Epidemiological study of work-related musculoskeletal disorders and related risk factors for the footwear industry in China

doi:10.21203/rs.3.rs-3104544/v1

Object:

To explore the prevalence and risk factors of work-related musculoskeletal disorders (WMSDs) among footwear industry workers in China, thereby providing a scientific basis for implementing health interventions.

Methods

Following a cross-sectional epidemiological survey method, modified Chinese version of the WMSDs Questionnaire was adopted to investigate the prevalence of WMSDs and related risk factors such as ergonomic load and psychosocial factors, among all workers in 26 footwear factories across China from 2018 to 2020. The data were statistically analyzed with chi-square tests and multivariate logistic regression analysis.

Results

A total of 7106 valid questionnaires were collected. The prevalence of WMSDs among footwear workers was 36.8%, and the symptoms mostly occurred in the neck (23.9%), shoulders (19.3%), and hands (14.9%). The Chi-square test revealed 26 factors that significantly differed from the prevalence of WMSDs (P < 0.05). Multivariate logistic regression analysis revealed that gender (OR = 1.348, 95%CI:1.122–1.621, P < 0.01), marital status (OR = 1.822, 95%CI:1.320–2.515, P < 0.01), physical conditions (OR = 1.423, 95%CI:1.302–1.555, P < 0.01), working in an uncomfortable position (OR = 1.371, 95%CI:1.261–1.491, P < 0.01), cold or temperature variations at work (OR = 1.350, 95%CI:1.184–1.540, P < 0.01), working outdoors (OR = 1.324, 95%CI: 1.017–1.724, P < 0.05), and multiple repetitive operations per minute (OR = 1.207, 95%CI:1.150–1.268, P < 0.01) were key risk factors in the prevalence of WMSDs. By contrast, monthly income (OR = 0.728, 95% CI:0.667–0.794, p < 0.01) was a protective factor for WMSDs.

Conclusion

WMSDs show high prevalence among footwear industry worker and are associated with gender, marital status, uncomfortable position, variable temperatures, working outdoors, and multiple repetitive operations. Preventive measures on the basis of these risk factors should be implemented to reduce the adverse effects on the health of footwear industry workers.

Occupational Medicine

work-related musculoskeletal disorders

cross-sectional study

occupational population

related factors

The high prevalence of work-related musculoskeletal diseases (WMSDs) and the wide range of industries involved have caused serious adverse effects on the health of occupational groups and the development of enterprises, thus becoming a major occupational health problem in many countries. WMSDs refers to the health problems of muscles, tendons, bones, cartilage, ligaments, nerves and other motor systems caused by occupational activities (repetitive operation, bad posture and mechanical load), including all forms of health diseases from mild and temporary injuries to irreversible and incapacitated injuries, the most common lower back injuries, neck and shoulder pain and carpal tunnel syndrome [1]. In the United States, WMSDs accounts for 65% of all occupational diseases and is the fastest growing occupational injury, causing major economic losses [2]. Another survey showed that more than 40% of occupational hazard claims in the EU are related to WMSD [3]. The prevalence of WMSDs in Chinese occupational population is high, reaching 20% − 90% [4].

WMSDs are related to many factors, including various demographic characteristics, work organization, work style and the influence of psychosocial factors, such as repetitive work, uncomfortable posture [5–7], vibration and cold working environment [8, 9]. China has developed rapidly in recent years. In 2013, the working population of the country exceeded 10 million. Due to price competition, many enterprises increase productivity and efficiency by extending working hours to gain profits, which may increase the pressure and workload of workers and prone to musculoskeletal problems. In particular, poor working posture (such as long-term sitting posture and uncomfortable posture) will increase the risk of WMSDs. Many local and international studies have examined the symptoms and ergonomic load of WMSDs in different industries. Many studies have a large sample size, but few large sample studies from China lack the technology and methods to identify and evaluate the risk factors of WMSDs.

In China, as an industry highly dependent on manual operation, the shoemaking industry has a higher risk of WMSDs than other industries, such as monotonous repetitive actions and unreasonable working hours in the process of work [10]. The footwear production process usually includes the following steps: (1) modeling, (2) cutting, (3) sewing, (4) assembly and (5) finishing [11]. The main basis of manufacturing process is task allocation. This tissue characteristic constitutes the threshold of WMSDs. Therefore, a better understanding of the risk factors and control measures of WMSDs in the footwear industry may have a significant impact on the quality of life of workers and even on national productivity. Therefore, this study aims to investigate the prevalence and distribution of WMSDs among Chinese shoemakers and the risk factors related to WMSDs, so as to provide theoretical basis for the prevention and control of the occurrence and development of WMSDs.

