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. Author manuscript; available in PMC: 2015 Jul 21.
Published in final edited form as: Occup Environ Med. 2013 May 3;70(8):529–537. doi: 10.1136/oemed-2013-101365

Personal and workplace psychosocial risk factors for carpal tunnel syndrome: a pooled study cohort

Carisa Harris-Adamson 1,2, Ellen A Eisen 1, Ann Marie Dale 3, Bradley Evanoff 3, Kurt T Hegmann 4, Matthew S Thiese 4, Jay M Kapellusch 5, Arun Garg 6, Susan Burt 7, Stephen Bao 8, Barbara Silverstein 8, Fred Gerr 9, Linda Merlino 9, David Rempel 10,11
PMCID: PMC4508843  NIHMSID: NIHMS701981  PMID: 23645610

Abstract

Background

Between 2001 and 2010, six research groups conducted coordinated multiyear, prospective studies of carpal tunnel syndrome (CTS) incidence in US workers from various industries and collected detailed subject-level exposure information with follow-up symptom, physical examination, electrophysiological measures and job changes.

Objective

This analysis of the pooled cohort examined the incidence of dominant-hand CTS in relation to demographic characteristics and estimated associations with occupational psychosocial factors and years worked, adjusting for confounding by personal risk factors.

Methods

3515 participants, without baseline CTS, were followed-up to 7 years. Case criteria included symptoms and an electrodiagnostic study consistent with CTS. Adjusted HRs were estimated in Cox proportional hazard models. Workplace biomechanical factors were collected but not evaluated in this analysis.

Results

Women were at elevated risk for CTS (HR=1.30; 95% CI 0.98 to 1.72), and the incidence of CTS increased linearly with both age and body mass index (BMI) over most of the observed range. High job strain increased risk (HR=1.86; 95% CI 1.11 to 3.14), and social support was protective (HR=0.54; 95% CI 0.31 to 0.95). There was an inverse relationship with years worked among recent hires with the highest incidence in the first 3.5 years of work (HR=3.08; 95% CI 1.55 to 6.12).

Conclusions

Personal factors associated with an increased risk of developing CTS were BMI, age and being a woman. Workplace risk factors were high job strain, while social support was protective. The inverse relationship between CTS incidence and years worked among recent hires suggests the presence of a healthy worker survivor effect in the cohort.

Introduction

Carpal tunnel syndrome (CTS) is a common peripheral entrapment neuropathy resulting from compression of the median nerve under the transverse carpal ligament at the wrist. CTS is an important driver of workers' compensation costs, lost time, lost productivity and disability.1,2 Although not as common as other upper extremity disorders, CTS is an important occupational health problem because of higher disability and overall costs than virtually any other upper extremity disorder.3 Prior studies have related CTS to both personal and occupational risk factors,48 however, the strength of these associations and the exposure–response relationships are not well described.1 To date, few large prospective studies using rigorous case criteria, individual-level exposure data, and appropriate control for confounding by personal factors have examined associations between occupational psychosocial and biomechanical risk factors and CTS incidence.7 To address this and other gaps in the literature, six research groups designed coordinated, multiyear, prospective epidemiological studies of US production and service workers from a variety of industries. Subsequent to completion of the studies, data on detailed subject-level exposure information was pooled with longitudinal assessment of symptoms, physical examination results, electrophysiological measures and biomechanical factors due to job changes.9 In the current manuscript, we describe the relationships between personal factors, occupational psychosocial factors and duration of employment, with CTS incidence, while adjusting for effects of confounding variables. Workplace biomechanical factors were collected and will be presented in a future paper, and thus, are not included in these analyses.

Population-based CTS incidence rates have ranged from 0.23 per 100 person-years10 to 11 per 100 person-years depending on study sample, occupational sectors and case definitions.11,12 Although numerous studies have identified associations between occupational risk factors, such as high hand force and repetitive hand activities and CTS,1315 relatively few studies have assessed the role of occupational psychosocial factors.1618 Moreover, variability in CTS case definitions have limited comparisons of results across studies.19 Thus, relatively little is known about how occupational psychosocial factors (such as job strain) and work organisational factors independently contribute to the risk of CTS.20

Associations between CTS and age, female gender, pregnancy and body mass index (BMI), have been reported in numerous studies.2125 However, detailed descriptions of the exposure– response relationships between these personal risk factors and CTS are not available, especially for occupational cohorts. In addition to demographic characteristics, comorbid conditions, such as rheumatoid arthritis,23,26 diabetes mellitus23,2628 and thyroid disease,24,29 have also been associated with CTS risk. Associations between CTS and other risk factors, such as gout and smoking status are uncertain26 and have not been assessed with adequate power in occupational studies.

In the current analysis, we examine associations between personal demographic and health characteristics, occupational psychosocial stress and work organisational factors, and incident CTS in a large cohort of industrial workers. In addition, the healthy worker survivor effect30 has rarely been taken into account in studies of musculoskeletal injuries, though a study of CTS may be particularly vulnerable to this bias depending upon the extent of the associated morbidity. If workers highly exposed to repetition and forceful movements, for example, are more likely to leave the workforce due to CTS symptoms, then the remaining exposed workers may have lower risk of developing CTS. Therefore, a secondary aim was to examine evidence for healthy worker bias in this first report of a pooled prospective cohort study of CTS.

Methods

Study participants and procedures

Participants

The 4321 individuals in the current analyses were recruited into six prospective epidemiological studies of risk factors for work-related upper-extremity musculoskeletal disorders (UEMSDs) conducted between 2001 and 2010. Details on each study design, health outcome pooling methods and baseline CTS prevalence are provided elsewhere.9 Common inclusion criteria were: (1) full-time work in industries primarily engaged in manufacturing, production, service and construction and (2) availability of individual-level exposure information. This analysis was restricted to the 3515 participants for whom follow-up data were available and who did not have baseline CTS or previous carpal tunnel surgery release (n=338), or baseline polyneuropathy (n=58).9 There was varied representation of workers across standard industrial classification (SIC) divisions with the majority of subjects coming from the manufacturing (n=2256), services (n=673) and construction (n=335) sectors. Other SIC divisions represented included agriculture (n=148), wholesale trade (n=47) and retail trade (n=49).

