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Preplanned Studies: Epidemiological Data of Work-Related Musculoskeletal Disorders — China, 2018–2020

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  • Summary

    What is already known about this topic?

    In recent decades, work-related musculoskeletal disorders (WMSDs) have become increasingly prominent and have become an important issue that is of universal concern and an urgent need to be solved in all countries of the world.

    What is added by this report?

    The top three industries or occupational groups with the highest standardized prevalence rate of WMSDs were flight attendants, medical staff, and vegetable greenhouses in that order. Women workers were 1.5 times more likely to suffer from WMSDs than men workers.

    What are the implications for public health practice?

    This study has found the prevalence and distribution characteristics of WMSDs in key industries in China. It is urgent to draw up relevant measures to prevent and control occupational populations with WMSDs.

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  • 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. 131031109000150003)
  • [1] Niu SL. Background and significance of revision of list of international occupational diseases 2010 edition. Chin J Ind Hyg Occup Dis 2010;28(8):599 − 604. http://dx.doi.org/10.3760/cma.j.issn.1001-9391.2010.08.013 (In Chinese). CrossRef
    [2] Kuorinka I, Jonsson B, Kilbom A, Vinterberg H, Biering-Sørensen F, Andersson G, et al. Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms. Appl Ergon 1987;18(3):233 − 7. http://dx.doi.org/10.1016/0003-6870(87)90010-xCrossRef
    [3] Salvendy G. Handbook of human factors and ergonomics. 4th ed. Hoboken: John Wiley & Sons, Inc. 2012. https://onlinelibrary.wiley.com/doi/book/10.1002/9781118131350.https://onlinelibrary.wiley.com/doi/book/10.1002/9781118131350
    [4] Jia N, Zhang HD, Ling RJ, Liu YM, Li G, Ren ZL, et al. Preplanned studies: investigation on work-related musculoskeletal disorders — China, 2018−2019. China CDC Wkly 2020;2(18):299 − 304. http://dx.doi.org/10.46234/ccdcw2020.077CrossRef
    [5] GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;393(10190):1789 − 858. http://dx.doi.org/10.1016/S0140-6736(18)32279-7CrossRef
    [6] Safiri S, Kolahi AA, Hoy D, Buchbinder R, Mansournia MA, Bettampadi D, et al. Global, regional, and national burden of neck pain in the general population, 1990-2017: systematic analysis of the Global Burden of Disease Study 2017. BMJ 2020;368:m791. http://dx.doi.org/10.1136/bmj.m791CrossRef
    [7] Robinson ME, Wise EA. Gender bias in the observation of experimental pain. Pain 2003;104(1 − 2):259 − 64. http://dx.doi.org/10.1016/s0304-3959(03)00014-9CrossRef
    [8] Andersson HI. Increased mortality among individuals with chronic widespread pain relates to lifestyle factors: a prospective population-based study. Disabil Rehabil 2009;31(24):1980 − 7. http://dx.doi.org/10.3109/09638280902874154CrossRef
  • TABLE 1.  WMSD prevalence and risk for different demographic groups among key industries or occupational groups in China, 2018–2020.

