting Index, back compressive force, strength requirement (minimum percent population capable) and load moment. Covariates studied included baseline individual and psychosocial factors.

iii

In multivariate analyses, Lifting Index and load moment showed evidence of association (p30 days in total in past 1 year (long), questionnaire

2.35 (long)

1E04

1.58-3.49

heavy physical work did not show a relationship with any outcome at baseline which was attributed to 'healthy worker' effect risk estimates are provided for followup period

LBP with medical consultation or sick leave >7

1.6 (females)

0.05

0.9-2.8

heavy physical work was a risk factor in both men and women

lab tests for physical capacity, physical exam, questionnaire

1.4 (males)

0.05

0.9-2.2

interaction between physical and psychophysical work factors seemed to be important

LBP > 1 day,

2.01

0.004

1.25-3.23

Heavy physical work was carrying heavy loads

-

-

-

-

-

-

took treatment for LBP,

2.17

0.012

1.17-4

visited health professional for LBP, sick leave for LBP

2.06

0.001

1.4-3.04

2.02

0.122

0.81-5.04

P-

value

Confidence Interval

consecutive days during past 24 years general population, n = 484

Ozguler, 2000

Crosssectional study, working population. n=725

Questionnaire

LBP >30 days, LBP intensity >3,

13

Table 2.1(contd.): Review of literature for heavy physical work Study (first author, year)

Study design, population, sample size

Exposure method of data collection

Astrand, 1988

Retrospective (22 years follow-up),

Back pain,

industrial (milling jobs: heavy work, office jobs: light work)

questionnaire, structured interview, physical exam

LBP outcome, method of data collection

Findings

Comments

-

-

Study (first author, year)


Heavy work over time associated with increase in back pain

n = 391 males Liles, 1984

Crosssectional, industrial, n = 453

Direct observation, inspection of production records,

lifting injury to back. outcome: injury rate, diability injury rate, severity injury rate, recorded

4.5

Injury rate (# lifting back injuries/100 FTE)

-

or reported interview with workers and supervisors

40

Krause, 2004

Prospective cohort, transit (vehicle) operators, n = 1233

Questionnaire

disability injury rate (> 1 lost workdays)

3

Reported LB injury, questionnaire

2.76

_

severity injury rate (# lost days per disabling back injury) 1.24-6.14

high risk for severe low back injury (medically diagnosed postlaminectomy syndrome, spinal stenosis, herniated d lumbar disc, sciatica, or spinal instability)

14 Table 2.1(contd.): Review of literature for heavy physical work Study (first author, year)




Hoogendorn, 2002

Prospective cohort, workers, n = 732 (no LBP > 3 months before baseline)

Video analysis

rate of sickness absence > 3 days due to LBP, Questionnaire

Findings

Comments



2.31

1.35-3.92

Never lifting > 10 kg/working day, compared to never lifting loads (unadjusted RR)

2.76

1.78-4.39

Never lifting > 25 kg/working day

3.6

2.18-5.99

1-15 lifts >25

3.81

2.14-6.68

15-25 lifts >25 kg/working day

2.16

1.0-4.7

lifting 44.5 N at least once/min throughout the day

kg/working day

Punnett, 1991

Case-referent (retrospective), automotive assembly, n = 219 (95 cases, 124 referents)

video analysis of postures, biomechanical data

back pain (>3 episodes, > 1 episode lasting > 1 week), interview

healthy worker effect observed only current job was analyzed, assuming short term relationship between job factors and LBP Skov, 1996

Bakker, 2007

Crosssectional, Danish salespeople, n = 1306

self-report

Prospective inception cohort (people with acute LBP 12 weeks), phone interview

1.07

0.991.15

spinal mechanical load (measured by 24HS sum scores), variable entered final multivariate model for persistent LBP (p < 0.1)

15

Lifting and forceful movements Nine studies on lifting and forceful movement were reviewed. Of these, 4 were prospective cohort studies with risk estimates ranging from 1.4-4.75, three of which employed questionnaires to collect exposure data; the other study used quantitative measures. Three studies were cross-sectional in design (all employed questionnaires for exposure data collection) and showed risk estimates ranging from 1.12-5.17. One casereferent retrospective cohort study was found which employed quantitative means to collect exposure data and showed a risk estimate of 2.1. One case control study was reviewed which used questionnaire to collect exposure data; risk estimates for different exposures ranged from 3.1-3.6. It must be noted that the strongest relationships between lifting and forceful movements and LBP outcomes were recorded when exposures were measured quantitatively (Chaffin and Park, 1973; Marras et al., 1995). A summary of the reviewed studies is provided in Table 2.2

It can be concluded that lifting and forceful movements could be a strong predictor for LBP/LBD. A need for quantified job demands is observed. The conclusion from this review is also supported by NIOSH which considers that there is strong evidence of association between lifting and forceful movements and LBD (Bernard, 1997).

16 Table 2.2: Review of literature for lifting and forceful movements





Chaffin, 1973

Prospective cohort (1 yr), industrial workers, n = 411

Measurement of Lifting strength rating (LSR) and lifting frequency

Punnett, 1991

Case-referent (retrospective), automotive assembly, n = 219 (95 cases, 124 referents)

Video analysis of postures, biomechanical data

Comments

Findings Risk estimate

pvalue

Medical visit due to LB complaint

-4.75

-

back pain (>3 episodes, >1 episode lasting > 1 week), interview

2.16

Confidence Interval incidence rate (low back injuries per 1000 man hours) increased with increase in LSR 1.0-4.7

lifting 44.5 N at least once/min throughout the day

healthy worker effect observed only current job was analyzed, assuming short term relationship between job factors and LBP Liles, 1984

Crosssectional, industrial, n = 453

Direct observation, inspection of production records.

lifting injury to back, outcome: injury rate, disability injury rate, severity injury rate, recorded or reported

4.5

Marras, 1995

Crosssectional, industrial, n = 403 jobs

Measured using lumbar motion monitor

LBD risk. medical and injury records

5.17

3.17

Injury rate (# lifting back injuries/100 FTE)

-

3.19-8.38

maximum load moment (23.6 Nm)

2.19-4.58

maximum weight (8.3 lbs)

17 Table 2.2 (contd.): Review of literature for lifting and forcefiil movements





Kelsey, 1984

Case-control, general population, n = 232 matched case-control pairs (20-64 yrs old)

Interview and questionnaire

acute prolapsed lumbar inter vertebral disc, medical records, interview, diagnostic tests

Ghaffari, 2006

Andersen, 2007

Landry, 2008

Cross-sectional, industrial (manufacturing, Iran), n = 3174

Prospective cohort (2 yrs), industrial and service, n = 1513

Cross-sectional, health professionals (Kuwait), n = 344

Questionnaire

Questionnaire

Questionnaire

LBP in past 12 months, LBP with sick leave in past 12 months, questionnaire

Sick leave > 2 weeks from records

LBP within last 24 hrs (acute LBP), questionnaire

Comments


p-value

Confidence Interval

3.5

-

1.5-8.5

lifting > 11.3 kg >25 times/day

3.1

-

1.3-7.5

6.1

-

1.3-27.9

lifting > 11.3 kg, >5 times/day, twisting the body half the time lifting > 11.3 kg, twisting body with knees almost straight

1.12

-

101-1.24

heavy lifting related to LBP in past 12 months

1.34

-

1.08-1.66

1.4

-

0.9-2.0

heavy lifting related to LBP with sick leave in past 12 months lifting 1-99 kg/hr compared to no lifting

1.9

-

1.3-2.8

1.47

100 kg/hr compared to no lifting acute LBP risk increased with number of daily patient lifts/transfers increase

18

Table 2.2 (contd.): Review of literature for lifting and forceful movements


Van Nieuwenhuyse, 2006




Prospective cohort (1

Questionnaire

LBP >7 consecutive days, questionnaire

3.13

Questionnaire

LBP in past 12 months, LBP intensity > 5, disability (LB)

2.1

2.24

yr), participants from healthcare and distribution company, n = 716

Bovenzi, 2009

Prospective cohort (1 yr), drivers, n = 537

Comments


pvalue

Confidence Interval 1.18-8.33

lifting or carrying loads > 25 kg, > 12 times/hr

1 episode lasting > 1 week), interview

Comments


Pvalue

Confidence Interval

8.09

1.5-44.0

time in nonneutral postures

4.9

1.4-17.4

non-neutral postures

5.7

1.6-20.4

mild flexion

5.9

1.6-21.4

sever flexion 5 variable trunk kinematic model predicts LBD risk 5 variable trunk kinematic model predicts LBD risk lifting > 11.3 kg, >5 times/day, twisting the body half the time

Marras, 1995



LBD risk, medical and injury records

10.6

4.8-23.1

Marras, 1993



LBD risk, medical and injury records

10.7

4.9-23.6

Kelsey, 1984

Case-control, general population, n = 232 matched case-control pairs (20-64 yrs old)


acute prolapsed lumbar intervertebral disc, medical records, interview,

3.1

1.3-7.5

diagnostic tests

6.1

1.3-27.9

LBP, self report

no association

Magora, 1973

Crosssectional, not described in article, n = 1970

observation, interview (bending, reaching, rotation, sudden maximal efforts)

lifting >11.3 kg, twisting body with knees almost straight authors concluded that many awkward postures are actually sudden maximal efforts incidentally carried out at that moment in a certain position of the spine

21 Table 23 (contd.): Review of literature for bending, twisting or awkward postures Exposure Study Study Findings LBP method of (first design, outcome, Risk pConfidence author, population, data method of value Interval year) sample size collection data estimate collection Riihimaki, Prospective Questionnaire LBP no cohort (3 1994 symptoms in association yrs), past 7 days, 12 months, machine lifetime, operators, carpenters, sciatic pain office radiating to workers, n legs = 591 questionnaire males

Comments

Static postures (prolonged sitting, standing, working in back bent posture) Static postures can be classified as prolonged standing, sitting or working with the back in a bent posture.

Five studies were reviewed (see Table 2.4) that looked at static postures of which 4 were cross-sectional and one was case-control in design. All the studies employed questionnaire, observation or interview to record exposure. Risk estimates were low (0.81-2.4) in general with the exception of one cross-sectional study on cadaveric spines (Videman et al., 1980) which showed a risk estimate of 24.6 for sedentary work (exposure was collected from talking to family).

This limited review found that there was a lack of prospective cohort studies and none of the studies measured exposures. Insufficient evidence of relationship between static postures and LBD was concluded by NIOSH (Bernard, 1997).

22

Table 2.4: Review of literature for static postures




Magora, 1973

Crosssectional, not described in article, n = 1970

observation, interview (bending. reaching. rotation, sudden maximal efforts)

Kelsey, 1975

Casecontrol, general population, n = 217 matched case-control pairs (20-64 yrs old)


Holstrom, 1992

Walsh, 1989

Crosssectional, construction workers, n = 1773

Crosssectional, postal workers, n = 436 (males and females)

questionnaire

questionnaire

LBP outcome, method of data collection LBP, self report

acute prolapsed lumbar intervertebral disc, medical records, interview, diagnostic tests

LBP history. LBP for>l to 7 days, LBP with any degree of functional impairment

self-reported LBP, interview

Comments


pvalue

Confidence Interval

no association

authors concluded that many awkward postures are actually sudden maximal efforts incidentally carried out at that moment in a certain position of the spine sitting > half the time < 35 years (number of cases=number of controls)

0.81

p 35 years (number of cases>number of controls) stooping > 4 hrs (reference: rarely stooping)

kneeling > 4 hrs (reference: rarely kneeling) driving > 4 hr/day (males)

sitting >2 hrs/day (females)

23

Table 2.4 (contd.): Review of literature for static postures





Videman, 1990

Crosssectional, workers (sedentary, mixed, driving. heavy), n = 86 (deceased)

Work history from family, job title

degree of degeneration, objective post-mortem pathological examination

Findings

24.6

Comments



sedentary vs. not sedentary

24

Whole body vibration Whole body vibration can occur from exposure while driving vehicles or equipment (e.g. fork lift trucks), due to exposure while handling tools that vibrate or due to vibration from the floor while standing close to machinery.

Seven studies were reviewed for whole body vibration. Two were prospective cohort studies employing questionnaires to record exposure, and showed risk estimates from 1.39-2.43. One retrospective cohort study was reviewed which classified exposure by job title and duration of employment, risk estimates ranged from 1.32-3.28 in this study. One case-control study was reviewed, risk estimate = 4.67 which recorded exposure using interview and questionnaire. Two studies were cross-sectional studies; both used questionnaires to record exposures. However, one of these studies also used quantitative measures of whole body vibration (duration, magnitude and dose of vibration) and risk estimates in this study were the highest reported ranging from 2.1-8.4 (Boshuizen, 1990). The value in quantitative exposure measurement can therefore be appreciated from this study.

From the magnitude of risk estimates and quality of studies reviewed, it appears that whole body vibration can be considered a strong predictor for LBP. NIOSH also supports the conclusion drawn from this review. A strong evidence of association between whole body vibration and LBP was concluded in the NIOSH review (Bernard, 1997). The summary of studies reviewed is presented in Table 2.5.

25 Table 2.5: Review of literature for whole body vibration

Comments





Skov, 1996

Crosssectional, Danish sales people, n = 1306

self-report

musculoskeletal symptoms, questionnaire

2.79

1.5-5.1

annual driving distance

Bovenzi, 2009

Prospective cohort (1 yr), drivers, n = 537

Questionnaire

LBP in past 12 months, LBP intensity > 5, disability (LB)

2.43

1.08-5.44

> 7 hrs of exposure to vibration (reference: 10 yrs vs. having disc prolapse

3.9-6.1

p 45 minutes was more hazardous when compared to repetitive work < 9 minutes (HR = 1.7, 95% CI = 1.2-2.6). The outcome was LBP with sick leave. Gheldof and colleagues also conducted a prospective cohort study (18 month follow-up) on 1,294 industrial workers and found that repetitive movements were predictive of long term LBP (> 30 days in year prior to follow-up) in the group reporting 1-30 days pf LBP prior to baseline (n = 120, crude OR = 1.31,95% CI = 1.03-1.66, adjusted OR was not significant). The third study was a cross-sectional study of 3,174 car manufacturing company workers found that repetitive work was predictive of sick leave due to LBP (OR = 1.2; 95% CI = 1.3-1.52). All studies used questionnaire to collect information on this factor.

