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CTS Risk Assessment Model

The development of risk assessment models for carpal tunnel syndrome: a casereferent study

HEECHEON YOU,1* ZACHARY SIMMONS,2 ANDRIS FREIVALDS,3 MILIND J. KOTHARI,2 SANJIV H. NAIDU4 and RONDA YOUNG1

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Department of Industrial Engineering, Pohang University of Science and Technology,

Pohang, Kyungbuk 790-784, Korea (R.O.K.) 2

Division of Neurology, The Pennsylvania State University College of Medicine, The

Milton S. Hershey Medical Center, Hershey, Pennsylvania 17033, USA 3

Department of Industrial and Manufacturing Engineering, The Pennsylvania State

University, University Park, Pennsylvania 16802, USA 4

Department of Orthopedics and Rehabilitation, The Pennsylvania State University

College of Medicine, The Milton S. Hershey Medical Center, Hershey, Pennsylvania 17033, USA

*Correspondence to Heecheon You. E-mail: [email protected]

Running title: CTS Risk Assessment Model

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The development of risk assessment models for carpal tunnel syndrome: a casereferent study

Key words:

Carpal Tunnel Syndrome; Risk Assessment Model; Case-referent Design; Work-relatedness; Injury Causation.

The present study developed risk assessment models for carpal tunnel syndrome (CTS) which can provide information of the likelihood of developing CTS for an individual having certain personal characteristics and occupational risks. A case-referent study was conducted consisting of two case groups and one referent group: (1) 22 work-related CTS patients (W-CTS), (2) 25 non-work related CTS patients (NW-CTS), and (3) 50 healthy workers (HEALTHY) having had no CTS history. The classification of CTS patients into one of the case groups was determined according to the type of insurance covering their medical costs. Personal characteristics, psychosocial stresses at work, and physical work conditions were surveyed by using a questionnaire tailor-designed to CTS (reliability of each scale ≥ .7). By contrasting the risk information of each case group to that of the referent group, three logistic regression models were developed: W-CTS/HEALTHY, NW-CTS/HEALTHY, and C-CTS/HEALTHY (C-CTS, the combined group of W-CTS and NW-CTS). ROC analysis indicated that the models have satisfactory discriminability (d′ = 1.91 to 2.51) and high classification accuracy (overall accuracy = 83% to 89%). Both W-CTS/HEALTHY and C-CTS/HEALTHY include personal and physical factors, while NW-CTS/HEALTHY involves only personal factors. This suggests that the injury causation of NW-CTS patients should be attributable mainly to their ‘high’ personal susceptibility to the disorder rather than exposure to adverse work conditions, while that


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of W-CTS patients be attributable to improper work conditions and CTS-prone personal characteristics in combination.


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Nomenclature A, Area under ROC curve d′, Discriminability P, Significance probability pc, Cut-off probability R, Partial correlation


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1.

Introduction

Carpal tunnel syndrome (CTS), a disorder of the median nerve at the wrist, has been a major problem in industry due to its work-relatedness, significant incidence rate, and high cost. Hand intensive activities at work can cause and/or aggravate the damage of the median nerve by increasing the hydrostatic pressure around the nerve within the carpal tunnel (Herbert et al. 2000, Adams et al. 1997, Guidotti 1992, Moore 1992, Armstrong et al. 1984). According to a workplace injury and illness survey by the Bureau of Labor Statistics (BLS 2001), a total of 26,266 CTS cases (1.29 cases per 1,000 equivalent fulltime workers (FTWs)) were reported during 1998 as work-related in US private industry; 99.2% of the cases were attributed to repetitive motion (0.5% due to overexertion); and 43.2% of the cases involved more than one month away from work (median = 24 days). The nerve disorder is the cause of considerable costs including medical expenses, production loss, and decreased quality of life. Epidemiological and experimental research has identified that CTS has multifactorial origins including personal attributes, psychosocial stresses at work, and physical work conditions (figure 1). Personal factors such as old age, female gender, type A personality, avocational activities involving excessive and/or repetitive use of the hands (e.g., woodworking and crocheting), medical conditions related to increased pressure within the carpal tunnel (e.g., hypothyroidism and diabetes mellitus), pregnancy, squaretype wrist, and/or obesity may increase the susceptibility of an individual to the focal median nerve injury (Adams et al. 1997: 1358-1359, Hagberg et al. 1995, Nathan and Keniston 1993, Cannon et al. 1981). Next, psychosocial factors such as negative perceptions at work (e.g., work monotony, time pressure, work load, and social conflict)


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have been found to be significantly related to undesirable health outcomes (musculoskeletal discomfort, fatigue, or injury/illness) (Kasl and Amick 1996, Bongers et al. 1993); however, a fundamental question has remained unresolved on whether psychosocial stress is either a cause or a result of the reduced physical capacity (Kasl and Amick 1996). Lastly, physical work factors such as awkward posture, forceful exertion, repetitive motion, and exposure to vibration of the hand and wrist for extended periods of time contribute to the development of the neuropathy (Bernard 1997, Hagberg et al. 1995).

[Insert figure 1 about here] For effective control and prevention of CTS, information on the relative contribution of non-occupational (personal) and occupational (psychosocial and physical) factors to the nerve injury is important, but little is known. Identifying major risk factors of the nerve injury and their relative significance is necessary because monitoring or controlling all CTS-related factors at the workplace may not be practical and limited resources for prevention should ideally be allocated based on the risk level. Several studies (Matias et al. 1998, McCauley-Bell and Crumpton 1997, Moore and Garg 1995, Nathan and Keniston 1993, Nathan et al. 1992a, 1992b, de Krom et al. 1990, Wieslander et al. 1989, Silverstein et al. 1987) have examined the relative importance of risk factors, including different partial sets of CTS risk factors, different case definition criteria, and different exposure assessment methods. The differences among the studies make their results difficult to compare and/or integrate; and the lack of comprehensiveness of the studies has limited the generalizability of their findings.