Baseline information

In all six studies, questionnaires were administered at study enrolment (baseline) to collect information on work history, demographics, medical history and musculoskeletal symptoms. Survey or interview questions regarding the psychosocial work environment were administered either at study enrolment or at 6 months after being hired. Five of the six studies included items from the Job Content Questionnaire (JCQ)31 necessary to calculate the psychological job demand and decision latitude scores. Five of six studies administered an electrodiagnostic study (EDS) of all workers' median and ulnar nerves at baseline, while one study administered EDS only to those reporting symptoms consistent with CTS. All studies administered physical examinations either to all subjects or for those reporting upper limb symptoms.9 In all studies, investigators responsible for collecting health outcome information were blinded to exposure status.

Periodic follow-up

Symptoms were assessed at regular intervals during follow-up, though the interval length differed across the six studies. Physical examinations and EDS were administered either in response to positive symptoms or annually, depending on the particular study design.9

Electrodiagnostic procedures

Electrophysiologic measures obtained across the wrist included median nerve sensory latency, median nerve motor latency and ulnar nerve sensory latency. Four different recording devices were used, and the comparability of EDS methods has been described elsewhere.9 All sensory latency values were normalised to a distance of 14 cm. All latencies (motor and sensory) were adjusted for measured skin temperature.9 Latencies not quantifiable but clearly abnormal (ie, absent evoked response) were classified as abnormal.

Measures

Personal and occupational psychosocial factors

All studies collected participant age, gender, height, weight, BMI, race/ethnicity, education, smoking status, hand dominance and comorbid medical conditions, such as rheumatoid arthritis and diabetes mellitus. Most studies also collected information about pregnancy status, gout and thyroid disease. Previous carpal tunnel release and disorders of the distal upper extremity were also assessed. The time spent engaged in non-occupational, non-aerobic hand-intensive activities (ie, knitting, gardening, housework) and non-occupational, aerobic, non-hand-intensive activities (ie, jogging, walking, swimming, basketball, soccer) was assessed at baseline and summed to provide the total number of hours spent in each category of activity per week. Neither variable included hand-intensive aerobic activity (ie, biking). General health was assessed on a 5-point scale.

Information on occupational psychosocial factors was collected at baseline or within 6 months of being newly hired, with scales from the JCQ. The JCQ psychological job demand and decision latitude scales were each dichotomised by splitting the distributions at their respective median values. The four-category job strain variable was created by assigning participants to one of the four quadrants resulting from the two split distributions (ie, high demand, low control; low demand, low control; high demand, high control; and low demand, high control).31 The a priori putative high job strain was defined as the job strain quadrant characterised by high demand and low control. In addition to the demand and control domains, a dichotomous social support variable was created by summing the JCQ coworker and supervisor support scale scores, and then splitting the resulting distribution at the median. The self-reported years worked at the current employer at enrolment, and the total time enrolled in the study up to the endpoint (ie, loss to follow-up, censoring, or end of study) were summed and used as a surrogate for total exposure. For analyses comparing time on the job, recent hires were defined as those hired within a year of enrolment.

Outcome

The primary outcome was CTS of the dominant hand. The case definition for CTS required symptoms that met study criteria (below) and median neuropathy based on an EDS consistent with median nerve mononeuropathy at the wrist.32,33 Symptom information was collected by survey or interview, and the symptom criteria were tingling, numbness, burning, and/or pain in one or more of the first three digits (thumb, index finger, and long finger) since the prior symptom collection date. The minimum requirement for triggering a physical examination was ‘occurring three times or within the last seven days’. The symptom questions used have been shown to have good to excellent test-retest reliability34,35 Median mononeuropathy was defined as temperature and distance adjusted: (1) peak median sensory latency >3.7 ms or onset median sensory latency >3.2 ms at 14 cm, (2) motor latency >4.5 ms, (3) transcarpal sensory difference of >0.85 ms (the difference in sensory latencies between the median and ulnar nerves across the wrist) and/or (4) an absent latency value consistent with an abnormal NCS. The latency thresholds for the pooled EDS data were determined by the consortium members prior to data analysis. Thresholds were selected based on the literature, and where there was a range, thresholds were selected that increased specificity.9 Subjects who met the study case definition for CTS at baseline were excluded from analyses. Incident cases were those who met both symptom and EDS criteria concurrently during follow-up. Polyneuropathy cases were defined as those meeting CTS case criteria with concurrent temperature-corrected peak ulnar sensory latency >3.68 ms or onset ulnar sensory latency >3.18 ms at 14 cm. Polyneuropathy cases were censored at the time the polyneuropathy case definition was met, and were not included as CTS cases. Individuals who were symptomatic without a subsequent EDS were censored at their last date of known cases status.

Statistical analysis

Dominant hand CTS incidence rates and crude incidence rate ratios (IRR) were calculated for each demographic and health-related factor, as well as for occupational psychosocial characteristics and years worked. HRs were estimated using Cox proportional hazards regression, with robust CIs, and adjusted for potential confounding. Years worked was categorised based on the distribution of cases to ensure an adequate number of cases in all categories. To account for left truncation bias due to follow-up of subjects hired before baseline,36 we also stratified the analysis of years worked and CTS by date of hire; subjects hired within a year of enrolment were considered separately. Covariates, including age, gender, BMI and medical conditions were considered potential confounders. Confounding was assessed using a 10% change in coefficient criterion of the magnitude of the primary exposure effect. The interactions of gender and comorbidities (BMI ≥30 kg/m2 or the existence of a comorbid medical condition) were assessed by stratification and inclusion of interaction terms in the models. The functional form of the relationship between CTS and age and BMI were assessed using penalised splines37 in a Cox model (R Core Team, Vienna, Austria). All analyses were implemented with the Stata Statistical Package (Stata, College Station, Texas, USA).