From the above review it appears that repetitive work is a moderate risk factor for LBP. Andersson's review also reported that repetitive work is related to sickness absence (Andersson, 1997).

30

2.1.2 Job metrics Job physical exposure measures or 'job metrics' are computed using job analysis methods (discussed in section 2.2). Examples of job physical exposure metrics are back compressive force, shear force, load moment and minimum percent capable. Literature regarding job metrics is discussed in sections 2.1 and 2.2.

Load moment Load moment can be considered the simplest job analysis method and is computed as the product of load weight and horizontal distance between the load and the body. Marras and associates have studied load moment using the lumbar motion monitor system in 2 studies (Marras et al., 1993; Marras et al., 1995). In the 1993 study (Marras et al., 1993), 403 jobs were studied employing a cross-sectional design, and lifting frequency, maximum twisting velocity, maximum load moment, maximum sagittal flexion and maximum lateral velocity were studied in relation to LBD risk. Maximum load moment was found to have the highest odds ratio (OR = 5.17, CI = 3.19-8.38, p < 0.05), average moment was also predictive of LBD risk (OR = 4.08, p < 0.05). The other study by Marras and colleagues (Marras et al., 1995), also on 403 industrial jobs, employing a cross sectional study design found load moment was a significant risk factor for LBD risk. Average load moment was predictive of LBD with OR = 2.23 (CI = 1.33-2.74) and maximum load moment was predictive with OR = 4.04 (2.03-8.06). Recently, Lavender and associates have studied load moment using the METS (moment exposure tracking system) in a cross-sectional study of 195 distribution center workers on 50 jobs. The authors concluded that the METS had very good specificity (87%) and

31

sensitivity (73%) indirectly implying that load moment is a good measure of LBP risk (Lavender et al., 2009). Norman and associates case-control study on 104 cases and 130 controls on risk factors of reported LBP found peak moment was a risk factor with OR = 1.9 (p < 0.05, CI =1.4-2.6). Prospective cohort studies of load moment as a risk factor for LBP are lacking, but from the literature reviewed, it can be inferred that load moment is an important factor and a strong predictor of LBP.

Back compressive and shear forces The Revised NIOSH Lifting Equation (RNLE, discussed later in the document) uses biomechanical back compressive force limits of 3.4KN (safe limit) and 6.4KN (maximum permissible or hazard limit) for sagittal lifting tasks. Peak back compressive force was studied by several researchers (Herrin et al., 1986; Norman et al., 1998; Neumann et al, 2001). Herrin's study is discussed in section 2.2. Norman and colleagues studied biomechanical risk factors of reported LBP in a case control study (104 cases, 130 controls) of hourly paid workers (Norman et al., 1998). They reported a conservative odds ratio of 1.9 (p < 0.05, CI = 1.4-2.6) for peak compression and OR = 2.3 (p < 0.05, CI = 1.6-3.4) for peak shear. Neumann and others (same study group as Norman et al. 1998) found a significant OR = 2.0 for peak compression (Neumann et al., 2001). It appears the same data pool was used for this study also as in the Norman study, except sample size for cases and controls was 104 and 129 respectively.

Cumulative compressive force has also been studied. A case-control study of 137 cases and 244 control/referents in the automobile manufacturing industry found that

32

cumulative lumbar disc compression was a significant risk factor (OR = 2.0, 95% CI = 1.22-3.59), for work-related LBP for cumulative compression loads greater than 4.6x 106 Nsec/shift (Kerr et al., 2001). This study also found that peak lumbar shear force greater than 190N was predictive of LBP (OR = 1.7, 95% CI = 1.02-2.86).

From the above information, it can be inferred that both compressive and shear force may be moderate but important risk factors for LBP. Again, none of these studies used a prospective cohort study design.

2.1.3 Work organization/work practice and other occupational factors Shift work has been studied by some researchers. Evening and night shift workers were found to have higher low back injury rate when compared to day shift workers in a large prospective cohort study on home depot workers (Kraus et a., 1997). Engkvist found that duration of > 35 hours on the job among nurses (in a Swedish case-control study) was a risk factor for LBP (OR = 2.4, CI = 1.6-3.6; Engkvist et al., 2000). It can be inferred that shift time could be related to LBP.

Frazer and associates reported that job rotation was a risk factor for LBP and increase in risk was greater for those that rotated into the demanding job than out of the demanding job, however, only 2 jobs with 1 operator performing each job were studied Frazer et al., 2003). Presence of other jobs was significantly related to current LBP with OR = 2.4, p = 0.05, CI = 1.06-5.55 (Okunribido, 2008). NIOSH's review of backbelts concluded that use of backbelts for back injury risk reduction among uninjured workers remained

33

unproven (NIOSH, 1994). Increased discomfort were reported by novices lifting loads greater than 8Nm while experienced subjects reported lower discomfort ratings irrespective of load handled (Parakkat et al., 2007).

2.2 Job analysis methods Job analysis methods are tools that employ biomechanical, physiological or psychophysical criteria, or a combination of two or more of these criteria to evaluate the risk of injury to the body. Of the various job analysis methods available, only the Revised NIOSH Lifting Equation (RNLE) and Three Dimensional Static Strength Prediction Program (3DSSPP) will be used in the proposed research. A brief description of these methods and their validity are presented below. The predictive validity of a job analysis method is described as the ability of the method to estimate the same level of risk as that observed (Marras et al., 1999). A method is said to have to predictive value if it can differentiate between jobs with different levels of OLBP risk. Other job analysis methods are discussed very briefly later in this section.

2.2.1 Revised NIOSH Lifting Equation (RNLE) Brief Description: The National Institute of Occupational Safety and Health (NIOSH) first recommended two limits for lifting tasks performed in the sagittal plane (NIOSH, 1981). These limits were based on epidemiological, biomechanical, psychophysical and physiological criteria. The action limit (AL) was designed as a 'safe limit' to protect most workers (75% females and 99% males) from musculoskeletal injuries and corresponded to a

34

compressive force limit of 3.4KN and an energy expenditure (metabolic rate) limit of 3.5 Kcal/min. The maximum permissible limit (MPL), calculated as three times the AL, was designed as an 'upper limit' for lifting, beyond which musculoskeletal injury and severity rates were considered to increase significantly. The MPL corresponded to 6.4KN of compressive force and energy expenditure of 5.0 Kcal/min and was considered acceptable for only 25% male and 1% female workers. The equation on which these limits were based, accounted for horizontal and vertical locations of the load, lifting frequency and vertical travel distance of lift (NIOSH, 1981). The 1981 NIOSH limits did not account for asymmetry/twisting and assumed good couplings among other assumptions.

In 1991, NIOSH revised these limits to also account for asymmetry, different coupling conditions and a wider range of lifting tasks using the 'Revised NIOSH Lifting Equation' (Waters et al., 1993). The RNLE was based on biomechanical (compressive force limit = 3.4 KN), physiological (energy expenditure limit = 4.7 Kcal/min for whole body work and 2.2 Kcal/min for upper body work) and psychophysical (weight limit acceptable to 75% female and 99% male workers) criteria. This new equation included a recommended weight limit (RWL) based on task variables and a lifting index (LI) for the RWL. The LI was calculated as the ratio of the RWL to the actual weight lifted. A lifting index of > 1.0 was considered to pose an increased risk of lifting related LBP to some workers, while a lifting index > 3.0 was considered to pose elevated risk to many workers. An applications manual was also published (Water, Putz-Anderson and Garg, 1994) which provided guidelines for use of the equation and proper interpretation of the

35

lifting index. This work also provided a method to evaluate jobs that involved lifting tasks predominantly (given that other MMH activities were limited to a minimum) using the Composite Lifting Index (CLI) (Water, Putz-Anderson and Garg, 1994).

Validity: The predictive validity of a job analysis method is defined as The Revised NIOSH Lifting Equation is universally recognized and used (Russell et al., 2007) by various researchers (Grant et al., 1995; Wang et al., 1998; Waters et al., 1999; Marras et al., 1999; Mirka et al., 2000; Sesek et al., 2003; van der Beek et al., 2005; Russell et al., 2007). However, few researchers have studied the validity of the RNLE (Marras et al., 1999; Waters et al., 1999).

In a cross-sectional study of 353 industrial jobs (manufacturing setting) involving only highly repetitive MMH jobs without job rotation, Marras and associates investigated RNLE validity and found that this tool was predictive of LBD (Odds ratio for high risk to low risk jobs = 3.1, p < 0.05). However, they also concluded that this tool misclassified low and medium risk jobs as high risk jobs (Marras et al., 1999). It must be noted that the results of this study are subject to the following limitations: (i) a crosssectional design which restricts result interpretation and, (ii) computation of lifting index using average weight and average horizontal distance which deviates from the computation method suggested in the NIOSH Applications Manual (Waters, PutzAnderson and Garg, 1994). Waters and others also conducted another cross-sectional study to assess validity of the RNLE. This study involved 204 jobs and 80 individuals

36

employed in lifting jobs and non-lifting jobs respectively. This study showed a generally increasing trend between Lifting Index and LBP risk and found the highest risk corresponding to 2.0 < LI > 3.0 (OR = 2.45, p < 0.05), but showed reduced LBP risk for LI >3.0 (OR = 1.63, p > 0.05). When adjusted for psychosocial and individual confounders, the 2.0 < LI > 3.0 group showed an OR of 2.2 (p < 0.05). Results of this study are also limited by cross-sectional design and using clerical workers as the control/low risk group who are exposed to prolonged sitting which itself may have contributed to LBP.

In addition, the biomechanical and epidemiological evidence supporting the 3.4 KN compressive force limit used in both the 1981 and 1991 equations has been questioned (Waters, Baron, et al., 1998; Jager and Luttmann, 1999). Hence, an urgent need to validate this popular tool using better designed studies is observed.

2.2.2 3D Static Strength Prediction Program (3DSSPP) Brief Description: The Three Dimensional Static Strength Program (3DSSPP) by the University of Michigan is the work of 30 years of research involving the biomechanical and static strength capabilities of a worker given the physical demands of work (University of Michigan, 2010). Researchers at the University of Michigan first developed a two dimensional biomechanical model (the 2D Static Strength Prediction Model, licensed in 1984) to account for work performed in the sagittal plane. The 3DSSPP was programmed by Garg and Chaffin in 1975 and licensed in 1989 by the University of

37

Michigan. It compares population strength capabilities with various muscle joint strengths. This model allows asymmetric exertions and one or two handed tasks modeling. The inputs to this model are hand forces and directions, body joint angles, anthropometry and gender. The outputs of this model include back compressive force at the L5/S1 joint and percent population capable of performing the task at the elbow, shoulder, trunk (lumbo-sacral), hip, knee and ankle joints among other data (Chaffin, 1997). The 3DSSPP model uses biomechanical and physiological criteria to evaluate strengths and compressive force for a lifting/lowering, pushing/pulling or carrying/holding type MMH tasks. It can only be used to analyze static tasks or rather 'slow movements of manual material handling tasks' (Chaffin and Anderson, 1991). It must be noted that model is highly sensitive to postural changes and a 10° error in specifying a joint angle associated with most limiting muscle strengths could result in a 30% error in the resulting population strength prediction (Chaffin and Erig, 1991).

Validity: The validity of the 3DSSPP model has not been directly addressed so far. Chaffin and Park (1973) conducted a pilot study (5 month period) of 38 industrial jobs on 135 people and concluded that Lifting Strength Ratio (computed as ratio of maximum load lifted to strength of a large strong man lifting in the same position) greater than or equal to 0.2 be considered potentially hazardous to some people. The LSR was developed on the biomechanical theory that LBP incidence increased when spine compressive forces increased, indirectly providing validation of compressive forces. Herrin and associates also studied the biomechanical model used in the 3DSSPP in an epidemiological study

38

of 2 years of retrospective and 1 year of prospective medical visit data. This study involved 6912 employees and found that there was significant positive correlation between maximum back compressive force for each job and all type of incidents and apparently significant negative correlation between minimum percent population capable for each job and all types of incidents. In addition, the study concluded that the maximum back compressive at the L5/S1 joint was a good predictor of back injuries (medical visit due to back injuries) and overexertion injuries in general. This study showed that the lowest incidence of back incidents was associated with less than 1000 lbs (4.4KN) of maximum back compressive force and greater than 90% of minimum percent population capable for each job. Other validation studies of strength and back force prediction models used in the 3DSSPP cited by Chaffin (1997) compared predicted strengths to group strengths and predicted torso muscle responses to external torso load moments using EMG estimates.

It is clear from the above discussion that there is limited validity of the 3DSSPP model in regard to its ability to predict LBP risk.

39

2.2.3 Other job analysis methods The following are other available job analysis methods which were not employed in the current research.

State of Washington Checklist The State of Washington Checklist (State of Washington, 2000) is a simple, practical and comprehensive checklist among other available checklists. It is based on the RNLE, but has not been validated as yet.

Maximum Acceptable Weights and Forces Maximum acceptable weights and forces for various manual materials handling such as lifting, lowering, pushing, pulling and carrying are widely used to analyze jobs (Snook, 1978; Snook and Ciriello, 1991). These are based on the psychophysical principle that a subject adjusts the weight or force required to perform a task until a maximum acceptable level is reached using his own integrated body response to the stress from the task as an input. This tool provides weights and forces acceptable to 90, 75, 50, 25 and 10% of males and females for a given combination of object width, frequency, task height and vertical distance of lift (travel distance). There is limited evidence of the predictive validity of this tool in literature (Snook, 1978; Ayoub et al., 1983; Liles, et al., 1984; Herrin et al., 1986).