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The present study was intended to identify, out of a comprehensive set of risk factors, the relatively important risk factors to CTS and to develop a quantitative risk assessment model for the disorder. This study hypothesized that the relative importance of non-occupational and occupational risk factors varies depending on the workrelatedness of the nerve injury. Using the major risk factors and their relative importance, an attempt was made to develop a statistical model for CTS risk assessment.

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Materials and methods

2.1. Case-referent study design A case-referent study was designed consisting of three groups: (1) 25 work-related CTS patients (W-CTS), (2) 25 non-work related CTS patients (NW-CTS), and (3) 50 healthy workers (HEALTHY). The sample sizes were determined by considering four statistical parameters: type I error probability (α), type II error probability (β), variability of odds ratios (σ), and minimum outcome difference in odds ratio (δ) to be detected (You 1999). CTS patients were classified into one of the case groups according to the type of insurance covering their medical costs: health insurance for NW-CTS and workers’ compensation insurance for W-CTS. The two case groups were defined to determine if the relative contribution of risk factors varies depending on the work-relatedness of CTS. Patients diagnosed with unilateral or bilateral CTS (based on both a history of CTS symptoms and standard nerve conduction studies in the affected hand(s)) at the Electromyography Laboratory, Hershey Medical Center were asked to participate in this study immediately after their nerve conduction evaluation.


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For the referent group, workers having had no medical history of CTS were recruited at four work sites in Pennsylvania: a window, door, and roof manufacturing plant; a garment factory; a food processing service; and a library. To screen healthy participants for possible CTS, the condition of each hand and wrist was evaluated by means of a modified Levine et al. CTS symptom severity assessment questionnaire (You et al. 1999). For those reporting ‘mild’ severity on secondary symptoms such as pain, weakness, and clumsiness (You et al. 1999), the Phalen’s test (Putz-Anderson, 1988) was administered. Only workers reporting no symptoms or those showing mild secondary symptoms with a negative Phalen’s sign were selected. Furthermore, the present study used ‘at least one-year of work experience on the current job’ as a selection criterion for all participants. Any individuals who were not employed or whose work experience on the current job was less than one year were not recruited. The study protocol was approved by the Institutional Review Board at the Medical Center.

2.2. Study hypothesis The present study assumed distinctive hypothetical features for each of the case and referent groups with respect to personal susceptibility to CTS, psychosocial stress at work, and physical risk exposure (see figure 2). Compared to healthy workers, most NW-CTS patients are highly susceptible to CTS and most W-CTS patients are moderately susceptible. Exposure to occupational risks is higher in W-CTS than in both NW-CTS and HEALTHY; and the distributions of occupational exposure of NW-CTS and HEALTHY are similar to each other.


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[Insert figure 2 about here] These hypothetical features of the study groups indicate that for non-work related CTS, non-occupational factors, for work-related CTS, occupational factors alone or occupational and non-occupational factors in combination contribute to the nerve disorder. When the distributions of the case groups are compared with that of the referent group, the development of CTS among NW-CTS patients should be mainly attributable to their ‘high’ personal susceptibility to the nerve injury rather than exposure to psychosocial and physical risk conditions, whereas for W-CTS patients the opposite could be concluded. In addition to this dichotomous causation, some portion of W-CTS patients may be explained by the combined contribution of personal susceptibility and occupational exposure, i.e., workers exposed to a ‘moderate’ level of occupational risk may or may not develop CTS depending on their CTS susceptibility level. As an illustration, in figure 3, although the occupational risk exposure levels of A and B are about the same, A develops CTS, but B does not, because the susceptibility of A is high enough to cause the cumulative risk exposure level of A to exceed the ‘threshold’ for the CTS onset.

[Insert figure 3 about here]

2.3. Risk assessment questionnaire A risk assessment questionnaire directed toward CTS, developed by You (1999), was used to collect information on non-occupational and occupational risk conditions for each individual. The questionnaire consists of three sections: (1) personal susceptibility, (2)


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psychosocial stress at work, and (3) physical risk at work (illustrated in figure 4). To develop the assessment instrument, You conducted a comprehensive survey regarding CTS risk factors and corresponding valid metrics (e.g., Bortner (1969) scales for type A personality, Edinburgh Handedness Inventory for hand dominance (Kucera and Robins 1989), and psychosocial scales by Kasl and Amick (1996)). For factors which do not have commonly accepted metrics, new metrics were developed. The questionnaire also incorporated use of measurement devices wherever applicable for more accurate assessment: anthropometer for measurement of body dimensions and grip dynamometers for assessment of use of power/pinch grip forces. He evaluated the reliability of the questionnaire instrument for 20 participants using the test-retest method and identified 98 metrics (63 for personal factors, 4 for psychosocial factors, and 31 for physical factors) as having a reliability of greater than .7.

[Insert figure 4 about here] The CTS risk assessment questionnaire was administered to each participant in a secure place by one, same investigator. The participants gave written informed consent and their participation was compensated. Any questions that arose during completion of the questionnaire, such as clarification of technical terms, were answered by the investigator.

