a case study on ergonomic hazards

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Success Stories

The success stories below are accounts that have been submitted to OSHA, or that were based on information obtained by OSHA from secondary sources, where employers have implemented ergonomics programs or utilized best practices and have reported successful results. They can be found according to category, by NAICS Code, or by doing a search. A complete list is also available .

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• NAICS 22112 - Logan Generating - Cleaning and Inspecting Boiler Backpass System

Construction

• NAICS 2362 - Hensel Phelps Construction Co.

Food Manufacturing

(naics 311).

• NAICS 311611- ConAgra Foods
• NAICS 311611 - Taylor, an Excel Food Solutions Company
• NAICS 311615 - Gold Kist, Inc.

( OSHA Strategic Partnership Program Participant )

Apparel Manufacturing

(naics 315).

• NAICS 3152 - LL Bean

Wood Product Manufacturing

(naics 321).

• [NAICS 321214] - Clearing Product Jams
• NAICS 321214 - Georgia Pacific Corporation

Paper Manufacturing

(naics 322).

• NAICS 32212 - Emptying Cutter Scrap Receptacles
• NAICS 322121 - Glue Table for Paper Roll Headers
• NAICS 322121 - Inserting Spindle into Paper Roll
• NAICS 322211 - Moving Paper Rolls
• NAICS 322211 - Plastic Strap Spool Assembly
• NAICS 322211 - Replacing Wire Spool on Baler
• NAICS 322211 - Rotary Die Cut Stacker Operations
• NAICS 322211 - Stacking Corrugated Boxes with Corner Stacker
• NAICS 322211 - Transport and Instillation of Bailing Wire Coil
• NAICS 322211 - Transporting Cutting Dies with Hand Truck
• NAICS 322299 - Cart to Transport Heavier Items Long Distances
• NAICS 322299 - Operation for Cap Sheets and Corner Posts
• NAICS 322211 - International Paper, Shorewood Packaging (OSHA Voluntary Protection Programs Participant)

Printing and Related Support Activities

(naics 323).

• NAICS 323111 - Quad Graphics Inc.

Chemical Manufacturing

(naics 325).

• NAICS 325110 - Infineum USA LP (OSHA Voluntary Protection Programs Participant)
• NAICS 325199 - PPG Industries

Machinery Manufacturing

(naics 333).

• NAICS 3333 - Pitney Bowes
• NAICS 333618 - Workstation Redesigns
• NAICS 333618 - "Ergo Dolly"

Computer and Electronic Product Manufacturing

(naics 334).

• NAICS 334112 - Exabyte Corporation
• NAICS 334413 - Intel Corporation
• NAICS 334118 - Sun Microsystems, Inc.
• NAICS 334413 - Xandex Inc.

Electrical Equipment, Appliance, and Component Manufacturing

(naics 335).

• NAICS 3353 - Rockwell Automation
• NAICS 33591 - Duracell

Transportation Equipment Manufacturing

(naics 336).

• NAICS 3361 - Motorized Carts and Racks
• NAICS 3361 - Orientation Training
• NAICS 3361 - Spring-Loaded Tool Fixture
• NAICS 3363 - Siemens VDO Automotive
• NAICS 336390 - Tube Setting Process
• NAICS 336390 - Roving Strand Application Machine
• NAICS 336390 - Robotic De-Palletizing System
• NAICS 336390 - Semi-Automatic Stretch Wrapper
• NAICS 3363 - DENSO Sales California, Inc.
• NAICS 336390 - TitanX Engine Cooling Inc.
• NAICS 336413 - Ducommun Aerostructures New York, Inc.

Furniture and Related Product Manufacturing

(naics 337).

• NAICS 33721 - HON Industries
• NAICS 337920 - Assembly Tables
• NAICS 337920 - Material Handling and Storage System
• NAICS 337920 - Replacement of Heavy Steel Parts in Machine Press Dies with Light-Weight Aluminum
• NAICS 337920 - Work Station Evaluations and Adjustments

Retail Trade

(naics 44-45).

• NAICS 445110 - Lowering of Tractor Trailer Landing Gear
• NAICS 4421100 – Lifting Program in the Furniture Warehouse

Transportation and Warehousing

(naics 48-49).

• NAICS 485113 - San Mateo County Transit District
• NAICS 493110 - Installing LP Tank on Forklift with Hoist

Information

• NAICS 511110 - New York Times Company
• NAICS 515120 - Turner Broadcasting Systems

Finance and Insurance

• NAICS 524114 - Blue Cross and Blue Shield of Kansas
• NAICS 524114 - Blue Cross and Blue Shield of Rhode Island
• NAICS 524113 - Erie Insurance Group

Professional, Scientific, and Technical Services

• NAICS 54171 - Lockheed Martin Systems Integration

Educational Services

• NAICS 611310 - Colby College

Health Care and Social Assistance

( OSHA Safety and Health Recognition and Achievement Program Site )

• NAICS 623110 - Borderview Rehabilitation and Living Center
• NAICS 623110 - Citizens Memorial Healthcare (OSHA Voluntary Protection Programs Participant)
• NAICS 623110 - Diversicare Windsor House, Inc. dba Windsor House
• NAICS 623110 - Heritage Enterprises, Inc.
• NAICS 6231 - Missouri Slope Lutheran Care Center

Public Administration

• NAICS 921110 - OSHA – Directorate of Technical Support and Emergency Management, Salt Lake Technical Center

Additional Success Stories

• Asset Distribution Facility
• Dual Monitors
• Navy Safety Center
• Washington State Department of Labor and Industries

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The effectiveness of ergonomic interventions in material handling operations

Steven j wurzelbacher, michael p lampl, stephen j bertke, chih-yu tseng.

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Corresponding Author: Steven J. Wurzelbacher

Issue date 2020 Sep.

This study evaluated the effectiveness of ergonomic interventions in material handling operations involving 33 employers and 535 employees from 2012–2017. Outcomes included employee-reported low back/upper extremity pain and safety incidents at baseline, every three months, and annually for up to two years. A total of 32.5% of employees completed at least one survey, while 13.6% completed all nine surveys over two years. Among highly exposed employees (who reported handling >= 50 lbs. > 33% of the time), upper extremity pain frequency and severity were lower among those who reported using the intervention routinely versus those that reported using their body strength alone to handle objects >= 50 lbs. After excluding from analyses one employer that used anti-fatigue mats, low back pain frequency was also significantly lower among highly exposed intervention users. In conclusion, there was some evidence that the interventions were effective in reducing employee-reported pain for highly exposed employees.