    CharacteristicNumberAny body partNeckShouldersLower back
    No. of casesRate, %OR (95%CI)No. of casesOR (95%CI)No. of casesOR (95%CI)No. of casesOR (95%CI)
    Gender
    Male37,240 14,05737.717,77416,41915,5141
    Female20,2619,61247.41.5 (1.4−1.5) *6,7131.9 (1.8−2.0) *5,6471.9 (1.8−1.9) *3,9351.4 (1.3−1.5) *
    Age (years)
    <2512,0854,42636.612,38912,02711,4621
    25–26,13911,19642.81.3 (1.2−1.4) *6,9671.5 (1.4−1.6) *5,7411.4 (1.3−1.5) *4,5771.5 (1.4−1.6) *
    35–12,3015,29443.01.3 (1.2−1.4) *3,4861.6 (1.5−1.7) *2,8881.5 (1.4−1.6) *2,2381.6 (1.5−1.7) *
    45–5,8022,27139.11.1 (1.0−1.2) *1,3851.2 (1.2−1.4) *1,1871.3 (1.2−1.4) *9641.4 (1.3−1.6) *
    55–1,17448241.11.2 (1.1−1.4) *2601.2 (1.0−1.3) *2231.2 (1.0−1.4) *2081.6 (1.3−1.8) *
    Working age (years)
    <216,0615,49834.212,95512,53611,8861
    2–12,0724,98941.31.3 (1.3−1.4) *3,0111.5 (1.4−1.6) *2,5091.4 (1.3−1.5) *1,8571.4 (1.3−1.5) *
    4–7,2993,10642.61.4 (1.3−1.5) *1,9661.6 (1.5−1.7) *1,6541.6 (1.5−1.7) *1,2921.6 (1.5−1.7) *
    6–9,7174,36144.91.6 (1.5−1.6) *2,8051.8 (1.7−1.9) *2,3021.7 (1.6−1.8) *1,8531.8 (1.7−1.9) *
    8–12,3525,71546.31.7 (1.6−1.7) *3,7501.9 (1.8−2.0) *3,0651.8 (1.7−1.9) *2,5612.0 (1.8−2.1) *
    Education
    Junior high school15,3695,54336.113,23012,81512,2251
    Senior high school21,9018,63639.41.2 (1.1−1.2) *4,9901.1 (1.1−1.2) *4,1741.1 (1.0−1.1) *3,3991.1 (1.0−1.2) *
    University degree19,2318,94946.51.5 (1.5−1.6) *5,8411.6 (1.6−1.7) *4,7291.5 (1.4−1.5) *3,6261.4 (1.3−1.5) *
    Graduate degree1,00054154.12.1 (1.8−2.4) *4262.8 (2.4−3.2) *3482.4 (2.1−2.7) *1991.5 (1.2−1.7) *
    BMI
    <18.56,0062,45940.911,48711,21719081
    18.5–39,32816,13041.01.0 (0.9−1.1)9,9731.0 (0.9−1.1)8,3891.1 (0.9−1.1)6,4141.1 (1.0−1.2) *
    25–12,1675,08041.81.0 (1.0−1.1)3,0271.0 (0.9−1.1)2,4601.0 (0.9−1.1)2,1271.2 (1.1−1.3) *
    Smoking
    No36,52715,49642.419,89518,22716,0741
    Occasionally10,1113,61635.80.8 (0.7−0.8)2,0490.7 (0. 6−0.7) *1,7080.7 (0.6−0.7) *1,4530.8 (0.8−0.9) *
    Frequently10,8634,55741.91.0 (0.9−1.0)2,5430.8 (0. 8−0.9) *2,1310.8 (0.8−0.9) *1,9221.1 (1.0−1.1) *
    Sporting
    No17,9477,85943.814,77214,03813,3751
    Occasionally32,79713,27240.50.9 (0.8−0.9) *8,1470.9 (0.8−0.9) *6,7490.9 (0.8−0.9) *5,1160.8 (0.7−0.8) *
    Frequently6,7572,53837.60.8 (0.7−0.8) *1,5680.8 (0.8−0.9) *1,2790.8 (0.7−0.8) *9580.7 (0.6−0.7) *
    Abbreviations: WMSDs=work-related musculoskeletal disorders; BMI=body mass index.
    * P<0.05.
    Download: CSV

    TABLE 2.  Prevalence of WMSDs in key industries or occupational groups in China, 2018–2020.

    IndustryNumber (n)Any body partNeckShouldersUpper backLower backElbowsWrists/HandsHips/ThighsKneesAnkles/Feet
    npip'npip'npip'npip'npip'npip'npip'npip'npip'npip'
    Total57,501 23,66941.240.914,48725.224.812,06621.020.88,39914.614.29,44916.416.84,1697.37.37,55313.112.96,06510.510.66,18410.811.08,00213.912.8
    Automobile manufacturing21,5608,96941.643.55,04723.425.24,21419.520.63,14814.615.33,46016.018.11,5717.37.33,21014.914.02,21910.311.12,58412.012.33,88318.016.8
    Electronic equipment manufacturing8,1163,15838.940.42,06025.425.21,75821.722.41,15614.214.21,12913.913.95156.36.488911.010.97018.68.45727.08.18009.910.9
    Footwear industry7,1062,61636.834.21,70123.921.61,36819.317.984611.911.594313.312.45077.17.11,05814.914.46038.58.55247.47.05958.48.2
    Medical staff6,7663,79456.154.22,74940.639.72,22432.932.51,49022.021.91,71225.324.54626.87.678211.612.11,12616.616.292213.614.01,07215.815.0
    Furniture manufacturing4,4711,32029.528.570115.715.062313.913.748110.810.645910.39.94109.29.055612.412.14299.69.64189.39.661213.712.9
    Shipbuilding and related equipment manufacturing3,4881,43241.140.178722.621.667219.318.849114.113.565818.918.43269.38.945213.012.341812.011.748814.013.041311.811.5
    Coal mining and cleaning industry1,50058639.138.436224.123.731120.720.222314.913.025917.315.61338.97.616811.210.218812.511.624416.315.020013.30.1
    Construction industry1,37933224.123.41349.79.514710.710.51027.47.116512.011.6554.03.9896.55.9634.64.6503.63.5634.64.5
    Flight attendants1,35669651.355.750437.238.238728.533.720315.020.127520.388.4523.84.8987.27.01218.910.014310.511.715611.511.2
    4S automobile store1154417732.538.68816.223.17814.316.87012.915.49216.923.2275.08.5509.214.5478.612.3509.215.26111.216.2
    Toy manufacturing33316750.249.011935.734.211634.831.68425.224.29127.325.37121.320.19729.128.35516.514.96318.918.96419.219.4
    Animal husbandry2469639.039.76225.227.34116.717.7208.18.66426.027.1197.78.34719.120.6239.310.13514.214.2156.16.3
    Biopharmaceutical manufacturing24315764.648.411045.334.17731.724.76526.720.95321.817.7135.35.03414.088.43614.811.32911.99.25221.418.0
    Vegetable greenhouse24314760.550.75121.018.74317.715.0166.64.57932.527.152.11.5166.64.23012.310.35723.516.6135.33.7
    Petrochemical industry1502214.711.5128.07.074.73.542.71.6106.76.532.01.474.72.764.04.553.31.932.01.4
    Chi-square test1,336.71,525.7992.4550.4
    P value0000
    Note: Pi: Actual prevalence rate, P’: Standardized prevalence rate.
    Abbreviation: WMSDs=work-related musculoskeletal disorders.
    Download: CSV