2.4. Statistical methods Three statistical methods were used to develop risk assessment models: logistic regression analysis, receiver operating characteristic (ROC) analysis, and jack-knife


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technique. SPSS for Windows® Release 8.0 was used for statistical analysis. Logistic regression analysis develops a linear model relating discrete or continuous risk scales to logit, the natural logarithm of odds (odds is the ratio of the probability of a risk condition belonging to one of two study groups (p) to that of the risk condition belonging to the other group (1-p)). The exponential of a coefficient in the model represents the change in odds ratio (OR, the ratio of the odds of a risk condition to that of the baseline condition). Using the logistic regression model, the probability of a certain risk condition belonging to a group of interest can be estimated by equation 1.

p=

1 1+ e

− (α + β1 X 1 + β 2 X 2 +...+ β p X p )

(equation 1)

ROC analysis was then performed to examine the classification performance of a risk assessment model over cut-off probability (pc) in terms of sensitivity, specificity, and overall accuracy. Depending on pc, the sensitivity (proportion of correct classification of cases), specificity (proportion of correct classification of referents), and overall accuracy (mean of sensitivity and specificity) of the model vary; sensitivity and specificity run each other in an opposite direction—an increase of sensitivity results in a decrease of specificity. The present study found an optimal pc of the model where both sensitivity and specificity simultaneously reach an equivalent, high value, assuming an equal importance of correct classification of cases and that of referents. Furthermore, the discriminability of a ROC curve (d′ = 0, very poor; d′ = 2.33, nearly perfect (Proctor and Van Zandt 1994)) and the area under the curve (A = 0, very poor; A = 1, nearly perfect) were estimated. As


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a ROC curve pulls away from the diagonal, both d′ and A increase, indicating better discriminability of the model between the groups. Finally, the jack-knife method (Afifi and Clark 1990), a cross-validation method, was used to obtain a less biased estimate of the classification performance of a risk assessment model. Since a statistical technique develops a model which best fits the data, an evaluation of the model using the same data may be biased. For an unbiased evaluation, a statistical model needs to be validated over the data that has not been used to build the model. The jack-knife algorithm is an effective method for cross-validation of a model in case the sample size is small (Afifi and Clark 1990). The general steps of the algorithm are as follows: (1) exclude a single observation from the original data, then develop a model and establish a pc with the remaining data; (2) using the model, estimate a probability for the excluded observation; (3) classify the observation based on pc; (4) repeat steps 1 to 3 for the rest of the observations; and (5) based on the classification results, compute the model’s performance (which is less biased).

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Results

3.1. Survey of risk assessment In the CTS risk assessment survey, 22 W-CTS patients, 25 NW-CTS patients, and 50 healthy workers participated. The mean time for each participant to complete the questionnaire was about one hour on average (range = 40 min. to 1.5 hours).

3.2. Screening of risk scales


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To develop parsimonious and stable, yet valid, risk assessment models in the present study, 98 reliable metrics were screened by pseudo-univariate logistic regression analysis for three pairs of case and referent groups. The case groups were W-CTS, NW-CTS, and C-CTS (combined group of W-CTS and NW-CTS), the referent group was HEALTHY, and the pairs were denoted as W-CTS/HEALTHY, NW-CTS/HEALTHY, and CCTS/HEALTHY. The pseudo-univariate regression is a multiple logistic regression including one particular risk scale of interest, two stratification variables age and gender (commonly known confounders of the risk of CTS), and the interaction of age and gender. All scales except female-specific ones (e.g., menopause, history of hysterectomy, and use of oral contraceptives) were individually regressed along with age, gender, and age × gender in the pseudo-univariate analysis; for female-specific scales, only age was included as a confounder. Adjusting the effects of the confounders in each model, the pseudo-univariate analysis can better estimate the significance of the particular risk scale. As an example of the pseudo-univariate analysis, table 1 displays results of three recreational activity scales (light use, repetitive use, and strenuous use of the hands and wrists for recreational activity) for W-CTS/HEALTHY, NW-CTS/HEALTHY, and CCTS/HEALTHY, respectively. The table summarizes (1) frequency of cases and frequency of referents for each risk condition; (2) OR estimate after adjusting the effects of the confounders (OR > 1 indicates an increased risk of CTS and OR < 1 does the opposite when compared to a reference condition), its significance probability (P), and 95% confidence interval (CI) for each risk condition; and (3) χ2-test statistic (used for testing the significance of a risk scale having more than two levels), degrees of freedom (d.f.), and corresponding P for each risk scale. The χ2-values and OR estimates in the


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table indicate at α = .25: W-CTS patients are less active for recreational activities involving repetitive use of the hands/wrists; NW-CTS patients are less active for those involving light use of the hands/wrists, but more active for those involving repetitive use of the hands/wrists; and combined CTS patients are less active for those involving light use or strenuous use of the hands/wrists.

[Insert table 1 about here] Of the 98 risk scales, 27 for W-CTS/HEALTHY, 21 for NW-CTS/ HEALTHY, and 24 for C-CTS/HEALTHY were screened by using two criteria: (1) P of a risk scale ≤ .25 and (2) the direction of association of a risk scale with the risk of CTS agrees with related common findings in previous CTS studies. The former criterion is to screen risk scales having certain statistical significance, and the latter is to develop a model agreeing with the common understanding of CTS.