Results

The baseline cohort included 4321 participants. After excluding prevalent CTS (n=338) and polyneuropathy cases (n=58) and those lost to follow-up (n=410), the pooled prospective cohort included 3515 individuals (table 1). Approximately half the cases were women, and just over half were less than 40 years of age. Eleven percent were college graduates (n=336) of which only 11 became incident cases. Most subjects in the pooled cohort had worked with their current employer for more than a year prior to enrolment (referred to as ‘non-recent hires’); the average years worked at baseline was 6.2 years (SD=8.2). A sizeable subset (n=1237), however, was enrolled within a year of hire (ie, ‘recent hires’) (table 2).

Table 1. Demographics and health-related characteristics.

Total n=3515 %
Gender
 Male 1860 53
 Female 1654 47
Age (years)
 <30 years of age 1089 31
 ≥30 and <40 years of age 836 24
 ≥40 and <50 years of age 933 26
 ≥50 years of age 656 19
Ethnicity
 Caucasian 1901 60
 Hispanic 524 16
 African–American 499 16
 Asian 160 5
 Other 89 3
Education
 Some high school or less 572 16
 High school graduate or above 2914 84
 Right hand dominant 3205 91
Body mass index
 Body mass index (<30 kg/m2: normal or overweight) 2324 66
 Body mass index (≥30 kg/m2: obese) 1176 34
General Health
 Very good or excellent 891 43
 Good 897 43
 Fair or poor 281 14
Medical condition
 No medical condition 3164 90
 Current medical condition 346 10
  Diabetes mellitus 123 4
  Rheumatoid arthritis 66 2
  Thyroid disease (hyper- or hypothyroid) 159 5
  Pregnancy 19 1
 Gout 42 1
Previous DUE MSD
 No previous DUE MSD 2559 90
 Previous DUE MSD 297 10
Smoking status
 Never smoked 1897 54
 Currently smoked 1006 29
 Previously smoked 596 17
Weekly aerobic activity
 >3 h/week 1040 65
 ≤3 h/week 548 35
Weekly hand intensive activity (non-occupational)
 >3 h/week 727 34
 ≤3 h/week 1399 66

DUE, Distal upper extremity; MSD, Musculoskeletal disorder

Table 2. Summary of workplace factors.

Total n=3515 %
Total years worked (recent hires*)
 ≤3.5 years 517 42
 >3.5 years 720 58
Total years worked (full cohort)
 ≤3.5 years 755 22
 >3.5 years and ≤7 years 1302 37
 >7 years and <=15 years 886 25
 >=15 years 551 16
Job strain
 Low job strain (low demand and high control) 424 27
 Active (high demand and high control) 308 20
 Passive (low demand and low control) 364 23
 High job strain (high demand and low control) 462 30
Social support
 Low support 681 43
 High support 895 57
Physically exhausted
 None to slightly physically exhausted 1378 64
 Moderate to severely physically exhausted 775 36
Mental exhaustion
 None to slight mentally exhausted 1616 75
 Moderate to severely mentally exhausted 549 25
Job satisfaction
 Very satisfied 1172 38
 Satisfied 1513 49
 Dissatisfied or very dissatisfied 407 13
*

Recent hire defined as hired within one year of study enrolment.

There were 204 (5.8%) incident cases of dominant-side CTS observed during the 8833 person-years of follow-up for an incidence rate of 2.3 (95% CI 2.0 to 2.7) per 100 person-years. Follow-up time across studies ranged from 2 to 7 years.9 Twenty-eight individuals were censored due to the development of polyneuropathy, and 159 individuals were censored at their last time of known case status due to incomplete health outcome information (ie, positive symptoms without a subsequent EDS). To examine the temporal pattern of CTS development, we evaluated the baseline status of the 204 incident cases. Approximately 20% (n=40) of the incident CTS cases were both symptom free and had a normal EDS at baseline, while 63% (n=128) had an abnormal EDS with no symptoms, and 11% (n=22) had symptoms with a normal EDS. Fourteen cases had either negative symptoms or a negative EDS at baseline with the other criterion missing. By contrast, among non-cases, 71% were symptom free with normal EDS, 21% had abnormal EDS only, and 8% had symptoms only. The adjusted HR for incident CTS among those with baseline symptoms only was 5.48 (95% CI 3.29 to 9.14), and for abnormal EDS only was 8.83 (95% CI 5.98 to 13.02). The mean years worked among the cases of the non-recent hires was 11.0 (SD=8.5) compared with 3.7 years (SD=1.3) in the subset recently hired. The crude incidence rate ratio comparing those hired more than a year before enrolment to those hired less than a year was 3.30 (95% CI 2.33 to 4.77).

Women had 1.7 times the CTS incidence rate of men (table 3), and a 30% increase in risk when assessed while adjusting for age and BMI (HR=1.3; 95% CI 0.98 to 1.72). Increasing age was associated with greater CTS risk; those over 50 years old had a CTS incidence rate more than three times that of those under 30 years of age. When assessed as a continuous variable, risk of developing CTS increased approximately linearly with age (see online supplementary figure S1). Above 50 years of age, the CIs widen due to sparse data. A BMI greater than or equal to 30 kg/m2 almost doubled the risk of CTS (table 4) and, when assessed as a continuous variable, the HR increased approximately linearly with increasing BMI (see online supplementary figure S2). When each of four medical conditions (diabetes mellitus, thyroid disease, rheumatoid arthritis, pregnancy) was considered separately, they were all positively associated with CTS (except for pregnancy with zero cases), though only thyroid disease (IRR=1.81; 95% CI 1.01 to 3.01) was statistically significant (table 3). When the four medical conditions were combined and adjusted for gender, age and BMI, medical condition incurred no increased risk for developing CTS, and none of the conditions were statistically significant predictors of risk when analysed in separate adjusted models (table 4). There was no evidence for effect modification by gender of the associations with age, BMI, or medical condition.

Table 3. Incidence rate ratios for personal risk factors.