1: Introduction:

Work-related musculoskeletal injuries and illnesses associated with biomechanical risk factors such as overexertion, repeated movements, bodily reaction, and awkward body postures accounted for approximately 34% (295,830 of 882,730) of the non-fatal injuries and illnesses involving days away from work in US private industry in 2017 ( BLS, 2019 ). Liberty Mutual has estimated overall direct employees’ compensation claim costs to US industry to be $55.4 billion in 2016, with $13.1 billion due to overexertion injuries alone ( Liberty Mutual, 2019 ). This estimate does not include indirect costs (such as lost productivity, overtime and training costs to replace injured workers) which some research suggest are equal to or greater than corresponding direct costs for the same disabling injuries ( OSHA 2020 ; NIOSH 2020 ; Huang et al. 2009 ). Musculoskeletal injuries and illnesses also are among those for which patients are most often prescribed opioids both initially and on a long-term basis ( Thumala et al. 2018 ), suggesting an additional worker and societal burden.

Major public health goals set forth by the US National Institute for Occupational Safety and Health (NIOSH) include reducing work-related musculoskeletal injuries and illnesses in part by assessing the effectiveness of interventions. Although many ergonomic interventions are designed to reduce biomechanical risk factors involved in common tasks such as manual material handling, few high quality quasi-experimental, or randomized controlled trial (RCT) studies have been conducted to determine whether the interventions reduce future pain symptoms and injuries. Recent literature reviews on ergonomic intervention effectiveness, have found that RCTs were largely focused on office-based interventions ( van Eerd et al. 2016 ; Driessen et al. 2010 ) or on specific occupations such as dental care practitioners ( Mulimani et al. 2018 ).

Most ergonomic intervention effectiveness studies in industries such as manufacturing and construction have used quasi-experimental designs (e.g. pre- and post- intervention studies without control groups or randomization). A systematic review found that studies which have investigated the effectiveness of ergonomic engineering interventions alone have typically used short-term workload assessments as outcomes rather than reported pain symptoms or incidents and have been mixed in quality and findings ( van der Molen et al. 2005 ). The engineering interventions tested have typically included material handling devices or other workstation changes evaluated in laboratory experiments (Resnick and Chaffin 1997; Mirka et al. 2002 ) or limited field trials ( Bongers et al. 2001 ; Devereux et al. 1997 ; McGlothlin et al. 1996 ). Relatively few studies have involved longer-term field trials of patient handling equipment ( Collins et al. 2004 ; Li et al. 2004 ), material handling equipment ( Van der Molen et al. 2010 ; Marras et al. 2000 ; Vink et al. 1997 ) or other worksite engineering changes ( Luijsterburg et al. 2005 ). Two systematic reviews identified several studies that have examined the combined effect of ergonomic programs that include engineering, administrative, and work practice interventions and involve both management and employees in the improvement process (Tompa et al. 2009; Sultan-Taïeb et al. 2017 ). Tompa found strong economic evidence for ergonomic program intervention effectiveness at a firm level in certain industries (manufacturing and warehousing), moderate evidence in others (administrative support health care sectors), and limited or insufficient evidence in other sectors. Sultan-Taïeb by contrast found evidence for program effectiveness among studies largely in the healthcare sector.

There is a need to conduct additional research that examines the effectiveness of ergonomic engineering interventions such as material handling equipment especially in high-risk industries. A partnership between the Ohio Bureau of Workers’ Compensation (OHBWC) and the NIOSH Center for Workers’ Compensation Studies (CWCS) has been conducting a series of such studies. In 1999, the OHBWC developed a safety intervention grant program through which it awards insured employers grants to purchase engineering interventions to reduce safety hazards. Two previous studies ( Park et al. 2009 ; Fujishiro et al. 2005 ) found that the program could reduce workers’ compensation claims for impacted employees for select industries. More recently, a large OHBWC-NIOSH study ( Wurzelbacher et al. 2014 ) evaluated the program from 2003–2009 for 468 employers for intervention effectiveness. Overall, the study determined that the program did significantly reduce workers’ compensation claim frequencies in nine of ten industry sectors, and for three of four intervention types (i.e., ergonomic, safety, and multi-purpose). In summary, claim frequency per 100 employees was reduced by 66% (95%CI: 42–81%), while cost per employee was reduced by 81% (95%CI: 73–88%), and cost per claim reduced by 30% (95%CI: 13–44%).

In a subsequent study, researchers evaluated 153 OHBWC safety intervention grant case study reports completed by employers in the construction industry between 2003 and 2016 ( Lowe et al. 2020 ). Employers prepared these reports one year after implementation of interventions as a requirement of the program. Reports covered various elements related to interventions including cost-benefit analyses, and changes in quality, productivity, safety hazards, and ergonomic-related risk factors. The review indicated variability in the quality of the case study reporting and that interventions ranged in effectiveness in reducing ergonomic-related exposures and safety risks. Nearly all case studies reported some risk reduction. The authors identified 17 high-quality case studies, with the most complete information, that were also ranked highly in terms of quantified reduction in risk factors. The equipment in these case studies included electrical cable feeding/pulling systems, concrete sawing equipment, skid steer attachments for concrete breaking, and boom lifts.

In summary, earlier studies found that the OHBWC sponsored intervention program reduced workers’ compensation claims ( Wurzelbacher et al. 2014 ; Park et al. 2009 ; Fujishiro et al. 2005 ) and reduced some ergonomic and safety risk factors ( Lowe et al. 2020 ). However, other studies have shown that employees underreport injuries and pain symptoms ( Azaroff et al. 2013 ; Lipscomb et al. 2009 ; Scherzer and Wolfe, 2008 ; Fan et al. 2006 ; Azaroff et al. 2002 ; Rosenman et al. 2000 ; Biddle et al. 1998 ).

The specific aims of the current study were to understand how the funded interventions affected employee-reported symptoms and safety incidents. This study targeted heavy material handling tasks because overexertion associated with these types of tasks remains a leading cause of injury ( BLS, 2019 ; Liberty Mutual 2019 ) despite some prior research that indicates ergonomic material handling equipment can reduce biomechanical risk factors for musculoskeletal disorders ( Lowe et al. 2020 ; Mirka et al. 2002 ; Bongers et al. 2001 ; Marras et al. 2000 ;) and workers’ compensation claims ( Wurzelbacher et al. 2014 ; Park et al. 2009 ; Fujishiro et al. 2005 ; Marras et al. 2000 ) or other worksite engineering changes ( Luijsterburg et al. 2005 ).

2.1: Study Design

This study evaluated the effectiveness of ergonomic interventions in material handling operations using a prospective, quasi-experimental design involving 33 employers and 535 employees at baseline from 2012–2017. The study population included volunteer employees working at OHBWC-insured employers who volunteered to participate in a research study with OHBWC and NIOSH. OHBWC insures all employers with 1 to 499 employees in the state of Ohio. Employers with 500 or more employees can self-insure if fiscally able to do so.