3.3. Development of risk assessment models Risk assessment models were developed for W-CTS/HEALTHY (table 2), NWCTS/HEALTHY (table 3), and C-CTS/HEALTHY (table 4) by multiple logistic regression. The stepwise variable selection algorithm (.15 and .20 as criterion probability of inclusion and removal of risk scales from the model, respectively (Hosmer and Lemeshow 1989)) was used in the logistic regression analysis for the risk scales screened by the pseudo-univariate analysis. Each logistic regression table summarizes (1) risk scales included in the model; (2) estimated coefficients ( βˆ ) and their standard errors (SE( βˆ )); (3) Wald statistics (W), d.f., and P; and (4) partial correlations (R). The


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magnitude of R indicates the partial contribution of a risk scale to the likelihood of the corresponding case event’s occurrence. Goodness-of-fit of each model was tested by the Hosmer-Lemeshow statistics (Hc*) at α = .05 and all the models were found to be statistically appropriate: for W-CTS/HEALTHY, Hc* = 5.03 (d.f. = 8, P = .754); for NW-CTS/HEALTHY, Hc* = 9.47 (d.f. = 7, P = .221); and for C-CTS/HEALTHY, Hc* = 6.35 (d.f. = 8, P = .610).

[Insert table 2 about here] [Insert table 3 about here] [Insert table 4 about here]

Each risk assessment model includes a different set of risk scales, having no psychosocial factors. Table 5 summarizes risk scales selected in each model and highlights those having a relatively large influence (R > .1) in predicting the risk of the corresponding case event. Note that no occupational factors are included in NWCTS/HEALTHY, while both non-occupational and occupational factors are contributors to the other two models; and that no psychosocial factors are found significant in the models.

[Insert table 5 about here] The coefficient estimates of each model were used to compute the probability of belonging to the case group for an individual with certain personal attributes and working under certain job conditions. For example, the probability of belonging to W-CTS is


calculated as .783 (=

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{1 + e [

}

− − 34.67 + 2.93× 0 + .39× 68.1+ 3.66× 0 + ( 3.33×0 + 3.12× 0 +1.99×1) + 2.92×1+ ( 4.50×1+1.68× 0 ) ] −1

; for

categorical scale, multiply the corresponding coefficient by one if related, by zero if not; for continuous risk scale, multiply the number by the corresponding coefficient) for an individual who is male, having a wrist ratio of 68.1%, with no history of musculoskeletal disorders at the hands and wrists during the last five years, whose work includes use of heavy power grip forces for more than 2 hours per day, use of heavy pinch grip forces for more than 1 hour per day, and highly repetitive motions for .5 to 1 hour per day.

3.4. Classification of participants using the models Based on the probability information from each risk assessment model, a criterion probability was established (pc), which resulted in 83% to 89% overall accuracy. A pc should be established to classify an individual into one of the groups—if the probability of an individual for belonging to a case group is greater than pc, then the individual would be classified into the case group; otherwise, the referent group. In this study, the criterion probability of each risk assessment model was determined to be where both sensitivity and specificity reach an equal, high value. For example, in figure 5, for CCTS/HEALTHY, the sensitivity and specificity curves intersect each other at about .50 (pc for C-CTS/HEALTHY; sensitivity = 87%, specificity = 88%, and overall accuracy = 88%). Likewise, pc was about .35 for W-CTS/HEALTHY (sensitivity = 91%, specificity = 88%, and overall accuracy = 89%) and .37 for NW-CTS/HEALTHY (sensitivity = 84%, specificity = 82%, and overall accuracy = 83%), respectively.

[Insert figure 5 about here]


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ROC curves (figure 6) of the models were plotted, showing that the models have a high power of discriminating cases from referents. The discriminability (d′) of each ROC curve was computed by adding two z-scores corresponding to the sensitivity and specificity at pc: 2.51 for W-CTS/HEALTHY, 1.91 for NW-CTS/HEALTHY, and 2.31 for C-CTS/HEALTHY. The area (standard error) under each ROC curve was estimated by the nonparametric method as .96 (.02) for W-CTS/HEALTHY, .87 (.05) for NWCTS/HEALTHY, and .94 (.03) for C-CTS/HEALTHY; all the area values were significant (P < .001).

[Insert figure 6 about here] 3.5. Cross-validation of the models Classification accuracy of the risk assessment models was cross-evaluated by the jackknife procedure and resulted in a reduction of 2% to 7%. For cross-validation, about half of the participants were randomly selected from each case and referent group: 12 for W-CTS, 13 for NW-CTS, and 25 for HEALTHY. Then, each selected participant was removed one at a time from the corresponding original data set, generating 37, 38, and 50 test data sets for W-CTS/HEALTHY, NW-CTS/HEALTHY, and C-CTS/HEALTHY, respectively. Multiple logistic regression was conducted over each test data set, a probability was estimated for each excluded participant using the corresponding logistic regression model, and classification was made for each excluded participant based on the related pc. Cross-evaluated overall accuracy of the models is 84%, 76%, and 86% for WCTS/HEALTHY, NW-CTS/HEALTHY, and C-CTS/HEALTHY, respectively.


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4.

Discussion

To control for possible incorrect selection of participants for the case and referent groups, the present study used a combination of symptom reports, physical signs, and nerve conduction studies. For the CTS groups, patients were recruited having both clinical symptom history and electrodiagnostic evidence of CTS. For the referent group, healthy workers (no medical history of CTS) were screened for possible CTS by using the modified Levine et al. (1993) symptom assessment questionnaire (You et al. 1999) and Phalen’s wrist flexion test. Workers either reporting no symptoms or reporting a mild severity on nonspecific CTS symptoms (You et al. 1999) but showing a negative Phalen’s sign were selected. It has been reported, in CTS diagnosis, that the specificity of electrophysiologic testing is greater than 95% (AAEM et al. 1993), the sensitivity of Phalen’s test ranges from 71% to 75% (Moore 1992), and use of a combination of symptom reports and objective testing increases the diagnosis accuracy (Gerr et al. 1991). Based on these findings, it was estimated that there was less than a 5% possibility of false inclusion of healthy workers in the case groups and less than a 25% possibility of false inclusion of workers having CTS in the referent group, respectively. To obtain valid occupational exposure information, this study used ‘at least oneyear experience on the current job’ as selection criterion. The participants were asked to assess their psychosocial and physical conditions on the current job only because the assessment of the previous jobs may be inaccurate due to recall bias. Furthermore, it was assumed that participants having less than one year of experience on the current job may provide insufficient information of their occupational conditions and the functional status of their hands may be better explained by the past jobs rather than the current job.