    TABLE 3.  Analysis of pain scores of WMSDs with different demographic characteristics in China, 2018–2020.

    CharacteristicNeckShouldersUpper backLower backElbowsWrists/HandsHips/ThighsKneesAnkles/Feet
    M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2
    Gender
    Male 0(0, 6) −40.5** 0(0, 6) −37.8** 0(0, 5) −16.9** 0(0, 6) −15.8** 0(0, 2) −0.7 0(0, 5) −6.0** 0(0, 5) −10.6** 0(0, 5) −9.1 0(0, 6) −10.9**
    Female 3(0, 7) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    Age (years)
    <25 0(0, 6) 888.4** 0(0, 5) 619.5** 0(0, 5) 287.3** 0(0, 5) 684.8** 0(0, 0) 97.7** 0(0, 5) 38.3** 0(0, 4) 152.5** 0(0, 4) 182.4** 0(0, 6) 262.3**
    25− 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 2) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    35− 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 4) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    45− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 4)
    55− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 5) 0(0, 1) 0(0, 4) 0(0, 4) 0(0, 5) 0(0, 4)
    Working age (years)
    <2 0(0, 6) 1740.3** 0(0, 5) 1225.6** 0(0, 5) 667.1** 0(0, 5) 1300.8** 0(0, 0) 86.9** 0(0, 5) 36.5** 0(0, 4) 343.5** 0(0, 4) 664.9** 0(0, 5) 102.2**
    2− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    4− 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 2) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    6− 0(0, 7) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    8− 2(0, 7) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 4) 0(0, 5) 0(0, 5) 0(0, 6) 0(0, 6)
    BMI
    <18.5 0(0, 6) 6.4* 0(0, 6) 10.3** 0(0, 5) 15.5* 0(0, 6) 76.6** 0(0, 1) 8.4 0(0, 5) 2.5 0(0, 5) 3.6 0(0, 5) 49.2** 0(0, 5) 49.7**
    18.5− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    25− 0(0, 6) 0(0, 6) 0(0, 6 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    Smoking
    No 0(0, 6) 421.9** 0(0, 6) 327.2** 0(0, 5) 102.3** 0(0, 6) 214.1** 0(0, 2) 38.3** 0(0, 5) 53.0** 0(0, 5) 62.9** 0(0, 5) 104.9** 0(0, 5) 268.6**
    Occasionally 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 5) 0(0, 2) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 5)
    Frequently 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 4) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    Sporting
    No 0(0, 7) 26.6** 0(0, 6) 39.8** 0(0, 6) 56.7** 0(0, 6) 128.5** 0(0, 3) 10.9** 0(0, 5) 84.4** 0(0, 5) 41.5** 0(0, 5) 28.4** 0(0, 6) 72.0**
    Occasionally 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 2) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    Frequently 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 1) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 5)
    Abbreviations: WMSDs=work-related musculoskeletal disorders; BMI=body mass index.
    * P<0.05.
    ** P<0.01.
    Download: CSV

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Epidemiological Data of Work-Related Musculoskeletal Disorders — China, 2018–2020

View author affiliations

Summary

What is already known about this topic?

In recent decades, work-related musculoskeletal disorders (WMSDs) have become increasingly prominent and have become an important issue that is of universal concern and an urgent need to be solved in all countries of the world.

