Open Access

Prevalence and risk factors associated with self-reported carpal tunnel syndrome (CTS) among office workers in Kuwait

  • Sudha R Raman1, 2,
  • Becher Al-Halabi1,
  • Elham Hamdan1 and
  • Michel D Landry1, 2, 3Email author
BMC Research Notes20125:289

DOI: 10.1186/1756-0500-5-289

Received: 8 February 2012

Accepted: 13 June 2012

Published: 13 June 2012

Abstract

Background

The prevalence of carpal tunnel syndrome (CTS) is not well understood in many Arabian Peninsula countries. The objective of this study was to investigate the prevalence and factors associated with self-reported CTS in Kuwait.

Findings

A cross-sectional, self-administered survey of CTS-related symptoms was used in this study. Multivariate logistic regression was also used to estimate adjusted odds ratios for factors of interest. Participants in this study were adult office workers in Kuwait (n = 470, 55.6% males), who worked in companies employing more than 50 people. Self-reported CTS was reported in 18.7% of the group (88/470). CTS was significantly associated with the following demographic factors: female gender, obesity and number of comorbid conditions. Self-identification of CTS was also associated with key symptoms and impairment in daily activities (e.g., wrist pain, numbness, weakness, night pain, difficulty carrying bags, difficulty grasping [Chi-Square Test for Association: P < 0.05 for all symptoms/activities]). However, symptoms such as wrist pain, weakness, and functional disabilities were also frequently reported among those who do not self report CTS (range: 12.1%–38.2%).

Conclusions

Prevalence of self-reported CTS among office workers in Kuwait is 18.7%, and the risk factors for CTS in this population included female gender, obesity and number of related comorbidities. The frequency of symptoms in the sample who did not self report CTS suggest that CTS may be under-recognized, however further research is required to assess the prevalence of clinically diagnosed CTS.

Keywords

Carpal tunnel syndrome Prevalence Occupational exposure Kuwait

Background

Carpal tunnel syndrome (CTS) is a common musculoskeletal condition, and while diagnostic criteria for CTS can vary, the clinical profile typically includes a combination of clinical assessment of symptoms (e.g., numbness, tingling, night pain and paraesthesia), signs (e.g.,Tinel’s sign, Phalen’s sign) and/or nerve conduction velocity (NCV) testing of the median nerve across the carpal tunnel [1, 2]. Prevalence estimates of CTS in the general adult population range from approximately 1% to 16% [36]. Little is known about the prevalence, costs and potential contributing factors of CTS in the Arabian Peninsula region, including Kuwait [7]. In Kuwait, high levels of overweight and obesity, diabetes mellitus, and cigarette smoking [8, 9], and other known risk factors for CTS [10, 11] may suggest that CTS is an emerging workplace and occupational health issue. The purpose of this study was to estimate the prevalence of self-reported CTS among office workers in Kuwait, and to identify risk factors associated with CTS in this population.

Findings

Methods

Participants

A convenience sample of companies in Kuwait City employing 50 or more people were approached by the research team between June and July 2008 about participation in the study. All companies who were approached agreed to collaborate by allowing their individual employees to voluntarily participate in a questionnaire regarding CTS. In order to be included in this study, individual participants were required to be 20 years old or older, have an office job where their work duties and tasks were primarily related to administrative, computer and/or desk work, and be willing to complete the survey in English. In each location or participating company, a list of all employed office workers who met the inclusion criteria was obtained by the appropriate human resource department. Trained data collectors then described the study purpose and objectives to all potential participants, and at that time it was clarified that choosing to participate represented informed consent to participate in this study. Questionnaires were then distributed to all employees who agreed to participate. Ethics approval for this study was obtained through the Fawzia Sultan Rehabilitation Institute.

Survey tool development

The data collection tool used in this study was a self-administered questionnaire that required approximately 15 minutes to complete. The questionnaire was developed specifically for the purpose of this study, and was pilot tested with 10 office workers employed by companies outside the study sample. Feedback from pilot testing resulted in clarification of the wording of 20 of 61 questions; and in all cases, changes were incorporated into the final questionnaire.