2.1 Sample selection

A cluster random sampling method was implemented on all on-the-job workers from 26 footwear factories in China from 2018 to 2020 as the research objects. A total of 7,106 valid questionnaires were obtained.

Inclusion criteria: workers with ≥ 1 year of service on the job.

Exclusion criteria: non-WMSDs patients with congenital spinal deformity, trauma, infectious diseases, and malignancies.

2.2 Data collection

A cross-sectional epidemiological survey was conducted by a cluster random sampling method on all workers working in 26 footwear factories in China from 2018 to 2020. Then, the data of this work was collected by modified Chinese version of the WMSDs Questionnaire provided by the National Institute of Occupational Health and Poison Control of China Center for Disease Control and Prevention. The questionnaire is suitable for use in occupational populations and has been tested for reliability and validity. The questionnaire consisted of three parts. The first part was about demographic characteristics, including age, height, weight, length of service, smoking, income, education, marital and physical status. The second part was about work conditions, including type of work, heavy lifting, poor posture, repetitive operations, working space, and time and use of vibrating tools. The third part was about the prevalence of WMSDs in different regions of the body. The surveyors were trained similarly to ensure uniformity of standards and methodology.

The survey was conducted in a 1: N arrangement, that is, one surveyor conducted a face-to-face form-filling survey with N respondents. Respondents scanned the electronic questionnaire QR code to answer the questions online. Each questionnaire was submitted and uploaded directly to the cloud-based database. Responses with incomplete data and obvious errors or those from workers with less than one year of service were excluded during the collation of the questionnaires.

All workers in the selected workshop who are qualified to participate in the survey agreed to participate in the survey. Workers usually queue up to work in a sitting position. This study recruited workers who have worked in the industry for at least one year. Those diagnosed with musculoskeletal system injury, rheumatoid arthritis, tumor, tuberculosis or infection were excluded. Finally, 7106 subjects were included in the study.

During the period from 2018 to 2020, a questionnaire with a cover letter explaining the purpose and procedure of the study was distributed to these workers. Those who agreed to participate provided their signatures as informed consent. The questionnaire was completed under the guidance of well-trained investigators and went through strict quality control. Subjects who did not return to fill in the questionnaire were contacted and reminded to respond to the survey.

2.3 Assessment for WMSDs

According to the National Institute for Occupational Safety and Health criteria for WMSDs, a musculoskeletal injury entails discomfort such as pain, tenderness, stiffness, burning, numbness, or tingling in any of nine body parts (neck, shoulder, upper back, lower back, elbow, hand, leg, knee, and foot) within the past 12 months (Nunes and Bush, 2012).

2.4 Statistical analysis

SPSS version 24.0 was used to statistically process the data. Descriptive analysis was performed using the median (P25-P75) because the description of the data did not conform to a normal distribution. Kolmogorov-Smirnov test was used to analyze the Normality test of data. Through linear regression test, there is no multicollinearity among age, current working experience and total working experience. Count data were expressed as rates, and differences between groups were tested by Chi-square test. Multivariate logistic regression models were used to analyze risk factors for WMSDs. The variables used by Multivariate logistic regression models were: WMSDs: No = 0,Yes = 1; Gender: Male = 0,Female = 1; Age: <33 = 0,33-=1,40-=2,47-=3; Height(cm): <156 = 0,156-=1,160-=2,165-=3; Weight (kg): <51 = 0,51-=1,58-=2,65-=3; Dominant hand: Right hand = 0,Left hand = 1; Current working experience (years): <2 = 0,2-=1,5-=2,9-=3; Total working experience (years): <3 = 0,3-=1,6-=2,11-=3; Physical conditions: Good = 0,fair = 1,Poor = 2,Poorest = 3; Education: Junior High School and below = 0,High School = 1,College = 2, Bachelor = 3, Master and above = 4; Marital status: Unmarried = 0, Married = 1, Divorced = 2; Monthly income : ≤1000 = 0,1001–3000 = 1,3001–5000 = 2,>5000 = 3; Physical activities: No = 0,Sometimes = 1,2–3 times/month = 2,1–2 times/week = 3,>2 times/week = 4; Smoking: No = 0,Sometimes = 1,Frequently = 2,Quitting smoking = 3; Working hours per day: <9 = 0,9-=1,13-=2; Prolonged standing/Prolonged sitting/ Prolonged squatting or kneeling/ Lifting heavy objects (> 5 kg)/ Lifting heavy objects (> 10 kg)/ Upper limb or hand exertion/ Uncomfortable position/ Multiple repetitive operations per minute/Using vibrating tools/ Driving the vehicle: Seldom/never = 0,Sometimes = 1,ofen = 2,Frequently = 3; Working on other jobs/ job rotation/ Working in the same workshop/Working outdoors/Variable temperatures at work/Doing the same job daily/Work changes daily/Need for communication with customers: No = 0,Yes = 1; Occupation types: Management staff = 0, Shoe sole handlers = 1, Molding workers = 2, Needle workers = 3, Cutting workers = 4, Shoe surface handlers = 5, Other supporting workers = 6; P < 0.05(two tailed) was considered statistically significant.