What is added by this report?

The top three industries or occupational groups with the highest standardized prevalence rate of WMSDs were flight attendants, medical staff, and vegetable greenhouses in that order. Women workers were 1.5 times more likely to suffer from WMSDs than men workers.

What are the implications for public health practice?

This study has found the prevalence and distribution characteristics of WMSDs in key industries in China. It is urgent to draw up relevant measures to prevent and control occupational populations with WMSDs.

  • 1. National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
  • 2. Chongqing Center for Disease Control and Prevention, Chongqing, China
  • 3. Hubei Provincial Hospital of Integrated Chinese&Western Medicine, Wuhan, Hubei, China
  • 4. Guangzhou Twelfth People’s Hospital Affiliated to Guangzhou Medical University, Guangzhou, Guangdong, China
  • 5. Liaoning Provincial Health Service Center, Shenyang, Liaoning, China
  • 6. Guizhou Province Occupational Disease Prevention and Control Hospital, Guiyang, Guizhou, China
  • 7. Shanghai Center for Disease Control and Prevention, Shanghai, China
  • 8. Shandong Academy of Occupational Health and Occupational Medicine, Jinan, Shandong, China
  • 9. Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu, China
  • 10. Civil Aviation Medical Center, Civil Aviation Administration of China, Beijing, China
  • 11. Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
  • 12. Tianjin Occupational Disease Prevention and Control Hospital, Tianjin, China
  • 13. Tianjin Center for Disease Control and Prevention, Tianjin, China
  • 14. Beijing Center for Disease Control and Prevention, Beijing, China
  • 15. Fujian Province Occupational Disease and Chemical Poisoning Prevention and Control Center, Fuzhou, Fujian, China
  • 16. Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, Guangdong, China
  • 17. Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
  • 18. Institute of Occupational Medicine of Jiangxi, Nanchang, Jiangxi, China
  • 19. Ningxia Hui Autonomous Region Center for Disease Control and Prevention, Yinchuan, Ningxia, China
  • 20. Sichuan Provincial Center for Disease Control and Prevention, Chengdu, Sichuan, China
  • 21. Shaanxi Provincial Center for Disease Control and Prevention, Xi’an, Shaanxi, China
  • Corresponding author:

    Zhongxu Wang, wangzx@niohp.chinacdc.cn

  • 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. 131031109000150003)
  • Online Date: April 30 2021
    doi: 10.46234/ccdcw2021.104
  • With the development of science and technology and the process of industrialization, the working conditions of workers have changed greatly. During their work, workers frequently undergo local muscle tension such as repetitive operation, poor working posture, excessive force load, continuous muscle tension, vibration contact, and other health effects caused by adverse working conditions. Work-related musculoskeletal disorders (WMSDs) caused by adverse ergonomics are becoming increasingly prominent. As early as 2002, the International Labor Organization (ILO) added WMSDs in the international list of occupational diseases and refined it in the latest edition of occupational diseases catalogue approved in 2010, including seven categories and an open clause (1). Currently, WMSDs are not included in the list of statutory occupational diseases in China. Rather, it is only perceived as work-related diseases, so there is no legal basis for preventing and controlling WMSDs among occupational groups. In 2019, China put forward in the Healthy China Action (2019–2030) that the prevention and control of WMSDs should be included in the national health action goal. Therefore, a large sample of people in key industries in different regions of China were investigated and studied to determine the prevalence and distribution characteristics of WMSDs in key industries of China and explore related epidemiological characteristics.

    The scope of this study covers seven regions of North, East, Central, South, Southwest, Northwest and Northeast China. Selection of key industries is based on representative industries closely related to WMSDs, i.e., involving 15 industries such as automobile manufacturing, footwear industry, biological medicine manufacturing, electronic equipment manufacturing, ship and related equipment manufacturing, petrochemical industry, construction industry, furniture manufacturing, coal mining and cleaning industry, animal husbandry, medical staff, automobile 4S shops, vegetable greenhouses, civil aviation flight attendants, and toy manufacturing. In this study, a cluster sampling method was adopted, and all workers on duty who met the inclusion criteria were selected as research objects from the representative enterprises in the key industries and above areas. The inclusion criteria was workers with more than one year’s service, and the exclusion criteria was congenital spinal deformity and non-WMSD patients due to trauma, infectious diseases, and malignant tumors.