This study utilized a randomized multiple baseline design in which all employers eventually received an intervention, but at different times. Researchers admitted employers to the study on a rolling basis from January 2012 through September 2014. Upon program application acceptance, employers were matched to other participant employers based on industry type, type of affected task, number of affected employees, prior loss history (experience modification rating), and proposed intervention. After matching, researchers randomly assigned employers to different intervention implementation schedules. Schedule A received the intervention immediately, and Schedule B received the intervention six months later. Researchers matched 26 employers (13 pairs) based on the above criteria. The remaining seven employers were not able to be matched, but were still randomized to receive the intervention according to one of the above schedules. Participating employers were not restricted from receiving additional OHBWC-sponsored services that they would otherwise choose and could freely engage in other safety/health practices.

2.2: Recruitment

NIOSH coordinated with the OHBWC to recruit employers to participate in this study using an informational flyer that was advertised on the OHBWC website and sent by NIOSH via postal mail to employers in targeted industries that involved heavy material handling. A main incentive to participate was that OHBWC provided 3:1 funding (up to $40k per employer) and that employers could receive certain equipment that was otherwise unavailable through the safety intervention grant program since the equipment (such as powered hand trucks) had been placed on a moratorium list. At the time, OHBWC was trying to ensure that employers from a diverse set of industries were utilizing the safety intervention grant program for a wide variety of interventions and had limited the availability of some of the more common types of equipment available.

Participation by individual employees was voluntary. Participating employers provided a contact list for all individuals performing material handling tasks directly impacted by the intervention, such as delivery, installation, and receiving operations. NIOSH emailed or postal mailed the flyer directly to all prospective employee recruits or called recruits if no email address was available. There was no random sampling of impacted employees. Additional flyers were also placed at each employer. If an employer agreed to participate, but no individual employees wished to participate by answering surveys, the employer was still provided the intervention. This protocol was followed to reduce the chance of employer coercion for individual employee participation in order to receive the intervention. Each employee participant was fully informed of the potential risks and benefits of participation and completed informed consent forms.

2.3: Data Collection

Pre- and post-intervention metrics included affected employee-reported low back/upper extremity pain symptoms collected at baseline, every three months, and annually for up to two years using online or paper surveys. Online surveys were only available from 2012–2013 such that all surveys eventually were completed in paper form. Participant employees were given time in their normal workday to complete all surveys. Participants were mailed a $5 debit card upon completion of each survey data collection (up to a total of $45 for the entire study). Participants were sent surveys two weeks prior to the expected collection date. Emails and phone call prompts were used to maximize response rates. If no response was returned within six weeks of the scheduled data collection date, the participant was considered withdrawn from the study. Participant employees who withdrew were contacted to conduct exit interviews.

2.4: Independent Variables

2.4.1: intervention:.

The interventions were implemented as part of the OHBWC safety intervention grant program with a special NIOSH research collaboration to target activities involving heavy material handling. Employers worked with OHBWC ergonomics consultants to identify at-risk workgroups and choose equipment for implementation based on the specific needs of that workplace. This process may have involved employee participation, but it was not required as part of the research study.

Interventions included a variety of equipment designed to improve material handling ergonomics and safety during delivery, installation, receiving, and other processes in construction, manufacturing, health care, and services. Specific example interventions included stair-climbing/powered hand trucks, powered truck lift gates, lift tables, and cranes/hoists. One employer used funds to install anti-fatigue mats. The total initial cost for these interventions for the 33 participating employers was $834,529 ($556,353 provided by OHBWC). Participating employers also provided regular scheduled maintenance for interventions as indicated by the manufacturer. Participating employers encouraged the use of the intervention but did not require their use. Affected employees were provided training by participating employers in the safe use of the intervention as outlined by the manufacturer.

2.4.2: Individual:

Two types of individual exposure surveys (employee-reported general work environment and health, and employee-reported specific job tasks and intervention use) were administered to employees directly impacted by the ergonomic interventions throughout the course of the study as outlined below:

Employee-reported general work environment and health: Surveys were administered to each employee up to three times (at baseline and every twelve months for up to two years) to collect self-reported data on co-variate health and work conditions. This survey was a subset of data collected for a past large musculoskeletal epidemiologic study ( Burt et al. 2011 ). See Table A1 (Example Survey) and Table A2 (Survey Outcome Scoring) in the Appendix for more information. Employee-reported exposure and intervention use: A second set of surveys were administered to each employee up to nine times (at baseline and every three months for up to two years) at the same time the pain symptom surveys (described below) were administered. This set of surveys was designed by NIOSH specifically for this study. Employees were asked to rate the distribution of their workload among tasks expected to be impacted by the intervention and those tasks where no impact was expected. As a specific example, employees were asked how often on average they handled objects or stacked loads over 100 lbs. (such as appliances, large electronics equipment) in the last three months. Employees were then asked corresponding questions about their usage of the interventions for specific tasks. For example, employees were asked how often the new safety grant equipment was used to handle objects or stacked loads over 100 lbs. See Table 1 , Table A1 (Example Survey), and Table A2 (Survey Outcome Scoring) in the Appendix for more information.

Survey Sections Used to Determine Exposure Control Groups

The above survey data were then categorized based on the frequency of exposure and intervention use into four comparison groups of employee surveys that were used for the analysis:

Least Exposed (Group 1): Employee reported handling < 50 lbs. < 33% of the time.

Highly Exposed Intervention User (Group 2): Employee reported handling >= 50 lbs. > 33% of the time AND reported using the sponsored intervention routinely > 33% of the time during tasks where >= 50 lbs. were handled.

Highly Exposed Other (Group 3): Employee reported handling >= 50 lbs. > 33% of the time, but did NOT report using the sponsored intervention routinely > 33% of the time during tasks where >= 50 lbs. were handled. The employee may have reported using other equipment to aid material handling during tasks where >= 50 lbs. were handled.

Highly Exposed Body User (Group 4): Employee reported handling >= 50 lbs. > 33% of the time, but did NOT report using the sponsored intervention routinely > 33% of the time during tasks where >= 50 lbs. were handled, and instead reported using their body strength alone > 33 % of the time during tasks where >= 50 lbs. were handled.

Note that individual employee participants were placed into one of the above groups based on their responses at the time of each data collection, but that individuals over time may have been placed into more than one group. The category of >= 50 lbs. was chosen to represent the highest level of risk, based on the NIOSH lifting equation that recommends employees never lift more than 51 lbs. ( NIOSH, 1994 ).The choice of “> 33% of the time” to designate “routine” intervention use was chosen as a cut-point to create comparison groups (Groups 2 versus 4) of roughly equivalent size. The original survey included responses to a categorical question where reported intervention usage responses included: 1 = Never (0% of the time), 2= Occasional (1–33% of the time), 3= Frequent (34–66% of the time), 4= Regular (67–100% of the time), and Not applicable (safety grant equipment not in place yet). See Table 1 , Table A1 (Example Survey), and Table A2 (Survey Outcome Scoring) for more information.