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However, according to the 1998 BLS occupational injury and illness statistics (BLS 2001), it is not unusual that those with less than one-year’s service on the current job report their CTS as work-related (about 13% of all CTS cases). The present study did not confine the occupation of participants, resulting in an unbalanced distribution in occupation between the case and referent groups. When the participants’ occupations were classified into six high-level aggregate groups defined by the 1998 Standard Occupational Classification (BLS 1999), the occupations of the patients distributed over five aggregate groups (management and professional; service; sales and office; natural resources, construction, and maintenance; and production, transportation, and material moving) except the military group, while those of the healthy workers from four work sites ranged over only two aggregate groups (management and professional; and production, transportation, and material moving). This difference in occupational diversity may confound the effects of CTS risk factors, which causes estimation errors on the contribution of some risk factors to the nerve disorder. A proper control of job diversity among the case and referent groups is needed to improve the accuracy of CTS risk assessment models. No validated quantitative criteria determining the work-relatedness of CTS being available, this study used workers’ compensation to classify certain CTS cases as workrelated. While controversy exists regarding the causation of CTS by work (Nathan et al. 1993, Barton et al. 1992, Kroemer 1992, Hadler 1990), a comprehensive literature survey by NIOSH (Bernard 1997) indicated that CTS has epidemiologic evidence of a positive association with ergonomics stressors (force, repetitiveness, and hand-arm vibration). However, no validated criteria have been established to determine the magnitude of


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exposure through work (such as forcefulness, repetitiveness (Kilbolm 1994), and postural awkwardness (Juul-Kristensen et al. 1997)) and the acceptable limit for each individual exposure and in combination (Sorock and Courtney 1996, Kroemer 1992, Moore 1992). Due to the absence of quantitative ergonomics criteria discriminating between hazardous and safe workplaces for CTS, the practitioners and claimants often experience difficulty and confusion in determining the compensability of a CTS case (Herbert et al. 2000, Derebery 1998). Thus, at present, use of workers’ compensation may be the most objective, reliable criterion to define CTS cases as work-related. Therefore, the dichotomous grouping of the CTS patients based on workers’ compensation must include classification errors. Use of workers’ compensation in surveillance of occupational CTS has limits due to significant under-reporting (Korrick et al. 1994, Cummings et al. 1989) and ineligible-reporting (Derebery 1998), which indicates that some CTS conditions caused by work might be misclassified into non-work related CTS and vice versa. However, the present study did not artificially change the patient classification by evaluating the possibility of work-relatedness of their CTS based on the patients’ work condition assessment because it could introduce more bias in the model developing process. The use of better criteria would reduce the possibility of CTS patient misclassification. To identify exposure levels for various risks for an individual within a reasonable time, the self-assessment methodology was used along with measurement devices and the technical assistance of an investigator. For the assessment of personal and psychosocial risks, use of self-reports with easy, straightforward questions is common (however, for assessment of medical conditions, Atcheson et al. (1998) showed that use of patient


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reports might not be adequate for proper evaluation of the work-relatedness of a CTS due to the possibility of undiagnosed CTS-contributing diseases). Also, for the assessment of physical risks, self-reports are often used in epidemiological research (Bernard 1997); however, for better objectivity in exposure assessment, alternative methods such as expert evaluation and direct measurement are recommended (Hagberg et al. 1995). This study did not employ the more objective methods due to practical limitations of getting access to worksites, the work status of patients, and the applicability of direct measurement in a workplace setting (interference with work). Potential inaccuracies in the participants’ self-reports could decrease the validity of risk assessment models in this study. To increase the validity of the self-reports, instruments (such as anthropometer and grip dynamometer) and technical assistance were provided in the questionnaire. During the survey, it was identified that the assessment questions for physical risks should be modified for diversified work. Participants whose job activities are irregular, acyclic, diverse, and/or varying by season (e.g., construction work, office work, or maintenance) showed difficulty in assessing their work conditions using the questionnaire. The assessment difficulty was caused by a combination of different temporal proportions of tasks and distributions of exposure to different risk conditions for each task. To reduce the assessment difficulty for diversified work, an analytic job evaluation and synthesis method may be employed: first, the temporal distribution of tasks for a job is estimated; second, risk assessment is performed for each individual task; last, the individual task assessments are combined by the task temporal proportions for an overall job assessment. However, this analysis-synthesis assessment approach will require a significant increase in time for the survey.