Person-time (100 person-years) Dominant hand CTS Incidence rate (per 100 person-years) Incidence rate ratio Lower 95% CI Upper 95% CI
Gender 83.30 204
 Male 39.41 81 1.85 1.00
 Female 43.89 123 3.12 1.69 1.27 2.27
Age (years) 83.31 204
 <30 years of age 29.18 32 1.10 1.00
 ≥30 and <40 years of age 19.45 47 2.42 2.20 1.38 3.57
 ≥40 and <50 years of age 20.28 69 3.40 3.10 2.01 4.88
 ≥50 years of age 14.40 56 3.89 3.55 2.26 5.66
Ethnicity 77.01 183
 Caucasian 47.42 126 2.66 1.00
 Hispanic 9.43 16 1.70 0.64 0.35 1.08
 African–American 14.28 25 1.75 0.66 0.41 1.02
 Asian 3.42 9 2.63 0.99 0.44 1.94
 Other 2.46 7 2.84 1.07 0.42 2.27
Educational level 82.82 200
 Some high school or less 9.92 34 3.43 1.00
 High school graduate or above 72.90 166 2.28 0.66 0.46 0.99
Body mass index 83.02 203
 Body mass index (<30 kg/m2: normal or overweight) 55.25 104 1.88 1.00
 Body mass index (≥30 kg/m2: obese) 27.77 99 3.56 1.89 1.42 2.52
Overall health status 43.73 161
 Very good or excellent 20.59 55 2.67 1.00
 Good 18.24 83 4.55 1.70 1.20 2.44
 Poor or fair 4.90 23 4.70 1.76 1.03 2.91
Medical condition 83.16 204
 No medical condition 75.21 176 2.34 1.00
 Medical condition 7.95 28 3.52 1.50 0.97 2.25
  Diabetes mellitus 2.75 7 2.56 1.08 0.43 2.26
  Rheumatoid arthritis 1.49 6 4.02 1.66 0.60 3.69
  Thyroid disease 3.77 16 4.24 1.81 1.01 3.01
 Gout 1.16 6 5.17 2.23 0.81 4.94
Previous DUE MSD 74.85 158
 No previous DUE MSD 67.17 127 1.89 1.00
 Previous DUE MSD 7.68 31 4.04 2.13 1.39 3.18
Smoking status 82.99 201
 Never smoked 44.80 105 2.34 1.00
 Currently smoked 23.17 59 2.55 1.09 0.78 1.51
 Previously smoked 15.02 37 2.46 1.05 0.70 1.54
Weekly aerobic activity 32.20 100
 <3 h/week 17.63 55 3.12 1.00
 ≥3 h/week 14.57 45 3.09 0.99 0.65 1.50
Weekly hand intensive activity (non-occupational) 42.56 145
 <3 h/week 11.73 53 4.52 1.00
 ≥3 h/week 30.83 92 2.98 0.66 0.47 0.94
*

Medical condition includes diabetes mellitus, rheumatoid arthritis, thyroid disease and pregnancy. There were no pregnant women who became cases.

DUE, Distal upper extremity; MSD, Musculoskeletal disorder

Table 4. Multivariable models for selected personal and health-related factors, adjusting for gender, age and BMI.

n n (CTS cases) HR Lower 95% CI Upper 95% CI p Value
Gender* 3500 203
 Male 1855 81 1.00
 Female 1645 122 1.30 0.98 1.72 0.07
Age 3500 203
 <30 years of age 1084 31 1.00
 ≥30 and <40 years of age 832 47 2.12 1.34 3.34 0.00
  (>=40 and& <50 years of age 930 69 2.84 1.85 4.37 0.00
  (>=50 years of age 654 56 3.04 1.96 4.71 0.00
BMI 3495 203
 BMI <30 kg/m2 (normal or overweight) 2321 104 1.00
 BMI ≥30 kg/m2 (obese) 1174 99 1.67 1.26 2.21 0.00
General health 2058 160
 Excellent to very good 891 55 1.00
 Good 890 83 1.71 1.21 2.42 0.00
 Fair or poor 277 22 1.52 0.91 2.54 0.11
Medical condition§ 3495 203
 No medical condition 3150 175 1.00
 Medical condition 345 28 0.95 0.62 1.44 0.79
Diabetes mellitus 3197 190
 No diabetes 3075 183 1.00
 Diabetes 122 7 0.64 0.30 1.40 0.27
RA 3375 197
 No RA 3309 191 1.00
 RA 66 6 1.13 0.50 2.57 0.77
Thyroid disease 3487 201
 No thyroid disease 3328 185 1.00
 Thyroid disease 159 16 1.24 0.72 2.12 0.44
Gout 3196 189
 No gout 3155 183 1.00
 Gout 41 6 1.57 0.72 3.44 0.26
Previous DUE MSD 2845 157
 No previous DUE MSD 2550 126 1.00
 Previous DUE MSD 295 31 1.58 1.05 2.37 0.03
Weekly aerobic activity 1574 99
 <3 h/wk 1030 55 1.00
 ≥3 h/wk 544 44 0.82 0.55 1.22 0.32
Weekly hand intensive activity (non-occupational) 2112 144
 <3 h/wk 720 53 1.00
 ≥3 h/wk 1392 91 0.58 0.41 0.82 0.00
*

Adjusted for age and BMI only.

Adjusted for gender and BMI only.

Adjusted for age and gender only.

§

Includes diabetes mellitus, rheumatoid arthritis, thyroid disease, pregnancy.

Includes wrist tendinitis, elbow epicondylitis and trigger finger.

BMI, Body mass index; DUE, Distal upper extremity; MSD, Musculoskeletal disorder; RA, Rheumatoid arthritis

In the cohort as a whole, the incidence of CTS either decreased or remained stable with years worked at the current company after adjustment for potential confounders, though the CIs were wide (table 5). When the analysis was restricted to those enrolled within one year of hire (eg, recent hires), the HR of 3.08 (95% CI 1.55 to 6.12) was significantly higher for those who worked up to 3.5 years (median time to become a case) compared with those who worked longer. The distributions of years worked were non-overlapping between recent and non-recent hires, precluding a direct comparison between the two subgroups.