2.5: Dependent Variables

2.5.1: low back pain.

Employee-reported low back pain was assessed with two surveys, both provided in the Appendix . The first survey was based on a modified Nordic discomfort assessment tool ( Kuorinka et al. 1987 ) that was administered to each employee up to three times (at baseline and every twelve months for up to two years). This survey included the following main outcome measure:

The second survey was the North American Spine Society (NASS) Lumbar Spine Outcome Assessment Instrument, that was administered to each employee up to nine times (at baseline and every three months for up to two years). The NASS instrument has been found to have acceptability, high re-test reliability, internal reliability, and validity for low back pain and disability in multiple language translations ( Bosković et al. 2009 ; Schluessmann et al. 2009 ; Schneider et al. 2007 ; Sigl et al. 2006 ; Weigl et al. 2006 ; Schaeren et al. 2005 ; Padua et al. 2001 ; Schochat et al. 2000 ; Pose et al. 1999 ; Daltroy et al. 1996 ). This survey included the following main outcome measures:

2.5.2: Upper Extremity Pain

Employee-reported upper extremity pain was assessed with two surveys, both provided in the Appendix . The first survey was based on a modified Nordic discomfort assessment tool ( Kuorinka et al. 1987 ) that was administered to each employee up to three times (at baseline and every twelve months for up to two years). This survey included the following main outcome measure:

The second survey used the Quick Disabilities of the Arm, Shoulder, and Hand (DASH) Outcome Measure with Work Module Option (Beaton et. al. 2001), that was administered to each employee up to nine times (at baseline and every three months for up to two years). The DASH outcome has been found to have acceptability, high re-test reliability, internal reliability, and validity for shoulder/arm pain and disability ( Adams et. al. 2005 ; Beaton et. al. 2005 ; Gay et. al. 2003 ; Solway et. al. 2002 ; Beaton et. al. 2001a , b ; Atroshi et. al. 2000 ; Hudak et. al. 1996 ). These instruments were jointly developed by the Institute for Work and Health (IWH) and the American Academy of Orthopaedic Surgeons (AAOS). This survey included the following main outcome measures:

2.5.3: Safety Incidents

Employee-reported safety incidents were assessed using a survey that was administered to each employee up to nine times (collected at baseline and every three months for up to two years). This survey was designed by NIOSH specifically for this study. See the Appendix for more information. This survey included the following main outcome measures:

2.6: Statistical Analysis

Poisson, two-part, and linear regression models with repeated measures were used to evaluate changes over time (pre- and post-intervention) in the frequency and severity of employee-reported employee low back pain, upper extremity pain, and safety incidents. One employer was excluded from analyses since they only reported seasonal work, averaging nine months per year. Comparisons over time were restricted to employees who completed all nine surveys. Regression models were used to compare Highly Exposed Intervention Users (Group 2) versus Highly Exposed Body Users (Group 4) based on reported exposures and intervention usage among all employees who completed any surveys. All analyses were conducted using SAS 9.4 ( SAS Institute, Inc., Cary, NC ).

2.7: Human Subjects Review

This study was approved by the NIOSH Institutional Review Board.

3.1: Participant Demographics and Intervention Summaries

Table 2 provides a summary of participating employer industries, intervention types, impacted workgroup sizes, and number of completed surveys. Overall, there were seven different major industry groups represented, with most in construction, manufacturing, and services (except public safety). All employers had at least one employee submit a consent form to participate. For two employers, no employee surveys were ever completed. The mean impacted workgroup size was 16.2 employees, ranging from 1–50 employees. Among affected employees at baseline, 32.5% (174/535) completed at least one survey, 19.4% (104/535) completed at least five surveys over one year, while 13.6% (73/535) completed all nine surveys over two years. The vast majority (95%,165/174) of participating employees were male. Total employer size (based on a count of all employees, not just those impacted by the interventions) ranged from 1 to 572 (mean = 64). Employer union status was not determined.

Employer Demographics and Intervention Summaries

3.2: Pain Baseline Comparison

Table 3 provides a baseline summary for reported employee demographics, symptoms, and job exposures. A majority reported at least some low back pain (NASS-Pain Frequency > 0, 67%) and upper extremity pain (DASH-Disability Frequency > 0, 55%) at baseline. The severity of reported low back pain was higher than reported upper extremity pain throughout the study. The table also compares Schedules A and B (intervention implementation) at baseline and shows that the two Schedules were generally not significantly different, though some differences were still notable. All the reported symptom outcome scores were higher in Schedule B and Schedule B participant employees tended to be older. Schedule B was higher than Schedule A in NASS-Pain frequency and score as well as Annual Low Back Symptom Frequency. There were no significant differences in baseline reported symptoms among employees who eventually completed nine surveys versus those that withdrew over the course of the study.

Pain Symptoms Comparison at Baseline *

One company performing seasonal work was not included.

3.3: Pain Time Trends

Table 4 presents employee-reported upper extremity/low back pain symptom frequency and severity trends over time. There were no significant trends in reported pain over time without controlling for reported exposure or intervention usage. The proportion of participant employees who were symptomatic (score > 0) decreased over time for four of six outcomes (Annual Upper Extremity Symptoms, Annual Low Back Symptoms, NASS-Neuro, and DASH-Disability) but increased for two outcomes (DASH-Work, NASS-Pain). Table 4 also depicts that the cumulative raw scores for Schedule A and B employees increased over time for three of four scored outcomes. There were no significant differences between symptoms reported for Schedule A and B employers.

Pain Symptom Frequency and Severity Time Trends *

Restricted to those with nine complete surveys; one company performing seasonal work was not included.

Schedule A employers received the intervention immediately; Schedule B received it after a six-month waiting period.

3.4: Pain, Exposure, and Intervention Usage Relationship

Among all employee surveys, an average of 39% reported routine intervention use (defined to be > 33% of the time) during tasks where >= 50 lbs. were handled, with some fluctuations over time (data not shown). Among highly exposed employees (who reported handling >= 50 lbs. > 33% of the time), an average of 18% reported routine intervention use, with a slightly greater proportion reporting routine use early in the study. An average of 37% of employees reported high exposures (handling >= 50 lbs. > 33% of the time) for the study, with a greater proportion reporting high exposures in the beginning of the study (50%) versus the end (30%).

Table 5 displays employee-reported symptom outcomes by exposure and intervention usage groups. For five of six symptom outcomes (all except Annual Upper Extremity Symptoms), the percent of participant employees who were symptomatic (score > 0) and mean symptom scores were lowest with the Least Exposed (Group 1). For all symptom outcomes, the percent of participants who were symptomatic and mean symptom scores were lower among Highly Exposed Intervention Users (Group 2) versus Highly Exposed Body Users (Group 4). Highly Exposed Intervention Users had significantly lower upper extremity mean symptom score outcomes (DASH-Work, DASH-Disability) and a lower frequency of upper extremity pain (Annual Upper Extremity Symptoms) than Highly Exposed Body Users (Group 4). After restricting analyses to only material handling equipment (excluding anti-fatigue mats), Highly Exposed Intervention Users had significantly lower reported low back pain frequency (NASS Pain > 0 and Annual Low Back Symptoms) than Highly Exposed Body Users (Group 4).

Pain Symptom Comparison by Exposure and Intervention Usage Groups *

Based on comparing Groups 2 and 4, with 95th percentile confidence intervals shown

• Group 1: Least Exposed

• Group 2: Highly Exposed Intervention User

• Group 3: Highly Exposed Other

• Group 4: Highly Exposed Body User

3.5: Safety Incidents Time Trends

Table 6 depicts the employee-reported frequency of safety incidents. There were no significant trends over time without controlling for reported exposure or intervention usage. There were no significant differences between Schedules A and B.

Safety Incident Frequency Time Trends *

3.6: Safety Incidents, Exposure, and Intervention Usage Relationship

Table 7 presents employee-reported incidents by exposure and intervention usage groups. Reported safety incident frequencies were lowest with the Least Exposed (Group 1). For material handling related safety incidents, reported incident frequencies were lower among Highly Exposed Intervention Users (Group 2) versus Highly Exposed Body Users (Group 4), though the differences were not significant.

Safety Incidents Comparison by Exposure and Intervention Usage Groups

One company performing seasonal work was excluded.

Based on comparing Groups 2 and 4, with 95th percentile confidence intervals shown.

4: Discussion

A number of employee participants in this study were symptomatic for low back and/or upper extremity pain. A majority reported at least some low back and upper extremity pain at baseline. The severity of reported low back pain was higher than reported upper extremity pain throughout the study. The level of low back pain (NASS-Pain, 21.3) was higher at baseline than that reported (15.5) in a study of warehouse employees ( Ferguson et al. 2008 ). The level of upper extremity pain (DASH-Disability, 2.3) at baseline was lower than Hunsaker et al. 2002 reported would be expected for the general population (10.1).

Although the DASH and NASS surveys were chosen for use in this study based on their demonstrated reliability and validity, researchers designed both surveys for use among patients recovering from injury, and not necessarily among working populations involving heavy material handling. A survivor effect may have contributed to the fact that DASH scores were relatively low. Although the current study collected the DASH and NASS surveys every three months, the symptom reporting timeframe for each survey was within the last week, so it is possible that employees did not report all episodic symptoms. The annual low back and upper extremity symptom surveys did ask participants to report all symptoms within the last year, but these surveys could have been more prone to recall bias.

This study had mixed results for reported back and upper extremity pain symptoms over time without controlling for reported exposure or intervention use. Although trends were not significant, fewer study participants were likely to report symptoms over the study period, but when participants did report symptoms, the symptoms were likely to be more severe over time. This was not due to a few highly-symptomatic individuals who tended to become worse over time. Upper extremity symptoms were generally low and skewed towards lower symptom severity while low back symptoms were higher and more normally distributed for severity (data not shown). These results are generally consistent with prior studies that have tracked musculoskeletal symptoms for the same individual over time and have shown that symptoms often persist or grow worse, especially among employees performing manually intensive tasks ( Oakman et al. 2016 ; Neupane et al. 2015 ; Neupane et al. 2013 ).

Although this study included a minor RCT design component (where employers were randomized to receive the intervention at different times, with one receiving the intervention immediately and the other six months later), Schedules A and B employees were different at baseline as all reported symptom scores were higher in Schedule B and Schedule B employees tended to be older. Originally, the study plan was to offset interventions by a year, but this offset duration was reduced to six months based on feedback during initial focus groups with OHBWC consultants to improve employer study participation. This relatively short offset between implementation schedules did not afford sufficient time or number of survey measurements for meaningful differences to develop between schedule groups. As a result, there were also no significant differences between intervention implementation Schedules A and B in terms of reported symptoms over time.

The relationship between reported symptoms and reported exposures and intervention usage was clearer. Among highly exposed participants (defined a priori to be those who reported handling >= 50 lbs. > 33% of the time), reported symptom frequency and mean symptom severity were lower among those who reported using the sponsored intervention versus those that reported using their body strength alone to handle objects >= 50 lbs. These differences were significant for upper extremity symptom frequency (Annual Upper Extremity Symptoms) and for upper extremity mean symptom severity outcomes (DASH-Work, DASH-Disability). After restricting analyses to only material handling equipment interventions (excluding anti-fatigue mats), low back pain frequency (NASS Pain > 0 and Annual Low Back Symptoms) were also significantly lower among highly exposed participant intervention users compared to those who reported using their body strength alone to handle objects >= 50 lbs.

This provides some evidence that the interventions when used did reduce self-reported symptoms among the most heavily exposed employees. These findings are similar to the relatively few prior studies that measured the impact of engineering controls on reported symptoms among non-office employees, including bricklayers ( Bongers et al. 2001 ; Luijsterburg et al. 2005 ) and healthcare employees ( Li et al. 2004 ). Other studies indicated mixed or no changes in reported symptoms after ergonomic engineering intervention among delivery drivers ( Devereux et al. 1997 ; McGlothlin et al. 1996 ), other construction employees ( Vink et al. 1997 ; Van der Molen et al. 2010 ), and manufacturing employees ( Johansson et al. 1993 ).

4.2: Safety Incidents

This study found no significant change in reported frequency of incidents over time without controlling for reported exposures or intervention use. Differences between intervention implementation schedules were not significantly different, likely due the relatively short six month offset between the schedules for employers receiving interventions. There were also no significant relationships between reported incident frequency and reported exposures and intervention usage. This was not unexpected, given that employers chose the interventions primarily to reduce biomechanical risk factors during material handling tasks rather than safety-related hazards.

4.3: Limitations

This is one of the largest prospective, multi-site quasi-experimental studies to assess the impact of ergonomic engineering interventions on reported pain symptoms and safety incidents in non-office work environments. However, there are a number of limitations associated with this research, including potential employer/employee selection bias, low employee participation rates, the use of employee-reported measures, and lack of control of other concurrent organizational interventions. Employers had to choose to participate in the overall intervention program, so results may not be generalizable beyond this study population. Once employers agreed to participate, employee participation in the surveys was still voluntary. Survey participation rates among affected employees at baseline were low, as only 19.4% completed at least five surveys over one year, while 13.6% completed all nine surveys over two years. There is no way to ascertain whether participating or non-participating employees from the study differed in terms of their pain levels, safety incidents, work exposures or intervention usage. Although exit interviews were attempted with employees who withdrew from the study, very few withdrawn employees responded. These few interviews indicated that the employees most often left the study because they left their current employer, and the decision was not due to musculoskeletal symptoms or safety concerns. Furthermore, baseline reported symptoms among employees who eventually completed nine surveys were not significantly different from those that withdrew over the course of the study. This provides some support that findings are still valid despite participant employee attrition. The reliability and validity of self-reported measures in general may be questioned, but this specific study used multiple measures of low back and upper extremity pain, including two validated measures (NASS, DASH) and nine repeated measures over a two-year period to address concerns. Finally, employers were free to engage in other interventions. It is possible that there were systematic differences between employers in terms of overarching safety/ergonomic programs which integrate such elements as management commitment, employee participation, hazard identification, hazard control, training, and evaluation ( NIOSH, 1997 ). Such differences could also have impacted reported symptoms and safety incidents.