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Furthermore, during the questionnaire reliability evaluation, it was identified that the participants’ responses of psychosocial conditions at work significantly varied over time. Of seven psychosocial factors, three (time pressure, decision authority, and social support from colleagues) were excluded due to a low reliability, which indicates a significant temporal variation of workers’ perceptions of the job contents and social relationships in the workplace. This temporal variability of psychosocial factors may require a careful interpretation of the significance of psychosocial factors in relation to the incidence/severity of occupational musculoskeletal disorders. To develop a valid model for CTS, this study used a holistic approach for risk factors and screened factors based on both their statistical and empirical significance. A comprehensive set of personal, psychosocial, and physical factors were identified by a literature review and included in the CTS risk assessment questionnaire. These risk factors were screened for statistical significance by the pseudo-univariate method. Also, their directional relationship to the risk of CTS development was compared to corresponding previous findings. Three CTS risk assessment models (NW-CTS/HEALTHY, W-CTS/HEALTHY, and C-CTS/HEALTHY) including different sets of risk scales support the study hypothesis on risk factor contribution and a necessity of careful participant selection in CTS research. NW-CTS/HEALTHY includes only personal factors, while the other two models have different sets of personal and physical factors; all the models do not include psychosocial factors. This risk factor inclusion pattern indicates that the nerve injury for NW-CTS patients can be attributable mainly to their ‘high’ personal susceptibility to CTS rather than exposure to adverse physical work conditions, whereas that for W-CTS


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patients can be attributable to ‘high’ physical risk or the combined contribution of personal susceptibility and work exposure. The significant variation of factors included among the assessment models suggests that CTS research establish rigorous definitions of the work-relatedness of CTS for valid comparison among studies. The CTS risk assessment models showed a satisfactory discriminability and high classification accuracy. The discriminability and accuracy of the models ranged 1.91 to 2.51 and 83% to 89%, respectively. In the cross-validation by the jack-knife method, the models’ classification accuracy was reduced by 2% to 7%. The models’ performance can be increased by careful case/referent selection, objective occupational CTS definition, and accurate risk exposure assessment. Applications of the CTS assessment models include the establishment of exposure limits, prioritization of control solutions, and placement of workers to tasks. Acceptable exposure limits tailored to individual workers can be established considering their personal susceptibility to CTS. Various job improvement actions can be prioritized based on their potential risk reduction for CTS. Lastly, undesirable worker placement can be reduced by avoiding placement of ‘highly’ CTS susceptible individuals to hand-intensive tasks. The present CTS models can be discussed with reference to the theoretical model (Strain Index) of Moore and Garg (1995), the fuzzy linguistic model of McCauley-Bell and Crumpton (1997), and the discriminant model of Matias et al. (1998). The strain index model is less specific to CTS by dealing with the distal upper-extremity musculoskeletal disorders including the median nerve injury. The discriminant model is specific to VDT tasks, while the others are for generic tasks because the studies did not


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confine the type of jobs for participants. The current logistic regression models are most inclusive of important CTS risk factors. The discriminant and logistic models were developed based on empirical data, while the strain index and fuzzy models were based on theoretical concepts and expert judgment, respectively. While the present study screened all measurement scales in terms of reliability, the discriminant model did for some scales and the others did not include any discussion of the issue. Of the models, only the discriminant model used objective methods to assess physical work exposure. While the discriminant, fuzzy, and logistic models can predict the likelihood of developing CTS, the strain index model does not have the prediction capability. Lastly, the discriminant and logistic models discussed cross-validation, while the others did not. Future work is needed to improve the CTS risk assessment models with more elaborated study group definitions and assessment methods. The occupational category of study groups should be specific to a properly limited application scope (such as industry plant workers only). Valid criteria to determine the work-relatedness of CTS should be developed for accurate classification of CTS patients into W-CTS and NW-CTS. A larger group of participants should be recruited for better statistical power and generalizability of the models. Finally, regarding risk exposure assessment, for personal factors, a comprehensive medical examination should be included for the proper identification of undiagnosed medical conditions; for psychosocial factors, more reliable questionnaires should be employed; and, for physical factors, more accurate (but practical) techniques should be used.


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Acknowledgments The authors would like to thank Kathy Conner and Joann Tucker for their assistance in this study. The authors also wish to express gratitude to all the participants in the study: CTS patients at the Hershey Medical Center; workers at Penn State Univ. Libraries, Penn State Univ. Food Services Division, Bayer Clothing Group, Inc., Clearfield, PA, and Hess Manufacturing Co., Quincy, PA.

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45, 585-592. HADLER, N. M. 1990, Cumulative trauma disorders: An iatrogenic concept, Journal of Occupational Medicine, 32, 38-41. HAGBERG, M., SILVERSTEIN, B., WELLS, R., SMITH, M. J., HENDRICK, H. W., CARAYON, P. and PERUSSE, M. 1995, Work-related Musculoskeletal Disorders (WMSDs): A Reference Book for Prevention (London: Taylor & Francis). HERBERT R., GERR F. and DROPKIN J. 2000, Clinical evaluation and management of work-related carpal tunnel syndrome, American Journal of Industrial Medicine,

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14, 59-86. KORRICK, S. A., REST, K. M., DAVIS, L. K. and CHRISTIANI, D. C. 1994, Use of state workers' compensation data for occupational carpal tunnel syndrome surveillance: a feasibility study in Massachusetts, American Journal of Industrial Medicine, 25, 837-850. KROEMER, K. H. E. 1992, Avoiding cumulative trauma disorders in shops and offices, American Industrial Hygiene Association, 53, 596-604. KUCERA, J. D. and ROBINS, T. G. 1989, Relationship of cumulative trauma disorders of the upper extremity to degree of hand preference, Journal of Occupational and Environmental Medicine, 31, 17-22. LEVINE, D. W., SIMMONS, B. P., KORIS, M. J., DALTROY, L. H., HOHL, G. G., FOSSEL, A. H. and KATZ, J. N. 1993, A self-administered questionnaire for the assessment of severity of symptoms and functional status in carpal tunnel syndrome. The Journal of Bone and Joint Surgery, 75, 1585-1592. MATIAS, A. C., SALVENDY, G. and KUCZEK T. 1998, Predictive models of carpal tunnel syndrome causation among VDT operators, Ergonomics, 41, 213-226. MCCAULEY-BELL, P. and CRUMPTON, L. 1997, Fuzzy linguistic model for the prediction of carpal tunnel syndrome risks in an occupational environment, Ergonomics, 40, 790-799.