    In the study, the epidemiological cross-sectional survey method and the electronic questionnaire system of Chinese version of musculoskeletal disorders questionnaire were used to investigate the prevalence of WMSDs among occupational groups in key industries in different regions of China. This electronic questionnaire system was based on Nordic Musculoskeletal Questionnaires (NMQ) (2), and after proper modification, the adapted NMQ proved to have good reliability and validity for use for Chinese occupational groups. The criteria of the US National Institute for Occupational Safety and Health (NIOSH) for musculoskeletal injury was used to determine WMSDs (3). The survey was conducted by an investigator using face-to-face survey on N respondents, and the respondents answered questions online by mobile phone or by tablet after scanning Quick Response (QR) codes. Up to now, 57,501 valid questionnaires have been received, and the effective rate of questionnaires was 100%. There were 37,240 male workers and 20,261 female workers. The age of the investigated population was (32.3±9.2) years and the length of service was (7.5±7.2) years.

    The standardized prevalence rate of WMSDs among the population in key industries in China was 41.2% (all patients suffering from WMSDS at any position are regarded as one patient). The standardized prevalence rate of WMSDs varied from 7.3% to 24.8%. The 3 parts with the highest prevalence were the neck (24.8%), shoulders (20.8%), and lower back (16.8%). Female workers had 1.5 times the risk of WMSDs compared to male workers. A significant difference in the prevalence of WMSDs was observed between different age groups and different working age groups (P<0.05). The prevalence rate of WMSDs increased gradually and decreased with age, and the highest prevalence rate was between 35 and 45 years old. The prevalence of WMSDs increased with increased length of service. Regular physical exercise could reduce the risk of suffering from WMSDs. The risk of neck, shoulders, and lower back of people with different demographic characteristics was shown in Table 1.

    CharacteristicNumberAny body partNeckShouldersLower back
    No. of casesRate, %OR (95%CI)No. of casesOR (95%CI)No. of casesOR (95%CI)No. of casesOR (95%CI)
    Gender
    Male37,240 14,05737.717,77416,41915,5141
    Female20,2619,61247.41.5 (1.4−1.5) *6,7131.9 (1.8−2.0) *5,6471.9 (1.8−1.9) *3,9351.4 (1.3−1.5) *
    Age (years)
    <2512,0854,42636.612,38912,02711,4621
    25–26,13911,19642.81.3 (1.2−1.4) *6,9671.5 (1.4−1.6) *5,7411.4 (1.3−1.5) *4,5771.5 (1.4−1.6) *
    35–12,3015,29443.01.3 (1.2−1.4) *3,4861.6 (1.5−1.7) *2,8881.5 (1.4−1.6) *2,2381.6 (1.5−1.7) *
    45–5,8022,27139.11.1 (1.0−1.2) *1,3851.2 (1.2−1.4) *1,1871.3 (1.2−1.4) *9641.4 (1.3−1.6) *
    55–1,17448241.11.2 (1.1−1.4) *2601.2 (1.0−1.3) *2231.2 (1.0−1.4) *2081.6 (1.3−1.8) *
    Working age (years)
    <216,0615,49834.212,95512,53611,8861
    2–12,0724,98941.31.3 (1.3−1.4) *3,0111.5 (1.4−1.6) *2,5091.4 (1.3−1.5) *1,8571.4 (1.3−1.5) *
    4–7,2993,10642.61.4 (1.3−1.5) *1,9661.6 (1.5−1.7) *1,6541.6 (1.5−1.7) *1,2921.6 (1.5−1.7) *
    6–9,7174,36144.91.6 (1.5−1.6) *2,8051.8 (1.7−1.9) *2,3021.7 (1.6−1.8) *1,8531.8 (1.7−1.9) *
    8–12,3525,71546.31.7 (1.6−1.7) *3,7501.9 (1.8−2.0) *3,0651.8 (1.7−1.9) *2,5612.0 (1.8−2.1) *
    Education
    Junior high school15,3695,54336.113,23012,81512,2251
    Senior high school21,9018,63639.41.2 (1.1−1.2) *4,9901.1 (1.1−1.2) *4,1741.1 (1.0−1.1) *3,3991.1 (1.0−1.2) *
    University degree19,2318,94946.51.5 (1.5−1.6) *5,8411.6 (1.6−1.7) *4,7291.5 (1.4−1.5) *3,6261.4 (1.3−1.5) *
    Graduate degree1,00054154.12.1 (1.8−2.4) *4262.8 (2.4−3.2) *3482.4 (2.1−2.7) *1991.5 (1.2−1.7) *
    BMI
    <18.56,0062,45940.911,48711,21719081
    18.5–39,32816,13041.01.0 (0.9−1.1)9,9731.0 (0.9−1.1)8,3891.1 (0.9−1.1)6,4141.1 (1.0−1.2) *
    25–12,1675,08041.81.0 (1.0−1.1)3,0271.0 (0.9−1.1)2,4601.0 (0.9−1.1)2,1271.2 (1.1−1.3) *
    Smoking
    No36,52715,49642.419,89518,22716,0741
    Occasionally10,1113,61635.80.8 (0.7−0.8)2,0490.7 (0. 6−0.7) *1,7080.7 (0.6−0.7) *1,4530.8 (0.8−0.9) *
    Frequently10,8634,55741.91.0 (0.9−1.0)2,5430.8 (0. 8−0.9) *2,1310.8 (0.8−0.9) *1,9221.1 (1.0−1.1) *
    Sporting
    No17,9477,85943.814,77214,03813,3751
    Occasionally32,79713,27240.50.9 (0.8−0.9) *8,1470.9 (0.8−0.9) *6,7490.9 (0.8−0.9) *5,1160.8 (0.7−0.8) *
    Frequently6,7572,53837.60.8 (0.7−0.8) *1,5680.8 (0.8−0.9) *1,2790.8 (0.7−0.8) *9580.7 (0.6−0.7) *
    Abbreviations: WMSDs=work-related musculoskeletal disorders; BMI=body mass index.
    * P<0.05.