3.1 General characteristics and the prevalence of WMSDs

After strict adherence to the inclusion and exclusion criteria, 7106 valid samples were obtained. Among the valid samples, 2214 (31.2%) were male and 4892 (68.8%) were female; the median (P₂₅–P₇₅) age was 40.0 (33.0–47.0) years, the median (P₂₅–P₇₅) length of service for current type of work was 5 (2–9) years, the median (P₂₅–P₇₅) total length of service was 6 (3–11) years, the median (P₂₅–P₇₅) height was 160.0 (156.0–165.0) cm, the median (P₂₅–P₇₅) weight was 58.0 (51.0–65.0) kg, and their daily working hours were 9.0 (8.0–11.0) h; most participants were married, 5751 (80.9%); were not smokers, 6042 (85.0%); were in good physical condition, 3115 (43.8%); had less than five years of service, 3521 (49.5%); had junior high school education or less, 5399 (76.0%); and had a monthly income of RMB 3001–5000, 3434 (48.3%). The prevalence was 31.1% for males and 39.4% for females, with a statistically significant difference (χ² = 44.822, P < 0.01). The prevalence tended to increase with the increase of age. However, after the age of 47, the prevalence decreases, with statistically significant differences between age groups (χ² = 39.090, P < 0.01). Statistically significant differences in height and weight (χ² = 28.297, 9.509, P < 0.01, 0.05) were also noted. The prevalence rate was as high as 43.4% when the length of service for the current type of work was more than 9 years, and significant differences occurred among various segments of service (χ² = 71.259, P < 0.01). A similar trend was found for total length of service (χ² = 50.243, P < 0.01). Married people and people living alone had a higher prevalence compared to unmarried people, and the differences were statistically significant (χ² = 39.261, P < 0.01). Monthly income was another important factor influencing the prevalence of WMSDs (χ² = 153.528, P < 0.01). Smoking, a risk behavior, was correlated with WMSDs as well (χ² = 8.921, P < 0.05). The prevalence of physical condition for good, fair, poor and very poor were 28.3%, 42.9%, 53.5%, and 41.1%, respectively, with statistically significant differences (χ² = 185.386, P < 0.01). However, the effects of the dominant hand, education level, and physical activity on the prevalence of WMSDs were not statistically significant. The demographic characteristics of participants was shown in Table 1.

3.2 Prevalence of WMSDs

The total prevalence of WMSDs was 36.8% (2616/7106). The prevalence of WMSDs in different regions of body (in descending order) was 23.9% (1701/7106) in the neck, 19.3% (1368/7106) in the shoulder, 14.9% (1058/7106) in the hands, 13.3% (943/7106) in the lower back, 11.9% (846/7106) in the upper back, 8.5% (603/7106) in the thighs, 8.4% (595/7106) in the feet, 7.4% (524/7106) in the knees, and 7.1% (507/7106) in the elbows, with a statistically significant difference (χ² = 1731.184, P < 0.01) (Fig. 1). The prevalence of each type of work in the footwear factories was 42.0% (276/657) for shoe surface handlers, 39.1% (305/780) for cutting workers, 38.6% (660/1708) for needle workers, 36.7% (845/2303) for molding workers, 32.8% (152/463) for other supporting workers, 33.0% (144/436) for shoe sole handlers, and 30.8% (234/759) for managers. The differences among the types of work were statistically significant (χ² = 29.378, P < 0.01) (Fig. 2).

3.3 Univariable analysis of the prevalence of WMSDs

3.3.1 The relationship between work conditions and prevalence of WMSDs

As shown in Table 2, poor posture included prolonged standing, prolonged sitting, prolonged squatting or kneeling, and positions that required upper limb or hand exertion and uncomfortable postures, all of which increased the risk of WMSDs (P < 0.01). Increased exertional load from heavy lifting was also a risk factor for WMSDs (χ² = 62.194, 52.147, P < 0.01, 0.01).