5: Conclusions

This study evaluated the effectiveness of a variety of ergonomic interventions in material handling operations in a number of employers and industries including construction, manufacturing, and services. Interventions included largely material handling equipment such as powered hand trucks and lift tables. Outcomes included employee-reported low back/upper extremity pain and safety incidents at baseline, every three months, and annually for up to two years. Although survey participation rates among affected employees at baseline were low, employees reported fewer symptoms while using the equipment for heavy material handling. Specifically, 32.5% of employees completed at least one survey, while 13.6% completed all nine surveys over two years. Among highly exposed employees (who reported handling >= 50 lbs. > 33% of the time), upper extremity pain symptom frequency and severity were lower among those who reported using the interventions routinely (> 33% of the time) versus those that reported using their body strength alone routinely to handle objects >= 50 lbs. After excluding from analyses one employer that used anti-fatigue mats, low back pain frequency was also significantly lower among highly exposed routine intervention users. In conclusion, there was some evidence that the insurer-supported material handling engineering interventions were effective in reducing self-reported pain symptoms for highly exposed employees. This study is consistent with prior research that has indicated that ergonomic material handling equipment can reduce biomechanical risk factors for work-related musculoskeletal disorders and workers’ compensation claims. These findings are also consistent with other research that has indicated that integrated safety/ergonomic programs can reduce injuries, as the use of such material handling equipment can represent important aspects of overall hazard control within these systems.

Example Survey

Survey Outcome Scoring

Publisher's Disclaimer: Disclaimer: The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention or the Ohio Bureau of Workers’ Compensation.