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30

SILVERSTEIN, B. A., FINE, L. J. and ARMSTRONG, T. J. 1987, Occupational factors and carpal tunnel syndrome, American Journal of Industrial Medicine, 11, 343-358. SOROCK, G. S. and COURTNEY, T. K. 1996, Epidemiologic concerns for ergonomists: Illustration from the musculoskeletal disorder literature, Ergonomics, 39, 562-578. YOU, H. 1999, The Development of a Risk Assessment Model for Carpal Tunnel Syndrome, Unpublished doctoral dissertation, Pennsylvania State University, University Park, PA. YOU, H., SIMMONS, Z., FREIVALDS, A., KOTHARI, M. K. and NAIDU, S. H. 1999, Relationships between clinical symptom severity scales and nerve conduction measures in carpal tunnel syndrome, Muscle & Nerve, 22, 497-501. WIESLANDER, G., NORBACK, D., GOTHE, C. J. and JUHLIN, L. 1989, Carpal tunnel syndrome and exposure to vibration, repetitive wrist movements, and heavy manual work: A case-referent study, British Journal of Industrial Medicine, 46, 43-47.


31

List of Tables Table 1. Pseudo-univariate logistic regression analysis for recreational activity risk scales. Table 2. Multiple logistic regression model of W-CTS/HEALTHY (cases: W-CTS patients, referents: healthy workers). Table 3. Multiple logistic regression model of NW-CTS/HEALTHY (cases: NW-CTS patients, referents: healthy workers). Table 4. Multiple logistic regression model of C-CTS/HEALTHY (cases: CTS patients, referents: healthy workers). Table 5. Risk factors included in CTS risk assessment models.

List of Figures Figure 1. Occupational and non-occupational risk factors of CTS. Figure 2. Hypothetical characteristics of personal susceptibility to CTS and occupational risk exposure for case groups (work-related CTS patients, W-CTS; non-work related CTS patients, NW-CTS) and referent group (healthy workers, HEALTHY). Figure 3. Contribution profiles of occupational and non-occupational risks to the development of CTS. The contribution profile diagram shows how the combined effects of personal, psychosocial, and physical risks can cause the disorder (e.g., case A develops CTS due to the higher susceptibility to the disorder while case B does not). Figure 4. Questions for physical factors in the CTS risk assessment questionnaire (illustration). Figure 5. Classification performance of C-CTS/HEALTHY. Figure 6. ROC curves of CTS risk assessment models.


32

Table 1. Pseudo-univariate logistic regression analysis for recreational activity risk scales†*. W-CTS / HEALTHY Frequency

Risk scales

Cases Light use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) High (> 3 hrs/week) Repetitive use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) Moderate (3-5 hrs/week) Heavy (> 5 hrs/week) Strenuous use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) Moderate (3-5 hrs/week) Heavy (> 5 hrs/week)

χ2 test

Adjusted for age and gender

Referents

Odds Ratio

P

95% CI

χ2

d.f.

P

5 10 7

23% 45% 32%

9 14 27

18% 28% 54%

1 1.27 0.54

0.747 0.418

0.30 - 5.40 0.12 - 2.39

1.77

2

0.413

10 7 2 3

45% 32% 9% 14%

20 8 14 8

40% 16% 28% 16%

1 2.20 0.29 0.60

0.266 0.170 0.572

0.55 - 8.82 0.05 - 1.71 0.11 - 3.46

4.58

3

0.206

17 3 1 1

77% 14% 5% 5%

22 15 8 5

44% 30% 16% 10%

1 0.36 0.24 0.46

0.182 0.229 0.524

0.08 - 1.61 0.02 - 2.43 0.04 - 5.05

2.81

3

0.422

NW-CTS / HEALTHY Light use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) High (> 3 hrs/week) Repetitive use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) Moderate (3-5 hrs/week) Heavy (> 5 hrs/week) Strenuous use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) Moderate (3-5 hrs/week) Heavy (> 5 hrs/week)

10 6 9

40% 24% 36%

9 14 27

18% 28% 54%

1 0.37 0.25

0.171 0.044

0.09 - 1.54 0.07 - 0.96

4.21

2

0.122

6 7 5 7

24% 28% 20% 28%

20 8 14 8

40% 16% 28% 16%

1 3.87 2.21 3.91

0.076 0.320 0.084

0.87 - 17.32 0.46 - 10.50 0.83 - 18.41

4.30

3

0.231

18 2 2 3

72% 8% 8% 12%

22 15 8 5

44% 30% 16% 10%

1 0.22 0.72 1.64

0.079 0.727 0.595

0.04 - 1.19 0.11 - 4.58 0.26 - 10.14

3.87

3

0.276

C-CTS / HEALTHY Light use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) High (> 3 hrs/week) Repetitive use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) Moderate (3-5 hrs/week) Heavy (> 5 hrs/week) Strenuous use of the hands and wrists for recreational activity Minimal (< 1 hr/week) Low (1-3 hrs/week) Moderate (3-5 hrs/week) Heavy (> 5 hrs/week)

15 16 16

32% 34% 34%

9 14 27

18% 28% 54%

1 0.67 0.35

0.518 0.073

0.20 - 2.23 0.11 - 1.10

3.45

2

0.178

16 14 7 10

34% 30% 15% 21%

20 8 14 8

40% 16% 28% 16%

1 2.75 0.83 1.65

0.101 0.772 0.440

0.82 - 9.19 0.24 - 2.89 0.46 - 5.91

3.83

3

0.281

35 5 3 4

74% 11% 6% 9%

22 15 8 5

44% 30% 16% 10%

1 0.29 0.44 0.98

0.047 0.300 0.980

0.09 - 0.99 0.09 - 2.09 0.20 - 4.84

4.57

3

0.207

†

Risk scales and corresponding risk conditions’ odds ratios having P ≤ .25 are in bold.