    Table 1.  WMSD prevalence and risk for different demographic groups among key industries or occupational groups in China, 2018–2020.

    The results showed statistical differences in the prevalence of WMSDs among occupational groups in different industries (P<0.05). The standardized prevalence rate of WMSDs in various industries from high to low was: flight attendants (55.7%), medical staff (54.2%), vegetable greenhouse (50.7%), toy manufacturing (49.0%), biopharmaceutical manufacturing (48.4%), automobile manufacturing (43.5%), electronic equipment manufacturing (40.4%), shipbuilding and related equipment manufacturing (40.1%), animal husbandry (39.7%), 4S automobile store (38.6%), coal mining and cleaning industry (38.4%), footwear industry (34.2%), furniture manufacturing (28.5%), construction industry (23.4%), and petrochemical industry (11.5%) (Table 2).

    IndustryNumber (n)Any body partNeckShouldersUpper backLower backElbowsWrists/HandsHips/ThighsKneesAnkles/Feet
    npip'npip'npip'npip'npip'npip'npip'npip'npip'npip'
    Total57,501 23,66941.240.914,48725.224.812,06621.020.88,39914.614.29,44916.416.84,1697.37.37,55313.112.96,06510.510.66,18410.811.08,00213.912.8
    Automobile manufacturing21,5608,96941.643.55,04723.425.24,21419.520.63,14814.615.33,46016.018.11,5717.37.33,21014.914.02,21910.311.12,58412.012.33,88318.016.8
    Electronic equipment manufacturing8,1163,15838.940.42,06025.425.21,75821.722.41,15614.214.21,12913.913.95156.36.488911.010.97018.68.45727.08.18009.910.9
    Footwear industry7,1062,61636.834.21,70123.921.61,36819.317.984611.911.594313.312.45077.17.11,05814.914.46038.58.55247.47.05958.48.2
    Medical staff6,7663,79456.154.22,74940.639.72,22432.932.51,49022.021.91,71225.324.54626.87.678211.612.11,12616.616.292213.614.01,07215.815.0
    Furniture manufacturing4,4711,32029.528.570115.715.062313.913.748110.810.645910.39.94109.29.055612.412.14299.69.64189.39.661213.712.9
    Shipbuilding and related equipment manufacturing3,4881,43241.140.178722.621.667219.318.849114.113.565818.918.43269.38.945213.012.341812.011.748814.013.041311.811.5
    Coal mining and cleaning industry1,50058639.138.436224.123.731120.720.222314.913.025917.315.61338.97.616811.210.218812.511.624416.315.020013.30.1
    Construction industry1,37933224.123.41349.79.514710.710.51027.47.116512.011.6554.03.9896.55.9634.64.6503.63.5634.64.5
    Flight attendants1,35669651.355.750437.238.238728.533.720315.020.127520.388.4523.84.8987.27.01218.910.014310.511.715611.511.2
    4S automobile store1154417732.538.68816.223.17814.316.87012.915.49216.923.2275.08.5509.214.5478.612.3509.215.26111.216.2
    Toy manufacturing33316750.249.011935.734.211634.831.68425.224.29127.325.37121.320.19729.128.35516.514.96318.918.96419.219.4
    Animal husbandry2469639.039.76225.227.34116.717.7208.18.66426.027.1197.78.34719.120.6239.310.13514.214.2156.16.3
    Biopharmaceutical manufacturing24315764.648.411045.334.17731.724.76526.720.95321.817.7135.35.03414.088.43614.811.32911.99.25221.418.0
    Vegetable greenhouse24314760.550.75121.018.74317.715.0166.64.57932.527.152.11.5166.64.23012.310.35723.516.6135.33.7
    Petrochemical industry1502214.711.5128.07.074.73.542.71.6106.76.532.01.474.72.764.04.553.31.932.01.4
    Chi-square test1,336.71,525.7992.4550.4
    P value0000
    Note: Pi: Actual prevalence rate, P’: Standardized prevalence rate.
    Abbreviation: WMSDs=work-related musculoskeletal disorders.