Study measures and variables

There were three sets of questions in the survey (Additional file 1). The first set of questions was related to participant characteristics such as gender, age, height, weight, and nature of occupation (i.e. type of job). Body mass index (BMI: weight (kg)/height (m)2) was calculated and categorized according to World Health Organization (WHO) guidelines [12]. The second set of questions was related to CTS status. After reading a definition of CTS, participants who indicated ‘yes’ to the question, “Do you think you have Carpal Tunnel Syndrome?” were considered to have self-reported CTS. Participants self-reporting CTS were asked to indicate the duration, frequency, and severity of symptoms, the effect of their symptoms on work and daily activities disability, the effect of work on their development of CTS and whether they had been diagnosed with CTS by a health professional. The third set of questions was designed to test for the presence of CTS symptoms and risk factors via a series of questions for all participants regarding wrist pain, numbness, weakness, motor function, history of trauma, computer use and whether they suffered from any of 12 co-morbidities identified a priori from the literature [1316]. Participants were also asked to indicate their smoking status, exercise frequency and perception of their general health.

Data entry and analysis

Data were entered into a data file located on a password-protected computer in the research offices of Fawzia Sultan Rehabilitation Institute. Statistical analyses were conducted using SAS (SAS Institute Inc, Cary, North Carolina). Proportions were calculated to describe the outcome of interest (prevalence of self-reported CTS), demographic characteristics and CTS symptoms and disability. Symptom prevalence in individuals reporting CTS was compared to individuals not reporting CTS using Chi-square tests of association. The primary outcome variable of interest was self-reported CTS. Odds ratios and 95% confidence intervals (95% CIs) comparing individuals who reported CTS and individuals who did not, were calculated for factors of clinical interest (gender, age, BMI, self-rated health, previous wrist injury, computer use, number of co-morbid conditions, exercise frequency and smoking status). A best-fit logistic regression model of predictor variables for self-reported CTS was found using backward selection, starting from the full set of nine variables listed above. Adjusted odds ratios and 95% CIs for the variables remaining in the best-fit model were also calculated.

Results

A total of 470 office workers from 12 different companies in Kuwait City participated in this study. Of those reporting, 55.6% were male, 60.7% were aged 20–30 years of age, and 60.6% were overweight or obese (Table 1).
Table 1

Sociodemographic and health characteristics of 470 office workers from 12 different companies in Kuwait City collected in June/July 2008

Characteristics

Total for Item

N (%)

Gender

466

 

 Male

 

259 (55.6)

 Female

 

207 (44.4)

Age group (years)

466

 

 20–30

 

283 (60.7)

 31–40

 

111 (23.8)

 41–50

 

55 (11.8)

 51+

 

17 (3.7)

Marital status

467

 

 Married

 

239 (51.2)

 Single

 

210 (45.0)

 Divorced/widowed

 

18 (3.9)

Work type

452

 

 Professional/white collar

 

444 (98.2)

 Labor

 

8 (1.8)

Body mass index (BMI)

432

 

 Underweight (BMI: < 18.5)

 

8 (1.9)

 Normal (BMI: 18.5 - < 25)

 

162 (37.5)

 Overweight (BMI: 25 - < 30)

 

172 (39.8)

 Obese (BMI: 30+)

 

90 (20.8)

Self-rated health

413

 

 Excellent/very good

 

174 (42.1)

 Good

 

164 (39.7)

 Fair/poor

 

75 (18.2)

Previous wrist injury

413

 

 Yes

 

84 (20.3)

 No

 

329 (79.7)

Computer Use

397

 

 None

 

8 (2.0)

 < 3 hours per day

 

115 (29.0)

 > 3 hours per day

 

274 (69.0)

Number of comorbidities

381

 

 0

 

247 (64.8)

 1

 

75 (19.7)

 2

 

35 (9.2)

 3+

 

24 (6.3)

Exercise frequency

369

 

