BioMed Research International
Volume 2020 (2020), Article ID 3164056, 9 pages
https://doi.org/10.1155/2020/3164056

Research Article

A Cross-Sectional Epidemiological Survey of Work-Related Musculoskeletal Disorders and Analysis of Its Influencing Factors among Coal Mine Workers in Xinjiang

Xianting Yong, Fuye Li, Hua Ge, Xuemei Sun, Xiaofan Ma, and Jiwen Liu

Department of Occupational Health and Environmental Health, College of Public Health, Xinjiang Medical University, Urumqi, China

Correspondence should be addressed to Jiwen Liu; liujiwendr@sina.com

Received 13 February 2020; Revised 15 July 2020; Accepted 31 July 2020; Published 10 August 2020

Academic Editor: José L. Campos

Copyright © 2020 Xianting Yong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This study is to investigate the prevalence of work-related musculoskeletal disorders (MSDs) and the influencing factors among coal mine workers employed in on-site operations. The job burnout scale and MSD scale were implemented to investigate a random sample of 1,500 coal mine workers working in on-site operations in Xinjiang, China. In total, 1,325 valid questionnaires were collected, with a recovery rate of 88.33% (1,325/1,500). The rate of job burnout was 90%, of which 39.8% were categorized as mild burnout, 43.8% as moderate burnout, and 6.4% as severe burnout; the average job burnout score was . The annual prevalence of MSDs was 65.6%, with the highest annual prevalence in the waist (50.7%), followed by the neck, shoulder, and knee, and the lowest prevalence in the elbow (18.8%). Of the areas of the body affected by work-related MSDs, the highest proportion of requests for leave of absence was related to the waist, accounting for 25.7% of requests, while the lowest proportion (13.4%) was related to the wrist. In addition, the incidence of MSDs increased with the years of service. The lowest incidence of MSDs was associated with the two-shift and three-group working pattern. The prevalence of MSDs in the neck and waist was higher in women than in men. The prevalence of MSDs in various body parts increased with the years of service. Moreover, multiple logistic regression indicated that three shifts with four groups (, 95% CI: 0.832-1.445), working more than 10 years (, 95% CI: 2.369-5.748), working more than 20 years (, 95% CI: 1.419-6.337), significant bending (, 95% CI: 1.400-3.038), forward neck tilting (, 95% CI: 1.071-2.281), maximum force operation within a short period of time (, 95% CI: 1.164-2.547), repeated movement of upper arms or fingers (, 95% CI: 1.034-2.161), slip or fall incidents (, 95% CI: 1.039-1.216), work under conditions of cold or temperature variations (, 95% CI: 1.342-2.720), mild burnout (, 95% CI: 1.016-2.191), moderate burnout (, 95% CI: 1.267-2.708), and severe burnout (, 95% CI: 1.145-3.496) were risk factors for MSDs. In conclusion, there is a high annual prevalence of MSDs among the coal mine workers employed in on-site operations in Xinjiang, China. Measures to reduce this prevalence are required.

1. Introduction

Work-related musculoskeletal disorders (MSDs) refer to an injury or dysfunction of muscles, bones, nerves, tendons, ligaments, joints, cartilage, and spine [1]. It has been reported that 30% of European workers suffer from back pain, and the economic loss caused by back pain in Holland accounts for 1.7% of the national wealth every year. Furthermore [2, 3], the number of days absent from work due to MSDs accounts for 15%-22% of the total as a consequence of industry-related diseases in the Federal Republic of Germany every year. The direct and indirect economic losses incurred in Canada due to MSDs are as high as nearly 100 billion US dollars every year. Thus, the serious consequences of MSDs have gradually aroused widespread concern in economically developed countries [4]. Indeed, MSDs can also be induced by adverse psychosocial factors, which in turn have negative effects on psychological and social states and further increase the risk of workers, thus exhibiting a two-way connection and feedback loop between the two [5–7]. Under cognitive and emotional stress, a longer period of rest time is required to allow muscle relaxation [8]. Although coal mine workers are high-risk groups for MSDs [9–11], the influence of psychosocial factors in this group remains to be clarified. There is a wide range of psychosocial factors, including job demands, job control, job support, social support, and job satisfaction [12]. Furthermore, some studies have shown that, especially in developing countries, individual physical, social, and organizational risk factors are independent predictors of lumbar back symptoms (LBS), although on their interactions have seldom been explored. Previous studies have shown that psychosocial factors are more likely to lead to LBS than physical factors, among which the night shift and unreasonable shift systems are also risk factors [13, 14]. Additionally, a study in Indonesian coal miners showed interactions between lumbago symptoms and physical and psychological risk factors, with a higher incidence of lumbago symptoms among smokers and permanent night shift workers although the mechanisms remain to be clarified [15].