    Table 2.  Prevalence of WMSDs in key industries or occupational groups in China, 2018–2020.

    In this study, 56.5%–88.7% of the occupational population chose the pain scores for the neck, shoulders, upper back, lower back (waist), elbow, wrist/hand, hip/thigh, knee, ankle/foot, etc., as 0, which means no pain occurred. Therefore, this study used 10–90 percentile to express the distribution of pain scores. The results demonstrated that the pain scores of female workers were higher than those of male workers except for elbow and knee, which were statistically significant (P<0.05). The pain scores of different age groups, different working age groups, smoking history, and physical exercise habits were statistically significant (P<0.05) (Table 3).

    CharacteristicNeckShouldersUpper backLower backElbowsWrists/HandsHips/ThighsKneesAnkles/Feet
    M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2M (Q10, Q90)Z/χ2
    Gender
    Male 0(0, 6) −40.5** 0(0, 6) −37.8** 0(0, 5) −16.9** 0(0, 6) −15.8** 0(0, 2) −0.7 0(0, 5) −6.0** 0(0, 5) −10.6** 0(0, 5) −9.1 0(0, 6) −10.9**
    Female 3(0, 7) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    Age (years)
    <25 0(0, 6) 888.4** 0(0, 5) 619.5** 0(0, 5) 287.3** 0(0, 5) 684.8** 0(0, 0) 97.7** 0(0, 5) 38.3** 0(0, 4) 152.5** 0(0, 4) 182.4** 0(0, 6) 262.3**
    25− 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 2) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    35− 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 4) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    45− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 4)
    55− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 5) 0(0, 1) 0(0, 4) 0(0, 4) 0(0, 5) 0(0, 4)
    Working age (years)
    <2 0(0, 6) 1740.3** 0(0, 5) 1225.6** 0(0, 5) 667.1** 0(0, 5) 1300.8** 0(0, 0) 86.9** 0(0, 5) 36.5** 0(0, 4) 343.5** 0(0, 4) 664.9** 0(0, 5) 102.2**
    2− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    4− 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 2) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    6− 0(0, 7) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    8− 2(0, 7) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 4) 0(0, 5) 0(0, 5) 0(0, 6) 0(0, 6)
    BMI
    <18.5 0(0, 6) 6.4* 0(0, 6) 10.3** 0(0, 5) 15.5* 0(0, 6) 76.6** 0(0, 1) 8.4 0(0, 5) 2.5 0(0, 5) 3.6 0(0, 5) 49.2** 0(0, 5) 49.7**
    18.5− 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    25− 0(0, 6) 0(0, 6) 0(0, 6 0(0, 6) 0(0, 3) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    Smoking
    No 0(0, 6) 421.9** 0(0, 6) 327.2** 0(0, 5) 102.3** 0(0, 6) 214.1** 0(0, 2) 38.3** 0(0, 5) 53.0** 0(0, 5) 62.9** 0(0, 5) 104.9** 0(0, 5) 268.6**
    Occasionally 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 5) 0(0, 2) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 5)
    Frequently 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 6) 0(0, 4) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 6)
    Sporting
    No 0(0, 7) 26.6** 0(0, 6) 39.8** 0(0, 6) 56.7** 0(0, 6) 128.5** 0(0, 3) 10.9** 0(0, 5) 84.4** 0(0, 5) 41.5** 0(0, 5) 28.4** 0(0, 6) 72.0**
    Occasionally 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 2) 0(0, 5) 0(0, 5) 0(0, 5) 0(0, 5)
    Frequently 0(0, 6) 0(0, 6) 0(0, 5) 0(0, 6) 0(0, 1) 0(0, 5) 0(0, 4) 0(0, 5) 0(0, 5)
    Abbreviations: WMSDs=work-related musculoskeletal disorders; BMI=body mass index.
    * P<0.05.
    ** P<0.01.

    Table 3.  Analysis of pain scores of WMSDs with different demographic characteristics in China, 2018–2020.