 0 hour/week

 

134 (37.7)

 1 hour/week

 

114 (30.9)

 ≥2 hours/week

 

116 (31.4)

Smoking status

362

 

 Yes

 

96 (26.5)

 No

 

266 (73.5)

Self-reported CTS

CTS was self-reported by 18.7% of the group (88/470). Of these individuals, over 30% reported that they experience the typical symptoms ‘daily’ or ‘always’ (Figure 1). Length of time since first noticing symptoms was less than one year for 37.5% of the sample, between 1 and 5 years for 43.2% of the sample, and longer than 5 years for almost 7% of the sample (12.5% did not indicate symptom duration). Only fifteen respondents indicated that they had taken sick leave because of their CTS. Of those self-reporting CTS, 71.6% (63/88) indicated that they believed their CTS was related to their work duties. Thirteen (14.8%) of those self-reporting CTS (or 2.8% of the entire sample) indicated that they had been previously diagnosed with CTS by a healthcare professional.
https://static-content.springer.com/image/art%3A10.1186%2F1756-0500-5-289/MediaObjects/13104_2012_Article_1522_Fig1_HTML.jpg
Figure 1

Frequency of reported CTS symptoms amongst those who self-report CTS (n = 88). Detailed Legend: Note that one individual did not indicate symptom frequency.

In bivariate analysis, self-reported CTS was significantly more likely to be found in females than in males, in individuals aged 31–40 years than in those aged 20–30 years, in obese individuals (BMI > 30), in those with a previous wrist injury, in those who rarely or never exercise, and in those who reported relevant comorbidities (Table 2). Due to small sample sizes for many of the investigated comorbidities, we focused on number of these comorbidities, rather than on the specific conditions. However, exploratory testing of the four most common conditions amongst the study population showed that CTS was more likely to be self-reported by individuals with depression (odds ratio, 95% CI: 3.1, 1.6-6.2), previous trauma to the cervical spine (4.2, 2.1-8.5), arthritis (2.5, 1.9-5.3) or diabetes (3.6, 1.5-7.6) than individuals without these individual conditions.
Table 2

Prevalence and odds ratios (OR) of self-reported CTS by sociodemographic and health factors of interest among office workers (Kuwait City, 2008)

 

Total respondents

% (n) with CTS

OR

95% CI for OR

Adjusted OR*

95% CI for Adjusted OR*

Gender

456

     

 Male

254

13.4 (34)

1.0

Reference

1.0

Reference

 Female

202

25.3 (51)

2.2

1.4, 3.5

4.7

2.1, 10.3

Age group (years)

456

     

 20–30

276

16.7 (46)

1.0

Reference

  

 31–40

109

27.5 (30)

1.9

1.1, 3.2

  

 41–50

54

16.7 (9)

1.0

0.5, 2.2

  

 51+

17

17.7 (3)

1.1

0.3, 3.9

  

Body mass index

422

     

 Underweight

8

0.0 (0)

NE

NE

omitted

omitted

 Normal

160

16.2 (26)

1.0

Reference

1.0

Reference

 Overweight

167

15.6 (26)

1.0

0.5, 1.7

0.8

0.3, 1.9

 Obese

87

29.9 (26)

2.2

1.2, 4.1

3.7

1.5, 9.6

Self-rated health

404

     

 Excellent/very good

170

10.0 (17)

0.4

0.2, 3.3

  

 Good

160

23.1 (37)

1.0

Reference

  

 Fair/poor

74

35.1 (26)

1.8

1.0, 3.3

  

Previous wrist injury

405

     

 Yes

82

29.3 (24)

1.9

1.1, 3.3

  

 No

323

18.0 (58)

1.0

Reference

  

Computer use

389

     

 None

8

0.0 (0)

NE

NE

  

 < 3 hours per day

111

14.4 (16)

1.0

Reference

  

 > 3 hours per day

270

21.1 (57)

1.7

0.9, 3.1

  

Number of comorbidities

373

     

 0

244

10.2 (25)