MSDs are a serious threat to health, working ability, and quality life and represent the primary reason for the decline in the working population aged from 20 to 55. In some industrialized countries, traditional occupational diseases caused by chemical toxicity, dust, and noise have been gradually controlled, while chronic MSDs of the waist, back, shoulder, neck, and wrist have become widespread occupational health problems associated with high economic losses [16]. Thus, the psychology and musculoskeletal health of coal mine workers in specialized environments require further investigation.

In China, the coal mine workers are in a large population alling into the category of specialized occupations as a consequence of the rich coal resources. Specifically, coal miners are engaged in long-term operations executed hundreds, or even thousands, of meters underground, where the working environment is complicated, with a high level of responsibility and long working hours, resulting in an unstable biological rhythm. In addition, the working environment contains occupational risk factors such as high temperature, noise, dust, vibration, and unreasonable working systems, which have adverse effects on the psychology of coal miners [17]. In this study, using a questionnaire survey, we investigated the prevalence of MSDs and related influencing factors among coal miners working in an on-site operation in Xinjiang. This information will provide a scientific reference for further improving the quality of life of coal miners and establishing a healthy and harmonious working environment.

2. Methods

2.1. Participants

This cross-sectional study was conducted via questionnaire using random cluster sampling. From March 2017 to December 2017, 1,500 coal mine workers employed in on-site operations at six coal mine enterprises in Xinjiang were randomly selected for inclusion in our study. The participants were aged 18 years or over and under 60 years with ≥1 year of service. A total of 1,500 questionnaires were distributed in this survey. After the exclusion of the invalid questionnaires, 1,325 valid questionnaires were finally returned, with a recovery rate of 88.33%.

2.2. Job Burnout

In this study, we used the job burnout scale revised by Li et al. [18] to measure job burnout. The questionnaire was designed to evaluate emotional exhaustion, depersonalization, and reduced sense of achievement, with five items included for each factor (15 items in total). The Cronbach α-coefficient of the questionnaire was 0.780 and the test-retest reliability was 0.843 [19]. Based on a previous report, these values indicate that the reliability and validity of the questionnaire are good and that the questionnaire is eligible for psychological evaluations [20]. There were four job burnout sublevels: zero burnout (, , and ), mild burnout (, , and ; , , and ; or , , and ), moderate burnout (, , and ; , , and ; or , , and ), and severe burnout (, , and ). The critical values of the three levels of the job burnout scale were defined as ≥25, ≥11, and ≥16.

2.3. MSDs

The MSD questionnaire jointly developed by Kapitan et al. was adopted in this study [21, 22]. The questionnaire, which has good reliability and validity, includes general conditions (17 items), health conditions (three sections evaluating work-related pain or discomfort in nine areas of the body (neck, shoulder, back, elbow, waist, wrist, hip, knee, and, ankle, and foot) in the past year and in the past week), and working conditions. The working conditions included movements requiring great effort (such as lifting, pushing, pulling, unfavorable posture, and trunk bending), dynamic loads (trunk movements and neck, shoulder, and wrist movements, etc.), static loads (including slight bending and severe bending), repetitive loads (i.e., work with the same movement), ergonomic environment (working environment, lack of support, slipping and falling, forward movement with tools, etc.), and environmental temperature. Most questions required a simple “yes” or “no” answer.

2.4. Quality Control

Quality control was monitored throughout the on-site investigations and study, including at the design stage (questionnaire design, investigators, on-site investigation, and data collation).

2.5. Statistical Analysis

All data were entered into the EpiData 3.0 database and analyzed using the SPSS 20.0 software package. All measurement data were reported as the (SD). Two independent-sample -tests were used to evaluate differences between two groups, whereas differences among several groups were evaluated by one-way analysis of variance (ANOVA). If an overall difference was found, Student–Newman–Keuls- (SNK-) tests were used for multiple comparisons. Comparison of rates was performed using chi-squared tests. A multivariate logistic regression model of MSDs of coal mine workers was established by incorporating variables with in univariate analysis into the logistic regression equation. Furthermore, the analysis of factors affecting job burnout and MSDs was conducted through univariate and multivariate logistic regression models. The test level was .

3. Results

3.1. Baseline Data

The baseline data for the participants are shown in Table 1. A total of 1,500 questionnaires were distributed and 1,325 valid questionnaires were returned, with a recovery rate of 88.3%. Specifically, the respondents comprised 1,220 men and 105 women, with a mean age of years and a mean length of service of years. Regarding education background, 43.8% of participants had received a junior high school education or lower, 31.5% had received a senior high school or technical secondary school education, 18.4% had received a junior college education, and 6.2% had a bachelor’s degree or higher.

Table 1: Baseline data.

Variables

%

Gender
Male
1,220
92.1

Female
105
7.9

Age (years)
<30
171
12.9

30+
254
19.2

40+
609
46

50+
291
22

Years of service
<10
402
30.3

10+
305
23

20+
618
46.6

Education
Junior high school or lower
581
43.8

Senior high school or technical secondary
418
31.5

Junior college
244
18.4

Bachelor’s degree or higher
82
6.2

Type of work
Coal miner
231
17.4

Coal caver
26
2

Coal drivage worker
238
18

Anchor worker
11
0.8

Craft worker
26
2

Inspection worker
31
2.3

Ventilation worker
48
3.6

Gangue collector
21
1.6

Electric locomotive driver
26
2

Bell man
38
2.9

Electrical fitter
158
11.9

Plugman
18
1.4

Operator
42
3.2

Blaster
20
1.5

Gas inspector
27
2

Backman
49
3.7

Monitor observer
22
1.7

Loading workman
25
1.9

Others
266
20.1

Shift system
Day shift only
490
37

Two shifts
187
14.1

Two shifts with three groups
536
40.5

Three shifts with four groups
112
8.5

Marital status
Unmarried
126
9.5

Married
1,156
87.2

Divorced or widowed
43
3.2

3.2. Investigation on Job Burnout among Coal Miners

In terms of job burnout among the coal mine workers, we recorded a mean score of , with job burnout detected in 90.0% of participants. The emotional exhaustion score was , the depersonalization score was , and the reduced sense of achievement score was . In terms of the degree of job burnout among the coal mine workers, 527 (39.8%) were classified as slight burnout, 581 (43.8%) were classified as moderate burnout, and 85 (6.4%) were classified as severe burnout.

3.3. Weekly Prevalence of MSDs among Coal Miners

The weekly prevalence of MSDs among coal mine workers is shown in Table 2. The highest weekly prevalence of MSDs among the coal mine workers was related to the waist (41.3%), followed by the neck (32.2%), shoulder (27.4%), elbow (14.4%), and ankle (14.4%). The highest rate of MSDs resulting in a request for leave of absence was related to the waist (14.8%), while the lowest rate was related to the wrist (9.4%).

Table 2: Weekly prevalence of MSDs among coal mine workers.

Localizations
Position
MSD
Leave of absence due to MSD

Number of patients
Weekly prevalence
Requests for leave of absence
Absence rate due to MSD

Upper limb
Neck
427
32.20%
43
10.10%

Shoulder
363
27.40%
37
10.20%

Back
300
22.60%
33
11.00%

Elbow
191
14.40%
25
13.10%

Wrist
245
18.50%
23
9.40%

Lower limb
Waist
547
41.30%
81
14.80%

Hip
146
11.00%
19
13.00%

Knee
328
24.80%
39
11.90%

Ankle
191
14.40%
28
14.70%

3.4. Annual Prevalence of MSDs among Coal Mine Workers

The annual prevalence of MSDs among coal mine workers is shown in Table 3. The annual prevalence of MSDs among coal mine workers was 65.6%, which was dominated by MSDs related to the waist, neck, and shoulder and knee (juxtaposed). The highest annual prevalence of MSDs was related to the waist (50.7%), while the lowest was related to the elbow (18.8%). The highest rate of MSDs causing requests for leave of absence was related to the waist (25.7%), while the lowest rate was related to the wrist (13.4%).

Table 3: Annual prevalence of MSDs among coal mine workers.

Localizations
Position
Illness
Request for leave of absence due to illness

Number of patients
Annual prevalence
Requests for leave of absence
Absence rate due to illness

Upper limb
Neck
528
39.80%
85
16.10%

Shoulder
448
33.80%
57
12.70%

Back
359
27.10%
59
16.40%

Elbow
249
18.80%
42
16.90%

Wrist
314
23.70%
42
13.40%

Lower limb
Waist
672
50.70%
173
25.70%

Hip
199
15.00%
39
19.60%

Knee
448
33.80%
86
19.20%

Ankle
289
21.80%
61
21.10%

3.5. Univariate Logistic Regression Model of MSDs of Coal Mine Workers

Details of the factors influencing MSD prevalence are shown in Table 4. The significant influencing factors identified for MSDs among coal mine workers were as follows: years of service, significant bending, forward neck tilting, maximum force operation over a period of time, repeated movements of the upper arms or fingers, slip or fall incidents, work under conditions of cold or temperature variations, and job burnout levels. The prevalence of MSDs among coal mine workers increased with the years of services. In terms of the shift system, the lowest incidence of MSDs was associated with the two-shift with three-group pattern. Job burnout was identified as a risk factor for MSDs, with severe job burnout associated with a significantly higher risk of illness compared with that of the no burnout group ().

Table 4: Factors influencing prevalence of MSD last year.

Variables

Prevalence of MSD last year

OR
95% CI

Upper limit
Lower limit

Gender

0.273

Male
65.20%

1

Female
70.00%

1.276
0.826
1.973

Age (years)

0.097

<30
59.60%

1

30+
63.00%

1.151
0.774
1.714

40+
68.80%

1.492
1.051
2.118

50+
64.60%

1.235
0.837
1.821

Years of service

0.001

<10
58.50%

1

10+
67.20%

1.457
1.068
1.988

20+
69.40%

1.613
1.241
2.096

Education

0.689

Junior high school or lower
63.90%

1

Senior high school or technical secondary
67.00%

1.148
0.881
1.497

Junior college
66.40%

1.118
0.816
1.532

Bachelor’s degree or higher
68.30%

1.219
0.743
2.000

Shift system

0.002

Day shift only
67.30%

1

Two shifts
71.70%

1.226
0.847
1.774

Two shifts with three groups
60.10%

0.730
0.565
0.942

Three shifts with four groups
74.10%

1.338
0.873
2.205

Significant bending

<0.001

No
54.10%

1

Yes
75.10%

2.723
2.141
3.462

Forward neck tilting

<0.001

No
53.10%

1

Yes
76.80%

2.967
2.337
3.767

Maximum force operation within a short period of time

<0.001

No
55.00%

1

Yes
75.30%

2.655
2.088
3.376

Repeated movement of upper arms or fingers

<0.001

No
56.80%

1

Yes
71.90%

1.941
1.538
2.450

Slip or fall incidents

<0.001

No
55.00%

1

Yes
76.20%

2.744
2.155
3.495

Work under conditions of cold or temperature variations

<0.001

No
52.00%

1

Yes
74.40%

2.688
2.121
3.405

Burnout level

0.001

Zero burnout
67.40%

1

Mild burnout
66.20%

0.904
0.599
1.365

Moderate burnout
61.80%

0.743
0.495
1.117

Severe burnout
84.70%

2.530
1.225
5.099

Note: ; .

3.6. Multivariate Logistic Regression Model of MSDs of Coal Mine Workers

The results of the multivariate logistic regression analysis of MSDs among coal mine workers are shown in Table 5. Significant bending, forward neck tilting, maximum force operation within a short period of time, repeated movements of the upper arms or fingers, slip or fall incidents, and work under conditions of cold or temperature variations had positive contributions to the prevalence of MSDs among coal mine workers (). Burnout increases the risk of musculoskeletal injuries. By comparing the coal miners without burnout, mild burnout, moderate burnout, and severe burnout, we found that with the increase of occupational burnout, the prevalence of musculoskeletal diseases of coal mine workers also increased. These results indicate that the higher the degree of job burnout, the greater the risk of illness.

Table 5: Multivariate logistic regression analysis of prevalence of MSD last year of coal mine workers.

Variables
Variable assignment

Wald

OR
95% CI

Upper limit
Lower limit

Shift system
Day shift only

25.635
<0.001
1

Two shifts
0.092
0.427
0.514
1.096
0.832
1.445

Two shifts with three groups
-0.437
3.294
0.070
0.646
0.403
1.306

Three shifts with four groups
0.811
17.294
<0.001
2.271
1.543
3.342

Years of services
<10

31.662
0.01
1

10+
1.306
33.332
0.000
3.690
2.369
5.748

20+
1.101
8.251
0.004
3.008
1.419
6.377

Significant bending

0.724
13.395
<0.001
2.062
1.400
3.038

Forward neck tilting

0.565
5.657
0.017
1.572
1.071
2.281

Maximum force operation within a short period of time
0.543
7.398
0.007
1.7222
1.164
2.547

Repeated movement of upper arms or fingers
0.402
4.575
0.032
1.495
1.034
2.161

Slip or fall incidents
0.117
8.475
0.004
1.124
1.039
1.216

Work under conditions of cold or temperature variations
0.647
12.915
<0.001
1.911
1.342
2.720

Job burnout
Zero burnout

11.462
0.009
1

Mild burnout
0.400
4.159
0.041
1.492
1.016
2.191

Moderate burnout
0.617
10.317
0.001
1.852
1.267
2.708

Severe burnout
0.694
5.935
0.015
2.001
1.145
3.496

4. Discussion

Coal mine workers represent a huge population in China’s workforce (nearly 100,000 are employed in Xinjiang), requiring special attention regarding their physical and mental health. Many risk factors for MSDs are associated with the daily activities of coal mine workers, including the physical stress caused by dynamic and static loads, as well as ergonomic and environmental factors. The prevalence of MSDs in the general working population ranges from 20% to 40% [23–26]. Our survey showed that the annual prevalence of MSDs among coal mine workers on site is 65.6%, which is higher than that of the general working population and lower than that (78.4%) reported in 2011 by Xu [27]. As previously reported [28, 29], the prevalence of MSDs for gas station employees is 51.2% and that for nursing staff is 78.58%. The top three positions in terms of the prevalence of MSDs for nursing staff [29], automobile manufactory workers [30], oil extraction workers [31], and power plant workers [32] are the waist, neck, and shoulder, which is consistent with the results obtained in this study. The waist is an important region for normal movement of the human body. The five postures of standing, lifting, squatting, sitting, and carrying all out strain on the waist, which are inevitable aspects of all types of work, thus having a greater impact on the work once the waist has been injured. Therefore, the waist is ranked first among the positions leading to absence due to illness, followed by the ankle, possibly due the pressure imposed on this joint by the long distances traveled by coal mine workers from their homes to the mine and the requirement to remain standing for long periods of time during their working hours.