6: References

• Adams J, Burridge J, Mullee M, Hammond A, Cooper C. 2005. Self-reported hand functional ability measured by the DASH in individuals with early rheumatoid arthritis. British Journal of Hand Therapy 10(1): 21–4. [ Google Scholar ]
• Atroshi I, Gummesson C, Andersson B, Dahlgren E, Johansson A. 2000. The disabilities of the arm, shoulder and hand (DASH) outcome survey. Reliability and validity of the Swedish version evaluated in 176 patients. Acta Orthopaedica Scandinavica 71(6): 613–618. 10.1080/000164700317362262 [ DOI ] [ PubMed ] [ Google Scholar ]
• Azaroff LS, Levenstein C, Wegman DH. 2002. Occupational injury and illness surveillance: conceptual filters explain underreporting. Am J Public Health 92:1421–1429. 10.2105/ajph.92.9.1421 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Azaroff LS, Davis LK, Naparstek R, Hashimoto D, Laing JR, Wegman DH. 2013. Barriers to use of employees’ compensation for patient care at Massachusetts community health centers. Health Serv Res 48(4):1375–1392. 10.1111/1475-6773.12045 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Beaton DE, Wright JG, Katz JN, Upper Extremity Collaborative Group. 2005. Development of the QuickDASH: Comparison of three item-reduction approaches. Journal of Bone & Joint Surgery - American Volume 87(5):1038–46. 10.2106/JBJS.D.02060 [ DOI ] [ PubMed ] [ Google Scholar ]
• Beaton DE, Davis AM, Hudak P, McConnell S. 2001a. The DASH (Disabilities of the Arm, Shoulder and Hand) Outcome Measure: What Do We Know About It Now? British Journal of Hand Therapy 6(4):109–118 [ Google Scholar ]
• Beaton DE, Katz JN, Fossel AH, Wright JG, Tarasuk V, Bombardier C. 2001b. Measuring the Whole or the Parts? Validity, Reliability & Responsiveness of the Disabilities of the Arm, Shoulder, and Hand Outcome Measure in Different Regions of the Upper Extremity. Journal of Hand Therapy 14(2):128–146. [ PubMed ] [ Google Scholar ]
• Biddle J, Roberts K, Rosenman KD, Welch EM. 1998. What percentage of employees with work-related illnesses receive employees’ compensation benefits? J Occup Environ Med 40:325–331 [ DOI ] [ PubMed ] [ Google Scholar ]
• Burt S, Crombie K, Jin Y, Wurzelbacher S, Ramsey J, Deddens J. 2011.Workplace and individual risk factors for carpal tunnel syndrome. Occup Environ Med 68(12):928–33. 10.1136/oem.2010.063677 [ DOI ] [ PubMed ] [ Google Scholar ]
• Bongers PM, Luijsterburg P, Van den Heuvel F, De Vroome E, Miedema MC, Douwes M. 2001. Evaluation of new working methods for a team of bricklayers: heightened bricklaying and mechanisation of transport. Hoofddorp: Nederlandse Organisatie voor toegepast-natuurkundig onderzoek (TNO). TNO publication R2016811/1070130 [ Google Scholar ]
• Bosković K, Todorović-Tomasević S, Naumović N, Grajić M, Knezević A. 2009. The quality of life of lumbar radiculopathy patients under conservative treatment. Vojnosanit Pregl Oct;66(10):807–12. [ DOI ] [ PubMed ] [ Google Scholar ]
• Bureau of Labor Statistics. 2019. TABLE R4. Number of nonfatal occupational injuries and illnesses involving days away from work by industry and selected events or exposures leading to injury or illness, private industry, 2017
• Collins JW, Wolf L, Bell J, Evanoff B. 2004. An Evaluation of a “Best Practices” Back Injury Prevention Program in Nursing Homes. Injury Prevention 10:206–211. 10.1136/ip.2004.005595 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Daltroy LH, Cats-Baril WL, Katz J, Fossel AH, Liang MH. 1996. The North American Spine Society Lumbar Spine Outcome Assessment Instrument: Reliability and validity tests. Spine 21, 741–749. 10.1097/00007632-199603150-00017 [ DOI ] [ PubMed ] [ Google Scholar ]
• Devereux J, Buckle P, Haisman M. 1997. The evaluation of a handhandle interface tool (HHIT) for reducing musculoskeletal discomfort associated with the manual handling of gas cylinders. Int J Ind Ergon 21(1):23–34. [ Google Scholar ]
• Driessen MT, Proper KI, van Tulder MW, Anema JR, Bongers PM, van der Beek AJ. 2010. The effectiveness of physical and organisational ergonomic interventions on low back pain and neck pain: a systematic review. Occup Environ Med 67(4):277–85. 10.1136/oem.2009.047548 [ DOI ] [ PubMed ] [ Google Scholar ]
• Fan ZJ, Bonauto DK, Foley MP, Silverstein BA. 2006. Underreporting of work-related injury or illness to employees’ compensation: Individual and industry factors. J Occup Environ Med 48:914–922. 10.1097/01.jom.0000226253.54138.1e [ DOI ] [ PubMed ] [ Google Scholar ]
• Ferguson S, Burr DL, Allread WG, Ashida S, Fujishiro K, Heaney CA, Marras WS. 2008. Prevalence of low back disorders in furniture distribution centers. Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting
• Fujishiro K, Weaver JL, Heaney CA, Hamrick CA, Marras WS. 2005. The effect of ergonomic interventions in healthcare facilities on musculoskeletal disorders. Am J Ind Med 48(5):338–47. 10.1002/ajim.20225 [ DOI ] [ PubMed ] [ Google Scholar ]
• Gay RE, Amadio PC, Johnson JC. 2003. Comparative responsiveness of the Disabilities of the Arm, Shoulder, and Hand, the Carpal Tunnel Survey and the SF-36 to clinical change after carpal tunnel release. Journal of Hand Surgery (American) 28A(2): 250–254. 10.1053/jhsu.2003.50043 [ DOI ] [ PubMed ] [ Google Scholar ]
• Huang YH, Leamon TB, Courtney TK, DeArmond S, Chen PY, Blair MF. 2009. Financial Decision Makers’ Views on Safety. Professional Safety 54(4):36–42. [ Google Scholar ]
• Hudak P, Amadio PC, Bombardier C, and the Upper Extremity Collaborative Group.1996. Development of an Upper Extremity Outcome Measure: The DASH (Disabilities of the Arm, Shoulder, and Hand). Am J Ind Med 29:602–608. [ DOI ] [ PubMed ] [ Google Scholar ]
• Hunsaker FG, Cioffi DA, Amadio PC, Wright JG, Caughlin B. 2002. The American Academy of Orthopaedic Surgeons Outcomes Instruments – Normative Values from the General Population. Journal of Bone and Joint Surgery 84-A(2):208–215. 10.2106/00004623-200202000-00007 [ DOI ] [ PubMed ] [ Google Scholar ]
• Johansson JÅ, Kadefors R, Rubenowitz S, Klingenstierna U, Lindström I, Engström T, et al. 1993. Musculoskeletal symptoms, ergonomic aspects and psychosocial factors in two different truck assembly concepts. Int J Ind Ergon 1993;12:35–48. [ Google Scholar ]
• Kuorinka I, Jonsson B, Kilbom A, Vinterberg H, Biering-Sørensen F, Andersson G, Jørgensen K. 1987. Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms. Appl Ergon 18(3):233–7. [ DOI ] [ PubMed ] [ Google Scholar ]
• Li J, Wolf L, Evanoff B. 2004. Use of mechanical patient lifts decreased musculoskeletal symptoms and injuries among health care employees. Inj Prev 10(4):212–6. 10.1136/ip.2003.004978 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Liberty Mutual Research Institute. 2019. Workplace Safety Index, 2019.
• Lipscomb HJ, Dement JM, Silverstein BA, Cameron W, Glazner JE. 2009. Who is paying the bills? Health care costs for musculoskeletal back disorders, Washington State carpenters, 1989–2003. J Occup Environ Med 51:1185–1192. 10.1097/JOM.0b013e3181b68d0a [ DOI ] [ PubMed ] [ Google Scholar ]
• Lowe BD, Albers J, Hayden M, Lampl MP, Naber S, Wurzelbacher SJ. 2020. Review of Construction Employer Case Studies of Safety and Health Equipment Interventions. J Constr Eng Manage 146(4): 1–11. 10.1061/(ASCE)CO.1943-7862.0001782 [ DOI ] [ Google Scholar ]
• Luijsterburg PA, Bongers PM, de Vroome EMM. 2005. A new bricklayers’ method for use in the construction industry. Scand J Work Environ Health 31(5):394–400 [ DOI ] [ PubMed ] [ Google Scholar ]
• Marras WS, Allread WG, Burr DL, Fathallah FA. 2000. Prospective validation of a low-back disorder risk model and assessment of ergonomic interventions associated with manual materials handling tasks, Ergonomics, 43:11, 1866–1886. 10.1080/00140130050174518 [ DOI ] [ PubMed ] [ Google Scholar ]
• McGlothlin JD, Clark BL, Elfers DL. 1996. Ergonomic interventions for the soft drink beverage delivery industry. Cincinnati (OH): National Institute for Occupational Safety and Health (NIOSH), US [ Google Scholar ]
• Department of Health and Human Services. DHHS (NIOSH) publication no 96–109.38
• Mirka GA, Smith C, Shivers C, Taylor J. 2002. Ergonomic interventions for the furniture manufacturing industry, Part I. International Journal of Industrial Ergonomics 29 (5) 263–273. 10.1016/S0169-8141(01)00067-1 [ DOI ] [ Google Scholar ]
• Mulimani P, Hoe VC, Hayes MJ, Idiculla JJ, Abas AB, Karanth L. 2018. Ergonomic interventions for preventing musculoskeletal disorders in dental care practitioners. Cochrane Database Syst Rev 10. 10.1002/14651858.CD011261.pub2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Neupane S, Miranda H, Virtanen P, Siukola A, and Nygård CH. 2013. Do physical or psychosocial factors at work predict multi-site musculoskeletal pain? A 4-year follow-up study in an industrial population. International Archives of Occupational and Environmental health 86:581–589. 10.1007/s00420-012-0792-2 [ DOI ] [ PubMed ] [ Google Scholar ]
• Neupane S, Leino-Arjas P, Nygärd CH, Miranda H, Siukol A, and Virtanen P. 2015. Does the association between musculoskeletal pain and sickness absence due to musculoskeletal diagnoses depend on biomechanical working conditions? International Archives of Occupational and Environmental Health 88:273–279. 10.1007/s00420-014-0957-2 [ DOI ] [ PubMed ] [ Google Scholar ]
• National Institute for Occupational Safety and Health (NIOSH). 1994. Applications Manual for the Revised NIOSH Lifting Equation https://www.cdc.gov/niosh/docs/94-110/ . Accessed August 16, 2019.
• National Institute for Occupational Safety and Health. 1997. Elements of Ergonomics Programs Publication 97–117. https://www.cdc.gov/niosh/docs/97-117/ . Accessed January 28, 2020.
• National Institute for Occupational Safety and Health. 2020. Mining Safety Pays https://www.cdc.gov/niosh/mining/content/economics/safetypayscostesttechguide.html .Accessed January 29, 2020.
• Safety Occupational and Administration Health. 2020. Safety Pays https://www.osha.gov/safetypays/background.html . Access January 29, 2020.
• Oakman J, Neupane S, and Nygård C. 2016. Does age matter in predicting musculoskeletal disorder risk? An analysis of workplace predictors over 4 years. International Archives of Occupational and Environmental Health 89:1127–1136. 10.1007/s00420-016-1149-z [ DOI ] [ PubMed ] [ Google Scholar ]
• Padua R, Padua L, Ceccarelli E, Romanini E, Bondì R, Zanoli G, Campi A. 2001. Cross-cultural adaptation of the lumbar North American Spine Society survey for Italian-speaking patients with lumbar spinal disease. Spine 26(15):E344–7. 10.1097/00007632-200108010-00012 [ DOI ] [ PubMed ] [ Google Scholar ]
• Park RM, Bushnell PT, Bailer AJ, Collins JW, Stayner LT. 2009. Impact of publicly sponsored interventions on musculoskeletal injury claims in nursing homes. Am J Ind Med 52(9):683–697. 10.1002/ajim.20731 [ DOI ] [ PubMed ] [ Google Scholar ]
• Pose B, Sangha O, Peters A, Wildner M. 1999. Validation of the North American Spine Society Instrument for assessment of health status in patients with chronic backache. Z Orthop Ihre Grenzgeb 137(5):437–41. 10.1055/s-2008-1037387 [ DOI ] [ PubMed ] [ Google Scholar ]
• Resnick M, Chaffin DB. An ergonomic evaluation of three classes of material handling device (MHD). Int J Ind Ergon 19(3):217–29. 10.1016/S0169-8141(95)00108-5 [ DOI ] [ Google Scholar ]
• Rosenman KD, Gardiner JC, Wang J, Biddle J, Hogan A, Reilly MJ, Roberts K, Welch E. 2000. Why most employees with occupational repetitive trauma do not file for employees’ compensation. J Occup Environ Med 42:25–34. [ DOI ] [ PubMed ] [ Google Scholar ]
• SAS version 9.4 (SAS Institute, Inc., Cary, NC: ) [ Google Scholar ]
• Schaeren S, Bischoff-Ferrari HA, Knupp M, Dick W, Huber JF, Theiler R. 2005. A computer touch-screen version of the North American Spine Society outcome assessment instrument for the lumbar spine. J Bone Joint Surg Br 87(2):201–4 [ DOI ] [ PubMed ] [ Google Scholar ]
• Scherzer T and Wolfe N. 2008. Barriers to employees’ compensation and medical care for injured Personal Assistance Services employees. Home Health Care Serv Q 27(1):37–58. 10.1300/J027v27n01_03 [ DOI ] [ PubMed ] [ Google Scholar ]
• Schluessmann E, Diel P, Aghayev E, Zweig T, Moulin P, Röder C. 2009. SWISSspine: a nationwide registry for health technology assessment of lumbar disc prostheses. Eur Spine J 8(6):851–61. 10.1007/s00586-009-0934-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schochat T, Rehberg W, von Kempis J, Stucki G, Jäckel WH. 2000. The North American Spine Society Lumbar Spine Outcome Assessment Instrument: translation and psychometric analysis of the German version in rehabilitation patients with chronic back pain. Z Rheumatol 59(5):303–13 [ DOI ] [ PubMed ] [ Google Scholar ]
• Schneider C, Krayenbühl N, Landolt H. 2007. Conservative treatment of lumbar disc disease: patient’s quality of life compared to an unexposed cohort. Acta Neurochir (Wien) 149(8):783–91 [ DOI ] [ PubMed ] [ Google Scholar ]
• Sigl T, Cieza A, Brockow T, Chatterji S, Kostanjsek N, Stucki G. 2006. Content comparison of low back pain-specific measures based on the International Classification of Functioning, Disability and Health (ICF). Clin J Pain 22(2):147–53 [ DOI ] [ PubMed ] [ Google Scholar ]
• Solway S, Beaton DE, McConnell S, Bombardier C. 2002. The DASH Outcome Measure User’s Manual, Second Edition. Toronto: Institute for Work & Health [ Google Scholar ]
• Sultan-Taïeb H, Parent-Lamarche A, Gaillard A, Stock S, Nicolakakis N, Hong QN, Vezina M, Coulibaly Y, Vézina N, Berthelette D. 2017. Economic evaluations of ergonomic interventions preventing work-related musculoskeletal disorders: a systematic review of organizational-level interventions. BMC Public Health 8;17(1):935. 10.1186/s12889-017-4935-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Thumala V, Wang D, Liu T-C. 2018. WCRI Correlates of Opioids Dispensing Cambridge, MA: Employees’ Compensation Research Institute. WC-18–48 [ Google Scholar ]
• Tompa E, Dolinschi R, de Oliveira C, Amick BC 3rd, Irvin E. 2010. A systematic review of workplace ergonomic interventions with economic analyses. J Occup Rehabil 20(2):220–34. 10.1007/s10926-009-9210-3 [ DOI ] [ PubMed ] [ Google Scholar ]
• Van der Molen HF, Sluiter JK, Hulshof CT, Vink P, Frings-Dresen MH. 2005. Effectiveness of measures and implementation strategies in reducing physical work demands due to manual handling at work. Scand J Work Environ Health 31 Suppl 2:75–87 [ PubMed ] [ Google Scholar ]
• Van der Molen HF, Frings-Dresen MH, Sluiter JK. 2010. The longitudinal relationship between the use of ergonomic measures and the incidence of low back complaints. Am J Ind Med 53(6):635–40. 10.1002/ajim.20830 [ DOI ] [ PubMed ] [ Google Scholar ]
• Van Eerd D, Munhall C, Irvin E, Rempel D, Brewer S, van der Beek AJ, Dennerlein JT, Tullar J, Skivington K, Pinion C, Amick B. 2016. Effectiveness of workplace interventions in the prevention of upper extremity musculoskeletal disorders and symptoms: an update of the evidence. Occup Environ Med 73(1):62–70. 10.1136/oemed-2015-102992 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vink P, Urlings IJM, Van der Molen HF. 1997. A participatory ergonomics approach to redesign work of scaffolders. Saf Sci Scand J Work Environ Health 26(1/2):75–87. 10.1016/S0925-7535(97)00030-1 [ DOI ] [ Google Scholar ]
• Weigl M, Ewert T, Kleinschmidt J, Stucki G. 2006. Measuring the outcome of health resort programs. J Rheumatol 33(4):764–70 [ PubMed ] [ Google Scholar ]
• Wurzelbacher SJ, Bertke SJ, Lampl MP, Bushnell PT, Meyers AR, Robins DC, Al-Tarawneh IS. 2014. The effectiveness of insurer-supported safety and health engineering controls in reducing employees’ compensation claims and costs. Am J Ind Med 57(12):1398–412. 10.1002/ajim.22372 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
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