1.0

Reference

1.0

Reference

 1

70

28.6 (20)

3.5

1.8, 6.8

4.9

2.0, 12.3

 2

35

31.4 (11)

4.0

1.8, 9.2

3.3

1.1, 9.7

 3+

24

62.5 (15)

14.6

5.8, 36.8

14.9

4.8, 46.5

Exercise frequency

361

     

 0 hours/week

137

24.8 (34)

1.0

Reference

  

 1 hour/week

112

21.4 (24)

0.8

0.5, 1.5

  

 ≥2 hours/week

112

9.8 (11)

0.3

0.2, 0.7

  

Smoking status

355

     

 Yes

94

19.1 (18)

1.0

Reference

  

 No

261

19.2 (50)

1.0

0.5, 1.8

  

Abbreviations: CI, confidence interval; CTS, carpal tunnel syndrome; NE, not estimable; OR, odds ratio.

*Estimated from multivariable logistic regression. Variables shown remained in best-fit model following backward selection starting from a model including all variables in the table; n = 245 respondents with no missing values.

The best-fit logistic regression with multiple predictors of self-reported CTS included a subset of the variables listed above. Female gender, BMI >30 and the presence of 1 or more of the selected comorbidities were independently associated with self-reported CTS (Table 2).

CTS symptoms in study population

Individuals self-reporting CTS were also more likely to indicate that they experience symptoms and difficulty with activities typically associated with diagnosed CTS than individuals who did not self-report CTS (Figure 2; P < 0.05 for each, Chi-square Tests of Association). Among those who did not report CTS, symptoms and difficulties were not uncommon (12%–38%; Figure 2). In contrast to the 4.5% (4/88) of individuals initially self-reporting CTS who indicated they were seeking treatment from a healthcare professional for their CTS, 68.4% (54/79) of those reporting CTS at the end of the survey indicated that they were now interested in seeking medical treatment.
https://static-content.springer.com/image/art%3A10.1186%2F1756-0500-5-289/MediaObjects/13104_2012_Article_1522_Fig2_HTML.jpg
Figure 2

Common symptoms of CTS reported by survey respondents. Detailed Legend: Common symptoms of CTS reported by proportion of group self-reporting CTS (black bars) and by group not self-reporting CTS (white bars). Significant difference between groups for each symptom (Chi-Square Tests for Association: P < 0.05, for each).

Conclusion

We report that the prevalence of self-reported CTS among office workers in Kuwait to be 18.7%, which is higher than has been reported in other non Arabian Peninsula countries [36]. The CTS prevalence estimate in this study may differ from previous studies due to real differences among office workers in Kuwait or the diagnosis of CTS in Kuwait. The fact that the definition of self-reported CTS in this study did not require current symptoms also may have contributed to higher estimate of self-reported CTS prevalence. Only 2.8% of the entire sample reported CTS diagnosis from a health profession, which supports the finding that prevalence of CTS by self-report tends to be much higher as compared to clinical exam and nerve conduction testing [5, 13, 14]. It would be expected that the estimated prevalence of CTS would decrease if clinical diagnosis and/or nerve conduction testing were used to validate the estimate; however, the magnitude of that decrease in this setting cannot be predicted. Similar to other research we found self-reported CTS to be more common in women than men [5, 6], and that there is an association between self-reported CTS and obesity and relevant comorbidities [10, 15]. As in recent reviews, computer use could not be shown as a definitive risk factor for CTS [1618]. This study is limited by the use of a self-report measure rather than a clinical assessment, and by the use of a questionnaire that was not formally validated. Additional results were presented to provide context for the self-report prevalence estimate, including the proportion of those with diagnosed CTS and the prevalence of individual symptoms, some of which are used to diagnose CTS in the clinical setting (nocturnal sensory symptoms, numbness in the median nerve distribution, aggravating and alleviating factors). Reassuringly, the symptoms and functional impairments experienced by those who self-reported CTS were consistent with the clinical profile of CTS.