40

Lumbar Motion Monitor The Lumbar Motion Monitor (LMM) was developed after several experiments at the Ohio State University's Biodynamics Laboratory (Marras et al., 1993). The LMM uses trunk kinematic data in a five variable model to predict LBD risk. The five input variables to this model are lifting frequency, maximum twisting velocity, maximum load moment, maximum sagittal flexion and maximum lateral velocity. Risk in this model is the probability of membership in high LBD risk group. It must be noted that the maximum load moment, measured manually, had the highest predictive value with odds ratio of 5.17 (Marras et al., 1993). The validity of LMM has been studied by Marras and associates who found that the LMM model was capable of predicting high risk jobs from low risk jobs with predictive value of 10 (Marras et al., 1993; Marras et al. 1995). However, these studies used a cross-sectional study design to analyze 403 industrial jobs. The LMM is also limited in its application since it can analyze only repetitive lifting jobs with no job rotation.

Energy expenditure The energy expenditure model developed by Garg (1976) provides an estimate of whole body fatigue using physiological principles. NIOSH used the energy expenditure model to develop the physiological basis for the RNLE. However, a relationship between energy expenditure and LBP has not been studied yet.

ACGIH Proposed TLV for Lifting (ACGIH, 2001) The American Conference for Governmental Industrial Hygienists (ACGIH) proposed a Threshold Limit Values (TLVs) for "workplace lifting conditions under which it is believed nearly all workers may be repeatedly exposed, day after day, without developing work-related low back and shoulder disorders associated with repetitive lifting tasks" (ACGIH, 2010). The TLVs are weight limits proposed for a given task defined by the task duration, task frequency, horizontal and vertical distance of the load.

2.3 Individual factors Several individual or personal factors have been researched in relation to LBP. The following review discusses some of the frequently addressed risk factors. Of these, past history of LBP appears to be a very important predictor of future LBP.

Age Three reviews were considered for this risk factor (Garg and Moore, 1992; Andersson, 1997; Manchikanti, 2000). All reviews reported that low back pain begins early in life with peak incidence between ages 35-55. Andersson's review showed that effect of age on LBP could be different in males and females. Frequency of LBP appeared to increase over 65 years of age (Manchikanti, 2000). This review also reported that LBP persistence increased with age and was highest among the elderly (27%) and concluded that age was a probable risk factor. Two of the reviews reported that most patients who undergo surgery for disc herniation are between 35-50 years of age (Garg and Moore, 1992; Andersson, 1997). This observation seems unsurprising considering that most of

the working population would fall within the 35-50 age group. Garg and Moore also reported that LBP related recurrence, symptom duration and disability increase with increasing age (Garg and Moore, 1992).

It appears that age can be considered as an important potential risk factor for LBP from these reviews.

Gender Reviews by Garg and Moore (1992), Andersson (1997) and Manchikanti (2000) consistently show differences in considering gender as risk factor for LBP. Garg and Moore's review reported that (i) LBP incidence was equal in males and females, (ii) LBP incidence is higher in females than males performing heavy physical work, (iii) majority of workers compensation claims are filed by males, and (iv) surgeries for disc herniation were twice as common in males as females. Andersson's report (1997) appeared to support statements (i), (ii) and (iv) and also added that length of sickness absence varied in different age groups for men and women. Women appeared to be absent longer in the 40-49 age group while men appeared to be absent longer in the 5059 age group (Andersson, 1997). Manchikanti's review (2000) supported statements (i) and (vi). This review also presented the following insights: (a) LBP prevalence appeared to be more strongly related to occupational factors than to gender, (b) some back pain reported by women in epidemiological studies also may be associated with menstruation, pregnancy or labor, (c) women with history of multiple childbirths are

43

more likely to have LBP complaints (Manchikanti, 2000). In addition, Manchikanti concluded that gender is a possible risk factor for LBP.

From the above discussion, it appears that gender may be an important risk factor for LBP, but its importance is only preceded by occupational factors. Also, it appears that there is some level of interaction between gender and other risk factors (such as age and occupational factors) in contributing to LBP.

Body Mass Index/Obesity/Body weight Garg and Moore reported that there is no strong correlation between body weight and LBP (Garg and Moore, 1992). Andersson's review showed that there was more evidence that weight (pure/indexed) was not associated with either back pain or disc herniation than to the contrary (Andersson, 1997). Manchikanti's review reported different views that obesity was considered a strong risk factor by some, a possible but not strong risk factor by some others and not a risk factor of LBP by yet other researchers (Manchikanti, 2000). This report also referred to another systematic review of body weight as risk factor for LBP which found it to be a possible weak predictor. However, Manchikanti concluded that obesity is a possible risk factor of LBP. From the reviews considered, it appears that pure (body weight) or indexed (BMI, obesity) measures of weight are not strong predictors for LBP.

44

Smoking Andersson (1997) concluded that smoking could be an indicator of other risk factors affecting LBP risk rather than being a risk factor by itself. In the same report he also included that smoking was found to be consistently predictive of developing a herniated disc, but its association with back pain was not confirmed (Andersson, 1997). Garg and Moore reported that some studies identified an association between smoking and LBP while others did not (Garg and Moore, 1992). Smoking, number of cigarettes and years of exposure to smoking were associated with LBP severity according to Frymoyer's review (Frymoyer et al., 1983). Manchikanti's report suggested that smoking should be only considered a weak risk factor for LBP, but not a cause for it even though smoking was concluded as a probable risk factor for LBP in the same report (Manchikanti, 2000).

Therefore it appears that there is only a weak association between smoking and LBP.

Physical fitness, sports and other activities Andersson reported that a good state of physical fitness was associated with reduced risk of chronic LBP and faster recovery after an episode of back pain. He also reported that the role of physical exercises in LBP prevalence was difficult to assess, while there was no relationship between leisure time activities and surgery for disc hernias in the general population (Andersson, 1997). Garg and Moore agreed that literature supported the importance of physical fitness and training in reducing musculoskeletal injuries, but also pointed out that the efficacy of these factors as primary intervention for preventing musculoskeletal and back injuries was not supported (Garg and Moore, 1992).

45

Manchikanti reported that some studies found a relationship between LBP/LBD measures and sports activities such as soccer, wrestling, gymnastics, tennis and weight lifting but concluded that physical activity was not related to LBP (Manchikanti, 2000).

Hence, physical fitness, sports and leisure activities appear to be weakly associated with LBP at best.

Strength Garg and Moore (1992) and Andersson (1997) reported that poor trunk strength (in abdominal or back muscles) is associated with LBP. Chaffin and associates also found that higher lifting strength ratios (LSR) were associated with increased LBP risk in two epidemiological studies (Chaffin and Park, 1973; Chaffin, 1974). Keyserling also found that medical incidents of LBP were higher in workers whose strength did not match their job requirement than those workers whose strength matched their job requirement (Keyserling et al., 1980).

In view of the above evidence, it appears that strength (job specific) appears to be a strong risk factor for LBP.

Anthropometry and Lumbar mobility Garg and Moore (1992) reported that there was generally no strong correlation between stature or body build and LBP, however taller people in some studies were reported to have LBP/LBD. Similar views were reported by Andersson (1997).

46

Regarding spinal/lumbar mobility, both reviews (Andersson, 1997; Garg and Moore, 1992) concluded that though mobility was reduced in people with LBP, it was not considered to play a role in LBP causation. Manchikanti concluded in his report that body height was not related to LBP (Manchikanti, 2000).

It therefore appears that anthropometry and lumbar mobility are not strongly associated with LBP in general with the exception of height as a risk factor.

Past history of LBP Past history of LBP is one of the most important individual risk factors for future LBP. Some epidemiological studies have looked at this variable. Definition of past history of LBP and the LBP outcome itself vary between studies.

Tubach and associates studied sick leave due to LBP in 2236 French workers in a 2 year prospective cohort study. They found that past history of LBP > 30 days before baseline was highly predictive of both LBP with sick leave (defined as sick leave > 8 days due to LBP; OR = 7.2,95% CI = 4.1-13) and low back pain itself (defined as LBP > 30 days without sick leave; OR = 8.7, 95% CI = 6.4-12). Questionnaires were used to collect LBP information in this study (Tubach et al., 2002). A Belgian prospective cohort study (1 year) was conducted on 716 young healthcare workers and distribution center (median age = 26 years). This study reported relative risk of 1.7 (95% CI = 1.1 - 2.8) for back complaints > 7 consecutive days in the previous year (risk factor). The outcome

was LBP > 7 consecutive days during the follow-up period. Both baseline and follow-up information were collected by questionnaire (Van Niewenhusye et al., 2006). Perhaps, the most interesting study of relationship between previous LBP and future LBP is the one by Smedley and associates. The study was a prospective cohort study of 2 years based on 838 female nurses (mean age = 38 years), who had no back pain > 30 days before baseline. Their findings regarding past history of LBP are shown in Table 2.7 (table reprinted from Smedley et al., 1997). It is evident that total duration of past history of LBP has a significant relationship with future LBP and also with future LBP leading to absence from work. The odds ratios reported for all LBP outcome range from 1.9-6.1 and are significant. LBP > 30 days in the past year appeared to be most predictive of all LBP (OR = 6.1) and LBP leading to work absence (OR = 7.3).

Table 2.7: Risk factors for LBP during follow-up in the Smedley study adjusted for age and height Source: (Smedley et al„ 1997)

Eartnr feiataqf af law hack pw Tint saw* last Tatrt *«!• Now 1-6 Days 1 4 Weeks * 1 Mont* 16 Day Si 1-4 Weeks * f Month

M la* hack paia Noel

Ma al

MfeMie

Heel

Mfe ralie

120 23 26 25 24 36 61

19(11 to 3.1) 2.2 11.3 to 3 6) 2 9 (17 to 5 Ot 3.4 (20 to 5 8) 3.1 (2.0 to 4.9) 61 (4 1 to 91)

35 9 12 4 3 8 21

10 27(1 11o6 6i 3.5 1 year > 1 year • 1 year s 1 yew « 1 year * I year

460 57 59 46 45 66 92

laartatk pala laailai Id baaaatiwt

55%) showed null association between poor social support at work (represented by poor supervisor and co-worker relations) and LBP with and without adjusting for confounders (Davis and Heaney, 2000). Hoogendorn and colleagues concluded that there is strong evidence for low social support in the workplace as a risk factor of back pain with reported risk estimates ranging from 1.3-1.9 (Hoogendorn et al., 2000). NIOSH's review of epidemiological evidence of psychosocial risk factors of LBP, found only one study that showed weak evidence of association between social support and LBD (Bernard, 1997). Hartvigsen's review reported three high quality studies which showed insignificant positive association with LBP.

It appears that social support at work may be a weak predictor of LBP.

Job control Most studies reviewed by Davis and Heaney found null association between job control or lack of influence on work and LBP with and without adjusting for confounders (Davis and Heaney, 2000). Insufficient evidence of an effect of low job control and low

54

decision latitude (a combination of job control and content) was concluded by another review (Hoogendorn et al., 2000). NIOSH found limited support for an association between low job control and LBP (Bernard, 1997). Therefore it appears that job control is not associated with LBP.

Perceived stress A recent review by Illes and colleagues concluded that even though potential for stress may predispose a worker to longer work disability, it was not predictive of failure to return to work in the non-chronic phase of non-specific LBP (lies, Davidson and Taylor, 2008). Davis and Heaney's review reported that a distribution of 40% (2), 40% (2) and 20% (1) of reviewed studies showed a positive association, null association and mixed (positive and null) association between high feeling of stress and LBP after adjusting for biomechanical factors (Davis and Heaney, 2000). Another review stated that no association was reported by three high quality studies, moderate association was found by one high quality study and only one of the five low quality studies showed a strong association between perception of work and LBP (Hartvigsen et al., 2004). It appears that perceived stress may not be related to LBP.

Job pace Hoogendorn and colleagues concluded insufficient evidence of an effect of high work pace and back pain due to inconsistent findings in their review of literature (Hoogendorn et al., 2000). NIOSH reported that intensified workload which included job pace was

55

found to be significantly associated with LBD in four out of five studies (Bernard, 1997).

More weight is given to the NIOSH review since it conducted a rigorous investigation of LBD risk factors and it is concluded that job pace may be related to LBP/LBD.

2.4.2 Individual psychosocial factors Individual psychosocial factors include feelings of hysteria, depression, anxiety, psychological distress, psychological disorders, feeling tense or edge or nervous, poor social support outside of work, family problems and alcoholism. Garg and Moore reported that patients with chronic and disabling LBP often demonstrate significant psychosocial problems like anxiety, hysteria, emotional instability, depression, family problems or alcoholism. They also reported that most of the above problems tend to become normal after successful physical rehabilitation (Garg and Moore, 1992). Andersson added that "experience of stress, anxiety and depression do not seem to be secondary to back problems" as some prospective studies reviewed found that back pain was predicted by psychological distress in people without prior back pain (Andersson, 1997). lies and associates reported moderate evidence of fear avoidance as a predictor, strong evidence that depression and stress/psychological strain are not predictors and moderate evidence that anxiety is not a predictor of failure to return to work in people with non-chronic non-specific LBP (lies, Davidson and Taylor, 2008).

56

It appears that individual psychosocial factors have not been given the same attention as occupational psychosocial factors and more research is required to conclude a relationship between these factors and LBP.

2.5 Summary of Literature Review From a review of literature, it is evident that low back pain is multi-factorial in nature, a view supported by NIOSH (Bernard, 1997; NRC, 2001). A lack of studies using quantified job physical demands was observed. Additionally, there are no high quality prospective cohort studies on LBP that have: 1. Assessed dose-response relationships between JPD and LBP. 2. Assessed the predictive validity of select current job analysis methods. 3. Investigated the effects of confounders on these relationships. A high quality study of risk factors of LBP in individuals with past LBP should include an investigation of occupational, individual and psychosocial factors employing a prospective cohort study design. The proposed research attempted to address this gap in the body of knowledge on risk factors for low back pain.