Table 5. Multivariable models for workplace factors, adjusting for gender, age and BMI.

n n (cases) HR Lower CI Upper CI p Value
Years worked at company for entire cohort 3480 200
 ≤3.5 years 752 25 1.00
 >3.5 years and ≤7 years 1299 64 0.63 0.39 1.03 0.06
 >7 years and <=15 years 881 69 1.04 0.62 1.73 0.89
 >15 years 548 42 0.86 0.49 1.50 0.59
Years worked for recent hires (<1 year) 1234 41
 >3.5 years 719 23 1.00
 ≤3.5 years 515 18 3.08 1.55 6.12 0.001
Job strain 1549 102
 Low job strain (low demand and high control) 423 23 1.00
 Active (high demand and high control) 307 24 1.48 0.83 2.66 0.18
 Passive (low demand and low control) 360 19 1.23 0.67 2.27 0.50
 High job strain (high demand and low control) 459 36 1.86 1.11 3.14 0.02
Social support 1568 49
 Low support 677 28 1.00
 High support 891 21 0.54 0.31 0.95 0.03
Physically exhausted 901 160
 None to slightly physically exhausted 133 83 1.00
 Moderate to severely physically exhausted 768 77 1.45 1.05 2.00 0.03
Mental exhaustion 2153 163
 None to slightly mentally exhausted 1607 109 1.00
 Moderate to severely mentally exhausted 546 54 1.34 0.96 1.87 0.08
Job satisfaction 3080 181
 Very satisfied 1170 58 1.00
 Satisfied 1505 100 1.43 1.03 1.99 0.03
 Dissatisfied or very dissatisfied 405 23 1.28 0.79 2.08 0.31

BMI, Body mass index.

Participants with a high psychological demand score had increased risk of CTS (HR=1.57; 95% CI 1.06 to 2.33), and those with high decision latitude had reduced risk (HR=0.73; 95% CI 0.51 to 1.04). Those with high job strain (high demand and low control) had a HR of 1.86 (95% CI 1.11 to 3.14) relative to those with low job strain (high control and low demand), and subjects with high social support had half the risk of incident CTS compared with those with low support (HR=0.54; 95% CI 0.31 to 0.95; table 5). There was no interaction between gender, BMI, or medical conditions with either job strain or social support on risk of CTS.

Discussion

This analysis provided a unique opportunity to assess the relationships between selected personal and workplace risk factors and CTS incidence with a larger sample size than most previous studies. The observed associations provide evidence for both modifiable and non-modifiable risk factors for CTS. The wide range of industries, jobs and locations represented in this cohort increases the generalisability of results. The CTS incidence rate in this worker cohort was 2.3 per 100 person-years. This incidence rate was higher than the 0.13 to 0.37 per 100 person-years reported from population studies,38,39 and higher than the 0.17 per 100 person-years reported from workers' compensation datasets.11 However, the incidence rate was at the low end of the range (1.2 to 11.0 per 100 person-years) of incidence rates reported by other prospective studies of working populations.8,11,40 In this analysis, we identified a near-linear relationship between CTS incidence and both age and BMI. CTS incidence was also higher in categories with high job strain, and decreased with higher social support at work after adjusting for confounding by age, gender and BMI.

The adjusted HR effect size of 1.3 observed for women in the current study is lower than the approximate doubling of CTS risk observed in other studies.41,42 A study by Silverstein43 found that among those with median neuropathy, women reported more symptoms than men. This suggests that a reporting bias might explain the disparity in risk by gender. Another explanation for the increased CTS risk among women could be the physiological differences such as lower strength relative to task demands or stature.7 A study by Violante et al7 found that both men and women with taller stature and longer forearm length had 40–50% decreased risk compared with those with short stature and shorter forearm length. Violante et al7 also found that gender was a particularly strong risk factor among those with high workplace exposures to forceful grip or repetitions. Given a woman's smaller stature and decreased strength, a task may require a greater percent of her maximum voluntary contraction than a male counterpart, and/or require greater deviations in wrist posture. Future analyses of our pooled cohort will assess the role of workplace biomechanical factors on CTS incidence and their relationship with gender.

There is growing interest in how to accommodate an aging workforce as the demographics of the Western working population change. We found an approximately linear relationship between age and increased risk for CTS among the pooled cohort across the entire working age range (through the sixth decade). Mondelli et al5 identified a peak risk in women during their fifth decade of life, and a bimodal relationship among men with the highest risk in the fifth and seventh decades of life. Unlike the Mondelli and other studies,42 the slope and linear relationship that we observed between age and CTS was almost identical when stratified by gender. Apportioning this age-related trend in risk between physiologic changes due to aging and cumulative workplace exposure with increasing years worked is difficult since age and work history duration are highly collinear. Despite this, it is clear that there should be awareness of the increased risk of CTS among older workers as well as efforts to identify effective prevention strategies for the older worker.

Similar to the general population, obesity poses an emerging health risk among Western workers. Previous studies have shown varying strengths of association between obesity and CTS with risks ranging from 1.57 to 2.5.44,45 Our analysis was comparable, and when BMI was assessed as a continuous variable, a near-linear trend for increasing risk of CTS was evident up to 45 kg/m2, after which data became sparse. The mechanism by which BMI contributes to risk for CTS is not well understood.44 Among other important health considerations, it appears that interventions addressing obesity may also have a positive impact on incidence of CTS. Further analysis of obesity and physical workplace exposures in this prospective study may help focus such programmes on those who are at the greatest overall risk.

Medical conditions, such as diabetes mellitus, rheumatoid arthritis, gout and thyroid disease have been linked to CTS in previous studies.22,45 In this cohort, the higher incidence rate for those with diabetes mellitus, rheumatoid arthritis and thyroid disease, disappeared after adjusting for age, gender and BMI, indicating that these conditions were not independent predictors of CTS in this cohort. However, if subjects with these chronic conditions, who develop CTS, are more likely to leave employment, then only their less susceptible coworkers would be included in this study. Such self-selection out of the workforce is consistent with the relatively low baseline prevalence for diabetes mellitus in this cohort (4.3%) relative to the general working population (10.0%).46

There have been inconsistencies in the associations reported between smoking and CTS. Geoghan et al26 found no association with smoking and CTS, and other studies found a slight increase in risk for those who ever smoked.7,47 In this pooled cohort, neither current nor previous smoking status was significantly associated with an increased risk of CTS.