To our knowledge, this study presents the first estimate of self-report CTS prevalence in Kuwait and the first to suggest that a substantial proportion of individuals who did not self-report CTS describe high frequency of CTS symptoms. Given the dearth of information of the prevalence of CTS in this region of the world, we feel that self-report estimates of CTS can be taken in context as support for the need for further research into the prevalence of clinically diagnosed CTS and the possibility that CTS may be under-recognized in the general population in Kuwait. These findings, together with the documented high prevalence of factors associated with CTS in Kuwait [1921] may signal that programs for the identification, prevention and intervention of musculoskeletal conditions such as CTS may have broad applicability across Kuwait. These data represent an important first step in recognizing what may be an emerging issue for occupational health across Kuwait.

Abbreviations

BMI: 

Body Mass Index

CI: 

Confidence Interval

CTS: 

Carpal Tunnel Syndrome

NCV: 

Nerve Conduction Velocity

NE: 

Not Estimable

OR: 

Odds Ratio

WHO: 

World Health Organization.

Declarations

Acknowledgements

The authors would like to acknowledge the Lothan Youth Achievement Centre (LOYAC), and Ahmad Al-Halabi, Fatima Dashi, Fatima Ali, and Zained Al-Oraier for their important contributions in data collection. The authors also thank Andrea Ottensmeyer for her assistance in preparing the manuscript during this study.

Authors’ Affiliations

(1)
Fawzia Sultan Rehabilitation Institute
(2)
Doctor of Physical Therapy Division, Dept of Community and Family Medicine, Duke University
(3)
Department of Physical Therapy, University of Toronto

