IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i23p16249-d993430.html
   My bibliography  Save this article

Analysis of Mining-Related Injuries in Chinese Coal Mines and Related Risk Factors: A Statistical Research Study Based on a Meta-Analysis

Author

Listed:
  • Jin Tian

    (Institute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin 300161, China)

  • Yundou Wang

    (Institute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin 300161, China)

  • Shutian Gao

    (Institute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin 300161, China)

Abstract

Background and Objectives: Coal mine injuries commonly occur, affecting both the safety and health of miners, and the normal operation of the coal mine. Accordingly, this study aimed to explore the regularity of injury and injury-related risk factors in coal mines in China so as to establish a scientific basis for reducing the incidence and promoting the prevention and control of injuries. Methods: A meta-analysis of casualty cases and injury-related risk factors from 1956 to 2017 in China was conducted utilizing data from six databases, including CNKI, Web of Science, PubMed, Medline, Embase, and Wanfang data. Summary estimates were obtained using random effects models. Results: There were statistically significant variations in coal mine accident types, types of work, injury sites, age, experience, months, and shifts ( p < 0.001). Eight types of accidents were susceptible to the risk of injury, and the greatest risk was presented by roof-related accidents (odds ratio (OR) = 0.46, 95% confidence interval (CI) = 0.32–0.6). Coal miners and drillers were at a greater risk of injury (OR = 0.39, 95% CI = 0.35–0.44; OR = 0.22, 95% CI = 0.17–0.26, respectively). The extremities and the soft tissues of the skin were at the greatest risk of injury (OR = 0.44, 95% CI = 0.3–0.58; OR = 0.23, 95% CI = 0.1–0.48, respectively). Compared with other ages, miners aged 21–30 were at a greater risk of injury (21–30 years, OR = 0.45, 95% CI = 0.42–0.47; 31–40 years, OR = 0.29, 95% CI = 0.25–0.32; <20 years, OR = 0.13, 95% CI = 0.03–0.23; >40 years, OR = 0.17, 95% CI = 0.09–0.25). Compared with other miners, those with 6–10 years of experience were at a greater risk of injury (6–10 years, OR = 0.29, 95% CI = 0.25–0.32; 2–5 years, OR = 0.33, 95% CI = 0.25–0.41; <1 year, OR = 0.22, 95% CI = 0.08–0.33; >11 years, OR = 0.22, 95% CI = 0.17–0.27). During the months of July to September, the risk of injury was elevated (7–9th months, OR = 0.32, 95% CI = 0.25–0.39; 10–12th months, OR = 0.24, 95% CI = 0.16–0.31; 1st–3rd months, OR = 0.22, 95% CI = 0.16–0.28; 4–6th months, OR = 0.21, 95% CI = 0.16–0.27). In the three-shift work system, the risk of injury was higher during night shifts (22:00–06:00, OR = 0.43, 95% CI = 0.3–0.56; 14:00–22:00, OR = 0.3, 95% CI = 0.23–0.27; 06:00–14:00, OR = 0.27, 95% CI = 0.18–0.35). Conclusions: The results of this research study reveal that coal mine injuries are prevalent among coal miners. These injuries are often related to the age, experience, months of work, and the three-shift work system of miners.