57

3.

METHODS

3.1

Description of Data Obtained for Current Research (Parent Study)

Data for this research (Figure 3.1) were acquired from a large four-year prospective cohort study for quantifying risk factors for low back pain (Garg, Hegmann & Moore, 2008), referred to as the 'parent study' for the remainder of this document.

3.1.1 Brief Description of Parent Study The parent study was approved by the Institutional Review Board of the University of Wisconsin-Milwaukee (IRB approval #: 04.02.049). The parent study employed two research teams (blinded to each other), one for Job Physical Exposure Assessment and the other for Health Outcomes Assessment in Wisconsin, Texas and Utah.

Prospective Cohort Study (4 years) Industrial Workers

Job Physical Exposure Assessment Team

Health Outcomes Assessment Team

Baseline • Interview (worker, work organization and work practice data) • Interview, observation, measurements, video analysis (job physical exposure data)

Baseline • Questionnaire (demographics, psychosocial data, hobbies & physical activities) • Structured Interview (medical history including LBP history) • Physical Examination (palpation, maneuvers, physician diagnoses)

Quarterly Follow-up • Worker Interview • Re-collect data if significant changes in job occurred

Monthly Follow-up • Questionnaire (LBP outcomes) • Physical Examination (LBP outcomes)

Figure 3.1: Progression of data collection in the parent study (adapted from Boda, S., Bhoyar, P, & Garg, A., 2010)

58

The Job Physical Exposure Assessment team collected baseline job physical demands, work history, work organization/work practice data and conducted quarterly follow-up of subjects if there were significant changes in job physical exposures.

The Health Outcomes Assessment team collected baseline information on demographics, anthropometry, LBP history and other medical history, hobbies, physical exercises, psychosocial information and physical examination performance using a questionnaire, structured interview and physical examination (not used in current study). The team also conducted monthly follow-up of subjects to monitor incidence (new event) of occupational LBP health outcomes and the status of prevalent LBP cases via interview and physical examination. Methods employed to collect data from baseline to end of the study are shown in Figure 3.2.

mms

1 MONTH

QJFU

Baseline Questionnaire Baseline Structured Interview Baseline Physical Exam Baseline Job Physical Exposure Assessment Monthly Follow-Up for LBP Status Changes Quarterly Job Follow-Up

Figure 3.2: Data collection methods from baseline (enrollment) to end of study

59

3.1.2 Study Subjects The study population consisted of industrial workers employed in 30 diverse companies in four states (Wisconsin, Illinois, Texas and Utah) in the Continental US. Workers were contacted at these facilities and enrolled by members of the research teams. Table 3.1 shows participation rates (percentage of workers enrolled, from those invited to participate) for all companies. The overall participation rate was 72% (897 enrolled of 1240 invited).

Subjects were enrolled without discrimination based on gender, race, ethnicity, physical or mental disorders in the parent study and were excluded if they: (i) were aged less than 18 or greater than 65 (unless they were not retiring within 3 years) at enrollment, (ii) were able to speak English or Spanish and give informed consent, (iii) were not planning retirement within 3 years of study enrollment, (iv) did not have a major congenital spine, lower limb or upper limb deformity or any major impairment, (v) did not have a history of spinal fracture or arthritis of the spine, (vi) were not diagnosed with current sciatica at baseline by a study-appointed physician, (vii) did not have a history of one or more low back surgical procedures (fusion, laminectomy, laminotomy, disk removal, bone-spur removal), (viii) did not perform non-quantifiable work (such as maintenance or mechanic work) or, (ix) did not perform manual material handling work.

60 Table 3.1: Worker participation in parent study Invited

Number of Workers Enrolled Consented

Participation Rate (%)

(A)

(B)

(C)

(C/A)xl00

Poultry processing

31

27

26

84

Metal automotive parts manufacturer (2 plants)

22

22

22

100

Repackaging operations

18

13

12

67

Plastic parts manufacturer

62

37

33

53

Metal parts manufacturer

21

17

14

67

Salt manufacturer

21

18

17

81

Soft drink distributions

5

5

5

100

Paint products manufacturer

50

15

13

26

Electric light manufacturer, warehouse

44

44

44

100

Lawnmower manufacturing

27

22

20

74

Small- engine manufacturer

18

18

18

100

Glass window and door manufacturer

43

21

20

47

Toilet seat manufacturer

68

45

42

62

Printing operations

95

43

41

43

Print and distribution center

217

216

216

100

Airbag manufacturer

49

46

46

94

Department of Alcoholic Beverage Control

5

5

5

100

Cabinetry manufacturer

3

3

3

100

Aluminum extruded products manufacturer

30

28

28

93

State & Employment Setting Wisconsin

Illinois

Utah

Garage door manufacturer

16

16

16

100

Medical technology manufacturer

69

65

63

91

Meat processor

70

41

38

54

Ice-cream manufacturer

4

2

2

50

Bakery

15

11

8

53

Grocery warehouses (3 warehouses)

161

95

86

53

Office chair manufacturer

22

20

20

91

Medical supply distributor

6

5

5

83

Salt manufacturer

19

15

13

68

Chemicals manufacturer

11

8

8

73

Cosmetics manufacturer

18

14

13

72

1240

937

897

72

Texas

Total

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3.1.3 Baseline and Follow-up Job Data - Description Work history and work organization/work practice data were collected by worker interview and occasionally, analyst observation. Job physical demands/exposure data were collected via measurement, analyst observation and videotaping. Job data collection forms used in the parent study are provided in Appendix A. Figure 3.3 illustrates the levels of job data that were collected. Most workers who participated in the study (-80%) did not rotate jobs (performed only one job).

In the parent study, a 'job' or 'job cycle' referred to a specific and unique set of activities (called tasks) performed by the worker for a certain number of hours in a given day or week. A 'task' was defined by a unique set of job physical demand variables such as task type (lift, lower, push, pull, walk or carry), object weight, horizontal distance of hands (in front of the body) from the center of the feet, vertical height of hands from the floor, travel distance (between origin and destination of the task), task frequency, and grasp. 'Cycle time' was defined as the time required for completing one job cycle.

Job 1

Task 2

Task 1

Worker

Job 2

Task 2

Task 3

Job 3

Figure 3.3: Job data hierarchy

Task 1

62

Position-specific data (Appendix A.l) included work-shift and individual job descriptions (including average and maximum hours worked, percentage of work-shift spent on each job and work organization information such as shift type), work history and information on 'second job' i.e. secondary employment outside the facility visited in the study.

Job specific data (Appendix A.3) included information on manual materials handling, assembly, paperwork, fork-truck driving and resting/waiting, exposure to whole body vibration, typical pace of work (line, piece-rate or self-paced) and work practice information such as the use of back-belts, insoles and anti-fatigue mats.

Job physical exposure data were collected for lifting, lowering, pushing and pulling tasks. Objective job physical exposure measures included: (i) object weights (using digital platform scale), (ii) pushing and pulling forces (using force gauge, model # CSD250, manufactured by Chatillon), (iii) horizontal and vertical hand locations from the center of feet for each hand (using tape measure), (iv) push/pull distances (using rolling tape measuring device), and (v) duration of task activities (using digital stopwatch).

For lifting/lowering tasks, task type (lift or lower), object/box weight, vertical location of hands from floor and horizontal distance of hands from the center of feet at the origin (beginning) and destination (end) of a task and grasp type were recorded. For pushing/pulling tasks, task type (push or pull), initial and sustained force required to

63

push/pull the object/cart, vertical location of hands, distance pushed/pulled and time taken to complete the task were recorded.

All jobs performed by workers were recorded real time on digital videotape using a hand-held video camera. Video was taken perpendicular to the sagittal plane, such that parallax error was minimized. Each job was recorded for a minimum of three cycles or 20 minutes. At least one complete job cycle was recorded for jobs with cycle times longer than 20 minutes. All workers in the same department were videotaped for 20-30 minutes each, for jobs with high variability in job physical demands (e.g. warehousing or distribution).

Some workers underwent significant job changes (changes in physical exposures such as object weight/applied force, hand locations, task frequency, number of hours) during the follow-up period. These changes were also recorded by the job physical exposure assessment team.

3.1.4 Baseline Individual and Psychosocial Data - Description Individual and psychosocial data were collected via a computerized questionnaire (Appendix B.l) and physical examination (Appendix B.3) administered at baseline. The questionnaire included questions on: (i) demographics, (ii) hobbies and physical exercises outside of work, (iii) smoking and alcohol use, (iv) physician-diagnosed medical conditions (e.g. diabetes mellitus, high cholesterol and family history of lumbar

64

radiculopathy), (v) gynecological health, (vi) back pain, (vii) psychosocial/psychological information and (viii) employment.

Psychosocial information included questions on (i) work satisfaction - using a modified work APGAR scale (Bigos, et al., 1991), (ii) depression - using a modified version of the Zung depression scale (Zung, 1965) and (iii) feelings of tension, edginess and nervousness (Garg, Hegmann & Moore, 2008) among other questions.

Three composite scores for the aforementioned psychosocial scales were computed. A numeric score was assigned to every individual question within each scale. The 'composite' score was computed as the sum score of all individual question scores for each scale. The seven work APGAR scale questions were answered using a three-point scale (almost always - 0, some of the time - 1, hardly ever - 2) and resulted in a composite score ranging of 0-14. The eight Zung depression scale questions were answered using a four-point scale (never - 0, sometimes - 1, often - 2, always - 3) and resulted in a composite score ranging 0-24. The tense-edge-nervous scale comprised of three questions answered on a four-point scale (never - 0, sometimes -1, often - 2, always - 3) and resulted in a composite score ranging 0-9. The lowest score (0) on a scale indicated no psychosocial stress and the highest score indicated highest psychosocial stress for each scale.

A physical examination (Appendix B.3) of subjects was conducted by occupational/ physical therapists and physicians using standardized procedures. This examination

included heart rate, blood pressure, anthropometry (height, weight and various body segment measurements), range of motion, palpation and maneuver tests and physician diagnoses of low back pain and sciatica.

3.1.5

Baseline LBP Data - Description

Information on past and present LBP was obtained via a Structured Interview at baseline (Appendix B.2). The interview addressed history of diagnosed musculoskeletal conditions (e.g. osteoarthritis, bulging disc), injuries/accidents and surgeries, detailed history of pain and assessment of current pain (today or in the past month) in the lower back among other topics.

Questions on lifetime history of low back pain included: single longest duration of pain, worst pain ever (on a pain scale of 0-10), duration of most recent episode of pain, age when pain lasting greater than 3 or 7 days was first experienced, number of episodes of pain lasting at least 3 and/or 7 days and their work-relatedness, treatments, diagnostic examinations and health care provider visits, missing work (lost duty), being placed on light/restricted duty and changing jobs because of pain.

Questions on current LBP i.e. pain at baseline included presence, intensity (0-10), total duration and accident-relatedness. Further, there were questions which inquired about sciatica related symptoms (e.g. numbness and tingling in the lower back traveling down into legs) and receiving workers compensation for missing work due to a work-related

66

injury to the back or any other body part. Lastly, workers with pain at baseline were required to mark the areas of pain on a low back pain diagram.

3.1.6 Follow-up Assessment of LBP Health - Description Prospective changes in LBP status were monitored every month via a computerized questionnaire (Appendix B.4) administered to subjects. The questionnaire recorded the date and time of the last visit and included questions regarding previous pain or numbness/tingling, new pain, health care provider visits, taking treatments and missing work or being placed on light/restricted duty due to new LBP. Subjects were also asked to fill out a 'low back pain diagram' indicating all areas of LBP and the region of worst pain.

3.2

Methodology of Current Research

3.2.1 Specific Aim 1 - Determination of Study Cohort Data obtained from the parent study for the current research were inspected for errors prior to being used in the current research. Errors in data entry were corrected by checking against the original paper forms and confirmed by discussions with research team members of the parent study. Videotapes of the jobs were analyzed in order to verify job physical demands data collected on field in the parent study (discussed later).

Workers with complete baseline data on job physical exposures, LBP history and LBP health follow-up for at least one month were considered for inclusion in the study cohort. Of these, only workers with a history of LBP and at least a 90 day LBP-free period prior to baseline (Norman et al., 1998; Kerr et al., 2001) were included in the cohort since occupational LBP patients are considered to require at least 12 weeks (-90 days) to recover functional performance (Ferguson, 1998).

Workers without history of LBP prior to baseline (virgin cohort) or those with a history of LBP and an LBP-free period less than 90 days prior to baseline were excluded (i.e. considered ineligible to become a case). Some workers who experienced LBP within 90 days prior to baseline became pain free for at least 90 days during the follow-up period. However, these workers were not included in the study cohort as past epidemiological studies have also excluded this group (Hoogendorn et al., 2002; Jarvik et al., 2005; Power et al., 2001; Van Niewenhuyse et al., 2006).

68

Figure 3.4 illustrates the process to determine the study cohort from the parent study population. The study cohort determined using this process comprised 130 subjects with complete job physical exposure and LBP history data, at least one monthly LBP health follow-up and were LBP free for at least 90 days prior to baseline.

69

Total Enrolled at Baseline (Parent Study)

n = 897

Passed exclusion criteria at Baseline?