The results in this analysis are consistent with previous observations that distal upper extremity disorders, such as fractures26 and wrist tendinitis,7,40 are associated with increased risk for CTS. This may be due to the disorders having similar biomechanical risk factors, or an increased susceptibility of individuals not fully recovered from a previous musculoskeletal disorder.

The finding that hand-intensive activities outside of work at baseline were associated with reduced risk of developing CTS, should be interpreted with caution. Although the temporal relationship is unclear, individuals with periodic median nerve symptoms or those exposed to high biomechanical risk factors might choose not to engage in hand-intensive activities outside of work. Future analysis of hand-intensive activities stratified by occupational biomechanical exposure levels may help clarify this hypothesis.

It has been shown that when prospective studies include workers hired well before study enrolment, exposure–response results may be attenuated.36 The bias occurs because only the workers who remain at work without prevalent disease are eligible for enrolment in a prospective incidence study. In this pooled study, approximately 25% of the cohort was hired within a year of recruitment. We therefore examined associations between work years and CTS incidence in the subset of recent hires. Among those recently hired (ie, less than 1 year), there was a substantial increase in risk associated with working less than 3.5 years compared with those working more than 3.5 years. Also consistent with a survivor bias, was the inverse trend seen for those with long seniority, but the association was attenuated (closer to the null).

High job strain was also associated with increased risk for CTS. This is consistent with findings by Silverstein et al15 who reported that those who developed incident CTS had significantly higher psychosocial job demands at baseline. Of equal interest, both supervisor support and coworker support were strongly protective for CTS. Silverstein et al15 did not report a significant difference in social support between those who developed CTS and those who remained asymptomatic. It is possible that those with high job strain or low social support have increased physiological stress placing them at higher risk for developing CTS. Alternatively, it is possible that reporting thresholds are affected by these psychosocial factors. Further analysis of this cohort will assess whether physical exposures at work alter the relationship between job strain, social support and CTS.

Limitations

Despite the increased power and generalisability of the pooled study findings, there were limitations. First, there were some differences in study design among the six studies that presented challenges when pooling the data.9 Consistent with the population study by Nathan et al,48 a large percentage of our subjects met the criteria for median mononeuropathy at baseline, but remained asymptomatic throughout the study. This supports previous recommendations that CTS diagnosis include both median nerve symptoms and prolonged median nerve latencies.32 Data on some medical conditions are likely underpowered. Some studies did not collect the data necessary to generate psychological demand and decision latitude subscale scores, therefore, the sample size was reduced by approximately half for the job strain and social support findings. The sample size was also smaller for some of the non-occupational activities. Additionally, it should be noted, that as in most occupational studies, years worked was based on the company start date, and did not reflect time spent working in the same or similar industry at a prior employer. Finally, because the study cohort is primarily comprised of non-recent hires, it represents a less susceptible survivor population that may lead to an underestimation of associations.

Conclusion

Female gender, older age and higher BMI were associated with CTS incidence in this broad-based worker cohort. High job strain increased risk, and high social support was protective. Further analysis will identify the biomechanical risk factors associated with CTS and clarify possible interactions between occupational psychosocial factors, personal factors, and workplace physical exposures.

Supplementary Material

Diagrams

What this paper adds.

  • CTS is an important driver of workers compensation costs, lost time, lost productivity and disability.

  • To date, few large prospective studies using rigorous case criteria, individual-level exposure data and appropriate control for confounding by personal factors have examined associations between occupational psychosocial and biomechanical risk factors and CTS incidence.

  • Personal factors associated with an increased risk of developing CTS were BMI, age and being female.

  • Workplace risk factors were high job strain while social support was protective.

Acknowledgments

We would like to acknowledge the efforts of the research assistants from each of the research study groups that made the collection of the data possible, and the study participants and employers for their time and willingness to participate in this study. We would especially like to acknowledge Joyce Fan and Caroline Smith for their effort in data collection and data management.

Funding This study was supported by research funding from the Center for Disease Control/National Institute for Occupational Safety and Health (R01OH009712). The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

Footnotes

Additional material is published online only. To view please visit the journal online (http://dx.doi.org/10.1136/oemed-2013-101365).

Contributors All coauthors participated in the planning of the research, the interpretation of the data analysis, and reviewed and edited the final manuscript. CH-A conducted the data analysis and wrote most of the paper. All authors have reviewed the article, revised it critically for its intellectual content, and have approved the submitted article.

Competing interests None.

Ethics approval University of California, San Francisco.

Provenance and peer review Not commissioned; externally peer reviewed.