References

  1. Palmer KT, Harris EC, Coggon D: Carpal tunnel syndrome and its relation to occupation: A systematic literature review. Occup Med (Oxf). 2007, 57 (1): 57-66.View ArticleGoogle Scholar
  2. Barcenilla A, March LM, Chen JS, Sambrook PN: Carpal tunnel syndrome and its relationship to occupation: A meta-analysis. Rheumatology. 2012, 51 (2): 250-261. 10.1093/rheumatology/ker108.PubMedView ArticleGoogle Scholar
  3. Tanaka S, Wild DK, Seligman PJ, Behrens V, Cameron L, Putz-Anderson V: The US prevalence of self-reported carpal tunnel syndrome: 1988 national health interview survey data. Am J Public Health. 1994, 84 (11): 1846-1848. 10.2105/AJPH.84.11.1846.PubMedPubMed CentralView ArticleGoogle Scholar
  4. Ferry S, Pritchard T, Keenan J, Croft P, Silman AJ: Estimating the prevalence of delayed median nerve conduction in the general population. Br J Rheumatol. 1998, 37 (6): 630-635. 10.1093/rheumatology/37.6.630.PubMedView ArticleGoogle Scholar
  5. Atroshi I, Gummesson C, Johnsson R, Ornstein E, Ranstam J, Rosen I: Prevalence of carpal tunnel syndrome in a general population. JAMA. 1999, 282 (2): 153-158. 10.1001/jama.282.2.153.PubMedView ArticleGoogle Scholar
  6. Shiri R, Miranda H, Heliovaara M, Viikari-Juntura E: Physical work load factors and carpal tunnel syndrome: A population-based study. Occup Environ Med. 2009, 66 (6): 368-373. 10.1136/oem.2008.039719.PubMedView ArticleGoogle Scholar
  7. Maghsoudipour M, Moghimi S, Dehghaan F, Rahimpanah A: Association of occupational and non-occupational risk factors with the prevalence of work related carpal tunnel syndrome. J Occup Rehabil. 2008, 18 (2): 152-156. 10.1007/s10926-008-9125-4.PubMedView ArticleGoogle Scholar
  8. Jackson RT, al-Mousa Z, al-Raqua M, Prakash P, Muhanna A: Prevalence of coronary risk factors in healthy adult Kuwaitis. Int J Food Sci Nutr. 2001, 52 (4): 301-311. 10.1080/09637480120057558.PubMedView ArticleGoogle Scholar
  9. Shah NM, Behbehani J, Shah MA: Prevalence and correlates of major chronic illnesses among older Kuwaiti nationals in two governorates. Med Princ Pract. 2010, 19 (2): 105-112. 10.1159/000273069.PubMedView ArticleGoogle Scholar
  10. Werner RA: Evaluation of work-related carpal tunnel syndrome. J Occup Rehabil. 2006, 16 (2): 207-222.PubMedView ArticleGoogle Scholar
  11. Nathan PA, Keniston RC, Lockwood RS, Meadows KD: Tobacco, caffeine, alcohol, and carpal tunnel syndrome in American industry. A cross-sectional study of 1464 workers. J Occup Environ Med. 1996, 38 (3): 290-298. 10.1097/00043764-199603000-00015.PubMedView ArticleGoogle Scholar
  12. World Health Organization: BMI Classification. Available at: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed on July 6, 2011
  13. Homan MM, Franzblau A, Werner RA, Albers JW, Armstrong TJ, Bromberg MB: Agreement between symptom surveys, physical examination procedures and electrodiagnostic findings for the carpal tunnel syndrome. Scand J Work Environ Health. 1999, 25 (2): 115-124. 10.5271/sjweh.413.PubMedView ArticleGoogle Scholar
  14. Lenderink AF, Zoer I, van der Molen HF, Spreeuwers D, Frings-Dresen MHW, van Dijk FJH: Review on the validity of self report to assess work related diseases. Int Arch Occup Environ Health. 2012, 85: 229-251. 10.1007/s00420-011-0662-3.PubMedPubMed CentralView ArticleGoogle Scholar
  15. Atcheson SG, Ward JR, Lowe W: Concurrent medical disease in work-related carpal tunnel syndrome. Arch Intern Med. 1998, 158 (14): 1506-1512. 10.1001/archinte.158.14.1506.PubMedView ArticleGoogle Scholar
  16. Thomsen JF, Gerr F, Atroshi I: Carpal tunnel syndrome and the use of computer mouse and keyboard: A systematic review. BMC Musculoskelet Disord. 2008, 9: 134-10.1186/1471-2474-9-134.PubMedPubMed CentralView ArticleGoogle Scholar
  17. Van Rijn RM, Huisstede BM, Koes BW, Burdorf A: Associations between work-related factors and the carpal tunnel syndrome–a systematic review. Scand J Work Environ Health. 2009, 35: 19-36. 10.5271/sjweh.1306.PubMedView ArticleGoogle Scholar
  18. Andersen JH, Fallentin N, Thomsen JF, Mikkelsen S: Risk factors for neck and upper extremity disorders among computers users and the effect of interventions: an overview of systematic reviews. PLoS One. 2011, 6 (5): e19691-10.1371/journal.pone.0019691.PubMedPubMed CentralView ArticleGoogle Scholar
  19. Memon A, Moody PM, Sugathan TN, El-Gerges N, Al-Bustan M, Al-Shatti A, Al-Jazzaf H: Epidemiology of smoking among Kuwaiti adults: Prevalence, characteristics, and attitudes. Bull World Health Organ. 2000, 78 (11): 1306-1315.PubMedPubMed CentralGoogle Scholar
  20. Al-Awadhi AM, Olusi SO, Moussa M, Shehab D, Al-Zaid N, Al-Herz A, Al-Jarallah K: Musculoskeletal pain, disability and health-seeking behavior in adult Kuwaitis using a validated Arabic version of the WHO-ILAR COPCORD core questionnaire. Clin Exp Rheumatol. 2004, 22 (2): 177-183.PubMedGoogle Scholar
  21. Al-Orifan FH, Badr HE, Se’edah MAS, Khadadah KE, Al-Kordi B, Abass A: Obesity and cardiovascular risk factors in Kuwaiti adults. Kuwait Med J. 2007, 39 (2): 162-166.Google Scholar

Copyright

© Raman et al.; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement