|Year : 2021 | Volume
| Issue : 2 | Page : 27-33
Prevalence and pattern of work-related musculoskeletal disorders amongst electricity utility workers in Lagos, Nigeria
U A C Okafor1, ME Danjuma1, SN Oghumu2, KI Oke3, AM Akinfeleye4, CE Mbada5
1 Department of Physiotherapy, Faculty of Clinical Sciences, College of Medicine, University of Lagos, Idi-Araba, Lagos, Nigeria
2 Department of Physiotherapy, Faculty of Allied Medical Sciences, College of Medical Sciences, University of Calabar, Calabar; Department of Physiotherapy, Faculty of Clinical Sciences, University of Lagos, Idi-Araba, Lagos, Nigeria
3 Department of Physiotherapy, School of Basic Medical Sciences, College of Medical Sciences, University of Benin, Benin City, Nigeria
4 Department of Physiotherapy, Lagos University Teaching Hospital, Idi-Araba, Lagos, Nigeria
5 Department of Medical Rehabilitation, Obafemi Awolowo University, Ile-Ife; Department of Physiotherapy, University of Medical Sciences, Ondo, Nigeria; Department of Health Professions, Manchester Metropolitan University, Manchester, United Kingdom
|Date of Submission||09-Jan-2021|
|Date of Decision||02-Apr-2022|
|Date of Acceptance||06-Jun-2022|
|Date of Web Publication||5-Jan-2023|
Dr. K I Oke
Department of Physiotherapy, College of Medical Sciences, University of Benin, Benin City
Source of Support: None, Conflict of Interest: None
Background: Electricity utility workers are often exposed to various types and degrees of work-related musculoskeletal disorders (WMSDs). However, there seems to be a neglect or underreporting of WMSDs amongst them in developing countries, including Nigeria. Hence, this study investigated the prevalence and pattern of WMSDs amongst electric utility workers in Lagos, Nigeria.
Materials and Methods: A cross-sectional survey using a 68-item questionnaire was conducted amongst 180 electricity utility workers in selected electric power companies in Lagos, Nigeria. Data collected included sociodemographic variables of respondents, 12-month and point prevalence of WMSDs as well as psychosocial risk factors of WMSDs. The disabilities of the arm, shoulder and hand (DASH) and their impacts on work performance of respondents were evaluated. Data were analysed using descriptive and inferential statistics.
Results: The 12-month prevalence of WMSDs amongst the respondents was 78.9%, while the point prevalence was 53.3%. The wrist/hand, lower back and the shoulders in descending order were the body parts most affected. Age and work experience had a significant association (P < 0.05) with the prevalence of WMSDs. Psychosocial risk factors had no association with the occurrence of WMSDs. However, a significant association (P < 0.05) was found between perceived physical work demand and prevalence of WMSDs. Furthermore, a significant association (P < 0.05) was found between each of the DASH with prevalence of WMSDs.
Conclusion: A high proportion of electric utility workers presented with WMSDs of which the wrist/hand was the most affected body part. Ergonomic interventions may have a great impact in the prevention of WMSDs amongst electricity workers.
Keywords: Electricity utility workers, prevalence, work-related musculoskeletal disorders
|How to cite this article:|
Okafor U A, Danjuma M E, Oghumu S N, Oke K I, Akinfeleye A M, Mbada C E. Prevalence and pattern of work-related musculoskeletal disorders amongst electricity utility workers in Lagos, Nigeria. Niger J Health Sci 2021;21:27-33
|How to cite this URL:|
Okafor U A, Danjuma M E, Oghumu S N, Oke K I, Akinfeleye A M, Mbada C E. Prevalence and pattern of work-related musculoskeletal disorders amongst electricity utility workers in Lagos, Nigeria. Niger J Health Sci [serial online] 2021 [cited 2023 Jun 1];21:27-33. Available from: http://www.https://chs-journal.com//text.asp?2021/21/2/27/367245
| Introduction|| |
Electricity utility workers are saddled with the responsibility of generating, transmitting and distribution of electric power from electricity corporations to the end users. Their work description is usually associated with work-related hazards of various types and degrees. These include biophysical injuries from defaults in body biomechanics or assaults related to occupational tools, electrical injuries and psychosocial factors which may include work overload, work stress and poor work environments.,,,, Although occupational associated deaths amongst electricity utility workers are highest with electrical injuries, there is an ever-increasing incidence of work-related musculoskeletal disorders (WMSDs) in electricity utility industries with adverse impact on the quality of life of workers, general health, fitness and productivity., Fordyce et al. while analysing injury types amongst the United States electricity utility workers reported a high prevalence of sprain and strains in that population. The Bureau of Labor Statistics of the United States asserts that musculoskeletal disorders encompass injuries of soft tissues (nerves, connective tissues and muscles) and the musculoskeletal systems as a result of traumatic and non-traumatic injuries, overexertion of bodily structures, repetitive movements from micro- and macro-tasks and mechanically induced reactions to bodily structures.,
According to the Electric Power Research Institute, workers in electric generation plants perform numerous routine machinery preventatives as well as recuperative maintenance tasks during intensely planned unit outages, unplanned outages as well as routine maintenance and repairs during normal operations. These tasks performed by workers in electric sector are commonly physically strenuous and demanding.,, Consequently, it is believed that electricity utility workers lack the physical capacity required to complete their service years because many of them sustain musculoskeletal injuries over years of exposure to high-force demands. Emerging report of injuries amongst electricity utility workers suggests that job characteristics of being young, male, line workers, mechanics, older welders and meter readers portend greater risks of sustaining injuries., Furthermore, body regions of the back, neck, shoulders, hands, wrists and knees are more common with presentation of musculoskeletal symptoms amongst electricity utility workers., Thus, electricity utility facilities have numerous design aspects of power plants which can cause operators and mechanics to be at increasing risk for developing WMSDs such as low back pain, shoulder tendinitis and bursitis, sprains, strains, wrist tenosynovitis and carpal tunnel syndromes.,
The economic burdens of WMSDs are enormous. Reports amongst electricity utility workers indicate reduced workers and society productivity, high rates of workers' sick leave, increased cost of workers' compensation and loss of millions of dollars as a result of electricity deprivation of a modern society whose commerce depends grossly on electricity., Hence, identification and prevention of WMSDs amongst workers of electricity utility industries are imperative.
Studies abound on WMSDs in different construction occupations, yet literature is sparse on WMSDS amongst electric utility workers in developing countries. According to a study by Saba et al. amongst electricity utility workers in Nigeria, injury rates amongst workers in the electricity sector may be worse than in other climes because of inadequacy of occupational health and safety measures. They also reported neglect and underreporting in the case of WMSD injuries amongst electricity utility workers in developing countries. Therefore, the aim of this study was to investigate the prevalence and pattern of WMSDs in electric utility workers in selected power companies in Lagos, Nigeria.
| Materials and Methods|| |
One hundred and eighty participants were conveniently recruited in a cross-sectional descriptive survey from the Lagos State electric utility companies involved in the generation, transmission and distribution of electricity. These Lagos State electricity utility companies are located in five Local Government Areas (Mushin, Surulere, Ikorodu, Ikeja and Lagos Island Local Government Areas) of Lagos metropolis. The generation companies were Egbin Power PLC, Ikorodu; Island Independent Power Project, Lagos Island and Mainland Independent Power Project, Ikeja, Lagos. The transmission companies included the Transmission Company of Nigeria, Akangba Sub-region, Surulere, and Transmission Company of Nigeria, Egbin Sub-region, Ikorodu, Lagos. The distribution companies included Eko Electricity Distribution PLC, Mushin Business District, Isolo, Aguda zone, Orile zone, Ijesha zone, Lawanson zone, Mushin-Ishaga zone, and Isolo District zone.
Inclusion criteria were electric utility field workers who are involved in any power generation, transmission or distribution with at least 1 year of work experience. Electricity utility workers were excluded from participating if they have < 1 year of work experience, have obvious physical deformity such as kyphosis and limb length discrepancy and if they are involved in administrative work.
The sample size for the study was calculated based on the findings of a previous study, using the formula n = (Z2pq/d2) where n = the desired minimum sample size, Z = the standard normal deviate (set at 95% confidence interval which is equalled to 1.96), p= the expected prevalence rate in the previous study (p = 87.6% = 0.876), q = 1-p= 0.13, d = error of margin usually set at 0.05. Thus, n = (1.962) x o.87x0.13/ 0.052=174. Thus, n = (1.962) × 0.87 × 0.13/0.052 = 174. We estimated an attrition rate of 20% which implies 174 × 1.25 yielding a total of 217 participants expected for the study.
Prior to the commencement of the study, ethical approval was sought and obtained from the health research and ethics committee of a university teaching hospital. Also, permission was obtained from the District managers of the electricity utility companies, while informed consents were obtained from the participants.
The study instrument was a 68-item self-administered questionnaire titled 'The Prevalence and Pattern of Work-Related Musculoskeletal Disorders amongst Electricity Utility Workers in Lagos, Nigeria' modified from two previously validated questionnaires which included the Standardised Nordic Musculoskeletal Questionnaire and the Quick Disabilities of the Arm, Shoulder and Hand Questionnaire (QuickDASH) interlaced with questions on psychosocial risks factors., The face and content validity of the questionnaire was validated by a focus group of academic and clinical experts of the department of physiotherapy in a university college of medicine. The questionnaire has four sections A to D. Section A sought information on participants' socio-demographic parameters. Section B consisted of questions obtained from the Modified Standardised Nordic Questionnaire which identified any region (s) of discomfort, ache or pain in the last 12 months or any discomfort in the last 7 days. Section C evaluated the disability of the arm, shoulder and hand and the Optional Work Module of the QuickDASH to evaluate the impact of the musculoskeletal symptoms on the worker's performance with regard to their ability to perform certain activities. Section D assessed the physical and psychosocial factors to establish the association between workplace psychosocial factors and musculoskeletal pain.
Sections B and D were scored on a rated scale of 0–3 with 0 for never, 1 for sometimes, 2 for often and 3 for always. All the questions under each section were summed and divided by the number of questions, and the result was multiplied by 3 and expressed in 100 scales by multiplying with 100. Analysis was done by finding the median values and classifying the variable as high and low for each section. Section C was scored in two components: the disability/symptom section, four items, scored 1–5 and the optional work modules, four items, scored 1–5. The collated scores for each section were converted into percentage by adding the assigned values for each response, divided by the total number of responses and multiplied by 25. The result obtained was converted on a scale of 0%–100%, with 0 indicating no disability and 100 indicating severe disability.
Procedure for data collection
Prior to the distribution of the questionnaire, the aims and objectives of the study and instructions on how to answer each questionnaire were clearly explained to the participants. Thereafter, the questionnaire was distributed to all participants and collected the same day after completion. A total of 210 copies of the questionnaire were distributed to the available participants.
The Statistical Package for Social Sciences (IBM SPSS) 22.0 version (IBM Corp, Armonk, NY, USA) for windows analyzed the collected data. Data were summarised using descriptive statistics of mean, standard deviation, bar chart, frequency and percentages, while inferential statistics of Chi-square sought associations between participants' characteristics and occurrence of WMSDs.
| Results|| |
One hundred and eighty questionnaires were returned yielding a response rate of 85.7%. The respondents were 165 (91.7%) males and 15 (8.3%) females. The mean age, body mass index (BMI), hours of work per day and hours of work per week were 40.38 ± 8.3 years, 25.38 ± 2.3 kg/m2, 8.822 ± 2.4 h and 43.494 ± 4.9 h, respectively [Table 1]. Respondents' level of education and job descriptions are presented in [Table 2]. Most of the respondents have a higher level of education, whereas one-third of the respondents have job experience within the range of 1–5 years [Table 2]. Amongst electricity generation workers, only those in maintenance sections have more than two workers [Table 2]. Furthermore, only linesmen electricity utility workers make up 15% of the total workforce [Table 2].
|Table 2: Level of education and job descriptions of electric utility workers|
Click here to view
Furthermore, the results revealed a 78.9% prevalence of work-related musculoskeletal disorders amongst electric utility workers in the past 12 months, while the point prevalence was 53.3% [Table 3]. The wrists/hands had the highest 12-month prevalence of 77 (42.3%) and point prevalence of 40 (22.9%), followed by the lower back which had 64 (35.6) 12-month prevalence and 36 (20.0%) point prevalence [Table 3]. Similarly, the pattern of WMSDs amongst respondents showed that the wrists/hands 77 (42.3) followed by the lower back 64 (35.6) and the shoulders 57 (31.7) were the most affected regions [Table 4]. Furthermore, workers involved in electricity distribution were found to have the highest prevalence of WMSDs than those in transmission and generation of electricity [Figure 1]. Furthermore, the result revealed that 33 (18.3%) participants reported mild disability of the arms, shoulder and hand, 5 (2.8%) reported moderate disability, while 168 (93.3%) had no disability [Table 4].
|Table 3: Prevalence of work-related musculoskeletal disorders amongst respondents|
Click here to view
|Figure 1: WMSD in the three divisions of electricity utility. WMSD: Work-related musculoskeletal disorder|
Click here to view
|Table 4: Pattern and severity of work-related musculoskeletal disorders amongst respondents|
Click here to view
The level of work-related psychosocial risk factors amongst the respondents is also presented in [Table 4]. One hundred and twenty-three respondents (68.3%) reported a high work burden, 96 (53.3%) of the respondents could count on their co-workers for support and 96 (53.3%) could count on their supervisors for their support. Ninety-six (53.3%) of the respondents reported a high physical workload in performing their regular activities at work. A significant association was found between each of the age, work experience and QuickDASH scores with the prevalence of WSMDs amongst the electric utility workers [Table 5]. Only perceived work demand amongst all the psychosocial factors analysed was significantly associated with WMSDs of electricity utility workers [Table 6].
|Table 5: Association of participants' sociodemographic, job descriptions and disability of arm shoulder and hand scores with occurrence of work-related musculoskeletal disorder|
Click here to view
|Table 6: Association between psychosocial risk factors and the prevalence of work-related musculoskeletal discomfort|
Click here to view
| Discussion|| |
This study explored the prevalence and patterns of WMSDs amongst electricity utility workers in a Nigerian population. The study revealed that majority of the respondents were overweight male with a mean age of 40 years. This finding of more males than females in this study is consistent with the report of male dominance in electricity works than females., Similarly, the mean age of respondents obtained in this study agrees with the report that the workforce of electricity utility industries consists largely of workers between the ages of 40 and 50. Furthermore, the mean BMI of the respondents in this study revealed the tendency of being overweight. Although this finding of electricity utility workers being overweight is compatible with the report of Martinez and Fischer, they opined that higher BMI of being overweight and obese is incongruent with increased physical activity associated with electricity utility workers. Conversely, Choi et al. opined that electricity utility workers performed their work most times in sedentary positions of sitting, kneeling, standing and stooping over long period. However, being overweight and obese is influenced by many factors which were not explored in this current study.
Although this study found that most of the respondents have a higher level of education, one-third of the respondents reported job experience of 1–5 years. This shows that fewer workers are being employed into electricity industries because workers with >10-year working experience accounted for more than half of the respondents in this study. In addition, amongst the electricity utility workers in this study, only those in maintenance sections have more than two workers with linesmen making up 15% of the total workforce. Thus, it can be inferred that the categories of electricity utility workers in this study are doing more jobs as a result of few workers which accounted for high prevalence of WMSDs. Techera and colleagues reported heavy work load as one of the causes of workers fatigue in electricity distribution workers.
The prevalence of WMSDs amongst electric utility workers in this study was observed to be 78.9% with a point prevalence of 53.3%. This is comparable with the findings of Moriguchi et al. who reported a prevalence of 87% of musculoskeletal symptoms amongst energy distribution network linemen in Sao Carlos, Brazil. Furthermore, this study found the highest prevalence of WMSDs in the wrists and hands followed by the low back and then the shoulder. This pattern of WMSDs observed in this study is consistent with the report of previous studies that found the highest prevalence of WMSDs in the wrists and fingers of electricians., This assertion that WMSDs were highest in the wrist and hands has been attributed to the increased use of grip strength in performing routine activities in several repetitions, while static loads, lack of rest, vibration, poor ergonomics in bending, lifting, sitting, standing and routine overhead activities were attributed to increased prevalence of low back and shoulder WMSDs amongst electricity utility workers by previous studies.,,,,
Furthermore, this study found that workers in the distribution of electricity had the highest prevalence of WMSDs followed by those in transmission and generation of electricity. This agrees with the report that occupational injuries in electricity distribution workers such as line workers and meter readers account for the highest percentage of injuries in electricity utility industries., This finding of higher prevalence of WMSDs among distribution workers could be as a result of their job descriptions that involves performing duties such as: working from bucket trunks; overhead activities that involves bending, twisting from a climbing position on a pole; underground work involving standing, crouching, kneeling in manholes and vaults that have ceilings as low as six feet high.,, On the other hand, the job description of transmission workers are concerned with relaying electricity from power plants to substations, while electricity generation workers mostly work at generating stations.,, Furthermore, the outcome of the research collaboration between Electric Power Research Institute, Marquette University and We Energies in the United States identified and elaborated on high-risk ergonomic factors in electricity distribution workers amongst electricity utility workers for which various interventions were provided and recommended.,,
Furthermore, more mild severity of disability than moderate severity of disability of WMSDs was reported amongst respondents of this study. This trend in severity of disability of WMSDs amongst respondents of this study follows the assertion that WMSDs are cumulative trauma disorders whose level of severity increases with time on the job. Similarly, this study found that age and work experiences of respondents were significantly associated with WMSDs. This corroborates the report of Rahmani et al. that occurrences of WMSDs are associated with age distribution of electricity utility workers and their working experience. Similarly, a significant association was found between disability of the arm, shoulder and hand with WMSDs amongst the respondents of this study. The combined use of the upper extremity in performing overhead activities, lifting, carrying, screwing, unscrewing, hammering and other routine activities may explain this association between disabilities of arm, shoulder and hands amongst the electricity utility workers as also reported in other studies.,
Finally, this study found that only perceived work demand out of several psychosocial factors was significantly associated with the prevalence of WMSDs in electricity utility workers. This corroborates the report that increased work pressures and elevated psychic demands are associated with the WMSD complaints of electricity utility workers.
| Conclusion|| |
This study found a high prevalence of WMSDs amongst electric utility workers in Lagos, Nigeria. The wrist/hand followed by the lower back was the most commonly affected body part amongst the respondents. Furthermore, the prevalence of WMSDs was higher amongst electricity distribution workers than amongst electricity transmission and generating workers. Perceived work demand was associated with the prevalence of WMSDs in electricity utility workers.
Based on the outcome of this study, the following recommendations were made. The work design for electric utility workers, particularly those who have had a long work experience, should be tailored to reduce stress and exposure to occupational risk factors of WMSDs. There is a need to provide occupational safety measures and ergonomic advice consisting of postural control of the wrist/hands, lower back and shoulders for electricity utility workers in the performance of their daily activities.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Choi SD, Yuan L, Barchardt JG. Musculoskeletal disorders in construction. Practical solution from the literature. Prof Saf 2016;61:26-32.
Moja SJ, Van Zuydam CS, Mphephu F. Hazard and risk assessment in electricity sector: A case of Swaziland electricity company. J Geogr Nat Disast 2016;S6:006.
Padmanathan V, Joseph L, Omar B, Nawawi R. Prevalence of musculoskeletal disorders and related occupational causative factors among electricity linemen: A narrative review. Int J Occup Med Environ Health 2016;29:725-34.
Volberg V, Fordyce T, Leonhard M, Mezei G, Vergara X, Krishen L. Injuries among electric power industry workers, 1995-2013. J Safety Res 2017;60:9-16.
Lingard H. An integrated approach to reducing the risk of work-related musculoskeletal disorders. In: Lingard H, Wakefield R, editors. Integrating Work Health and Safety into Construction Project Management. 1st
ed. New York, United States: Wiley Blackwell; 2019. p. 198-220.
Rahmani A, Khadem M, Madreseh E, Aghaei HA, Raei M, Karchani M. Descriptive study of occupational accidents and their causes among electricity distribution company workers at an eight-year period in Iran. Saf Health Work 2013;4:160-5.
Fordyce TA, Leonhard MJ, Watson HN, Mezei G, Vergara XP, Krishen L. An analysis of fatal and non-fatal injuries and injury severity factors among electric power industry workers. Am J Ind Med 2016;59:948-58.
Bureau of Labor Statistics. Injuries, Illnesses, and Fatalities: Occupational Safety and Health Definitions; 2017. Available from: https://www.bls.gov/iif/oshdef.htm
. [Last accessed on 2018 Dec 18].
EPRI-DOE Hand book Supplement of Energy Storage for Grid Connected Wind Generation Applications. Washington, DC: EPRI, Palo Alto, CA, and the U.S. Department of Energy; 2004. p. 1-144.
Seeley PA, Marklin RW. Business case for implementing two ergonomic interventions at an electric power utility. Appl Ergon 2003;34:429-39.
Saba TM, Atsumbe BN, Otor AF, Sado J. Appraisal of occupational health and safety practices in power holding company of Nigeria (PHCN) Plc Abuja distribution zone. Int J Sci Eng Res 2013;4:1-10.
Moriguchi CS, Alencar JF, Miranda-junior LC, Coury HJ. Musculoskeletal symptoms among energy distribution network linemen. Rev Bras Fisioter 2009;13:123-9.
Kuorinka I, Jonsson B, Kilborn A, Vinterberg H, Biering-Sorensen F, Anderson G, et al
. Standardized Nordic questionnaire for the analysis of musculoskeletal symptoms. Appl Ergon 1987;18:233-7.
Hudak PL, Amadio PC, Bombardier C. Development of an upper extremity outcome measure: The DASH (disabilities of the arm, shoulder and hand) [corrected]. The upper extremity collaborative group (UECG). Am J Ind Med 1996;29:602-8.
United States Department of Labor, “Quarterly Census of Employment and Wages,” Bureau of Labor Statistics. Available from: http://www.bls.gov/cew/
. [Last accessed on 2016 Nov 08].
Martinez MC, Fischer FM. Stress at work among electric utility workers. Ind Health 2009;47:55-63.
Techera U, Hallowell M, Littlejohn R. Worker fatigue in electrical-transmission and distribution-line construction. J Constr Eng Manage 2019;145:1943-7862.
Roli D, Ali I, Neekhra V. Work related musculoskeletal disorders in electrical, telecommunication and instrument mechanics of armed forces. IJOSH 2020;10:18-27.
Albers J, Estill C, MacDonald L. Identification of ergonomics interventions used to reduce musculoskeletal loading for building installation tasks. Appl Ergon 2005;36:427-39.
Marklin RW, Lazuardi L, Wilzbacher JR. Measurement of handle forces for crimping connectors and cutting cable in the electric power industry. Int J Ind Ergon 2004;34:497-506.
Saadatfar A, Ranjbarian M, Saremi M, Hashemian AH, Yazdian A. Risk assessment of musculoskeletal disorders in linemen of electric power distribution company of Kermanshah province using REBA method in 2015. J Rafsanjan Univ Med Sci 2016;15:593-606.
Sendin A, Sanchez-fornie MA, Berganza I, Simon J, Urrutia I. Electric power system concepts for telecommunication engineers. In: Telecommunication Network for the Smart Grid. 1st
ed. London: Artech House; 2016. p. 67-79.
Stone A, Usher D, Marklin R, Seeley P, Yager JW. Case study for underground workers at an electric utility: How a research institution, university, and industry collaboration improved occupational health through ergonomics. J Occup Environ Hyg 2006;3:397-407.
Marklin RW. General knowledge regarding engineering controls. In: Interventions, Controls, and Applications in Occupational Ergonomics. The Occupational Ergonomics Handbook. 2nd
ed. United States: CRC Press, Taylor & Francis; 2006. p. 270-75.
Stone A, Marklin R, Seeley P, Mezei G. A collaborative effort to apply ergonomics to electric utility workers at generating stations. Work 2011;39:103-11.
Lu J, Twu L, Wang MJ. Risk assessments of work-related musculoskeletal disorders among the TFT-LCD manufacturing operators. Int J Ind Ergon 2015;30:1-12.
Gemma SF, Primo R, Brittes JL, Misuta MS, Junior EP. Ergonomic and psychosocial aspects of electrical energy maintenance activities on transmission lines. In: Bagnara S, Tartaglia R, Albolino S, Alexander T, Fujita Y. Proceedings of the 20th Congress of the International Ergonomic Association (IEA 2018), Part of the Advances in Intelligent Systems and Computing Book Series, Springer Nature, Germany: AISC 824; 2019. p. 1757-60.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]