NOT Missing Baseline Data?

n = 537

NO

NO

Baseline Exclusions (55) Health Major congenital spine, lower limb or upper limb deformity or any major impairment (1) Diagnosis of current sciatica at baseline (21) Low back surgeries (15) Job Non-quantifiable work (maintenance/mechanic work, 5) No manual material handling activities (13)

Missing Baseline Data (305) • Missing job physical exposure data at baseline (158) • Missing data to determine LBP history at baseline (147)

YES

60% Have 2 1 Month LBP Follow-up Data?

n = 514 57%

Do not have 2 1 Month LBP Follow-up Data (23)

YES

LBP free for at least 90 days at Baseline

n = 258 29%

NO

NO

LBP within 90 days of Baseline (256) Became 90-day LBP free during follow-up (146) Never became 90-day LBP free during follow-up (110)

YES

Prior history of LBP (> 90 days before baseline)

NO No history of LBP prior to baseline (128)

YES Study Cohort 90-day pain free cohort

n = 130 (14%) Figure 3.4: Procedure to determine 90-day pain-free cohort (study cohort), n and percent of total enrolled at baseline in parent study are shown at each stage

70

3.2.2 Specific Aim 2 - Quantification of Job Physical Exposures 3.2.2.1 Initial Assessment of Job Physical Exposures at the Task Level Job physical exposure data at the task level were extracted from videotapes and verified against original paper forms for all manual material handling activities. For lifting/lowering tasks, hand locations (horizontal location from the center of feet, vertical height from floor and lateral location from the plane perpendicular to shoulders) for both hands at origin and destination of task, trunk twisting angle at origin and destination of task, vertical travel distance from beginning to end of lift/lower, lifting/lowering frequency, grasp (good, fair, poor) and use of separate hands (yes/no) were extracted. For pushing/pulling tasks, hand locations for each hand at origin, use of separate hands (yes/no), pushing/pulling frequency and duration were extracted. In addition to the above task level data, cycle time for each job was also extracted from videotape analysis.

Hand locations that were measured in the field and recorded on forms were used for further analyses. However, when hand locations estimated from videotapes did not concur (difference greater than 15 cm) with those in the forms, estimates from videotape analysis were used instead of field measurements. It was recognized that these differences could have resulted when it was not possible for analysts to measure hand locations accurately due to space/time constraints or due to data entry errors in the parent study.

71

Snapshots of the videotape were taken for each: (i) lifting/lowering task at the beginning and end of the task, and (ii) pushing/pulling task at the beginning of the task. These snapshots were used to obtain postural data (body joint angles) for 3D biomechanical analysis (discussed later).

A significant number of jobs (~30%) were performed in distribution centers and warehouses where day-to-day job physical exposures (characterized by weight, horizontal and vertical hand locations, travel distance, trunk twisting angle and frequency) varied greatly. Thus, videotape recordings of a single worker's job were only able to capture some of the variability of the actual job performed. In such cases, tasks performed by all the workers in a group (workers of the same gender, in the same department and company) were pooled to form a 'super cycle'. All tasks contributing to this super-cycle were assigned to all workers in the affected group.

3.2.2.2 Computation of 'Job Metrics' at the Task and Job Levels Job physical exposures were quantified using lifting index, back compressive force, strength requirement and load moment. These measures will be referred to as 'job metrics'.

Single task lifting indices (at origin and destination) for each lifting/lowering task and a composite lifting index for each job performed by a worker were computed using the methodology prescribed by Waters et al. (Waters, T.R., Putz-Anderson, V., Garg, A., 1994), with the following exception. The NIOSH lifting index was developed to design

72

jobs and not necessarily to determine risk for LBP. Thus, job physical demand variables in the original method were capped and multipliers for the variables were set to zero in order to enforce design limits. However in the current research, the multipliers were not set to zero but capped at the design limits in order to obtain an estimate of LBP risk beyond the design limits.

Back compressive force and strength requirement were estimated using the 3D Static Strength Prediction Program i.e. 3DSSPP (University of Michigan, 1997), version 5.0.8. Average anthropometry (50th percentile) and gender were specified for each worker. Body posture (limb and trunk angles) for each task performed by a worker was simulated in the program using the snapshots from videotape. Hand loads/forces for each hand were also inputted. If the lifting/lowering was two-handed, the hand load for each hand was inputted as half the object weight. For single-handed lifting/lowering tasks, the hand load was inputted as the total object weight for the hand moving the object and as zero for the other hand. Similarly, for pushing/pulling tasks hand force was inputted as half the initial push/pull force for each hand if two-handed or, as total initial push/pull force for the hand moving the object and zero for the other hand if onehanded. Back compressive force at the L5/S1 joint and minimum percent population capable of performing the task with respect to hip or torso muscle groups (measure of strength requirement) were obtained from the program's output. These metrics were estimated at both origin and destination for lifting/lowering tasks and only at origin for pushing/pulling tasks.

73

Load moment was computed as the simple product of object weight and moment arm at the L5/S1 joint. Moment arm was determined using hand locations from the center of feet (field measurements/video analysis estimates) and the estimated distance between center of feet and the L5/S1 joint as determined by 3DSSPP. Load moment was computed at both origin and destination for each lifting/lowering task.

3.2.2.3 Assigning Job Physical Exposures at Worker Level Job physical exposures quantified at the task and job levels were assigned to the worker level in order to facilitate future statistical analysis (discussed in section 3.2.5).

Traditionally, (i) peak/maximum exposure (Herrin et al. 1986, Marras et al., 1993), (ii) simple average (Marras et al., 1993), (iii) time-weighted average (Frazer et al., 2003, Mirka et al., 2000), (iv) frequency-weighted average (NIOSH 1981, Chen et al., 2003; Funakoshi et al., 2004) and (v) cumulative exposure (Norman et al., 1998; Kerr et al., 2001) approaches have been used to quantify exposures at the job and/or worker level.

These approaches, are believed to either underestimate or overestimate job physical exposures (Dempsey, 1999; Garg et al., 2004; Garg, 2006; Garg and Kapellusch, 2009), and may erroneously classify unsafe jobs as safe jobs and vice-versa (Garg and Kapellusch, 2009). An appropriate method for assigning job physical exposures at the worker level has not been clearly identified in literature. Some studies suggested that physical exposure when quantified as peak exposure at the job level was the best discriminator of LBP risk in the workplace (Herrin et al., 1986; Marras et al., 1995).

74

In the current research, peak and typical (Garg and Kapellusch, 2009) exposure methods were used to quantify job physical exposures at the worker level. Typical exposure is defined as the exposure from the job that is performed for the longest duration of a work-shift and is a novel approach in LBP research. Figure 3.5 shows examples of how peak and typical exposures were determined when there was a tie. Worker

Peak CLI = 4 Typical CLI = 2

Job 1

Job 2

5 hours, CLI = 2

2 hours, CLI = 4

Typical job

Peak Job

Job 3

1 hour, CLI = 1

Worker


Job 1

5 hours, CLI = 4

Job 2

2 hours, CLI = 4

Typical and Peak Job

Job 3

1 hour, CLI = 1

Worker


Job 1

Job 2

4 hours, CLI = 2

4 hours, CLI = 4 Typical and Peak Job

Figure 3.5: Sample jobs performed by a worker illustrating peak and typical exposure methods (adapted from Garg et al., 2010). (a) Job 1 is performed for the longest duration in the work-shift, thus typical Composite Lifting Index (typical CLI) from all jobs is assigned from Job 1. Job 2 is the job producing the highest Composite Lifting Index, thus peak CLI is assigned from this job (b) Job 1 is performed for the longest duration in the work-shift; therefore typical CLI is assigned from this job. Both jobs 1 and 2 produce the maximum CLI, therefore peak CLI can be assigned from either job (c) Jobs 1 and 2 are performed for the same duration in a work-shift. However, job 2 produces the maximum CLI; therefore typical and peak CLI are assigned from this job.

Variables resulting from quantification of job physical exposures at the worker level using peak and typical task or job level exposures are provided in Table 3.2. Six variables were used to quantify peak and typical physical exposures resulting from lifting/lowering only. Two additional variables were used to quantify peak physical exposures resulting from lifting, lowering, pushing or pulling.

3.2.3 Specific Aim 3 - Assessment of Individual and Psychosocial Risk Factors Individual and psychosocial data were only available for baseline from the parent study. Only select individual and psychosocial data collected in the parent study were used in the current research after inspection. Other data were believed to be insufficient (for analysis) or questionable as per discussions with data collection teams of the parent study. Individual data used included age, gender, body mass index (BMI), hobbies, physical activities outside of work, LBP history and other medical history. Psychosocial data used included only the three composite psychosocial scores.

76 Table 3.2: List of variables quantified at the worker level Worker Level Variable

Level obtained from /computed at

Source

Quantification method used at worker level

INDIVIDUAL JOB PHYSICAL EXPOSURE VARIABLES (only for descriptive purposes, no analyses performed) From lifting/lowering only

Maximum weight

Field measurement

Taskt

Peak

Maximum horizontal distance of hands from L5/S1

Field measurement/ Video analysis

Taskt

Peak

Maximum vertical height of hands from floor


Taskt

Peak

Minimum vertical height of hands from floor


Taskt

Peak

Maximum trunk flexion angle

Video analysis

Taskt

Peak

Maximum trunk twisting angle

Video analysis

Taskt

Peak

Lifting/lowering frequency

Video analysis

Taskt

Peak

Peak initial push/pull force

Field measurement

Taskt

Peak

Peak sustained push/pull force

Field measurement

Taskt

Peak

Maximum push/pull frequency

Video analysis

Taskt

Peak

Maximum push/pull distance

Field measurement

Taskt

Peak

From pushing/pulling only

JOB METRICS (Analyses performed to test a priori hypotheses) From lifting/lowering only

Peak Single Task Lifting Index (PSTLI) Peak Composite Lifting Index (PCLI)

Calculated

Taskt

Peak

Calculated

Job

Peak

Typical Composite Lifting Index (TCLI) Maximum back compressive force at L5/S1 joint (BCFLL)

Calculated

Job

Typical

Obtained from 3DSSPP

Taskt

Peak

Minimum percent population capable at hip/torso (MPCLL)


Taskt

Peak

Calculated

Taskt

Peak

Maximum back compressive force at L5/S1 joint (BCFALL)


Taskt

Peak

Minimum percent population capable at hip/torso (MPCALL)


Taskt

Peak

Load moment at L5/S1 joint (PLM) From tifting/lowering/pushing/pulling

tMaximum of exposure from origin and destination was assigned to the task level for lifting/lowering tasks.

Table 3.3 lists individual and psychosocial variables that were used in the current research to investigate their potential confounding effect on the relationship between quantified job physical exposures and occupational LBP outcomes. Though all workers in the study cohort had a history of LBP, a new binary variable to represent more severe LBP history was created in order to study its relationship with future occupational LBP. This variable termed "Combined LBP history" was coded as 'Yes' if a worker had ever used medication, had lost workdays, were placed on light/restricted duty relating to LBP, received workers compensation for back or had LBP traveling down one leg into the calf, or 'No' otherwise.

Certain missing individual and psychosocial data were imputed using techniques enlisted in Table 3.4. Missing data on demographics, other medical history, hobbies and physical exercises outside of work were not imputed. Subject (worker) height and weight were used to compute BMI and were not used otherwise in the current study.

78 Table 3.3: Individual and psychosocial variables studied for potential confounding effect Variable group Variable Demographics

Age Gender Race Education Marital Status Tobacco use Alcohol use

Anthropometry

BMI

LBP history (lifetime prevalence, NOT at baseline)

Combined LBP history*

Other medical history (diagnosed by a physician)

High cholesterol

Psychosocial factors

Job satisfaction (Modified APGAR composite score) Depression (Modified Zung composite score) Tense-edge-nervous composite score

Physical exercises outside of work

Any exercise Walking Aerobics Bicycling Running Swimming Weightlifting Basketball Baseball

Hobbies

Any hobby Housework Gardening Maintenance Woodwork Remodeling Snow shoveling

Motorcycle/ATV * LBP related medication use, healthcare provider visit, light/restricted duty, lost workdays, workers compensation or LBP traveling down leg into calf.

79 Table 3.4: List of imputed job, individual and psychosocial variables Variable Method of Imputation

Number of workers affected (% of study cohort, n = 130)

Individual variables

Subject height /stature

1

2(2%)

Subject weight

1

1 (1%)

Composite modified APGAR (job satisfaction) score

2

3 (2%)

Composite modified Zung (depression) score

2

9(7%)

Composite Tense-edge-nervous score

2

9 (7%)

Psychosocial variables

1 - Gender-specific mean from workers in the same company 2 - Median from workers in the same company

3.2.4 Specific Aim 4 - Determination of Incident Cases/Non-cases and Incidence Times for Occupational LBP Outcomes Occupational LBP was defined as self-reported pain of any intensity lasting at least one day in any of the five lumbosacral areas (L-left lumbar, M-immediately paraspinal, Nright lumbar, O-left gluteal and P-right gluteal) that did not occur as a result of something outside of work or an accident as per worker self-report.

The prospective design employed in this research allows measurement of risk using incidence. Incidence is defined as the number of new cases that have occurred during a given time period divided by the population at risk at the beginning of the time period (Dawson & Trapp, 2001). Incident cases/non-cases and time-to-events were determined for each occupational LBP outcome for every worker using monthly follow-up data.

Occupational LBP health outcomes for each worker were determined from monthly follow-up data. Cases and non-cases for the four occupational LBP health outcomes

80

were determined using case definitions provided in Table 3.5. A worker was considered a case for any of the four occupational LBP health outcomes if he/she met these case definitions and a non-case if otherwise. Table 3.5: Case definitions for occupational LBP outcomes Occupational LBP outcome 1.

Occupational LBP of any intensity lasting at least one day (OLBP-A)

Case Definition i.

ii. 2.

Occupational LBP with medication use (OLBP-M)

i.

ii.

3.

Occupational LBP resulting in visits to a health care provider (OLBP-H)

Occupational LBP resulting in light/restricted duty, or lost workdays (OLB-L)

Low Back Pain (LBP) of any intensity on a structured interview (areas L, M, N, 0, or P) lasting at least one day, Reported cause of LBP is NOT somethine outside of work or an accident (at work or outside of work) and

iii.

Reported taking over the counter (OTC), prescription, narcotic, non-narcotic or non-steroidal anti inflammatory (NSAID) medication for LBP.

i.

Low Back Pain (LBP) of any intensity on a structured interview (areas L, M, N, O, or P) lasting at least one day, Reported cause of LBP is NOT somethine outside of work or an accident (at work or outside of work) and

ii.