References

  • 1.Stapleton MJ. Occupation and carpal tunnel syndrome. ANZ J Surg. 2006;76:494–6. doi: 10.1111/j.1445-2197.2006.03770.x. [DOI] [PubMed] [Google Scholar]
  • 2.U.S. Bureau of Labor and Statistics, U.S. Department of Labor. Nonfatal occupational injuries and illnesses requiring days away from work, 2010. USDL report number: 11-1612. Retrieved 3 October 2012. http://www.bls.gov/news.release/osh2.nr0.htm.
  • 3.Foley M, Silverstein B, Polissar N. The economic burden of carpal tunnel syndrome: long-term earnings of CTS claimants in Washington State. Am J Ind Med. 2007;50:155–72. doi: 10.1002/ajim.20430. [DOI] [PubMed] [Google Scholar]
  • 4.Nordstrom DL, DeStefano F, Vierkant RA, et al. Incidence of diagnosed carpal tunnel syndrome in a general population. Epidemiology. 1998;9:342–5. [PubMed] [Google Scholar]
  • 5.Mondelli M, Giannini F, Giacchi M. Carpal tunnel syndrome incidence in a general population. Neurology. 2002;58:289–94. doi: 10.1212/wnl.58.2.289. [DOI] [PubMed] [Google Scholar]
  • 6.Bernard B. Musculoskeletal disorders and workplace factors: a critical review of epidemiologic evidence for work-related musculoskeletal disorders of the neck, upper extremity, and low back. Cincinnati: Department of Health and Human Services NIOSH; 1997. [Google Scholar]
  • 7.Violante FS, Armstrong TJ, Fiorentini C, et al. Carpal tunnel syndrome and manual work: a longitudinal study. J Occup Environ. 2007;49:1189–96. doi: 10.1097/JOM.0b013e3181594873. [DOI] [PubMed] [Google Scholar]
  • 8.Bonfiglioli R, Mattioli S, Armstrong T, et al. Validation of the ACGIH TLV for hand activity in the OCTOPUS cohort: a two-year longitudinal study of carpal tunnel syndrome. Scand J Work Environ Health. 2013;39:155–63. doi: 10.5271/sjweh.3312. [DOI] [PubMed] [Google Scholar]
  • 9.Dale AM, Harris-Adamson C, Rempel D, et al. Prevalence and incidence of CTS in US working populations: pooled analysis of six prospective studies. Scand J Work Environ Health. 2013 Feb 19; doi: 10.5271/sjweh.3351. epub ahead of print. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Roquelaure Y, Ha C, Pelier-Cady MC, et al. Work increases the incidence of carpal tunnel syndrome in the general population. Muscle Nerve. 2008;37:477–82. doi: 10.1002/mus.20952. [DOI] [PubMed] [Google Scholar]
  • 11.Gorsche RG, Wiley JP, Renger RF, et al. Prevalence and incidence of carpal tunnel syndrome in a meat packing plant. Occup Environ Med. 1999;56:417–22. doi: 10.1136/oem.56.6.417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Franklin G, Haug J, Heyer N, et al. Occupational carpal tunnel syndrome in Washington State, 1984–1988. Am J Public Health. 1991;81:741–6. doi: 10.2105/ajph.81.6.741. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Werner RA. Evaluation of work-related carpal tunnel syndrome. J Occup Rehabil. 2006;16:207–22. doi: 10.1007/s10926-006-9026-3. [DOI] [PubMed] [Google Scholar]
  • 14.Shiri R, Miranda H, Heliovaara M, et al. Physical work load factors and carpal tunnel syndrome: a population-based study. Occup Environ Med. 2009;66:368–73. doi: 10.1136/oem.2008.039719. [DOI] [PubMed] [Google Scholar]
  • 15.Silverstein BA, Fan ZJ, Bonauto DK, et al. The natural course of carpal tunnel syndrome in a working population. Scand J Work Environ Health. 2010;36:384–93. doi: 10.5271/sjweh.2912. [DOI] [PubMed] [Google Scholar]
  • 16.Leclerc A, Landre MF, Chastang JF, et al. Upper-limb disorders in repetitive work. Scand J Work Environ Health. 2001;27:268–78. doi: 10.5271/sjweh.614. [DOI] [PubMed] [Google Scholar]
  • 17.Werner RA, Franzblau A, Albers JW, et al. Median mononeuropathy among active workers: are there differences between symptomatic and asymptomatic workers? Am J Ind Med. 1998;33:374–8. doi: 10.1002/(sici)1097-0274(199804)33:4<374::aid-ajim7>3.0.co;2-u. [DOI] [PubMed] [Google Scholar]
  • 18.NIOSH Research Projects. Musculoskeletal Disorders. 1997 DHHS (NIOSH) Publication No. 97-109. [Google Scholar]
  • 19.Roquelaure Y, Mariel J, Dano C, et al. Prevalence, incidence and risk factors of carpal tunnel syndrome in a large footwear factory. Int J Occup Med Environ Health. 2001;14:357–67. [PubMed] [Google Scholar]
  • 20.Bongers PM, Kremer AM, ter Laak J. Are psychosocial factors, risk factors for symptoms and signs of the shoulder, elbow, or hand/wrist?: a review of the epidemiological literature. Am J Ind Med. 2002;41:315–42. doi: 10.1002/ajim.10050. [DOI] [PubMed] [Google Scholar]
  • 21.Atroshi I, Gummesson C, Johnsson R, et al. Prevalence of carpal tunnel syndrome in a general population. JAMA. 1999;282:153–8. doi: 10.1001/jama.282.2.153. [DOI] [PubMed] [Google Scholar]
  • 22.Stevens JC, Sun MD, Beard CM, et al. Carpal Tunnel Syndrome in Rochester, Minnesota, 1961 to 1980. Neurology. 1988;38:134–8. doi: 10.1212/wnl.38.1.134. [DOI] [PubMed] [Google Scholar]
  • 23.Solomon DH, Katz JN, Bohn R, et al. Nonoccupational risk factors for carpal tunnel syndrome. J Gen Intern Med. 1999;14:310–4. doi: 10.1046/j.1525-1497.1999.00340.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Tanaka S, Wild DK, Seligman PJ, et al. The US prevalence of self-reported carpal tunnel syndrome: 1988 National Health Interview Survey data. Am J Public Health. 1994;84:1846–8. doi: 10.2105/ajph.84.11.1846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Boz C, Ozmenoglu M, Altunayoglu V, et al. Individual risk factors for carpal tunnel syndrome: an evaluation of body mass index, wrist index and hand anthropometric measurements. Clin Neurol Neurosurg. 2004;106:294–9. doi: 10.1016/j.clineuro.2004.01.002. [DOI] [PubMed] [Google Scholar]