3.3.2 The relationship between work organization and prevalence of WMSDs

As shown in Table 3, the more frequent repetitive operations per minute, the higher the prevalence of WMSDs, and the difference was statistically significant (χ² = 237.475, P < 0.01). Similarly, being arranged to work in the same workshop was also relevant to the prevalence of WMSDs (χ² = 13.300, P < 0.01). Moreover, working hours per day was associated with WMSDs (χ² = 15.399, P < 0.01). However, job rotation did not have an impact on the prevalence of WMSDs among workers.

3.3.3 The relationship between work environment and prevalence of WMSDs

As shown in Table 4, the use of vibrating tools at work and driving a vehicle were linked to WMSDs (χ² = 75.180, 20.823, P < 0.01, 0.01). Workplace ambient factors such as those involving cold or temperature variations were also risk factors (χ² = 93.276, P < 0.01). Compared to 36.5% for working indoors, the prevalence of WMSDs for working outdoors was 43.1%, a difference which was statistically significant (χ² = 5.147, P < 0.05).

3.3.4 The relationship between psychosocial factors and prevalence of WMSDs

As shown in Table 5, doing the same job every day was associated with the prevalence of WMSDs (χ² = 10.888, P < 0.01), but the daily variation of jobs and the need to deal with customers did not influence the prevalence of WMSDs (Table 5).

3.4 Multivariate logistic regression analysis of WMSDs

By considering whether the subject had WMSDs as the dependent variable and the factors with statistically significant differences in the univariable analysis (P < 0.05) as the independent variables, the multivariate logistic regression analysis was performed. A total of 26 independent variables, such as gender, age, length of service, occupation types, poor posture, exertional load operation methods, and temperature changes were included. Gender (OR = 1.348, 95%CI:1.122–1.621, P < 0.01), length of service for the current type of work (OR = 1.177, 95%CI:1.090–1.272, P < 0.01), marital status (OR = 1.822, 95%CI:1.320–2.515, P < 0.01), poor physical health (OR = 1.423, 95%CI:1.302–1.555, P < 0.01), prolonged standing (OR = 1.092, 95%CI:1.023–1.167, P < 0.01), prolonged sitting (OR = 1.077, 95%CI:1.009–1.149, P < 0.05), using vibrating tools (OR = 1.092, 95%CI:1.012–1.179, P < 0.05), working in an uncomfortable position (OR = 1.371, 95%CI: 1.261–1.491, P < 0.01), working involving cold or changes in temperature (OR = 1.350, 95%CI: 1.184–1.540, P < 0.01), and doing repetitive operations per minute (OR = 1.207, 95%CI: 1.150–1.268, P < 0.01) were risk factors for WMSDs in footwear factory workers, but higher monthly income was a protective factor for WMSDs (OR = 0.728, 95%CI: 0.667–0.794, P < 0.01) (Table 6 and Fig. 3).

Nowadays, industries are constantly evolving, and the introduction of new technologies and changes in production strategies and product requirements have had a profound impact on the way workers perform and what they do. The consequent work-related musculoskeletal disorders (WMSDs) have become a common occurrence that plagues workers’ physical and mental health and reduces productivity. The footwear industry is typically labor-intensive. China produces over 10 billion pairs of various shoes annually, accounting for nearly 70% of the total global footwear manufacturing, making the country the largest footwear manufacturing base in the world [12]. However, while generating huge profits, workers in the footwear industry face a major risk of large-scale and progressive diseases [13], with WMSDs as the most common occupational diseases among footwear workers, mainly affecting the lower back, neck, and upper and lower limbs[14].In this study, we examined the prevalence of WMSDs and possible risk factors among workers in 26 footwear factories in China. The results revealed a high risk of WMSDs in the factories, with a total prevalence of 36.8%. In addition, the highest prevalence was 23.9% in the neck (by body parts) and 42.0% in shoe upper handlers (by work types). Working in an uncomfortable position, working with cold or temperature changes, and multiple repetitive operations per minute were the main risk factors for the development of WMSDs. These results could provide reference for understanding the prevalence of WMSDs in the footwear industry. WMSDs are prevalent in industries that require manual labor, heavy lifting and repetitive tasks, with particularly high prevalence in the manufacturing industry [15, 16], and the incidence and location vary among workers in different industries. However, previous studies on the prevalence of WMSDs in the footwear industry are mostly small sample surveys, and relevant data are not sufficient to represent the actual condition of WMSDs in the footwear industry in China. Therefore, this work is expected to provide a basis for the development of preventive and control measures for WMSDs for workers in this industry.