Suggested Citation

  • Jin Tian & Yundou Wang & Shutian Gao, 2022. "Analysis of Mining-Related Injuries in Chinese Coal Mines and Related Risk Factors: A Statistical Research Study Based on a Meta-Analysis," IJERPH, MDPI, vol. 19(23), pages 1-16, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16249-:d:993430
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/23/16249/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/23/16249/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mery Gonzalez-Delgado & Héctor Gómez-Dantés & Julián Alfredo Fernández-Niño & Eduardo Robles & Víctor H Borja & Miriam Aguilar, 2015. "Factors Associated with Fatal Occupational Accidents among Mexican Workers: A National Analysis," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-19, March.
    2. Yu, Haimiao & Chen, Hong & Long, Ruyin, 2017. "Mental fatigue, cognitive bias and safety paradox in chinese coal mines," Resources Policy, Elsevier, vol. 52(C), pages 165-172.
    3. Yan Cui & Shuang-Shuang Tian & Nan Qiao & Cong Wang & Tong Wang & Jian-Jun Huang & Chen-Ming Sun & Jie Liang & Xiao-Meng Liu, 2015. "Associations of Individual-Related and Job-Related Risk Factors with Nonfatal Occupational Injury in the Coal Workers of Shanxi Province: A Cross-Sectional Study," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-13, July.
    4. Qiao, Wanguan & Liu, Quanlong & Li, Xinchun & Luo, Xixi & Wan, YuLong, 2018. "Using data mining techniques to analyze the influencing factor of unsafe behaviors in Chinese underground coal mines," Resources Policy, Elsevier, vol. 59(C), pages 210-216.
    5. Gilberto Montibeller & Detlof von Winterfeldt, 2015. "Cognitive and Motivational Biases in Decision and Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1230-1251, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei Zhang & Dongxiao Gu & Yuguang Xie & Aida Khakimova & Oleg Zolotarev, 2023. "How Do COVID-19 Risk, Life-Safety Risk, Job Insecurity, and Work–Family Conflict Affect Miner Performance? Health-Anxiety and Job-Anxiety Perspectives," IJERPH, MDPI, vol. 20(6), pages 1-21, March.
    2. Shuang Liu & Kirsten Maclean & Cathy Robinson, 2019. "A cost-effective framework to prioritise stakeholder participation options," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 221-241, November.
    3. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    4. Jaspersen, Johannes G., 2022. "Convex combinations in judgment aggregation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 780-794.
    5. Wang, Qun & Jia, Guozhu & Song, Wenyan, 2022. "Identifying critical factors in systems with interrelated components: A method considering heterogeneous influence and strength attenuation," European Journal of Operational Research, Elsevier, vol. 303(1), pages 456-470.
    6. Dimitrios Gouglas & Kendall Hoyt & Elizabeth Peacocke & Aristidis Kaloudis & Trygve Ottersen & John-Arne Røttingen, 2019. "Setting Strategic Objectives for the Coalition for Epidemic Preparedness Innovations: An Exploratory Decision Analysis Process," Service Science, INFORMS, vol. 49(6), pages 430-446, November.
    7. Qiao, Wanguan, 2021. "Analysis and measurement of multifactor risk in underground coal mine accidents based on coupling theory," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    8. Fangyuan Tian & Hongxia Li & Shuicheng Tian & Chenning Tian & Jiang Shao, 2022. "Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?," IJERPH, MDPI, vol. 19(1), pages 1-21, January.
    9. Siebert, Johannes Ulrich & Kunz, Reinhard E. & Rolf, Philipp, 2021. "Effects of decision training on individuals’ decision-making proactivity," European Journal of Operational Research, Elsevier, vol. 294(1), pages 264-282.
    10. Gary J. Summers, 2021. "Friction and Decision Rules in Portfolio Decision Analysis," Decision Analysis, INFORMS, vol. 18(2), pages 101-120, June.
    11. Mónica D. Oliveira & Inês Mataloto & Panos Kanavos, 2019. "Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(6), pages 891-918, August.
    12. Marttunen, Mika & Haara, Arto & Hjerppe, Turo & Kurttila, Mikko & Liesiö, Juuso & Mustajoki, Jyri & Saarikoski, Heli & Tolvanen, Anne, 2023. "Parallel and comparative use of three multicriteria decision support methods in an environmental portfolio problem," European Journal of Operational Research, Elsevier, vol. 307(2), pages 842-859.
    13. Parreiras, R.O. & Kokshenev, I. & Carvalho, M.O.M. & Willer, A.C.M. & Dellezzopolles, C.F. & Nacif, D.B. & Santana, J.A., 2019. "A flexible multicriteria decision-making methodology to support the strategic management of Science, Technology and Innovation research funding programs," European Journal of Operational Research, Elsevier, vol. 272(2), pages 725-739.
    14. Gerda Ana Melnik-Leroy & Gintautas Dzemyda, 2021. "How to Influence the Results of MCDM?—Evidence of the Impact of Cognitive Biases," Mathematics, MDPI, vol. 9(2), pages 1-25, January.
    15. Jin-Hwan Bae & Jin-Woo Park, 2021. "Research into Individual Factors Affecting Safety within Airport Subsidiaries," Sustainability, MDPI, vol. 13(9), pages 1-13, May.
    16. Anca M. Hanea & Marissa F. McBride & Mark A. Burgman & Bonnie C. Wintle, 2018. "The Value of Performance Weights and Discussion in Aggregated Expert Judgments," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1781-1794, September.
    17. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    18. Ismail, Siti Noraishah & Ramli, Azizan & Aziz, Hanida Abdul, 2021. "Influencing factors on safety culture in mining industry: A systematic literature review approach," Resources Policy, Elsevier, vol. 74(C).
    19. Yan Cui & Shuang-Shuang Tian & Nan Qiao & Cong Wang & Tong Wang & Jian-Jun Huang & Chen-Ming Sun & Jie Liang & Xiao-Meng Liu, 2015. "Associations of Individual-Related and Job-Related Risk Factors with Nonfatal Occupational Injury in the Coal Workers of Shanxi Province: A Cross-Sectional Study," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-13, July.
    20. Marek Kęsek & Romuald Ogrodnik, 2021. "Method for Determining the Utilization Rate of Thin-Deck Shearers Based on Recorded Electromotor Loads," Energies, MDPI, vol. 14(13), pages 1-14, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16249-:d:993430. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.