  • 26.Geoghegan JM, Clark DI, Bainbridge LC, et al. Risk factors in carpal tunnel syndrome. J Hand Surg. 2004;29:315–20. doi: 10.1016/j.jhsb.2004.02.009. [DOI] [PubMed] [Google Scholar]
  • 27.Stevens JC, Beard CM, O'Fallon WM, et al. Conditions associated with carpal tunnel syndrome. May Clin Proc. 1992;67:541–8. doi: 10.1016/s0025-6196(12)60461-3. [DOI] [PubMed] [Google Scholar]
  • 28.Chammas M, Bousquet P, Renard E, et al. Dupuytren's disease, carpal tunnel syndrome, trigger finger, and diabetes mellitus. J Hand Surg. 1995;20:109–14. doi: 10.1016/S0363-5023(05)80068-1. [DOI] [PubMed] [Google Scholar]
  • 29.Roquer J, Cano JF. Carpal tunnel syndrome and hyperthyroidism. Acta Neurol Scand. 1993;88:149–52. doi: 10.1111/j.1600-0404.1993.tb04207.x. [DOI] [PubMed] [Google Scholar]
  • 30.Eisen EA, Holcroft CR, Greaves IA, et al. A strategy to reduce healthy worker effect in a cross-sectional study of asthma and metalworking fluids. Am J Indus Med. 1997;31:671–77. doi: 10.1002/(sici)1097-0274(199706)31:6<671::aid-ajim1>3.0.co;2-u. [DOI] [PubMed] [Google Scholar]
  • 31.Karasek R, Brisson C, Kawakami N, et al. The Job Content Questionnaire (JCQ): an instrument for internationally comparative assessments of psychosocial job characteristics. J Occup Health Psychol. 1998;3:322–55. doi: 10.1037//1076-8998.3.4.322. [DOI] [PubMed] [Google Scholar]
  • 32.Rempel D, Evanoff B, Amadio P, et al. Consensus criteria for the classification of carpal tunnel syndrome in epidemiologic studies. Am J Public Health. 1998;88:1447–51. doi: 10.2105/ajph.88.10.1447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Gerr F, Letz R. The sensitivity and specificity of tests for carpal tunnel syndrome vary with the comparison subjects. J Hand Surg. 1998;23:151–5. doi: 10.1016/s0266-7681(98)80163-0. [DOI] [PubMed] [Google Scholar]
  • 34.Franzblau A, Salerno DF, Armstrong TJ, et al. Test-retest reliability of an upper extremity discomfort questionnaire in an industrial population. Scand J Work Environ Health. 1997;23:299–307. doi: 10.5271/sjweh.223. [DOI] [PubMed] [Google Scholar]
  • 35.Salerno DF, Franzblau A, Armstrong TJ, et al. Test-retest reliability of the Upper Extremity Questionnaire among keyboard operators. Am J Ind Med. 2001;40:655–66. doi: 10.1002/ajim.10024. [DOI] [PubMed] [Google Scholar]
  • 36.Applebaum KM, Malloy EJ, Eisen EA. Left truncation, susceptibility, and bias in occupational cohort studies. Epidemiology. 2011;22:599–606. doi: 10.1097/EDE.0b013e31821d0879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Eisen EA, Agalliu I, Coull B, et al. Smoothing methods applied to occupational cohort studies; illustrated by penalized splines. Occup Environ Med. 2004;61:854–60. doi: 10.1136/oem.2004.013136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Bongers FJM, Schellevis FG, van den Bosch WJM, et al. Carpal tunnel syndrome in general practice (1987 and 2001): incidence and the role of occupational and non-occupational factors. Br J Gen Pract. 2007;57:36–9. [PMC free article] [PubMed] [Google Scholar]
  • 39.Gelfman R, Melton LJ, 3rd, Yawn BP, et al. Long-term trends in carpal tunnel syndrome. Neurology. 2009;72:33–41. doi: 10.1212/01.wnl.0000338533.88960.b9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Gell N, Werner RA, Franzblau A, et al. A longitudinal study of industrial and clerical workers: incidence of carpal tunnel syndrome and assessment of risk factors. J Occup Rehabil. 2005;15:47–55. doi: 10.1007/s10926-005-0873-0. [DOI] [PubMed] [Google Scholar]
  • 41.Jenkins PJ, Srikantharajah D, Duckworth AD, et al. Carpal tunnel syndrome: the association with occupation at a population level. J Hand Surg Eur. 2013;38:67–72. doi: 10.1177/1753193412455790. [DOI] [PubMed] [Google Scholar]
  • 42.Atroshi I, Englund M, Turkiewicz A, et al. Incidence of Physician-Diagnosed Carpal Tunnel Syndrome in the General Population. Arch Intern Med. 2011;171:943–4. doi: 10.1001/archinternmed.2011.203. [DOI] [PubMed] [Google Scholar]
  • 43.Silverstein BA, Fan ZJ, Smith CK, et al. Gender adjustment or stratification in discerning upper extremity musculoskeletal disorder risk? Scand J Work Environ Health. 2009;35:113–26. doi: 10.5271/sjweh.1309. [DOI] [PubMed] [Google Scholar]
  • 44.Werner RA, Albers JW, Franzblau A, et al. The relationship between body mass index and the diagnosis of carpal tunnel syndrome. Muscle Nerve. 1994;17:632–6. doi: 10.1002/mus.880170610. [DOI] [PubMed] [Google Scholar]
  • 45.Atcheson SG, Ward JR, Lowe W. Concurrent medical disease in work-related carpal tunnel syndrome. Arch Intern Med. 1998;158:1506–12. doi: 10.1001/archinte.158.14.1506. [DOI] [PubMed] [Google Scholar]
  • 46.Costello S, Eisen EA, Brown D, et al. Incident ischemic heart disease and occupational exposure to particulate matter in aluminum manufacturing workers. Presented at Society of Epidemiology (SER) Meeting; Minneapolis, MN. 2012. [Google Scholar]
  • 47.Nathan PA, Keniston RC, Lockwood RS, et al. Tobacco, caffeine, alcohol, and carpal tunnel syndrome in American industry. A cross-sectional study of 1464 workers. J Occup Environ Med. 1996;38:290–8. doi: 10.1097/00043764-199603000-00015. [DOI] [PubMed] [Google Scholar]
  • 48.Nathan PA, Keniston RC, Myers LD, et al. Natural history of median nerve sensory conduction in industry: relationship to symptoms and carpal tunnel syndrome in 558 hands over 11 years. Muscle Nerve. 1998;21:711–21. doi: 10.1002/(sici)1097-4598(199806)21:6<711::aid-mus2>3.0.co;2-a. [DOI] [PubMed] [Google Scholar]

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