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A systematic review on seafarer health: Conditions, antecedents and interventions

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  • Li, Xue
  • Zhou, Yusheng
  • Yuen, Kum Fai

Abstract

Seafarer health is critical for sustainable shipping management. During the COVID-19 pandemic, the unfavourable working environment has posed physical and psychological problems to seafarers. Consequently, seafarer health has been receiving increasing attention from academics, the shipping industry and international organizations. The objective of this review is to identify key characteristics of the articles on seafarer health published in the past two decades, and summarize key investigated health problems, antecedents and health promotion interventions. The review suggests that cardiovascular diseases, transmittable diseases, fatigue and stress have been the most widely studied health issues. Moreover, seafarer health conditions can be influenced by the (1) natural environment; (2) physical working environment; (3) social environment; (4) personal characteristics; (5) management style; (6) industry-specific characteristics; (7) health crisis. Furthermore, medical care provided by telehealth, effective regulatory and education interventions can improve seafarer health. This review contributes to the existing literature by consolidating the scattered research on seafarer health. Combining the literature review results and recommendations from international organizations on public health and maritime transport, this study proposes several future research directions.

Suggested Citation

  • Li, Xue & Zhou, Yusheng & Yuen, Kum Fai, 2022. "A systematic review on seafarer health: Conditions, antecedents and interventions," Transport Policy, Elsevier, vol. 122(C), pages 11-25.
    • Handle: RePEc:eee:trapol:v:122:y:2022:i:c:p:11-25
    DOI: 10.1016/j.tranpol.2022.04.010
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    Cited by:

    1. Li, Xue & Seah, Rachel & Wang, Xueqin & Yuen, Kum Fai, 2022. "Investigating the role of sociotechnical factors on seafarers’ psychological capital and mental well-being," Technology in Society, Elsevier, vol. 71(C).
    2. Fan, Shiqi & Yang, Zaili, 2024. "Accident data-driven human fatigue analysis in maritime transport using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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