  • The epidemiological characteristics of WMSDs in key industries in China from January 2018 to June 2020 were investigated in this study. On the basis of data published last year (4), this paper continues to expand the sample size, reaching data of nearly 60,000 people, which is the largest population survey on WMSDs in China so far. The results of this study showed that the prevalence rate of WMSDs in any body part was 41.2%, and the most common parts were neck, shoulders, and lower back. The risk of WMSDs among female workers was 1.5 times that of male workers. With increases in age, the prevalence rate of WMSDs increased gradually and then decreased. A study on the burden of 354 diseases in 195 countries and regions demonstrated that from 1990 to 2017, lower back pain was the first disease leading to years lived with disability (YLD), and the prevalence rate of musculoskeletal disorders, lower back pain, and neck pain was 38.4% (36.4% to 40.2%), 30.0% (27.9% to 31.9%), and 44.4% (41.9% to 47.0%), respectively (5). According to the data, in 2017, the spot prevalence rate of neck pain in women was higher than that in men, although the results were not significant at P=0.05. It was also found that the prevalence rate of pain in the neck increased up to age 70–74 years and then decreased (6), which was similar to the results obtained from this study.

    The results showed that biopharmaceutical manufacturing, vegetable greenhouses, medical personnel, civil aviation flight attendants, toy manufacturing, automobile manufacturing, and shipbuilding and related equipment manufacturing were industries or occupational groups with high prevalence rate of WMSDs exceeding 40%. Differences in the occurrence position of WMSDs depended on features of occupational activities. WMSDs of shipbuilding and related equipment manufacturing industry, construction industry, coal mining and cleaning industry, civil aviation flight attendants, automobile 4S shops, automobile manufacturing industry, petrochemical industry, and medical personnel were mainly concentrated in the neck, shoulders, and lower back. WMSDs in electronic equipment manufacturing and biopharmaceutical manufacturing occurred mainly in the upper back, and WMSDs in the furniture manufacturing industry occurred mainly in the ankles. However, in toy manufacturing, animal husbandry, and footwear industry, WMSDs not only occurred in the neck and shoulders but also the wrist. WMSDs occurred in the knees of vegetable greenhouse workers except for the lower back and neck. The disparity in results may be related to differences in affected parts, labor intensity, working conditions, and working methods. The prevalence rate of WMSDs in vegetable greenhouse workers was very high, which exceeded that of most workers in industrial and mining enterprises.

    The pain scores in many parts of the female population were higher than those of the male population, which might be related to the fact that women were more sensitive to pain than men and were more willing to report pain (7). This study also found that the pain scores of those with BMI above 25, those who smoke, and those without physical exercise were higher than those of the corresponding low-dose groups. A prospective population study investigated the relationship between chronic pain and lifestyle factors and a correlation was found between pain and lifestyle such as smoking and infrequent physical exercise (8).

    The study was subject to some limitations. First, research objects came from workers of 15 industries in China and some key industries related to WMSDs were not investigated, so the generalizability of the results was limited. Second, because of the nature of cross-sectional studies, making causal inference between risk factors and WMSDs was impossible. Finally, because the questionnaire survey was used in this study and the time period of the questionnaire survey was limited to past year, the resulting reporting bias and recall bias could influence the results.

    In conclusion, the prevalence rate of WMSDs in key industries or occupations in China was relatively high. The most affected body parts were in the neck, shoulders, and lower back, and the results showed increases with increasing age and length of service. Women were more likely to suffer from WMSDs than men. The top three industries or occupational groups with the highest prevalence of WMSDs were pharmaceutical manufacturing, vegetable greenhouses, and medical staff. As a result, it is necessary to strengthen the publicity and education of ergonomics knowledge and improve the awareness of the occupational population on the basis of this study of WMSDs to promote effective intervention and control measures among the occupational population in order to reduce the impact of WMSDs. WMSDs in key industries should also be considered to be included in China's list of statutory occupational diseases.

    Acknowledgments: All the participants involved in this study from Chongqing, Shanghai, Jiangsu, Zhejiang, Tianjin, Beijing, Hubei, Ningxia Hui Autonomous Region, Sichuan and Shaanxi Provincial Centers for Disease Prevention and Control, Hubei Provincial Hospital of Integrated Chinese & Western Medicine, Guangzhou Twelfth People’s Hospital Affiliated to Guangzhou Medical University, Liaoning Provincial Health Service Center, Guizhou Province Occupational Disease Prevention and Control Hospital, Shandong Academy of Occupational Health and Occupational Medicine, Civil Aviation Medical Center of China Civil Aviation Administration, Tianjin Occupational Disease Prevention and Control Hospital, Fujian Province Occupational Disease and Chemical Poisoning Prevention and Control Center, Guangdong Province Hospital for Occupational Disease Prevention and Treatment, and the Institute of Occupational Medicine of Jiangxi.

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