*

Classification of recreational activities: Activity type Activities with a light use of the hands and wrists Activities with a repetitive use of the hands and wrists Activities with a strenuous use of the hands and wrists

Examples Walking, jogging, hiking, bicycling, swimming, aerobic dancing, and playing soccer. Crocheting, knitting, playing the piano, gardening, painting, playing table tennis, and playing basketball. Bowling, woodworking, racquet sports, lifting weights, boating, sailing, and water-skiing.


33

Table 2. Multiple logistic regression model of W-CTS/HEALTHY (cases: W-CTS patients, referents: healthy workers)†. W-CTS / HEALTHY Risk scales Gender (GENDER) Male Female Wrist ratio - right hand (WR_R) Musculoskeletal disorder during last 5 years at the hands and wrists (MD_H_5) None Yes Use of heavy power grip forces (> 88.9 N) of the dominant hand (PW_20_D) Minimal (< .5 hr/day) Low (.5-1 hr/day) Moderate (1-2 hrs/day) High (> 2 hrs/day) Use of heavy pinch grip forces (> 22.2 N) of the dominant hand (PC_5_D) Low (1 hr/day) Very highly repetitive motions (< 1 sec./operation) of the dominant hand (RE_1_D) Minimal (< .5 hr/day) Low (.5-1 hr/day) Moderate (1-2 hrs/day) High (> 2hrs/day) Constant

†

(β)

Standard error (SE(β))

2.93 0.39

1.407 0.147

4.33 7.12

1 1

0.04 0.01

0.16 0.24

3.66

1.340

7.47 6.58

1 3

0.01 0.09

0.25 0.08

3.33 3.12 1.99

1.421 1.740 1.520

5.48 3.21 1.71

1 1 1

0.02 0.07 0.19

0.20 0.12 4.7) Weight (WT) Wrist ratio - right hand (WR_R) Female by WT AGE by WR_R Constant

†

Standard error (SE(β))

d.f.

P

Partial correlation (R )

5.91

2

0.05

0.14

Wald (W )

1.29 1.97

0.814 0.818

2.52 5.81

1 1

0.11 0.02

0.07 0.20

0.23 0.01 0.0012 -23.46

0.090 0.0041 0.0005 6.974

6.80 6.40 5.55 11.32

1 1 1 1

0.01 0.01 0.02 .1) to the model are in bold.


35

Table 4. Multiple logistic regression model of C-CTS/HEALTHY (cases: CTS patients, referents: healthy workers)†. C-CTS / HEALTHY Risk scales Age (AGE) Light use of the hands and wrists for recreational activity (LU) Minimal (< 1 hr/week) Low (1-3 hrs/week) High (> 3 hrs/week) Weight (WT) Wrist ratio - right hand (WR_R) Musculoskeletal disorder during last 5 years at the hands and wrists (MD_5_D) None Yes Use of heavy pinch grip forces (> 22.2 N) of the dominant hand (PC_5_D) Very highly repetitive motions (< 1 sec./operation) of the dominant hand (RE_1_D) Minimal (< .5 hr/day) Low (.5-1 hr/day) Moderate (1-2 hrs/day) High (> 2 hrs/day) Exposure of the hands and wrists to extremely cold temperature (< 10 °C) (CO_E) Low (< .25 hr/day) Moderate (.25-.5 hr/day) High (> .5 hr/day) Female by WT Female by PC_5_D Female by Low (< 1 hr/day) Female by High (> 1 hr/day) Constant

†

(β) 0.08

Standard error (SE(β)) 0.042

1.88 1.09

0.941 0.847

4.00 1.67

1 1

0.05 0.20

0.12 80%)

Use of the hands

1

2

3

4

5

6

;7

Natural (within 5°)

1

2

3

;4

5

6

7

Moderate (5 to 30°)

1

2

3

;4

5

6

7

Extreme (above 30°)

1

2

;3

4

5

6

7

Figure 4. Questions for physical factors in the CTS risk assessment questionnaire (illustration).


41

correct classification probability

1.0 0.8 0.6

Sensitivity (C-CTS/C-CTS) Specificity (HEALTHY/HEALTHY) Overall accuracy

0.4 0.2

0.0 0.0

0.2

0.4

0.6

0.8

cut-off probability (p c )

Figure 5. Classification performance of C-CTS/HEALTHY.

1.0


42

1.0

Sensitivity

0.8

0.6

0.4

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0

1-Specificity

(a) W-CTS/HEALTHY

1.0

Sensitivity

0.8

0.6

0.4

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0

1-Specificity

(b) NW-CTS/HEALTHY

1.0

Sensitivity

0.8

0.6

0.4

0.2

0.0 0.0

0.2

0.4

0.6

0.8

1.0

1-Specificity

(c) C-CTS/HEALTHY Figure 6. ROC curves of CTS risk assessment models.