4.

Low Back Pain (LBP) of any intensity on a structured interview (areas L, M, N, O, or P) lasting at least one day and Reported cause of LBP is NOT somethine outside of work or an accident (at work or outside of work).

iii.

Reported seeking a health care professional such as a medical doctor (MD), chiropractor, physical therapist or massage therapist,

i.

Low Back Pain (LBP) of any intensity on a structured interview (areas L, M, N, O, or P) lasting at least one day,

ii.

Reported cause of LBP is NOT somethine outside of work or an accident (at work or outside of work) and

iii.

Reported taking sick leave (lost workdays) or being placed on modified/restricted duty at work due to LBP.

The time unit of analysis in this study was one day. Workers who reportedly developed non-occupational LBP during the study due to an accident or an event outside of work (treated as non-cases) were censored one day prior to the injury. Workers who dropped out of the study contributed person-time data to the analyses until the day they dropped

81

out (Kaplan, 1958). Time-to-event (Incidence time data) definitions for each occupational LBP outcome for cases and non-cases are provided in Table 3.6.

Table 3.6: Time-to-event Definitions for Cases/Non-cases for Occupational LBP Outcomes Occupational LBP outcome

1.

2.

3.

4.

Occupational LBP of any intensity lasting at least one day (OLBP-A)

Occupational LBP with medication use (OLBP-M)

Occupational LBP resulting in visits to a health care provider(OLBP-H)

Occupational LBP resulting in modified/restricted duty, or lost workdays (OLBP-L)

Case/Non-case

Time-to-event definition

Case

Time from baseline/enrollment till the reported start date of the first occupational LBP episode during follow-up.

Non-case

Time from baseline/enrollment till the worker's exit, end of study or, the day before the report of non-occupational LBP episode during follow-up.

Case

Time from baseline/enrollment till the time* the first occupational LBP related medication use was reported during follow-up.

Non-case


Case

Time from baseline/enrollment till the time* the first occupational LBP related healthcare provider visit was reported during follow-up.

Non-case


Case

Time from baseline/enrollment till the first occupational LBP related lost workdays* OR modified duty/restricted duty placement were reported during follow-up.

Non-case


* Exact start dates of medication use, health care provider visits or lost workdays were not available from monthly follow-up data. Therefore an approximate start date was estimated as (i) the mid-point date between the start date of the related occupational LBP episode and the date of the monthly follow-up when medication use/healthcare provider visit/lost workdays were reported (if occupational LBP occurred in the first half of the same month as medication use/healthcare provider visit) or, (ii) the day when the related occupational LBP episode was reported (if occupational LBP occurred in the second half of the same month as medication use/healthcare provider visit/lost workdays) or, (iii) as the mid-point date between the date of monthly follow-up when medication use/health provider visit/lost workdays were reported and the date of the previous month's follow-up (if medication use/healthcare provider visit were reported in other monthly follow-ups succeeding the first report of occupational LBP).

Many workers in the study cohort experienced more than one episode of occupational LBP during the follow-up period. Occupational LBP recurrence information was available from the parent study. However, the current research only considers the

82

conservative analysis of time to first occupational LBP event during follow-up, following an LBP- free period of at least 90 days prior to baseline (enrollment).

3.2.5 Specific Aim 5 - Determination of Relationships between Quantified Job Physical Exposures and Occupational LBP Outcomes/Statistical Analysis All statistical analyses were performed using R (version 2.13.2). Baseline descriptive statistics were computed for all variables enlisted in Tables 3.2 and 3.3. Lifetime prevalence of taking medications, seeking a healthcare provider and having lost workdays or being placed on light/restricted duty because of LBP were calculated. Prevalence was computed by dividing the number of workers reporting LBP symptoms (for a given occupational LBP outcome) by the total population of the study cohort. Incidence rates (number of new cases per 100 full-time workers per year) for each occupational LBP health outcome were also determined.

The Cox proportional hazard regression model (Cox, 1972) was used to analyze relationships between LBP health outcomes and independent variables (job metrics, individual and psychosocial factors). This statistical method is commonly employed for analysis of survival data (time data till the occurrence of an event) in clinical studies and has also been used recently in LBP research (Alexopoulos et al., 2008; Brage et al., 2007; Jarvik et al., 2005). Risk for an occupational LBP outcome was measured using hazard ratio, defined as the relative risk of a condition (here LBP) based on comparison of event rates (hazard rates) between two groups (Spruance, Reid, Grace & Samore, 2004).

83

3.2.5.1 Univariate Analyses - Relationships between Quantified Job Physical Exposures and Occupational LBP Outcomes Unadjusted univariate hazard ratios and 95% confidence intervals were computed for quantified job physical exposures/job metrics (table 3.2) using Cox proportional hazard regression with time-varying covariates (to account for changes in job physical exposures during this study).

Job physical exposures listed in Table 3.2 were treated as continuous variables. However, the continuous range of a variable was categorized in order to identify safe limits (cut-points) of job physical exposures and provide occupational LBP risk estimates above and below these limits. Two approaches used in the current research to study occupational LBP risk for different levels (doses) of job physical exposures are described as follow. 1. Testing literature-suggested limits for job metrics (Table 3.7): Research relating to the development of the 1981 and 1991 NIOSH lifting equations, suggests limits for lifting index and back compressive force. Snook (1978) also studied maximum acceptable limits for manual handling tasks (another measure of strength capability) and concluded that workers were three times more prone to low back injury if performing a manual handling task that was acceptable to less than 75% of the working population. However, there is limited evidence of association between risk of occupational LBP and suggested limits. Therefore this research aimed to investigate the relationship between job metrics and low back pain for these literature-suggested cut-points. Unadjusted

hazard ratios were computed for each category using the lowest exposure group as the reference. Table 3.7: Literature-suggested limits (cut-points) for job metrics

Job Metric Lifting Index Back compressive force Strength requirement (Minimum percent population capable)

Cut-points (Source) 1.0 (NIOSH, 1981; Waters et al., 1993) 3400 N (NIOSH, 1981; Waters et al., 1993) 75% (Snook, 1978t)

tStrength requirement measures in Snook study are maximum acceptable weights and forces

2. Post-hoc analyses of iob metrics: Post-hoc analyses were performed to categorize job metrics based on their functional form. Smoothed Martingale residual plots were used to study the functional form of how each metric was entered into the Cox regression model (Therneau, Gramsch & Fleming, 1990). Loess smoothing curve (Ruppert, Wand & Carrol, 2003) with a span of 0.5 was used for smoothing. The plots were first divided into quantiles (based on number of cases). The job metric was dichotomized at the quantile which visually appeared to maximize the difference in risk between the two categories. The quantiles were chosen such that at least five cases were ensured in each dichotomized category of the job metric. Unadjusted hazard ratios were then computed for each category using the lower exposure group as the reference.

85

3.2.5.2 Univariate Analyses - Relationships between Individual, Psychosocial Factors and Occupational LBP Outcomes Unadjusted univariate hazard ratios and 95% confidence intervals were computed for individual and psychosocial variables (collected at baseline). These variables were treated as time-independent covariates. Continuous variables age, BMI, Zung and the three composite psychosocial scores (Modified APGAR, modified Zung and Tenseedge-nervous) were dichotomized to determine risk in each category using the same procedure as described above (see post-hoc analyses of job metrics).

3.2.5.3 Multivariate Analyses - Relationships between Quantified Job Physical Exposures and Occupational LBP Outcomes Relationships between quantified job physical exposures and occupational LBP outcomes were determined using the following procedure. 1. Identifying significant covariates: A covariate was selected for inclusion into the multivariate model if it was (i) biologically meaningful in terms of direction of risk and (ii) the p-value of the likelihood ratio test from univariate analysis was < 0.05 (for OLBP-A and OLBP-M outcomes) or < 0.2 (for OLBP-H and OLBP-L). All covariates meeting these criteria were then tested for co-linearity using Spearman's rank correlation test. If two variables were correlated (r2>0.8), then the variable with lower pvalue for the likelihood ratio test in univariate analyses was selected for inclusion in the multivariate model. All variables were selected for inclusion if no correlation was observed.

86

2. Building a multivariate model using significant covariates: Significant covariates selected from step 1 (above) were entered into an initial multivariate model for each occupational LBP outcome. A step-wise backward selection process based on p-value from the Wald test was used to eliminate covariates from the initial multivariate model. Additionally, the Akaike Information Criterion (AIC) score (Akaike, 1974) was used to check the impact of removal. Variables were removed in sequence from the model based on the highest remaining p-value of variables in the model. The reduced model's AIC score was compared to the AIC score of the full model. The variable was permanently removed if the reduced model had a lower AIC score, otherwise it was retained and the variable with the next highest p-value was removed. The final multivariate-covariate model was one with least AIC score (Gharibvand, 2008). 3. Building a final multivariate for each job metric with significant covariates: Final multivariate models were developed only for those job metrics whose pvalue for the likelihood ratio test from univariate analyses was < 0.2. For each occupational LBP outcome, a final multivariate model was constructed by adding a job metric into the final covariate model determined in step 2. If the pvalue from the maximum likelihood ratio test for the job metric was < 0.05, the association between the job metric and the occupational LBP outcome was considered statistically significant while adjusting for covariates. Otherwise, insufficient evidence of association between the job metric and the outcome was concluded.

87

4.

RESULTS

4.1

Descriptive Statistics for Job, Individual and Psychosocial Data at Baseline

Baseline data on the study cohort (130 subjects) were collected between February 2004 and August 2005. Subjects were followed for a total of 156 person-years. The maximum follow-up period was 4.5 yrs during which decrease in participation was primarily due to job loss (lay-off) or close of companies. Summary statistics of follow-up times for cases and non-cases for OLBP-A outcome are provided in Table 4.1.

Table 4.1: Descriptive statistics of follow-up time for total cohort, cases and non-cases for OLBP-A outcome Follow-up time (years)

n

Mean

SD

Minimum

Maximum

Total cohort

130

1.2

1.0

0.1

4.5

Cases

60

0.7

0.5

0.1

2.5

Non- cases

70

1.2

1.1

0.1

4.5

Figure 4.1 shows the mean follow-up times in person-years for cases and non-cases (OLBP-A outcome) for every 6 months of follow-up. This indicates that the mean follow-up time for cases increased at a higher rate than non-cases and that total followup time for cases was less than that for the non-cases.

Descriptive statistics of demographics, anthropometry, LBP and other medical history, psychosocial factors, physical exercises outside of work and hobbies at baseline are summarized in Table 4.2. Other individual and psychosocial variables with a small sample size (•

|500 52 Of

^ 400

i» 5 0

£ 200 c

31.6 kg/m2

48 (42) 12(28)

1.00 0.49

0.26 - 0.92

0.031

No

23 (43)

1.00

-

-

Yes

37 (27)

2.38

1.41 -4.01

o.ooitt

-

-

0.48

0.39

Anthropometry

BMI

-

-




45 (59) 1.00 No 15(11) 0.70 1.12 0.63 - 2.01 Yes *LBP related medication use, healthcare provider visit, light/restricted duty, lost workdays, workers compensation or LBP traveling down leg into calf. tSignificant variables selected for inclusion into initial covariate model. JSignificant variables that remained in final covariate model. High cholesterol

94 Table 4.4 (contd.): Univariate hazard ratios for individual and psychosocial variables for OLBP-A outcome p-value 95% (Wald Confidence Cases Hazard Category Variable (non-cases) Interval test) Ratio Psychosocial factors

Job satisfaction (modified APGAR composite score)

5.5

30 (50) 30 (20)

1.00 2.04

1.23-3.40

0.006ti

Depression (modified Zung composite score)

< 10 > 10 4

15(9)

2.58

1.43-4.68

0.002tt

Any exercise

No Yes

21 (25) 39 (45)

1.00 1.32

0.77 - 2.34

0.31

Walking

No Yes

25 (25) 35 (45)

1.00 0.78

0.47 - 1.30

0.34

Aerobics

No Yes

56 (65) 4(5)

1.00 0.88

-

-

0.32 - 2.42

0.80

Bicycling

No Yes

50 (52) 10(18)

1.00 0.77

0.39-1.51

0.44

Running

No Yes

53(61) 7(9)

1.00 1.17

0.53 - 2.58

0.69

Swimming

No Yes

54 (63) 6(7)

1.00 1.44

0.62 - 3.35

0.40

Weightlifting

No Yes

40 (57) 20(13)

1.00 1.95

1.14-3.35

Baseball

No Yes

59 (63) 1 (7)

1.00 0.20

-

-

0.03 - 1,44

0.11

No Yes

54 (56) 6(14)

1.00 0.63

-

-

0.27-1.45

0.28

Any hobby

No Yes

1.00 1.19

0.51 -2.75

Housework

No Yes

6(10) 54 (60) 19 (24) 41 (45)

1.00 1.16

-

-

0.67- 1.99

0.60

No Yes

32 (31) 28 (39)

1.00 0.77

0.47-1.28

Maintenance

No Yes

46 (54) 14(16)

1.00 1.09

Woodwork

No Yes

56 (63) 4(7)

1.00 1.06

0.38 - 2.94

0.91

Remodeling

No Yes

56(64) 4(6)

1.00 0.91

0.33 - 2.52

0.86

Snow shoveling

No Yes

29 (36) 31 (34)

1.00 1.06

-

-

0.64- 1.76

0.82

No 56 (63) 4(7) Yes t Significant variables selected for inclusion into initial covariate model, tSignificant variables that remained in final covariate model.

1.00 0.85

0.31 -2.35

Tense-edge-nervous composite score

-


Basketball

-

-

o.oi+t

Hobbies

Gardening

Motorcycle/ATV

-

0.69

-

0.60-1.99

0.32 0.78

-

-

-

-

-

0.76

Increased BMI (>31.6 kg/m2), having a combined history of LBP, having job satisfaction (modified APGAR composite score), being tense, on edge or nervous (Tense-edge-nervous composite score > 4) and weightlifting (as an exercise) showed a statistically significant relationship with OLBP-A outcome (p10) showed a possible relationship with OLBP-A (p=0.06). Other variables did not show evidence of a relationship (p>0.2) or had a very small sample size (n3) and biological plausibility. Significant variables that were considered were BMI, combined history of LBP, job satisfaction (modified APGAR composite score), Tense-edge-nervous composite score and weightlifting (as an exercise). These variables were entered together into a Cox proportional hazards model (initial covariate model) and variables were removed (based on p-value) till a final covariate model was obtained. Of the variables inputted into the model initially, only combined history of LBP, job satisfaction (modified APGAR composite score), Tense-edge-nervous composite score and weightlifting (as an exercise) remained in the final covariate model.

96

4.2.3 Univariate analyses for job metrics using literature-suggested cut-points 4.2.3.1 Univariate hazard ratios for job metrics using literature-suggested cutpoints Univariate hazard ratios were computed for job metrics using literature-suggested cutpoints (Table 4.5) as discussed in Chapter 3. PSTLI, TCLI, BCFALL and BCFLL showed a relationship with OLBP-A outcome (p0.2). Table 4.5: Univariate hazard ratios for job metrics using literature-suggested cut-points for OLBP-A outcome Variable PSTLI PCLI TCLI BCFALL BCFLL MPCALL MPCLL

Category

Cases (non-casest)

Hazard Ratio

95% Confidence Interval

p-vaiue (Wald test)

1.0

56 (58)

2.58

0.93-7.11

0.07*

1.0

59 (59)

9.11

1.26-65.78

0.03

< l.o

6(16)

1.00

-

-

> 1.0

54 (54)

2.20

0.95-5.12

0.07*

3400 N

38 (36)

1.50

0.88 - 2.53

0.13*

3400 N

37 (35)

1.48

0.88 - 2.48

0.14*

>75%

28 (32)

1.00

-

-

75%

30 (34)

1.00

-

-

1.11

0.67 - 1.84

0.69

1.0

56 (58)

2.49

0.90 - 6.93

0.08

5.5

30 (20)

1.70

1.00-2.89

0.05


4

15(9)

2.26

1.21-4.20

0.01

LBP History

No

23 (43)

1.00

-

-

Yes

37 (27)

2.01

1.17-3.43

0.01

No

40 (57)

1.00

-

-

Yes

20(13)

2.07

1.20-3.57

0.009

Variable PSTLI

APGAR composite score (job satisfaction)

Weight lifting

p-value (Wald test)

Table 4.7: Multivariate hazard ratios forTCLI using literature-suggested cut-points while accounting for significant covariates for OLBP-A outcome 95% Cases Hazard Confidence p-value (Wald test) Variable Category (non-cases) Ratio Interval < 1.0 6(16) TCLI 1.00 > 1.0

54 (54)

2.39

5.5

30 (20)

1.58


4

15(9)

LBP History

No


Weight lifting

1.01 -5.64

0.05

0.92 - 2.69

0.1

2.69

1.43-5.04

0.002

23 (43)

1.00

-

-

Yes

37 (27)

2.15

1.26-3.67

0.005

No

40 (57)

1.00

-

-

Yes

20(13)

1.89

1.10-3.28

0.02

98

The peak single task lifting index (PSTLI) showed a positive relationship with OLBP-A (Hazard ratio, HR=2.49) while accounting for significant covariates. This relationship while not statistically significant at p 3400 N

38 (36)

1.27

0.74-2.18

0.38

5.5

30 (20)

1.62

0.95 - 2.75

0.07


4

15(9)

2.34

1.26-4.34

0.007

LBP History

No

23 (43)

1.00 1.21 -3.53

0.008

1.17-3.49

0.01

Variable BCFALL


Weight lifting

Yes

37 (27)

2.07

No

40 (57)

1.00

Yes

20(13)

2.02

Table 4.9: Multivariate hazard ratios for BCFLL using literature-suggested cut-points while accounting for significant covariates for OLBP-A outcome

Variable BCFLL

APGAR composite score (job satisfaction) Tense-edge-nervous composite score LBP History Weight lifting


p-value (Wald test)

1.22

0.72 - 2.09

0.46

1.00 1.63

0.96 - 2.76

0.07

1.00 2.34

1.26-4.35

0.007

1.21-3.53

0.008

1.17-3.49

0.01

Category 3400 N

37 (35)

5.5

30 (20)

4

15(9)

No

23 (43)

1.00

Yes

37 (27)

2.07

No

40 (57)

1.00

Yes

20(13)

2.02

99

The maximum back compressive force from lifting, lowering, pushing and pulling tasks (BCFALL) and maximum back compressive force from lifting/lowering tasks (BCFLL) did not show evidence of a relationship with OLBP-A (p>0.2, HR.~1.25) while accounting for significant covariates despite showing association in univariate analyses.

4.2.4 Univariate hazard ratios for cut-points determined using post hoc analyses of job metrics Cut-points for job metrics were determined using Martingale's residual plots with Loess smoothing curves as explained in Chapter 3. Figure 4.2 illustrates an example of a Martingale's residuals for null model of OLBP-A outcome against PSTLI values. The data were divided into deciles (thin vertical black lines) of cases (shown against top horizontal axis, total n is shown against bottom horizontal axis). The quantile (here decile) which showed a visual change in curvature/shape of the Loess curve (thin dotted horizontal line across chart) was chosen for univariate analyses.

o

o

£

° o

«•)

9 ~

•

•

••

•

i •

i

T1hiii,Tii inn

n« TITITi ill i ill! ill J 2

I I

H

1_U

3 PSTU

Figure 4.2: PSTLI vs. Martingale's residuals for OLBP-A null model

_Lj

I

LL.

100

4.2.4.1 Univariate hazard ratios for job metrics using cut-points determined from post hoc analyses Table 4.10 shows the univariate hazard ratios for categories of job metrics based on the cut-points determined using this method. PSTLI, PCLI, TCLI, BCFALL, BCFLL and PLM showed a relationship with OLBP-A outcome (p0.2). Peak lifting index measures (PSTLI and PCLI) and peak load moment (PLM) showed moderate relationship with OLBP-A outcome (HR> 2.0). Back compressive force measures and CLI from typical job (TCLI) showed a weak-moderate relationship with OLBP-A (HR< 2.0).

Table 4.10: Univariate hazard ratios for job metrics from post hoc analyses for OLBP-A outcome Variable PSTLI PCLI TCLI BCFALL BCFLL MPCALL MPCLL PLM

Category

Cases (non-casesf)

Hazard Ratio


p-value (Wald test)

< 1.2

5(15)

1.00

-

-

> 1.2

55 (55)

2.64

1.06-6.59

0.04*

< 1.4

6(18)

1.00

-

-

> 1.4

54 (52)

2.87

1.23-6.68

0.01*

2.0

38 (34)

1.66

0.98-2.81

0.06*

4230 N

19(15)

1.66

0.96 - 2.86

0.07*

< 4230 N

41 (56)

1.00

-

-

> 4230 N

19(14)

1.80

1.04-3.10

0.04*

>86%

15(21)

1.00

-

-

87 %

14 (20)

1.00

-

-

52 N-m * Job metrics selected for multivariate modeling. t n for non-cases is for job physical exposure measured at baseline.

1.00

-

-

2.73

1.09-6.82

0.03*

101

4.2.4.2 Multivariate hazard ratios for job metrics using cut-points determined from post hoc analyses while adjusting for significant covariates Multivariate models were constructed for PSTLI, PCLI, TCLI, BCFALL, BCFLL and PLM (Tables 4.11-4.16).

PSTLI, PCLI and PLM showed a moderate-strong positive relationship with OLBP-A (HR>2.5, p 4230 N

19(14)

1.59

0.92 - 2.76

0.10

5.5

30 (20)

1.61

0.95 - 2.73

0.08


4

15(9)

2.39

1.28-4.44

0.006

LBP History

No

23 (43)

1.00

Yes

37 (27)

2.04

1.20-3.48

0.009

No

40 (57)

1.00

Yes

20(13)

1.99

1.15-3.44

0.01

Variable BCFLL


Weight lifting

Category < 4230 N

Table 4.16: Multivariate hazard ratios for PLM using cut-points determined from post hoc analyses while accounting for significant covariates for OLBP-A outcome 95% Cases Hazard Confidence p-value Category Variable (non-cases) (Wald test) Ratio Interval < 52 N-m PLM 5(16) 1.00

APGAR composite score (job satisfaction) Tense-edge-nervous composite score LBP History Weight lifting

> 52 N-m

55 (54)

2.59

5.5

30 (20)

1.60

4

15(9)

2.31

No

23 (43)

1.00

Yes

37 (27)

2.08

No

40 (57)

1.00

Yes

20(13)

1.99

1.03-6.49

0.04

0.94 - 2.73

0.08

1.24-4.31

0.008

1.22-3.57

0.007

1.15-3.44

0.01

It appeared that combined history of LBP and tense-edge-nervous composite score were moderate predictors (HR>2.0) and weight lifting was a weak-moderate predictor (HR~2) of OLBP-A across various multivariate models, however the modified APGAR composite score for job satisfaction was only a weak-moderate predictor of OLBP-A outcome and this relationship only approached statistical significance (0.05 5.5), being depressed (modified Zung composite score >10) and being tense, on edge or nervous (Tense-edge-nervous composite score > 4) showed a statistically significant relationship with OLBP-M outcome (p0.2) or had a very small sample size (n 56 years

45 (72) 7(7)

1.00 1.19

-

-

0.54 - 2.63

0.67

Male Female

35 (57) 17 (22)

1.00 1.18

-

-

0.66-2.11

0.57

White Hispanic or Latino Black or African American Other Missing

35 (44) 10(15)

1.00 0.95

0.47- 1.91

5(11) 2(6) 0(3)

0.59 0.69 0.00

4(2)

High school graduate or GED Some college

Demographics

Age Gender Race

Education

Marital Status

Tobacco use

Alcohol use

Some school

-

0.84 0.87

0.00-

0.27 0.60 0.99

1.00

-

0.14

22 (34)

0.45

0.16-1.32

0.15

23 (38)

0.52

0.18-1.50

0.22

0.23-1.51 0.17-2.85

College graduate (Bachelor's degree or higher)

3(2)

0.92

0.21 -4.15

0.92

Missing

0(3)

0.00

0.00-

0.99

Married

23 (42)

1.00

-

0.05

Single

16(25)

1.35

0.71 -2.55

0.36

Other (divorced, separated or widowed/widowered)

13(9)

1.94

1.94-0.98

0.06

Missing

0(3)

0.00

0.00-

0.99

28 (44)

1.00

-

0.30

11 (19)

0.81

0.40- 1.62

0.54

Currently using

13(16)

1.51

0.78 - 2.92

0.22

No Yes

49 (76) 3(3)

1.00 1.26

< 33.1 kg/m2

46 (54)

>33.1 kg/m2

6(25)

1.00 0.35

No

18 (48)

1.00

-

-

Yes

34 (31)

2.89

1.63-5.14

co.oom

Never Used in the past, not currently

-

-

0.39 - 4.04

0.70

-

-

0.15-0.83

omn

Anthropometry

BMI




38 (67) No 1.00 14(12) 1.21 0.65 - 2.23 0.55 Yes •LBP related medication use, healthcare provider visit, light/restricted duty, lost workdays, workers compensation or LBP traveling down leg into calf. tSignificant variables selected for inclusion into initial covariate model. ^Significant variables that remained in final covariate model. High cholesterol

106

Table 4.17 (contd.): Univariate hazard ratios for individual and psychosocial variables for OLBP-M outcome

Category

Cases (noncases)

Hazard Ratio


p-value (Wald test)


5.5

38 (67) 14(12)

1.00 1.93

1.05-3.57

0.04ft

Depression (modified Zung composite score)

< 10 > 10 4

38 (68) 14(11)

1.00 2.56

1.38-4.76

0.003ti

No Yes

17 (30) 35 (49)

1.00 1.54

-

-

0.86 - 2.76

0.14

No Yes No Yes No Yes No Yes

20 (31) 32 (48) 49 (73) 3(6)

1.00 0.94

-

-

0.54- 1.64

0.83

Variable Psychosocial factors

Tense-edge-nervous composite score Physical exercises outside of work

Any exercise Walking Aerobics Bicycling

1.00 0.85 1.00 0.76

-

0.27 - 2.74

-

0.79

No Yes

44 (59) 8(20) 46 (69) 6(10) 47 (71) 5(8)

No Yes

36 (62) 16(17)

1.00 1.69

Baseball

No Yes

51 (72) 1(7)

1.00 0.24

Basketball

No Yes

47 (64) 5(15)

1.00 0.66

-

-

0.26- 1.66

0.38

Any hobby

No Yes

5(11) 47 (68)

1.00 1.28

0.51 -3.21

0.60

Housework

No Yes

15 (29) 37 (50)

1.00 1.32

-

-

0.72 - 2.40

0.37

No Yes

26 (38) 26(41)

1.00 0.97

0.56-1.67

Maintenance

No Yes

41 (60) 11 (19)

1.00 0.91

Woodwork

No Yes

73 (47) 4(7)

1.00 1.24

-

-

0.45 - 3.44

0.68

No Yes

48 (73) 4(6)

1.00 1.04

0.37 - 2.89

No Yes

25 (41) 27 (38)

1.00 0.98

0.57 - 1.69

0.93

No 49(71) 3(8) Yes fSignificant variables selected for inclusion into initial covariate model, ^Significant variables that remained in final covariate model.