Our results demonstrated that the prevalence of musculoskeletal diseases in various body regions of footwear factory workers was in the range of 7.1–23.9%, with the highest prevalence in the neck (23.9%), followed by the shoulders (19.3%), and hands (14.9%), in line with previous findings[17]. Moreover, the prevalence of WMSDs in shoe surface handlers (42.0%), cutting workers (39.1%), and needle workers (38.6%) were higher than those of other types, which may be related to the fact that their work content and requirements require sitting for long periods of time and maintaining poor posture to complete repetitive tasks.

Investigation of demographic characteristics factors suggested that the prevalence of WMSDs was higher in females than in males. This outcome was consistent with prior findings which indicated that a difference occurred in the prevalence of musculoskeletal symptoms between females and males in different occupations, with females being more likely to have WMSDs than males [18, 19]. Taking into account the nature of work, females would be exposed to more detailed job contents than males, which suggests that females are given higher work demands. Additionally, high work demands, low freedom of decision making, and low work support were synergistic factors of mechanical exposure and WMSDs [20]. Like the conclusion of most previous studies, age is also a risk factor of WMSDs in this study, which may be due to the fact that older workers are exposed to efficacy risk factors for a longer time, and the aging of joints and muscles is more significant[14, 21]. Besharati et al. similarly identified an increase for WMSDs in the neck, shoulders, and wrists with age[22]. The reason why older workers had less chance of experiencing occupational injuries might be that the knowledge/experience of older workers compensated for the reduced physical and cognitive abilities at work, whereas younger workers did not have enough experience to meet the ergonomic risk factors[23–25]. Our results indicated that the length of service for current type of work might be a risk factor for the occurrence of WMSDs. Research on the prevalence of WMSDs among Swedish female physiotherapists also found that the proportion of older female physiotherapists suffering from WMSDs was higher than the average level for the Swedish working population [3]. However, total length of service did not show a significant difference on the prevalence of WMSDs in the present research, further suggesting the contribution of the length of service for the current type of work to the development of WMSDs in footwear factories. Furthermore, married and divorced workers were at a higher risk of WMSDs than unmarried workers, and low-income workers were at a higher risk than higher-income workers, which may be related to the impacts of WMSDs on the mental health of the workers. This may be due to the high proportion of women workers in the shoemaking industry. Married or divorced women workers still have to go home after work to continue to complete all kinds of housework, reducing the time for rest and exercise, and aggravating musculoskeletal strain, especially in low-income groups. Mohd et al. also concluded that neck pain was significantly related to physical and psychosocial factors in their research on the influencing factors of WMSDs in dentists [26]. This phenomenon suggested that psychosocial factors played a crucial role in the occurrence of WMSDs. Finally, poor physical health would likewise increase the risk of WMSDs in workers, thereby implying that underlying disease promoted the development of WMSDs.

In addition to individual factors, biomechanical and work organization factors also influenced the development of WMSDs. Studies reported that poor blood circulation, inadequate blood supply to the corresponding parts, and failure of muscles and bones to absorb nutrients in time would occur after long-term repeated bad postures, which could lead to muscle tissue ligament strain and WMSDs given a sustained low load or short-term strong load impact[27]. This study demonstrated that standing, sitting, holding an uncomfortable working posture for a long time, and repetitive operations per minute could result in an increased risk of WMSDs (P < 0.05), thereby confirming that reduced bad posture work and rational arrangement of work patterns had a profound effect on the prevention and treatment of WMSDs.

Su and Yong et al. respectively found an association between the use of vibrating tools, work involving temperature changes such as cold, and the occurrence of WMSDs in a cross-sectional survey of cabinet factory workers and coal mine site workers[28–29]. The multifactorial regression analysis in this work also indicated that using vibrating tools, working outdoors, and work involving cold or temperature changes were the risk factors for WMSDs. Michael et al. proposed that controlling the physical environment might be a way of mitigating WMSDs [30]. Therefore, mitigating the threat of WMSDs to the health of workers and the sustainability of footwear factories by creating a good working environment is necessary.

5.1 Strengths

This study provided in-depth analysis of the prevalence and risk factors of WMSDs among workers of 26 footwear factories in China, on the basis of five aspects ( demographic characteristics, biomechanical factors, work organizational factors, environmental factors, and psychosocial factors) and helped to fill the blank in the knowledge about this industry in China. This work is valuable for understanding the prevalence and risk factors of WMSDs in this industry locally and providing a basis for taking preventive measures.

5.2 Limitations

The limitation of this study is that, first of all, the data only come from 26 footwear industries in China, which may not fully reflect the real situation of WMSDs prevalence in the entire footwear industry in China. Second, the data is collected through electronic questionnaires, and some subjective biases may appear among the respondents who fill in the questionnaire items, resulting in untrue data. Third, as a cross-sectional study, it cannot provide more powerful evidence than the longitudinal study. It only analyzes the correlation between various risk factors and the disease. In the future, cohort study should be carried out. Fourth, the intermediary effect between various risk factors has not been considered.

Our research confirmed that workers in the footwear industry are at risk of WMSD, with an overall prevalence rate of 36.8%. The main factors leading to the high prevalence of WMSD among footwear workers include demographic characteristics (such as gender, age and marital status), working conditions, work organization, work environment and (represented by bad posture), Environmental factors (such as using vibration tools, working outdoors and involving cold temperature changes) and work organization factors (such as repetitive tasks). Therefore, the following protective measures should be taken for the above risk factors:(1) Strengthen health education and training. Cognitive behavior intervention is an effective means to prevent WMSD. Health education and training can effectively improve the ability of workers in the footwear industry to identify work hazards and expand their awareness of self-protection and self-care.(2) Guide workers to correct working posture and reasonable movement: they should keep their body in a balanced and natural state as far as possible, and make the center of gravity of the weight close to the body, so as to effectively reduce the posture load.(3) Improve working conditions: workers should use auxiliary tools to replace heavy physical activities, automation should replace manual operations, repetitive work per minute should be reduced, and the working environment should be optimized to increase warmth and moisture resistance. The working hours of workers in the unavoidable working environment should be reduced.(4) Reasonably arrange labor organization: reasonably arrange labor content, disperse labor intensity, relieve muscle tension, effectively stretch and recover, which can better match the physical and psychological abilities of workers.

Authors’ contributions :ZHD performed the questionnaire survey, analyzed the data and wrote the manuscript. NJ and CFQ designed the study and collected the data. DY,XPL,LC,HXD,RHR, YML,JCC,XLF,YQL,BS performed the questionnaire survey. ZHD and ZXW got financial support for the project of this study and assume supervision responsibility. All authors read and approved the final manuscript.

Funding: The Project of Occupational Health Risk Assessment and National Occupational Health Standard Formulation of National Institute of Occupational Health and Poison Control (Project No.: 131031109000160004) provided the funding for this work.

Data Availability Statement：The data that support this study are available in the article and in the accompanying online supplementary material.

Ethics approval and consent to participate：This research protocol was approved by the Ethics Committee of Chinese Center for Disease Control and Prevention (Grant No. NIOHP20212) to commencing the study in accordance with relevant guidelines and regulations. Each questionnaire had attached an informative letter at frst, which clarifed the free participation, anonymity, and confdentiality of this study. Participants were informed of the study details and signed electronic informed consent forms prior to the survey.

Consent for publication：Not applicable.

Competing interests:No conflict of interest arises from the submission of this manuscript, and the draft was approved by all authors for publication.

Acknowledgments: The authors recognize the contribution of all study participants who were from Chongqing, Guangdong, Fujian, Zhejiang, and Jiangxi，China.

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Table 1. Demographic characteristics of participants (N=7106)