In terms of demographic factors, a previous study revealed a higher prevalence of MSDs related to the waist and neck in women than in men [31]. The prevalence of MSDs in the hip and knee also increased with the age of the participants, with the highest prevalence rate in the waist in individuals aged between 40 and 50 years. In contrast, there were no significant differences in the prevalence of MSDs at other positions. Our results showed that the prevalence of MSDs increased with the number of years of services, which is consistent with previous studies [33–35] especially after the 10 or more years of services. This phenomenon results in a more rapid increase in the prevalence of MSDs in this group than that of workers with less than 10 years of service, in contrast to slowed increase in the prevalence of MSDs when the years of service exceed 20. In general, coal mine workers, who are mainly engaged in manual labor, are prone to sickness caused by overwork under the influence of various adverse factors. However, with the experience that comes with increasing years of services, these workers learn strategies to avoid adverse working postures. On the other hand, under the influence of their own MSDs, the working efficiency of these individuals decreases and they cannot maintain their previous workload. As a result, the prevalence of MSDs increases less in the groups with 10 to 20 years of service and those with more than 20 years of service. The univariate logistic regression analysis of the groups classified according to years of services revealed that the risk of MSDs in the groups with more than 20 years and between 10 and 20 years of service was higher than that of the group with less than 10 years of services, respectively. These results confirm that the increase in the number of years of services is a risk factor for MSDs in coal mine workers. Furthermore, the results also show that the prevalence of MSDs among coal mine workers is the lowest in those employed under the system of two shifts with three groups. This employment practice was also shown to be protective against MSDs compared with that of individuals working day shifts only. The comprehensive evaluation based on the establishment of a logistic regression model revealed the existence of numerous influencing factors for MSDs among coal mine workers including significant bending, forward neck tilting, maximum force operation within a short period of time, and repeated movements of upper arms or fingers, all of which are inevitable and occur frequently in the labor process [36]. In addition, low temperature and humidity in the coal mine also have substantial influences on the work, as causes of slip and fall incidents and showing an association with environmental changes [7].

The prevalence of MSDs in various positions of coal miners with severe burnout is much higher than that of coal miners affected by job burnout to a lesser degree, suggesting that there is a certain relationship between job burnout and MSDs in this population and confirming that job burnout is a risk factor for MSDs. Psychosocial factors have been reported to have a noticeable effect on MSDs [37, 38], with adverse psychosocial factors shown to change human behavior and lead to the occurrence of MSDs [39] via a two-way feedback between psychosocial factors and MSDs [40]. Studies outside China have also shown that psychological factors and physical load simultaneously influence the prevalence of MSDs [41].

This study focused on the coal mine workers on site in Xinjiang; therefore, further studies are required to determine and verify our results in other regions. In addition, we investigated the prevalence of MSDs without clarification of the mechanism and etiology of MSDs in coal miners; thus, a cohort study is required to explore the causes and mechanisms of MSDs among coal miners.

5. Conclusions

In this cross-sectional study, we showed a high prevalence of MSDs among coal mine workers on site in Xinjiang. The prevalence of MSDs of the neck and waist was higher in women than in men. Furthermore, the prevalence of MSDs at each position increased with the years of service. In addition, the prevalence of MSDs in the neck of coal mine workers varied with educational background, while the prevalence in the wrist and knee varied with shift systems. Numerous factors influence the prevalence of MSDs among coal mine workers including shift system, years of services, significant bending, forward neck tilting, maximum force operation within a short period of time, repeated movements of upper arms or fingers, slip and fall incidents, work under conditions of cold or temperature variations, and job burnout, suggesting interventions to control these factors will be beneficial to both the body and mind of coal mine workers.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Ethical Approval

The study was approved by the ethics committee of the Xinjiang Medical University (Approval No. 8176120048).

Consent

All participants provided written informed consent.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The study was supported by the National Natural Science Foundation of China [Grant No. 81760581], University Research Projects of Xinjiang Province [Grant No. XJEDU2019Y025], and Graduate Innovation and Entrepreneurship Program of Xinjiang Medical University [No. CXCY2018057].

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