1.00 0.73

0.24 - 2.45

0.65

Running Swimming Weightlifting

1.00 1.22 1.00 1.53

-

-

0.36- 1.62

0.48

-

-

0.52 - 2.86

0.64

-

-

0.60 - 3.86

0.37

-

-

0.94 - 3.06

0.08

-

-

0.03 - 1.76

0.16

Hobbies

Gardening

Remodeling Snow shoveling Motorcycle/ATV

-

-

0.47 - 1.78

-

-

-

0.91 -

0.79

-

0.94 -

107

4.3.2 Significant covariates Significant variables that were considered for potential confounding effect on OLBP-M outcome were BMI, combined history of LBP, job satisfaction (modified APGAR composite score), depression (modified Zung composite score) and Tense-edge-nervous composite score. Of the variables inputted into the model initially, only BMI, combined history of LBP, job satisfaction (modified APGAR composite score) and Tense-edgenervous composite score remained in the final covariate model.

4

4.3.3 Univariate analyses for job metrics using literature-suggested cut-points 4.3.3.1 Univariate hazard ratios for job metrics using literature-suggested cutpoints Univariate hazard ratios for job metrics using literature-suggested cut-points are shown in Table 4.18.

Table 4.18: Univariate hazard ratios for job metrics using literature-suggested cut-points for OLBP-M outcome Cases p-value 95% Confidence (nonHazard Category (Wald test) Ratio casest) Variable Interval < 10 PSTLI 4(12) 1.00 PCLI TCLI BCFALL BCFLL MPCALL MPCLL

> 1.0

47 (67)

1.97

0.71 - 5.47

0.19*

1.0

50 (68)

7.09

0.98-51.32

0.05

< l .o

5(17)

1.00

-

-

> 1.0

46 (62)

1.66

0.71 - 3.90

0.24

< 3400 N

19(37)

1.00

-

-

> 3400 N

32 (42)

1.48

0.84 - 2.61

0.17*

3400 N

31 (41)

1.43

0.82-2.51

0.21

>75%

23 (37)

1.00

-

-

75%

24 (40) 33.1 kg/m2

6(24)

0.41

Variable PSTLI LBP History

BMI

-

-

1.31 -4.28

0.004

>

0.17-0.96

0.04

109

Table 4.20: Multivariate hazard ratios for BCFALL using literature-suggested cut-points while accounting for significant covariates for OLBP-M outcome

Tense-edge-nervous composite score BMI

Hazard Ratio 1.00


p-value (Wald test)

-

-

0.32

< 3400 N

19 (37)

> 3400 N No

32 (42) 18 (48)

1.35 1.00

0.75 - 2.42 -

-

Yes

32 (32)

1.32-4.31

0.004

-

-

0.94-3.41

0.07

7

14(12)

1.8

4

14(10)

1.00 2.36

< 33.1 kg/m2

46 (54)

1.00

> 33.1 kg/m2

6(24)

0.43

-

-

1.25-4.46

0.008

_

_

o


Cases (non-cases)

00

LBP History

Category

©

Variable BCFALL

0.05

BCFALL did not show evidence of any relationship with OLBP-M outcome (HR0.2) in the presence of significant covariates. Having a combined history of LBP and being tense, on edge or nervous (tense-edge-nervous composite score >4) were moderate predictors of OLBP-M (HR>2.13). Having poor job satisfaction (modified APGAR composite score >7) was a weak predictor of OLBP-M (HR 33.1 kg/m2) appeared to reduce the risk of OLBP-M outcome (HR=0.41).

110

4.3.4 Univariate hazard ratios for job metrics using cut-points determined from post hoc analyses 4.3.4.1 Univariate hazard ratios for job metrics using cut-points determined from post hoc analyses Table 4.21 shows the univariate hazard ratios for categories of job metrics based on the cut-points determined from post hoc analyses. PSTLI, PCLI, TCLI, BCFALL, BCFLL, MPCLL and PLM showed a relationship with OLBP-M outcome (p0.2). Only PCLI showed a strong relationship with OLBP-M outcome (HR> 2.0, p87%

9(22)

1.00

-

-

87%

10(24)

1.00

-

-

52 N-m 63 (42) * Job metrics selected for multivariate modeling. t n for non-cases is for job physical exposure measured at baseline.

Ill

4.3.4.2 Multivariate hazard ratios for job metrics using cut-points determined from post hoc analyses while adjusting for significant covariates Multivariate models for job metrics using cut-points determined from post hoc analyses are summarized in Tables 4.22-4.29.

PLM showed the strongest association with OLBP-M outcome in comparison to other metrics (HR=2.31), however the result only approached statistical significance at p=0.08. PCLI showed a moderate relationship with OLBP-M (HR~2.0, p7) was a moderate predictor of OLBP-M (HR0.2) or had a very small sample size (n 28.6 kg/m2

10(58) 5(57)

1.00 0.46

0.16- 1.33

0.15t

3(63) 12 (52)

1.00 5.25

1.48-18.62

o.oioti

Category

95%

Confidence Interval

Demographics

Age Gender Race

Education

Marital Status

Tobacco use

Problems due to alcohol use

< 32 years >32 years Male Female White Hispanic or Latino Black or African American Other Missing Some school High school graduate orGED Some college College graduate (Bachelor's degree or higher) Missing Married Single Other (divorced, separated or widowed/widowered) Missing Never Used in the past, not currently Currently using

-

-

0.40 -3.46

0.76

-

0.41 -3.51 -

0.12-2.37

-

-

0.19-2.90

-

-

0.74 0.14 0.40 0.99 0.92 0.99 0.21

-

Anthropometry

BMI



No Yes

.


High cholesterol

No 8(96) 1.00 Yes 7(19) 2.90 1.05-8.00 0.04tt *LBP related medication use, healthcare provider visit, light/restricted duty, lost workdays, workers compensation or LBP traveling down leg into calf. ISignificant variables selected for inclusion into initial covariate model. ^Significant variables that remained in final covariate model.

117

Table 4.30 (contd.)i Univariate hazard ratios for individual and psychosocial variables for OLBP-H outcome

p-value (Waid test)

Category

Cases (noncases)

Hazard Ratio



3.6

5(35) 10 (80)

1.00 0.77

-

-

0.26 - 2.26

0.63

Depression (modified Zung composite score) Tense-edge-nervous composite score

3 3

6(24) 9(91) 13(82) 2(33)

1.00 0.33 1.00 0.55

No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes

4(42) 11 (73) 6(44) 9(71) 13(108) 2(7) 11(91) 4(24) 15 (99) 0(16) 11 (106) 4(9) 10(87) 5(28) 15 (107) 0(8) 14 (96) 1 (19)

1.00 1.98 1.00 0.80 1.00 2.02 1.00 1.65 1.00 0.00 1.00 5.77 1.00 1.63 1.00 0.00 1.00 0.49

1 (15) No 14(100) Yes Housework No 3(40) Yes 12(75) Gardening No 6(57) Yes 9(58) Maintenance No 10 (90) 5(25) Yes Woodwork No 13(106) 2(9) Yes Remodeling 15(105) No 0(10) Yes Snow shoveling No 6(59) 9(56) Yes 14 (105) Motorcycle/ATV No 1(10) Yes fSignificant variables selected for inclusion into initial covariate model, ^Significant variables that remained in final covariate model.

1.00 1.68 1.00 2.07 1.00 1.46 1.00 1.86 1.00 2.35 1.00 0.00 1.00 1.36 1.00 0.86

Variable Psychosocial factors

-

-

0.12-0.94

0.04

-

-

0.12-2.43

0.43


Any exercise Walking Aerobics Bicycling Running Swimming Weightlifting Baseball Basketball

-

-

0.63 - 6.22

0.24

-

-

0.28 - 2.25

0.67

-

-

0.46 - 8.99

0.35

-

-

0.52-5.19

0.39

-

0.00-

1.81 - 18.42

-

0.99 -

0.003

-

-

0.56 - 4.78

0.37

-

-

0.00-

0.99

-

-

0.06 - 3 70

0.49

Hobbies

Any hobby

-

-

0.22 - 12.79

0.62

-

-

0.58 - 7.33

0.26

-

-

0.52-4.10

0.47

-

-

0.64 - 5.46

0.26

-

-

0.53 - 10.50

0.26

-

0.00-

-

0.99

-

-

0.48 - 3.83

0.56

-

0.11-6.52

-

0.88

118

4.4.2 Significant covariates Significant variables that were considered for potential confounding effect on OLBP-H outcome were BMI, combined history of LBP and high cholesterol. Of the variables inputted into the model initially, only combined history of LBP and high cholesterol remained in the final covariate model.

4.4.3 Univariate analyses for literature-suggested cut-points of job metrics 4.4.3.1 Univariate hazard ratios for literature-suggested cut-points of job metrics Univariate hazard ratios for job metrics using literature-suggested cut-points are shown in Table 4.31. In general, variable categories did not have sufficient sample size (n2.0), but these relationships showed low statistical significance (p>0.05). PSTLI, PCLI, TCLI, MPCALL and MPCLL did not show evidence of association with OLBP-H. Table 4.31: Univariate hazard ratios for job metrics using literature-suggested cut-points for OLBP-H outcome 95% Cases p-value (nonHazard Confidence Category Variable casest) Ratio Interval (Wald test) < 1.0 PSTLI 0(16) 1.00 PCLI TCLI BCFALL BCFLL MPCALL MPCLL

> 1.0

15 (99)

-

< 1.0

0(12)

1.00

> 1.0

15(103)

-

< l .o

1(21)

> 1.0

14 (94)

< 3400 N

-

-

-

-

1.00

-

-

2.84

0.37-21.56

0.32

3(53)

1.00

-

-

> 3400 N

12 (62)

3.29

0.93-11.68

0.07*

< 3400 N

4(54)

1.00

-

-

> 3400 N

11(61)

2.38

0.76 - 7.48

0.14*

>75%

6(54)

1.00

-

-

75%

6(58) 9(57)

1.00

-

-

1.83

0.65-5.15

0.25

0.2) in the presence of significant covariates. Table 4.32: Multivariate hazard ratios for BCFALL using literature-suggested cut-points while accounting for significant covariates for OLBP-H outcome

Variable BCFALL LBP History High cholesterol

Category 3400


p-value (Wald test)

-

-

0.69 - 8.95

0.17

12 (62)

2.48

No

3(63)

1.00

-

-

Yes

12 (52)

4.57

1.28- 16.31

0.02

No

8(96)

1.00

-

-

Yes

7(19)

2.40

0.86-6.71

0.09

Table 4.33: Multivariate hazard ratios for BCFLL using literature-suggested cut-points while accounting for significant covariates for OLBP-H outcome

Category

Cases (noncases)

Hazard Ratio


p-value (Wald test)

BCFLL

3400 No

11 (61) 3(63)

1.76 1.00

0.55 - 5.63

0.34

LBP History

-

-

Yes

12(52)

1.32- 16.82

0.02

No

8(96)

4.70 1.00

-

-

Yes

7(19)

2.52

0.90 - 7.03

0.08

Variable

High cholesterol

120

4.4.4 Univariate post hoc analyses of job metrics 4.4.4.1 Univariate hazard ratios from post hoc analyses of job metrics Table 4.34 shows the univariate hazard ratios for categories of job metrics based on the cut-points determined from post hoc analyses. PCLI, TCLI, MPCALL and MPCLL showed moderate-strong relationships with OLBP-H outcome (HR>2.0, p0.2). It appears that strength requirement (minimum percent population capable) measures were strong predictors of OLBP-H outcome. Table 4.34: Univariate hazard ratios for job metrics from post hoc analyses for OLBP-H outcome

Variable PSTLI PCLI TCLI BCFALL BCFLL MPCALL MPCLL PLM

Category

Cases (non-cases t)

Hazard Ratio


p-value (Wald test)

2.0

10(61)

1.98

0.68 - 5.81

0.21

2.3

10 (60)

2.04

0.69 - 5.98

0.20*

2.3

10 (50)

2.65

0.90 - 7.76

0.08*

3430 N

10(61)

1.86

0.64 - 5.46

0.26

3410 N

10(61)

1.80

0.61 - 5.27

0.29

>40%

10(103)

1.00

-

-

40%

10(104)

1.00

-

-

126N-m 5(29) * Job metrics selected for multivariate modeling. t n for non-cases is for job physical exposure measured at baseline.

121

4.4.4.2 Multivariate hazard ratios from post hoc analyses of job metrics while adjusting for significant covariates Multivariate models for job metrics using cut-points determined from post hoc analyses are summarized in Tables 4.35-4.38. TCLI, MPCALL and MPCLL showed strong relationships with OLBP-H outcome (HR>2.5) while accounting for significant covariates that approached statistical significance (p=0.06). Combined history of LBP and high cholesterol were predictive of OLBP-H outcome. PCLI did not show evidence of any relationship with OLBP-H outcome (p > 0.2).

Table 4.35: Multivariate hazard ratios for PCLI using cut-points determined from post hoc analyses while accounting for significant covariates for OLBP-H outcome

Variable PCLI LBP History High cholesterol

Category

Cases (non-cases)

Hazard Ratio


p-value (Wald test)

2.3 No

10(60) 3(63)

1.64 1.00

0.54 - 4.94

0.38

-

-

Yes

12 (52)

4.87

1.37- 17.35

0.01

No

8(96)

1.00

Yes

7(19)

2.43

-

-

0.85 - 6.90

0.10

Table 4.36: Multivariate hazard ratios for TCLI using cut-points determined from post hoc analyses while accounting for significant covariates for OLBP-H outcome

Variable TCLI LBP History

High cholesterol

Category

Cases (non-cases)

Hazard Ratio


p-value (Wald test)

2.3

10 (50) 3(63)

2.79 1.00

0.95 - 8.24

0.06

No

-

-

Yes

12 (52)

5.4

1.52-19.26

0.009

No

8(96)

1.00

Yes

7(19)

2.47

-

-

0.89 - 6.84

0.08

122

Table 437: Multivariate hazard ratios for MPCALL using cut-points determined from post hoc analyses while accounting for significant covariates for OLBP-H outcome

Variable MPCALL LBP History High cholesterol

Category

Cases (non-cases)

Hazard Ratio


p-value (Wald test)

>40%

10(103)

1.00

-

-

40 %

10(104)

1.00

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