Variables		Total (%)	Prevalence（%）	c²	P value
Gender
	Male	2214(31.2)	31.1	44.822	<0.001^**
	Female	4892(68.8)	39.4
Age
	<33	1649(23.2)	32.1	39.090	<0.001^**
	33-	1739(24.5)	38.9
	40-	1936(27.2)	41.2
	47-	1782(25.1)	34.5
Height(cm)
	<156	1690(23.8)	40.7	28.297	<0.001^**
	156-	1397(19.6)	39.2
	160-	1731(24.4)	36.2
	165-	2288(32.2)	33.0
Weight(kg)
	<51	1645(23.2)	39.2	9.509	0.023^*
	51-	1839(25.9)	37.7
	58-	1652(23.2)	34.3
	65-	1970(27.7)	36.1
Dominant hand
	Right hand	6624(93.2)	36.9	.057	0.811
	Left hand	482(6.8)	36.3
Current working experience (years)
	<2	1383(19.4)	31.7	71.259	<0.001^**
	2-	2138(30.1)	32.6
	5-	1698(23.9)	39.0
	9-	1887(26.6)	43.4
Total working experience (years)
	<3	1647(23.2)	30.5	50.243	<0.001^**
	3-	1648(23.2)	35.3
	6-	2005(28.2)	39.3
	11-	1806(25.4)	41.2
Physical conditions
	Good	3115(43.8)	28.3	185.386	<0.001^**
	fair	3705(52.2)	42.9
	Poor	230(3.2)	53.5
	Poorest	56(0.8)	41.1
Education
	Junior High School and below	5399(76.0)	37.5	7.286	0.122
	High School	1478(20.8)	35.0
	College	173(2.4)	31.8
	Bachelor	44(0.6)	36.4
	Master's degree and above	12(0.2)	16.7
Working on other jobs
	No	4756(66.9)	36.3	1.830	0.176
	Yes	2350(33.1)	37.9
Marital status
	Unmarried	1128(15.9)	29.8	39.261	<0.001^**
	Married	5751(80.9)	37.7
	Divorced	227(3.2)	48.5
Monthly income(￥)
	≤1000	43(0.6)	20.9	153.528	<0.001^**
	1001–3000	2870(40.4)	45.3
	3001–5000	3434(48.3)	31.9
	＞5000	759(10.7)	28.1
Physical activities
	No	2404(33.8)	36.3	1.252	0.870
	Sometimes	3810(53.6)	37.0
	2–3 times/month	234(3.3)	36.3
	1–2 times/week	443(6.3)	38.8
	>2 times/week	215(3.0)	35.8
Smoking
	No	6042(85.0)	37.5	8.921	0.030^*
	Sometimes	603(8.5)	31.7
	Frequently	405(5.7)	34.8
	Quitting smoking	56(0.8)	33.9
Occupation types	Management staff	759(10.7)	30.8	29.378	<0.001^**
	Shoe sole handlers	436(6.1)	33.0
	Molding workers	2303(32.5)	36.7
	Needle workers	1708(24.0)	38.6
	Cutting workers	780(11.0)	39.1
	Shoe surface handlers	657(9.2)	42.0
	Other supporting workers	463(6.5)	32.8

*Significant at P<0.05; **Significant at P<0.01

Table 2. The relationship between work conditions and prevalence of WMSDs

Variables		Total (%) N=7106	Prevalence（%）	c²	P value
Prolonged standing
	Seldom/never	2670(37.6)	35.8	32.883	<0.001^**
	Sometimes	2126(29.9)	34.6
	often	1545(21.7)	37.2
	Frequently	765(10.8)	45.9
Prolonged sitting
	Seldom/never	1722(24.2)	38.0	108.033	<0.001^**
	Sometimes	1851(26.1)	28.6
	often	2362(33.2)	37.3
	Frequently	1171(16.5)	47.1
Prolonged squatting or kneeling
	Seldom/never	6011(84.6)	35.8	32.311	<0.001^**
	Sometimes	927(13.1)	40.1
	often	124(1.7)	54.0
	Frequently	44(0.6)	59.1
Lifting heavy objects (>5 kg)
	Seldom/never	4387(61.7)	34.4	62.194	<0.001^**
	Sometimes	1954(27.5)	37.9
	often	545(7.7)	45.3
	Frequently	220(3.1)	55.5
Upper limb or hand exertion
	Seldom/never	2171(30.6)	31.2	122.927	<0.001^**
	Sometimes	1748(24.5)	32.8
	often	2086(29.4)	39.4
	Frequently	1101(15.5)	49.4
Uncomfortable position
	Seldom/never	5164(72.7)	31.6	248.155	<0.001^**
	Sometimes	1458(20.5)	47.7
	often	335(4.7)	59.7
	Frequently	149(2.1)	61.1

*Significant at P<0.05; **Significant at P<0.01

Table 3. The relationship between work organization and prevalence of WMSDs

Variables		Total (%) N=7106	Prevalence（%）	c²	P value
Working hours per day
	<9	2886 (40.6)	35.6	15.399	<0.001^**
	9-	4090 (57.6)	37.2
	13-	130 (1.8)	52.3
Job rotation
	No	4536 (63.8)	37.3	1.282	0.258
	Yes	2570 (36.2)	36.0
Multiple repetitive operations per minute
	Seldom/never	1786(25.1)	24.1	237.475	<0.001^**
	Sometimes	1142(16.1)	35.7
	often	677(9.5)	30.0
	Frequently	3501(49.3)	45.0

*Significant at P<0.05; **Significant at P<0.01

Table 4. The relationship between work environment and prevalence of WMSDs

Variables		Total (%) N=7106	Prevalence（%）	c²	P value
Using vibrating tools
	Seldom/never	5430(76.4)	34.7	75.180	<0.001^**
	Sometimes	1055(14.8)	40.1
	often	438(6.2)	45.7
	Frequently	183(2.6)	60.7
Working outdoors
	No	6811 (95.8)	36.5	5.147	0.023^*
	Yes	295 (4.2)	43.1
Cold or temperature variations at work
	No	5683 (80.0)	34.0	93.276	<0.001^**
	Yes	1423 (20.0)	47.9

*Significant at P<0.05; **Significant at P<0.01

Table 5. The relationship between psychosocial factors and prevalence of WMSDs

Variables		Total (%) N=7106	Prevalence（%）	c²	P value
Doing the same job daily
	No	872 (12.3)	31.8	10.888	0.001^**
	Yes	6234 (87.7)	37.5
Need for communication with customers
	No	6679(94.0)	36.5	3.788	0.052
	Yes	427 (6.0)	41.2

*Significant at P<0.05; **Significant at P<0.01

Table 6. Odds ratios (95% CIs) of WMSDs by Factors

Factors	Variables	B	P value	Odds ratio	95%CI
Factors	Variables	B	P value	Odds ratio	Lower	Upper
Demographic characteristics	Gender	0.325	<0.001	1.384	1.162	1.648
Demographic characteristics	Age	0.115	<0.001	1.122	1.060	1.187
	Height(cm)	-0.026	0.390	0.974	0.917	1.034
	Weight(kg)	0.000	0.991	1.000	0.949	1.053
	Dominant hand	-0.043	0.685	0.958	0.780	1.177
	Occupation types	-0.004	0.798	0.996	0.963	1.029
	Current job experience(years)	0.221	<0.001	1.247	1.182	1.315
	Working on other jobs	0.131	0.020	1.140	1.021	1.272
	Education	-0.050	0.323	0.951	0.860	1.051
	Marital status	0.256	<0.001	1.292	1.120	1.490
	Monthly income(￥)	-0.350	<0.001	0.705	0.647	0.768
	Physical activities	0.021	0.446	1.022	0.967	1.079
	Smoking	0.044	0.427	1.045	0.938	1.163
Work conditions	Prolonged standing	0.099	0.003	1.104	1.034	1.178
	Prolonged sitting	0.105	0.001	1.111	1.042	1.184
	Prolonged squatting or kneeling	0.088	0.137	1.092	0.973	1.225
	Lifting heavy objects (>5 kg)	0.076	0.050	1.078	1.000	1.163
	Upper limb or hand exertion	0.045	0.118	1.046	0.989	1.106
	Uncomfortable position	0.337	<0.001	1.400	1.289	1.522
work organization	Working hours per day	0.036	0.498	1.037	0.934	1.150
	Job rotation	-0.013	0.814	0.987	0.886	1.100
	Multiple repetitive operations per minute	0.204	<0.001	1.227	1.169	1.287
work environment	Using vibrating tools	0.104	0.007	1.109	1.029	1.196
	Working outdoors	0.211	0.139	1.234	0.934	1.631
	Cold or temperature variations at work	0.292	<0.001	1.339	1.174	1.527
Psychosocial factors	Doing the same job daily	-0.104	0.226	0.902	0.763	1.066
	Need for communication with customers	0.163	0.180	1.177	0.927	1.495

Epidemiological study of work-related musculoskeletal disorders and related risk factors for the footwear industry in China

Status:

Version 1

Abstract

Object:

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1 Sample selection

2.2 Data collection

2.3 Assessment for WMSDs

2.4 Statistical analysis

3. Results

3.1 General characteristics and the prevalence of WMSDs

3.2 Prevalence of WMSDs

3.3 Univariable analysis of the prevalence of WMSDs

3.3.1 The relationship between work conditions and prevalence of WMSDs

3.3.2 The relationship between work organization and prevalence of WMSDs

3.3.3 The relationship between work environment and prevalence of WMSDs

3.3.4 The relationship between psychosocial factors and prevalence of WMSDs

3.4 Multivariate logistic regression analysis of WMSDs

4. Discussion

5. Strengths and limitations of the study

5.1 Strengths

5.2 Limitations

6. Conclusion

Declarations

References

Tables

Status:

Version 1